Abstract

   The incidence of cancer and Alzheimer’s disease (AD) increases
   exponentially with age. A growing body of epidemiological evidence and
   molecular investigations inspired the hypothesis of an inverse
   relationship between these two pathologies. It has been proposed that
   the two diseases might utilize the same proteins and pathways that are,
   however, modulated differently and sometimes in opposite directions.
   Investigation of the common processes underlying these diseases may
   enhance the understanding of their pathogenesis and may also guide
   novel therapeutic strategies. Starting from a text-mining approach, our
   in silico study integrated the dispersed biological evidence by
   combining data mining, gene set enrichment, and protein-protein
   interaction (PPI) analyses while searching for common biological
   hallmarks linked to AD and cancer. We retrieved 138 genes (ALZCAN gene
   set), computed a significant number of enriched gene ontology clusters,
   and identified four PPI modules. The investigation confirmed the
   relevance of autophagy, ubiquitin proteasome system, and cell death as
   common biological hallmarks shared by cancer and AD. Then, from a
   closer investigation of the PPI modules and of the miRNAs enrichment
   data, several genes (SQSTM1, UCHL1, STUB1, BECN1, CDKN2A, TP53, EGFR,
   GSK3B, and HSPA9) and miRNAs (miR-146a-5p, MiR-34a-5p, miR-21-5p,
   miR-9-5p, and miR-16-5p) emerged as promising candidates. The
   integrative approach uncovered novel miRNA-gene networks (e.g., miR-146
   and miR-34 regulating p62 and Beclin1 in autophagy) that might give new
   insights into the complex regulatory mechanisms of gene expression in
   AD and cancer.

   Keywords: genes, miRNAs, cancer, Alzheimer, inverse relationship, data
   mining, enrichment analysis, protein-protein interaction network

Introduction

   Cancer and dementia are complex pathologies that rise exponentially
   with age and dramatically affect quality of life and survival. Cancer
   and dementia are also described as the divergent manifestations of
   aging, the rival demons ([39]Campisi, 2013), emerging from the opposite
   ends of a biological spectrum: uncontrolled cell proliferation
   characterizes carcinogenesis, whereas a progressive neuronal death
   marks neurodegeneration ([40]López-Otín et al., 2013). Consistent
   epidemiological evidence suggests an intriguing inverse relationship
   between cancer and Alzheimer’s disease (AD) ([41]Driver et al., 2012;
   [42]Musicco et al., 2013; [43]Romero et al., 2014; [44]Freedman et al.,
   2016; [45]Frain et al., 2017; [46]Hanson et al., 2017). Four
   meta-analyses also support evidence on this hypothesis ([47]Ma et al.,
   2014; [48]Shi et al., 2015; [49]Zhang et al., 2015; [50]Catalá-López et
   al., 2017). Several theories and hypotheses have been proposed to
   address possible biological mechanisms underpinning this inverse
   association. For instance, in aging, cells undergo a metabolic
   reprogramming that leads to a divergent regulatory mechanism of
   metabolic pathways toward and away from respiration in mitochondria
   ([51]Harris et al., 2014). In cancers, an elevation of aerobic
   glycolysis plays an important role in the biosynthesis of
   macromolecules and in promoting cell proliferation. In contrast, in
   senile neurodegeneration, in response to age-dependent diminished
   energy production, the decreased aerobic glycolysis is compensated by
   an increase in mitochondrial oxidative phosphorylation. The metabolic
   dysregulation along with the selective pressure to meet the cellular
   metabolic needs might be a common mechanism linking the two diseases
   and has suggested the attractive idea of repositioning of cancer drugs
   for AD treatment ([52]Monacelli et al., 2016; [53]Vargas et al., 2018).
   Among others, the theory of antagonistic pleiotropy ([54]Campisi, 2013)
   offers a plausible interpretation of the two divergent manifestations
   of aging. This theory states that genes control more than one phenotype
   during the life span, and the same genes while promoting early-life
   benefits may exert negative actions in later life ([55]Chinta et al.,
   2015). In the case of cancer and dementia, this biological trade-off
   implies that the processes that, in younger ages, stimulate cellular
   repair may boost neoplastic growth or limit cell regeneration if
   dysregulated in older ages. Similarly, the same processes leading to an
   increased rate of cell death may drive senile neurodegeneration while
   protecting from abnormal cellular reproduction.

   A number of studies explored genes potentially involved in cancer and
   AD. Recently, three systematic reviews ([56]Li et al., 2014; [57]Shafi,
   2016; [58]Snyder et al., 2017) proposed sets of genes, pathways, and
   mechanistic and genetic links as possible actors in this complex
   biological context. Moreover, the molecular scenarios linking the two
   diseases were investigated in a few gene expression studies ([59]Ibáñez
   et al., 2014; [60]Klus et al., 2015; [61]Sánchez-Valle et al., 2017).
   These studies indicated that a metabolic dysregulation of processes,
   such as mitochondrial metabolism and protein degradation, might play a
   dual role favoring the onset of cancer or AD. Noteworthy, the age is a
   relevant factor influencing all of the metabolic processes occurring in
   diseases, such as cancer and AD ([62]Harris et al., 2014).
   Intriguingly, well-known genes associated with both diseases (e.g.,
   TP53, APOE) have been recently associated with longevity traits
   ([63]Tacutu et al., 2018). Apart from the age factor, the type of cell
   or tissue and other fine regulators, such as noncoding RNAs, impact
   biological processes underlying complex diseases, such as cancer and
   AD. Indeed, the role of miRNAs in complex diseases is now widely
   recognized ([64]Hébert and De Strooper, 2009; [65]Peng and Croce, 2016;
   [66]Quinlan et al., 2017; [67]Chen et al., 2018). MiRNAs exert
   pleiotropic effects because each miRNA can potentially target several
   mRNAs simultaneously, thereby influencing the expression of several
   genes and affecting a multitude of cellular pathways. As a consequence,
   miRNAs can have both beneficial and deleterious functions (antagonistic
   pleiotropy as, e.g., miRNA-34) ([68]Liu et al., 2012). Because genes
   associated with antagonistic pleiotropy are likely to be evolutionarily
   retained due to their earlier beneficial functions, miRNA pathways
   could provide mechanisms to suppress their potentially deleterious
   age-related activities ([69]Ahmad, 2017) but can also trigger the
   neurodegenerative process if they become dysregulated during aging
   ([70]Godlewski et al., 2019). Hence, miRNAs together with their target
   genes might play a key role in the inverse association between cancer
   and AD.

   The path of understanding the inverse relationship involves the
   exploration of the research from the field of –omics, the survey of the
   vast amount of cancer and AD literature, the investigation of the
   disease-specific databases, and the integration of cross-domain
   knowledge. The complexity of the cross-domain research might explain
   the fact that despite available knowledge on cancer and AD biology,
   only a few genes have been experimentally investigated with respect to
   the inverse relationship ([71]Lee Houck et al., 2018). Recently,
   computational approaches have been developed to facilitate the
   exploitation of the huge amount of diverse biological data aiming at
   the prioritization of genes in human diseases ([72]Moreau and
   Tranchevent, 2012). Text-mining approaches are effective in extracting
   biological information hidden in a massive amount of published
   biomedical articles and thus gaining insights on potential molecular
   biomarkers for complex diseases ([73]Doncheva et al., 2012). Indeed, by
   means of literature mining and network analysis approaches,
   [74]Bhasuran et al. (2018) have identified the functional association
   of genes in high-altitude diseases. Moreover, an integrative strategy
   that combined genomics data from various sources was instrumental to
   understand the molecular pathways involved in coronary artery diseases
   ([75]Zhao et al., 2016). PubMed provides knowledge (information
   accompanied with evidence) that is generated and published across
   distributed sources, a massive amount of data that can be used to
   derive gene-disease associations. Thus, text-mining tools (e.g.,
   Beegle; [76]ElShal et al., 2016) have been proposed to simplify the
   process of extracting from PubMed and other sources (e.g., Online
   Mendelian Inheritance in Man: OMIM) the most relevant gene sets known
   to be linked to a given disease. The usage of computational methods
   empowered also functional analysis by integrating different databases
   (e.g., Gene Ontology, KEGG), which might help the process of discovery
   of new biological hypothesis. Interestingly, the availability of
   comprehensive atlas of the human tissue transcriptome enables the
   integration of data mining results with additional information about
   the expression levels of protein-coding genes across many tissues
   (Genotype-Tissue Expression, GTEx project) ([77]Lonsdale et al., 2013).
   However, despite the effort of bioinformatics tools to integrate
   genomics data, the annotation of human genes or miRNAs for a specific
   purpose might be incomplete. To cope with this limitation, a manual
   annotation of candidate genes and miRNAs by cross-checking them in
   curated databases for both AD and cancer might be used. For AD, to our
   knowledge, two main databases are available: the Integrative Database
   for Gene Dysregulation in Alzheimer’s Disease (AlzBase) ([78]Bai et
   al., 2016) and the catalogue of 430 genes reported to be associated
   with AD from 823 publications (Alzgset) ([79]Hu et al., 2017). Focusing
   on cancer, two cancer databases can be used: the Cancer Gene Census
   (CGC) database ([80]Forbes et al., 2010) and the Pathology Atlas
   included in the Human Protein Atlas (HPA) project ([81]Uhlen et al.,
   2015). The Transcriptome Wide Association Study (TWAS) has been
   recently proposed to integrate gene expression measurements with
   genome-wide association studies (GWAS) to identify genes associated
   with complex traits, including AD and cancer ([82]Gusev et al., 2016).
   Likewise, the LongevityMap database, based on GWAS, is available to
   pick genes associated with longevity trait in human populations
   ([83]Tacutu et al., 2018).

   The research strategy conducted in this study aimed at identifying
   candidate genes, miRNAs, and biological hallmarks shared by cancer and
   AD. To maximize the benefits of multiple studies and –omics approaches,
   which on their own provided dispersed pieces of knowledge on cancer and
   AD, we adopted a multistep methodological approach by combining
   automatic exploration of biomedical literature, annotation by means of
   evidence-based databases and gene set functional enrichment analysis.

Methods

   A concise scheme of the computational strategy is depicted in
   [84]Figure 1 . The computational strategy included three main steps: a)
   identification of a gene set by the text-mining tool Beegle and gene
   annotation by referring to specific databases and genomic resources; b)
   gene set functional enrichment analysis, followed by network analysis
   and protein-protein interaction (PPI) module reconstruction by means of
   Metascape; c) identification of gene-miRNA interactions specific for AD
   and cancer, particularly focusing on the analysis of PPI modules.

Figure 1.

   [85]Figure 1
   [86]Open in a new tab

   Flowchart of the bioinformatics strategy.

Identification of Genes by Text-Mining and Annotation by Curated Databases

   By means of a text-mining tool, Beegle, using the combined keywords
   “cancer OR cancers AND Alzheimer OR Alzheimer’s disease,” we retrieved
   a list of genes (namely, ALZCAN gene set) associated to cancer and AD.
   To complete the description of ALZCAN gene set, we cross-checked it
   with gene sets currently present in the AD and cancer-specific
   databases. For this aim, we queried two AD-specific databases, namely,
   AlzBase, referred to as a meta-analysis database, including gene
   expression studies comparing AD versus healthy subjects in different
   brain areas ([87]http://alz.big.ac.cn/alzBase/ ) ([88]Bai et al.,
   2016), and Alzgset, a catalogue of 430 genes reported to be associated
   with AD from 823 publications ([89]Hu et al., 2017). To have a
   comprehensive annotation of gene expression levels, we referred to the
   GTEx database (GTEx v.6) ([90]https://www.gtexportal.org/home/datasets
   ), which provides the expression level for 53 human tissues, including
   12 different brain areas. Gene expression data as Reads Per Kilobase
   per Million mapped reads (RPKM) were downloaded from the UCSC Table
   Browser interface ([91]https://genome.ucsc.edu/ ). By using Matlab
   R2018b “clustergram and heatmap” function (Matlab software,
   MathWorks^©), the log10 transformed values (138 genes × 53 tissues)
   were visualized by a heat map applying an agglomerative hierarchical
   clustering method. For cancer information, we intersected the ALZCAN
   gene set with the CGC database ([92]Forbes et al., 2010)
   ([93]https://cancer.sanger.ac.uk/census ). We then downloaded the mRNA
   and protein expression data for both normal tissues and cancer types
   from HPA project v.18.1 ([94]https://www.proteinatlas.org/ ). Cancer
   data, available in the Pathology Atlas, included a prognostic
   classification of human genes based on the correlation analyses of the
   mRNA expression levels and the clinical outcome of more than 17
   different forms of human cancer. A gene was classified “prognostic” on
   the basis of the Kaplan-Meier survival analysis (p < 0.001). To assess
   the presence of associations between complex genetic traits (e.g., AD)
   and biological models (e.g., cancer types) for the genes being studied,
   TWAS resource ([95]http://twas-hub.org/ ) was queried. We retained as
   statistically significant only the associations with Chi^2 values ≤-3
   or ≥3. To flag if the ALZCAN gene was associated to longevity, the
   LongevityMap database (build 3) ([96]http://genomics.senescence.info/ )
   was queried, limiting the selection to Caucasian populations.

Functional Enrichment Analysis, Network, and PPI Module Reconstruction

   To perform the pathway enrichment analysis and the gene network
   reconstruction, we used the Metascape tool ([97]http://metascape.org )
   ([98]Tripathi et al., 2015) with the default parameters set. Imputing
   the gene set obtained from step a, pathway and enrichment analyses were
   carried out selecting the genomics sources: KEGG Pathway, GO Biological
   Processes, Reactome Gene Sets, Canonical Pathways, and CORUM
   ([99]Giurgiu et al., 2019). All genes in the genome were used as the
   enrichment background. Terms with p < 0.01, minimum count 3, and
   enrichment factor >1.5 (the enrichment factor being the ratio between
   observed count and the count expected by chance) were collected and
   grouped into clusters based on their membership similarities. More
   specifically, p values were calculated based on accumulative
   hypergeometric distribution, q values were calculated using the
   Benjamini-Hochberg procedure to account for multiple testing. Kappa
   scores were used as the similarity metric when performing hierarchical
   clustering on the enriched terms, and then sub-trees with similarity
   >0.3 were considered a cluster. The most statistically significant term
   within a cluster was chosen as the one representing the cluster. To
   further capture the relationship among terms, a subset of enriched
   terms was selected and rendered as a network plot, where terms with
   similarity >0.3 are connected by edges. The terms with the best p
   values from each of the 20 clusters were selected, with the constraint
   that there were no more than 15 terms per cluster and no more than 250
   terms in total. Subsequently, using Metascape default parameters, based
   on PPI enrichment analysis, we run a module network reconstruction
   based on the selected genomics databases. The resulting network was
   constructed containing the subset of proteins that form physical
   interactions with at least one other list member. Subsequently, by
   means of Molecular Complex Detection (MCODE) algorithm, we first
   identified connected network components, then a pathway and process
   enrichment analysis was applied to each MCODE component independently
   and the three best-scoring (by p value) terms were retained as the
   functional description of the resulting modules. The overall Metascape
   analysis allowed the identification of a list of significant
   overrepresented GO and KEGG categories, network plot, and PPI MCODE
   components (modules) linked to AD and cancer.

Identification of Gene-miRNA Interactions Specific for AD and Cancer

   To identify miRNAs that target the genes belonging to the ALZCAN gene
   set, first, we carried out a miRNA enrichment analysis by ToppGene
   ([100]Chen et al., 2009) using default parameters. Then, from ToppGene
   results, we considered only the data related to the experimentally
   validated miRNA gene targets referred to the miRTarBase database
   ([101]http://mirtarbase.mbc.nctu.edu.tw/ ) ([102]Chou et al., 2018). A
   q value threshold of 0.05 after Bonferroni correction was applied for a
   given miRNA to be included in the enriched data set. Last, to further
   explore the involvement of a given miRNA in the pathogenesis of AD and
   cancer, we manually consulted the Human microRNA Disease Database
   version 3.0 (HMDD v3.0) ([103]http://www.cuilab.cn/hmdd ) ([104]Li et
   al., 2014), which is a curated database that takes into account
   experiment-supported evidence for human microRNA and disease
   associations. By querying the database, we retrieved miRNAs associated
   with either cancer (any type of cancer) or AD, and we flagged them for
   belonging to both diseases. Then, by combining the results from steps a
   and b ([105] Figure 1 ), we formulated novel gene-miRNA interactions.

Results

Description of ALZCAN Gene Set Linked to Both Cancer and AD

   As a first step ([106] Figure 1 ), by means of Beegle, we retrieved a
   total of 138 genes (ALZCAN gene set) that were further annotated by
   intersecting them with the AD databases (AlzBase and Alzgset) as well
   as cancer databases (CGC and HPA) ([107] Figure 2 , [108]Supplementary
   Table S1 ). Only 4 out of 138 genes were not present in any of the
   selected databases. After querying of AlzBase, 123 out of 138 genes
   were computed as differentially expressed genes (DEG) with up and/or
   downmodulation ([109] Supplementary Table S1 ). In particular, we found
   that the modulation of gene expression levels of PIN1, UCHL1, PRKACA,
   CTNNB1, HSP90AA1, EGFR, NOTCH1, SQSTM1, and KRAS was significantly
   altered in AD ([110] Supplementary Table S1 ). Moreover, 46 out of 138
   genes were also indicated by GWAS studies as associated with AD
   (Alzgset database). A total of 14 genes were included neither in
   AlzBase nor in Alzgset ([111] Figure 2 ). By means of GTEx database, we
   retrieved the expression of the ALZCAN gene set across 53 human tissues
   ([112] Supplementary Table S2 ). Referring to the 12 human brain
   tissues, on average, we found that out of 138, 39 genes displayed gene
   expression levels below 10 RPKM whereas 21 had gene expression levels
   over 50 RPKM ([113] Supplementary Table S2 ). The clustering analysis
   by genes and tissues showed that the 12 human brain tissues clustered
   together ([114] Figure S1 ). In the heat map, we recognized a few genes
   displaying an opposite tuning of expression in the cluster of brain
   tissues compared to others. For example, we observed high brain tissue
   expression levels for FEZ1 and UCHL1 genes compared to other tissues
   or, vice versa, very low brain expression levels for both S100A9 and
   S100A8 genes. We flagged 16 genes as Cancer Gene Census ([115] Figure 2
   ). By referring to the HPA data ([116] Supplementary Table S3 ), 72
   among out of 138 genes were classified as disease-related genes.
   According to the Pathology Atlas, 115 genes were detected at protein
   level in normal or cancer tissues and 87 out of 138 genes were
   associated with either favorable or unfavorable prognostic scores
   depending on cancer type ([117] Supplementary Table S3 ). By means of
   TWAS, we recovered seven genes (APOC1, APOE, BIN1, HMGB1, PRKAA1,
   SQSTM1, and UCP2) significantly associated to AD traits and some cancer
   models ([118] Figure 2 , [119]Supplementary Table S1 ). Notably, we
   found two genes displaying a negative association between Alzheimer’s
   Disease (including proxy) trait and Lung Squamous Cell Carcinoma model
   with Chi^2 value of -18.3 (APOE) and -22.7 (APOC1) (see TWAS Trait
   association table at twas-hub.org/genes/APOE/and
   twas-hub.org/genes/APOC1/). Considering the LongevityMap database, we
   found 27 genes associated to longevity loci ([120] Supplementary Table
   S1 ).

Figure 2.

   [121]Figure 2
   [122]Open in a new tab

   Four-set Venn diagram of the overlap between the ALCAN gene set with
   four databases. A total of 134 out of 138 were found to have an
   intersection with AlzBase ([123]http://alz.big.ac.cn/alzBase/ ),
   Alzgset ([124]Hu et al., 2017), prognostic genes on the basis of HPA
   ([125]https://www.proteinatlas.org/ ), and CGC
   ([126]https://cancer.sanger.ac.uk/census ). Four genes (AKAP2, CAPN9,
   TKTL1, and WNT1) were not found overlapping in any of the selected
   databases. (*)TWAS ([127]http://twas-hub.org/ ) significantly
   trait-model associated genes. See [128]Supplementary Table S1 and
   [129]S3 for more details.

Functional Enrichment Analysis Identified the Most Relevant Genes and
Gene-Gene Modules

   As a second step ([130] Figure 1 ), by means of Metascape, we found
   many overrepresented GO-BP terms ([131] Figure 3A , [132]Supplementary
   Table S4 ) and 124 out of 138 genes were included in a biological
   network ([133] Figure 3B ). Among the retrieved GO-BP terms, those
   related to response to oxidative stress (54 genes), cellular response
   to nitrogen compound (50 genes), aging (30 genes), positive regulation
   of cell death (45 genes) were the most significant. Within the enriched
   KEGG pathways, we found that the Alzheimer’s pathway (hsa05010)
   associated with 18 ALZCAN genes.

Figure 3.

   [134]Figure 3
   [135]Open in a new tab

   Functional enrichment analysis by Metascape. (A) Bar chart of clustered
   enrichment ontology categories (GO and KEGG terms); (B) enrichment
   ontology clusters including 124 genes. Each term is represented by a
   circle node, where its size is proportional to the number of input
   genes falling into that term, and its color represents its cluster
   identity (i.e., nodes of the same color belong to the same cluster).
   Terms with a similarity score >0.3 are linked by an edge (the thickness
   of the edge represents the similarity score). The network is visualized
   with Cytoscape (v3.1.2) with “force-directed” layout and with edge
   bundled for clarity. See [136]Supplementary Table S4 for more details.

   The MCODE enrichment analysis based on PPI enrichment analysis resulted
   in a network ([137] Figure 4A ) characterized by the presence of four
   PPI modules, including 37 genes from which four genes, EGFR, APP, WNT1,
   CCR5, were defined as seed genes ([138] Figure 4B , [139]Supplementary
   Table S5 ). Among the top list of enriched terms of Module 1 (22
   genes), we found three major GO-BP clusters, positive regulation of
   programmed cell death, cellular response to oxidative stress,
   autophagy, and two KEGG pathways, glioma and longevity regulating
   pathway ([140] Table 1 ). The top list of enriched categories for
   Module 2 (8 genes) included regulation of Wnt signaling pathway (APP,
   ESR1, GSK3B, NOTCH1, PRKN, PIN1, and PSEN1) and regulation of growth
   (APP, GSK3B, NOTCH1, PRKN, and PIN1). Module 3 (4 genes) included
   regulation of cellular response to stress (BRCA1, CTNNB1, HSP90AA1, and
   WNT1) and breast cancer (BRCA1, CTNNB1, and WNT1). Module 4 (3 genes)
   was associated with G alpha (i) signaling events (ADRA2B, CASR, and
   CCR5).

Figure 4.

   [141]Figure 4
   [142]Open in a new tab

   MCODE enrichment analysis by Metascape. (A) PPI interaction network.
   MCODE algorithm was applied to clustered enrichment ontology terms to
   identify neighborhoods where proteins are densely connected. Each MCODE
   network is assigned a unique color. (B) PPI MCODE component. GO
   enrichment analysis was applied to each MCODE network to assign
   “meanings” to the network component. See [143]Supplementary Table S5
   for more details. Red, blue, green, and violet colors indicate modules
   1, 2, 3, and 4, respectively.

Table 1.

   Top ranked clusters of enrichment terms of Module 1.
   GO Biological Processes and KEGG terms* Genes
   GO:0043068-positive regulation of programmed cell death CDKN2A, ATF2,
   GSK3A, MAPT, PPARG, SNCA, SOD1, SOS1, TP53, SQSTM1, HDAC6, PPARGC1A
   GO:0034599-cellular response to oxidative stress EGFR, MAPT, PRKAA1,
   SNCA, SOD1, TP53, BECN1, HDAC6, PPARGC1A
   GO:0061919-process utilizing autophagic mechanism GSK3A, MAPT, PRKAA1,
   SNCA, TP53, UCHL1, BECN1, SQSTM1, HDAC6, PPARGC1A
   hsa05214: Glioma CDKN2A, EGF, EGFR, IGF1R, KRAS, SOS1, TP53
   hsa04211: Longevity regulating pathway ATF2, IGF1R, KRAS, PPARG,
   PRKAA1, TP53, PPARGC1A
   [144]Open in a new tab

   *Selected enrichment categories of Module 1. For more details, see
   [145]Supplementary Table S4 .

Identification of Gene-MiRNA Interactions Linked to Both AD and Cancer

   In the third step ([146] Figure 1 ), the ALZCAN gene set was enriched
   with the information on specific miRNA targets. By means of ToppGene,
   we performed an enrichment analysis of the ALZCAN gene set for
   experimentally validated miRNA target sites included in the miRTarBase
   database. The analysis resulted in 90 significantly enriched miRNAs
   having functional miRNA-target interactions (MTIs) with the genes
   belonging to the gene set ([147] Supplementary Table S6 ). For 61 of
   them, we found that strong experimental evidence (reporter assays,
   Western blot experiments, and qPCR assays) supported the MTIs.
   Moreover, according to the HMDD database, we found evidence referring
   to the involvement of 21 miRNAs to both AD and cancer ([148] Table 2 ).
   Interestingly, among the most significantly enriched miRNAs, we
   retrieved those miRNAs or members of miRNA families that are most
   widely recognized as involved in AD pathogenesis, including miR-16-5p,
   miR-15a-5p, miR-15b-5p, miR-195-5p (all members belonging to the
   miR-15/107 family), miR-34a-5p, miR-181a-5p, miR-155-5p, miR-125b-5p,
   miR-9-5p, and miR-146a-5p ([149]Femminella et al., 2015; [150]Miya
   Shaik et al., 2018).

Table 2.

   List of 21 miRNAs linked to both AD and cancer according to HMDD^a.
   miRNA ALZCAN (Gen)^b miRNA Targets
   hsa-miR-16-5p 29 (1,557) KRAS, CDKN2A, ABCC4, HTRA1, CIB1, ASCC1, WT1,
   RPSA, TKTL1, APP, PTGS2, GSK3B, HIGD1A, ADRA2B, IGF1R, BRCA1, EGFR,
   BSG, MTOR, SQSTM1, AGER, PRKAA1, ALDH2, SNCG, TARBP2, HSPA9, PRNP,
   TP53, HSP90AA1
   hsa-miR-34a-5p 20 (735) CDKN2A, RCAN1, WNT1, CAPN1, BAG3, HMGB1, PPARA,
   XRCC1, DLL1, SIRT1, AKR7A2, IGF1R, PPP3R1, BRCA1, PIK3CG, TNF, CTNNB1,
   BECN1, NOTCH1, TP53
   hsa-miR-26b-5p 25 (1,874) CAPN9, TSPAN12, HTRA1, CIB1, STUB1, NRDC,
   FOLH1, PARK7, BAG3, INSR, HMGB1, LRP1B, PTGS2, GSK3B, IGF1R, PPP3R1,
   BRCA1, PIK3CG, PRKAA1, TLR4, PRKN, ADAM17, ABCG2, ATP2A2, TARBP2
   hsa-miR-15a-5p 16 (717) CDKN2B, ASCC1,WT1, TKTL1, APP, ATF2, GSK3B,
   HIGD1A, ADRA2B, BRCA1, BSG, UCP2, PRKAA1, SNCG, TARBP2, TP53
   hsa-miR-181a-5p 14 (553) KRAS, HDAC6, GSKIP, PTGS2, SIRT1, BRCA1,
   PEBP1, UCHL1, PRKN, ADAM17, ABCG2, CTNNB1, IL1A, NOTCH1
   hsa-miR-155-5p 16 (904) KRAS, CDKN2A, ABCC4, HTRA1, PSEN1, ADAM10,
   SIRT1, GSK3B, AKR7A2, EGFR, SH3PXD2A, PEBP1, DHCR24, AKAP10, CTNNB1,
   IL6
   hsa-miR-125b-5p 12 (431) CDKN2A, ABCC4, NRDC, S100A8, IGF1R, EGFR,
   AKAP2, TNF, MMP2, NME2, BACE2, TP53
   hsa-miR-9-5p 11 (350) ESR1, PPARA, SIRT1, GSK3B, LMNA, PRKAA1, LRP1,
   MMP2, BECN1, IL6, NOTCH1
   hsa-miR-195-5p 12 (639) INSR, ASCC1, TKTL1, APP, GSK3B, HIGD1A, ADRA2B,
   BSG, AGER, PRKAA1, SNCG, TARBP2
   hsa-miR-146a-5p 8 (102) PTGS2, BRCA1, EGFR, SQSTM1, TLR4, IL6, NOTCH1,
   SOS1
   hsa-miR-17-5p 15 (1,181) ESR2, APEX1, HMGB1, RPSA,APP, AKR7A2, PPP3R1,
   SQSTM1, TNF, MMP2, NBR1, HSPB2, PRNP, TP53, HSP90AA1
   hsa-miR-27a-3p 10 (429) KRAS, APEX1, PPARG, GSK3B, EGFR, PIK3CG, SNCG,
   NME2, TP53, SOS1
   hsa-miR-15b-5p 12 (760) INSR, ASCC1, TKTL1, APP, GSK3B, HIGD1A, ADRA2B,
   BSG, PRKAA1, SNCG, ATP2A2, TARBP2
   hsa-miR-21-5p 11 (611) STUB1, HMGB1, PPARA, ATF2, ABCB1, IGF1R, BRCA1,
   EGFR, TLR4, MMP2, IL1B
   hsa-miR-142-3p 9 (389) ESR1, HMGB1, XRCC1, PIK3CG, ABCG2, CTNNB1, IL1A,
   ATP2A2, IL6
   hsa-miR-26a-5p 9 (457) ESR1, PTGS2, GSK3B, BRCA1, PIK3CG, PRKAA1,
   ADAM17, IL6, DNMT3B
   hsa-miR-100-5p 7 (251) ESR1, APEX1, HMGB1, IGF1R, MTOR, BECN1, ATP2A2
   hsa-let-7b-5p 13 (1,215) NRDC, RCAN1, SIGMAR1, HMGB1, RPSA, PTGS2,
   GSK3A, PPARGC1A, IGF1R, SH3PXD2A, TLR4, ATP2A2, HSP90AA1
   hsa-miR-7-5p 9 (578) SIGMAR1, IDE, AKR7A2, IGF1R, EGFR, SQSTM1, PIK3CG,
   TLR4, SNCA
   hsa-miR-375 8 (477) CDKN2B, IGF1R, PEBP1, CTNNB1, IFI6, TP53, HSP90AA1,
   DNMT3B
   hsa-miR-181c-5p 6 (291) KRAS, GSKIP, SIRT1, PEBP1, ADAM17, IL1A
   [151]Open in a new tab

   ^aHMDD, Human MicroRNA Disease Database (HMDD v3.0); ^bnumber of genes
   of ALZCAN gene set (total number of genes in the genome according to
   ToppGene). For more details, see [152]Supplementary Table S6.

   Limiting the enrichment analysis to the 37 genes belonging to the four
   modules, we highlighted subsets of 13, 9, and 10 significantly enriched
   miRNAs with functional MTIs for modules 1, 2, and 3, respectively
   ([153] Table 3 ). No significant miRNAs were found for genes within
   Module 4. Notably, all miRNAs but miR-34a-5p, which has target genes in
   both modules 1 and 3, were module-specific. According to HMDD v3.0,
   among the 13 enriched miRNAs targeting genes belonging to Module 1,
   eight miRNAs (namely, miR-16-5p, miR-27a-3p, miR-206, miR-125b-5p,
   miR-7-5p, miR-146a-5p, miR-21-5p, and miR-34a-5p) have been associated
   to both AD and cancer. In addition, miR-9-5p and miR-296-5p in Module
   2, as well as miR-139-5p and miR-375 in Module 3, have been linked to
   AD and cancer ([154] Table 3 ). It is worth noting that all of the
   eight miRNAs in Module 1 that are involved in both AD and cancer, apart
   from miR-34a-5p, share EGFR as a validated target gene; IGF1R is
   targeted by six miRNAs, and TP53 is targeted by four miRNAs. Last, a
   third receptor gene, NOTCH1, is the target of miR-9-5p and miR-296-5p,
   the two enriched miRNAs in Module 2 with involvement in AD and cancer.

Table 3.

   List of miRNAs and their target genes belonging to PPI modules.
   PPI module miRNA ID Target genes (miRTarBase)
   Module 1 hsa-miR-16-5p EGFR, CDKN2A, KRAS, SQSTM1, HSPA9, TP53, IGF1R,
   ALDH2, PRKAA1
   hsa-miR-30a-5p STUB1, EGFR, BECN1, TP53, IGF1R, PRKAA1
   hsa-miR-27a-3p EGFR, KRAS, TP53, PPARG, SOS1
   hsa-miR-206 KRAS, SOD1, IGF1R
   hsa-miR-125b-5p EGFR, CDKN2A, TP53, IGF1R
   hsa-miR-125b-1-3p TP53, IGF1R
   hsa-miR-7-5p EGFR, SQSTM1, IGF1R, SNCA
   hsa-miR-146a-5p EGFR, SQSTM1, SOS1
   hsa-miR-21-5p STUB1, EGFR, ATF2, IGF1R
   hsa-miR-548e-5p BECN1, SQSTM1, PRKAA1
   hsa-miR-877-5p KRAS, SQSTM1, UCHL1
   hsa-miR-34a-5p CDKN2A, BECN1, TP53, IGF1R
   hsa-miR-150-3p TP53, IGF1R
   Module 2 hsa-miR-9-5p NOTCH1, ESR1, GSK3B
   hsa-miR-744-5p PIN1, GSK3B, PRKACA
   hsa-miR-1910-5p ESR1, GSK3B
   hsa-miR-296-5p NOTCH1, PIN1
   hsa-miR-874-3p ESR1, PIN1
   hsa-miR-6073 ESR1, APP
   hsa-miR-935 NOTCH1, PSEN1
   hsa-miR-4709-3p ESR1, APP
   hsa-miR-22-3p ESR1, PRKACA
   Module 3 hsa-miR-34a-5p BRCA1, CTNNB1, WNT1
   hsa-miR-139-5p HSP90AA1, WNT1
   hsa-miR-152-3p HSP90AA1, WNT1
   hsa-miR-548l HSP90AA1, CTNNB1
   hsa-miR-148a-3p HSP90AA1, WNT1
   hsa-miR-1226-3p HSP90AA1, CTNNB1
   hsa-miR-1826 CTNNB1
   hsa-miR-101-3p HSP90AA1, CTNNB1
   hsa-miR-148b-3p HSP90AA1, WNT1
   hsa-miR-375 HSP90AA1, CTNNB1
   [155]Open in a new tab

   Bold, miRNA associated with both Alzheimer’s disease and cancer based
   on the Human microRNA Disease Database (HMDD v3.0).

Discussion

   The biological understanding of the inverse occurrence between the two
   diseases, widely supported by epidemiological studies, is still an open
   question. In recent years, a significant progress has been made in
   exploring the molecular mechanisms of both AD and cancers. Nowadays, by
   means of the advancements in integrative bioinformatics, we can exploit
   the massive amount of -omics data and biological information to tackle
   the complex nature of both these age-related diseases on much larger
   scales. In this study, by mining of biomedical literature and by
   delineating the interconnection of genes by means of pathway-based and
   network-based analyses, we retrieved a pool of 138 genes (ALZCAN gene
   set) and their interactions, thus obtaining valuable data for further
   analysis on their relationship with both cancer and AD. Moreover, by
   retrieving miRNAs targeting the ALZCAN gene set, we postulated some
   promising miRNA-gene interrelationships evidencing the complex gene
   expression regulation that might be shared between the two diseases.
   This integrated bioinformatics approach, by focusing on the biological
   function of genes, pathways, and network analysis, was not only
   instrumental in yielding a more comprehensive view of biological
   processes but also more robust in terms of the influence of
   false-positive genes that might be retrieved by means of basic
   automatic text-mining procedures.

   As revealed by functional enrichment analysis, we observed that 124 out
   of 138 ALZCAN genes are densely interconnected in many metabolic
   pathways and cellular processes ([156] Figure 3B ). Terms such as
   response to oxidative stress, cellular response to nitrogen compound,
   aging, and positive regulation of cell death were overrepresented in
   the ALZCAN gene set. Interestingly, many of these processes have been
   recently enlisted by [157]Lee Houck et al. (2018) that reviewed the
   biological and genetic overlap between cancer and neurodegeneration. As
   expected, because of the presence of an emerging body of scientific
   literature, we found that TP53, PIN1, and APP genes were top-ranked by
   text-mining via Beegle ([158] Supplementary Table S1 ), confirming them
   as important players in the inverse association between cancer and AD
   ([159]Shafi, 2016; [160]Feng et al., 2017; [161]Galvão et al., 2019).
   The scenarios underlying the onset of both AD and cancer are however
   pretty complicated, and many factors (e.g., aging, oxidative stress,
   inflammation) are affecting the cell performance in specific tissues.
   Of note, amid the biological processes highlighted by our study, we
   found processes implicated in the cellular homeostasis and adaptation
   to stress. The human tissues combat age-related diseases, such as
   cancer and neurodegeneration by enhancing intracellular processes
   linked to the protein turnover pathways (e.g., proteolysis and
   autophagy) by which cellular components are degraded and recycled
   ([162]Barbosa et al., 2018). The protein degradation process,
   responsible for the correct protein clearance, is a cooperating system
   between the ubiquitin-proteasome system (UPS) and autophagy for
   maintaining protein homeostasis, particularly within postmitotic cells
   (nervous system cells) ([163]Vilchez et al., 2014; [164]Leidal et al.,
   2018). It is important to highlight that aging is a manifestation of
   the accumulation of cellular damage over time that is, in turn,
   depending on nine hallmarks, including loss of proteostasis, reactive
   oxygen species, genome instability ([165]López-Otín et al., 2013).
   Hallmarks of aging are significant with respect to both cancer and AD.
   In the brain, impaired neuronal autophagy promotes the accumulation of
   toxic protein aggregates and damaged organelles linked to dementia.
   Intriguingly, autophagy has been shown to be important for multiple
   aspects of cancer biology, including cell metabolism, protein and
   organelle turnover, and cell survival ([166]Santana-Codina et al.,
   2017). Both the enhancement and inhibition of autophagy have been
   suggested as therapeutic strategies in cancer. Noteworthy, a recent
   transcriptomics meta-analysis conducted comparing AD and three cancer
   types indicated protein degradation as a candidate biological process
   orchestrating the comorbidity between AD and cancer ([167]Ibáñez et
   al., 2014). In addition, a meta-analysis conducted by taking into
   account the physicochemical properties of gene products involved in
   cancer and senile dementias strongly supported the role of protein
   unfolding as trade-off factors triggering pathological conditions
   ([168]Klus et al., 2015).

   By means of gene-gene network reconstruction analysis, we found four
   functional modules that rely on the crosstalk between several
   biological processes and pathways ([169] Figure 4B ). In Module 1, we
   found 10 genes that play a role in protein turnover system that
   includes autophagy and UPS ([170] Table 1 , [171]Supplementary Table S5
   ). Here, we focused the discussion on five genes: SQSTM1, UCHL1, STUB1,
   BECN1, and HSPA9. SQSTM1/p62 (sequestosome 1) is an autophagy receptor
   required for selective macroautophagy and functions as a bridge between
   polyubiquitinated cargo and autophagosomes. SQSTM1/p62 also serves as a
   signaling hub for multiple pathways associated with neurodegeneration,
   providing a potential therapeutic target in the treatment of
   neurodegenerative diseases ([172]Ma et al., 2019). By database
   cross-checking, we found that SQSTM1/p62 is classified as a
   disease-related gene and is upregulated in AD (according to AlzBase).
   Intriguingly, the upregulation and/or inefficient degradation of p62
   has been linked with tumorigenesis ([173]Zhang et al., 2019). By means
   of TWAS, we retrieved significant data on expression quantitative trait
   locus (eQTL) for SQSTM1, finding negative associations (Chi^2 value
   equal to -3) between AD trait and two tissue models, Brain Prefrontal
   Cortex and Thyroid Carcinoma, thus suggesting a link between the gene
   expression variation and the genotypes, deserving further investigation
   with respect to the inverse relationship between cancer and AD.
   SQSTM1/p62 is gaining attention because it is involved in dementia and
   cancers ([174]Bitto et al., 2014; [175]Taniguchi et al., 2016). UCHL1
   is an ubiquitin-protein hydrolase involved in the processing of both
   ubiquitin precursors and ubiquitinated proteins. Although it is highly
   expressed in human brain tissues (GTEx database, [176]Figure S1 ),
   UCHL1 is implicated in neurodegenerative diseases ([177]Bishop et al.,
   2016). This gene is also found consistently downregulated in AD
   (AlzBase data) but, conversely, upregulated in cancers with a
   prognostic score ([178] Supplementary Table S3 ). Recently, more
   attention has been paid to the relationship between malignancies and
   the UCH family, which plays different roles in the progression of
   several tumors ([179]Fang and Shen, 2017). STUB1(CHIP) gene encodes for
   an E3 ubiquitin-protein ligase, ubiquitously expressed in human
   tissues, targets misfolded chaperone substrates toward proteasomal
   degradation and modulates the activity of several chaperone complexes,
   including Hsp70, Hsc70, and Hsp90. STUB1 has also emerged as a hub gene
   in the regulation of biosynthetic processes in the brain of the
   accelerated senescence mice (SAMP8) used as a robust model of AD
   ([180]Cheng et al., 2013). The role of STUB1 in human malignant
   disorders has been reviewed by [181]Cao et al. (2016). Although the
   modulation of STUB1 expression in AD and cancers is controversial, it
   might be an interesting gene deserving to be further investigated.
   Concerning the molecular processes involved in controlling protein
   folding, we also found BECN1 (Beclin 1), a protein involved in the
   regulation of autophagy, which has been found reduced in patients with
   AD ([182]Jaeger et al., 2010). Notably, the gene expression level of
   BECN1 is found significantly altered in cancer and AD but in opposite
   manner ([183]Ibáñez et al., 2014). Beclin1 might act as a protein
   platform interlinking autophagy and apoptosis ([184]Kang et al., 2011)
   and, in turn, might be linked to inflammation ([185]Salminen et al.,
   2013). The relevance of Beclin1, as well as autophagy, is a matter of
   ongoing debate in cancer therapy ([186]Toton et al., 2014;
   [187]Galluzzi et al., 2015). Together with STUB1 and UCHL1, our
   analysis highlighted a member of the heat shock protein family, HSPA9
   (Mortalin), which plays a key role in preventing protein misfolding and
   aggregation. Its gene expression is oppositely regulated in AD and
   cancer. Lately, Mortalin has been suggested as a potential therapeutic
   target for AD ([188]Ou et al., 2014). In the cancer field, findings
   support its role in the induction of epithelial-mesenchymal transition
   (EMT), prompting further investigation of its therapeutic value for
   contrasting metastasis ([189]Na et al., 2016). Altogether, this
   supporting evidence suggests that protein clearance system in
   age-associated diseases might be attractive multifaceted molecular
   machinery, with therapeutic potential for both cancer and AD.

   Among the enriched biological processes of Module 1, we also obtained
   the terms aging and positive regulation of cell death, including
   CDKN2A, TP53, EGFR genes that play a pivotal biological role. These
   genes are well-known molecular players in cell proliferation, survival,
   adhesion, and apoptosis ([190]LaPak and Burd, 2014). By manual
   annotation, we found that CDKN2A and TP53 were present in all databases
   (AlzBase, Alzgset, CGC, and HPA) ([191] Figure 2 ). An increased level
   of CDKN2A (cyclin-dependent kinase inhibitor 2A) resulted in both brain
   and blood cells from APP/PS1 mice ([192]Esteras et al., 2012).
   Interestingly, linkage and association studies linked the CDKN2A locus
   (9p21.3) to late-onset AD (LOAD study) families ([193]Züchner et al.,
   2008). The gene is upregulated in AD (AlzBase) as well as in cancer.
   The tumor protein p53 (TP53) is one of the best-known hallmarks of
   cancer and has been linked to the longevity trait in the human
   population as well as to AD ([194]Lanni et al., 2012). TP53 network has
   been suggested as a candidate signaling cascade linked to the inverse
   relationship between cancer and AD. EGFR (epidermal growth factor
   receptor) is widely recognized for its importance in cancer and has
   been catalogued as an oncogene. Recently, a pivotal role of EGFR in AD
   has been proposed ([195]Shafi, 2016). The finding of the overexpression
   of EGFR in AD (AlzBase) is however in contrast with its absence in the
   central core of AD neuritic plaques ([196]Birecree et al., 1991). A
   polymorphism of this gene has been associated with glioma ([197]Sanson
   et al., 2011). Whether this gene polymorphism could influence its
   transcriptional activity in the etiology of AD deserves further
   investigation.

   Besides the multiple factors implicated in AD and cancer, increasing
   evidence points toward a role of Wnt signaling in the etiology of both
   diseases. Notably, Module 2 was enriched in the Wnt signaling pathway,
   where nine genes were interconnected ([198] Figure 4B ). Among them, we
   found PIN1 that, not surprisingly, was ranked by Beegle in the top
   position together with TP53 ([199] Supplementary Table S1 ). Recent
   studies have underlined the pivotal role of PIN1 (peptidylprolyl
   cis/trans isomerase, NIMA-interacting 1) in the inverse association
   between cancer and AD ([200]Lee et al., 2011; [201]Driver et al., 2012;
   [202]Harris et al., 2014; [203]Driver et al., 2015). In Module 2, PIN1
   connected to GSK3β and CTNNB1 genes ([204] Figure 4B ). GSK3β (glycogen
   synthase kinase 3 beta), a constitutively active protein kinase, acts
   as a negative regulator in the hormonal control of glucose homeostasis,
   Wnt signaling, and regulation of transcription factors and microtubules
   by phosphorylating and inactivating glycogen synthase. In Wnt
   signaling, GSK3β forms a multimeric complex with APC, AXIN1, and
   CTNNB1/beta-catenin and phosphorylates the N-terminus of CTNNB1,
   leading to its degradation mediated by ubiquitin/proteasomes. Neurons
   derived from iPSCs of sporadic AD reveal elevated Tau
   hyperphosphorylation, increased amyloid levels, and GSK3β activation
   ([205]Ochalek et al., 2017). The gene is mostly upregulated in AD
   studies (Alzbase) and has been associated with AD in GWAS studies
   (Alzgset). Aberrant nuclear GSK3β may represent a potential target for
   the clinical treatment of human breast and squamous cell carcinoma
   ([206]Ugolkov et al., 2018). Altogether, our analysis highlighted
   autophagy, UPS, and Wnt signaling as molecular mechanisms that could
   exert an opposite function according to the gene-environment conditions
   of the specific human tissues.

   As a third step of our investigation ([207] Figure 1 ), we considered
   the key role of miRNAs in the complex regulation of gene expression and
   their potential pleiotropic action. Focusing on the 37 genes belonging
   to PPI modules, we identified 12 miRNAs that turned out to be
   associated with both AD and cancer ([208] Table 3 ). Among these 12
   miRNAs, we found several targeting genes involved in autophagy and UPS,
   including miR-146a-5p (targets SQSTM1/p62), miR-34a-5p (targets BECN1),
   and miR-21-5p (targets STUB1/CHIP). MiR-146a-5p is upregulated in AD
   brain and in human neural cells following a number of different stimuli
   and stresses, including cytokines, β-amyloid, and oxidative stress
   ([209]Lukiw et al., 2008; [210]Li et al., 2011). In cancer, miR-146a-5p
   was found acting as both oncogene and oncosuppressor ([211]Li et al.,
   2010; [212]Pacifico et al., 2010; [213]Hou et al., 2012). Upregulation
   of miR-146a-5p leads to inflammatory response in AD but has an
   anti-inflammatory effect in cancer ([214]Lukiw et al., 2008; [215]Cui
   et al., 2010; [216]Rusca and Monticelli, 2011; [217]Khorrami et al.,
   2017). Moreover, polymorphisms in the miR-146a gene have been involved
   in the genetic susceptibility to both AD and cancer ([218]Cui et al.,
   2014; [219]Xie et al., 2014). MiR-146a-5p targets SQSTM1/p62 (Module
   1). Of note, p62 can inhibit neuronal death induced by β-amyloid
   ([220]Geetha et al., 2012) and its inactivation in mice leads to an
   age-dependent constitutive activation of GSK3β, resulting in
   hyperphosphorylated tau, neurofibrillary tangles, and neurodegeneration
   ([221]Ramesh Babu et al., 2008). Taken together, these data suggest
   that miR-146a-5p upregulation might promote neurodegeneration in AD
   through inhibition of p62 activity. On the contrary, loss of miR-146a
   and overexpression of p62 promote cell survival and proliferation in
   cancer ([222]Fang et al., 2014).

   MiR-34a-5p has been widely recognized as a key player in tumor
   suppression, and its expression is silenced in several cancers
   ([223]Farooqi et al., 2017; [224]Slabáková et al., 2017). Conversely,
   miR-34a-5p is overexpressed in AD patients and mouse models; increased
   miR-34a-5p levels repress genes involved in synaptic plasticity and
   energy metabolism ([225]Sarkar et al., 2016). MiR-34a knockout also
   promotes cognitive function in APP/PS1 mice by both inhibiting the
   amyloidogenic process and increasing synaptic plasticity ([226]Jian et
   al., 2017; [227]Xu et al., 2018). MiR-34a-5p targets in Module 1
   include BECN1. The elevated miR-34a-5p levels marking AD condition are
   expected to reduce the cellular amount of Beclin1 and hence to affect
   the efficiency of the autophagy machinery in clearing protein
   aggregates, providing a link between miR-34a dysregulation and
   β-amyloid pathology in AD. In tumors, the picture is more complicated
   because autophagy, in general, and Beclin1, in particular, might exert
   a dual role ([228]White, 2012; [229]Gong et al., 2013). MiR-34a-5p
   downregulation observed in many tumors would positively affect BECN1
   expression, leading to enhanced autophagy, but the effect of this
   improved autophagic flux is likely to be dependent on cancer type,
   cancer stage, and cell type.

   MiR-21-5p is frequently overexpressed in various human tumors and
   cancer stem cells and seems to play an important role in the oncogenic
   process because it has been associated with high proliferation,
   invasion, and metastatic potential, as well as with low apoptosis
   ([230]Pfeffer et al., 2015). Oppositely, miR-21-5p upregulation
   inhibits cell apoptosis induced by β-amyloid in a GSK3β-dependent way,
   suggesting a protective role in AD ([231]Feng et al., 2018). Moreover,
   Tau can enhance miR-21-5p activity ([232]Chauderlier et al., 2018).
   Because STUB1 is a miR-21-5p target, it can be speculated that in AD,
   augmented miR-21-5p levels can exacerbate the neurodegenerative process
   by reducing STUB1/CHIP activity, leading to less efficient Tau and
   β-amyloid clearance ([233]Lee et al., 2018). In cancer, miR-21-5p
   upregulation and consequent STUB1 downmodulation might have opposite
   effects, according to the oncogenic or tumor-suppressive effect of
   STUB1 ([234]Cao et al., 2016).

   Another interesting miRNA, miR-9-5p, is known as one of the most highly
   expressed miRNAs in the vertebrate brain and plays a pivotal role in
   its development ([235]Coolen et al., 2013; [236]Radhakrishnan and Alwin
   Prem Anand, 2016). MiR-9-5p levels are mostly downregulated in AD
   ([237]Miya Shaik et al., 2018), whereas both miR-9-5p upregulation and
   downregulation have been reported in human cancers, where it can either
   support or suppress tumor development ([238]Nowek et al., 2018).
   MiR-9-5p targets three genes belonging to Module 2, namely, NOTCH1,
   GSK3β, and ESR1. Interestingly, upregulation of miR-9-5p and consequent
   inhibition of the Notch signaling pathway were shown to stimulate
   neuron differentiation in an APP-overexpressing AD cell model ([239]Li
   et al., 2017). Here, we propose a complex interplay between miR-9-5p
   and NOTCH1, GSK3β, and ESR1 target genes, also involving genes of
   Modules 1 and 3 and their miRNAs ([240] Figure 5 ): NOTCH1 is targeted
   and negatively regulated by miR-9-5p, but its expression, in turn,
   depends on active Notch signaling ([241]Roese-Koerner et al., 2017). In
   addition, the GSK3β kinase is able to phosphorylate both NOTCH1 and
   ER-alpha (encoded by ESR1). Phosphorylation by GSK3β can lead to both
   upregulation and downregulation of NOTCH1 activity ([242]McCubrey et
   al., 2014), whereas ER-alpha activity is positively regulated by GSK3β
   ([243]Grisouard and Mayer, 2009). GSK3β also phosphorylates β-catenin,
   a miR-34a-5p target in Module 3, causing its degradation and leading to
   suppression of cell proliferation ([244]Mancinelli et al., 2017). On
   the other hand, GSK3β activity is inhibited by SQSTM1/p62 ([245]Ramesh
   Babu et al., 2008), a miR-146a-5p and miR-16-5p target belonging to
   Module 1. Therefore, our analyses highlight the existence of an
   intricate and highly complex regulatory network between enriched miRNAs
   and their ALZCAN target genes that may guide future experimental
   analysis to evaluate their potential role in the inverse occurrence of
   AD and cancer. Intriguingly, unfolded protein response and metabolism
   have gained attention as major biological targets for both cancer and
   AD, thus suggesting the exploitation of cancer drugs repositioning in
   AD ([246]Monacelli et al., 2016).

Figure 5.

   Figure 5
   [247]Open in a new tab

   Model of multiple miRNA-gene interactions. The interplay among
   miR-9-5p, miR-146a-5p, and miR-16-5p and NOTCH1, β-catenin (CTNN1),
   SQSTM1, ESR1, and GSK3β proteins originates a complex network. P
   indicates the phosphorylated form of the protein. Red, blue, and green
   colors indicate modules 1, 2, and 3, respectively.

   Our study has some limitations. The analyses relied on genes spotted by
   a computer-based approach that considers the scientific literature
   based on the MEDLINE abstracts and OMIM database, although these
   databases are highly curated, the list of candidate genes might be
   incomplete. Second, according to the quality process implemented by
   Beegle for discovering disease-gene relationship, we assumed all of the
   138 genes as true positives, but we cannot exclude the presence of
   spurious data. Nonetheless, a high percentage of ALZCAN genes (124 out
   of 138 genes) have been functionally clustered on the basis of
   significantly enriched GO-biological processes and KEGG terms, thus
   supporting the text mining analysis in terms of meaningful biological
   relevance. At the same time, despite various genomics databases
   included in Metascape, the bioinformatics analysis might be incomplete
   and other functional PPI modules could be computed. Overall, we are
   confident that the ALZCAN gene set and the selected miRNAs might be a
   useful resource for deeper investigation of biological processes
   underlying the inverse relationship of occurrence between cancer and
   AD.

Conclusions

   Here, the text-mining tool Beegle was instrumental in extracting a list
   of 138 candidate genes hidden in the huge amount of biomedical
   literature. By using the Metascape tool, we obtained significant
   clusters of molecular and biological processes that might help in the
   understanding of the intricate set of biological crosstalks between
   cancer and AD. Through the analysis at the miRNA level, we hypothesized
   interesting miRNA-gene interrelationships deserving further
   investigation. By deep investigation of our results, we highlighted
   three major biological mechanisms: autophagy, UPS, and cell death that
   might be included in the scenario of the dysregulated processes shared
   by cancer and AD. Among the ALZCAN gene set and miRNAs, we proposed
   nine genes (SQSTM1, UCHL1, STUB1, BECN1, CDKN2A, TP53, EGFR, GSK3Β, and
   HSPA9) and five miRNAs (miR-146a-5p, MiR-34a-5p, miR-21-5p, miR-9-5p,
   and miR-16-5p) as best candidates that warrant further investigation.
   Recently, the new avenue of drug repositioning has been proposed for
   the treatment, so far poorly effective, of AD. In this light, we
   believe that the comprehensive exploitation of the data here presented
   might provide potential insights for identifying innovative therapeutic
   approaches for AD.

Data Availability

   All datasets analyzed for this study are included in the manuscript and
   the [248]Supplementary Files .

Author Contributions

   Full access to all of the data in the study and taking responsibility
   for the integrity of the data and the accuracy of the data analysis:
   CB. Study concept and design: CB. Acquisition of data: CB, MV, FA, NJ,
   and AS. Analysis and interpretation of data: CB and MV. Drafting of the
   manuscript: CB. Critical revision of the manuscript for important
   intellectual content: CB, FA, MM, and GB. Text mining and statistical
   analysis: CB, MV, AO, RS, and AS. Study supervision: CB. All authors
   read and approved the final manuscript.

Conflict of Interest Statement

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Acknowledgments