Abstract

Background

   Significant miss‐expressed gene indicators contributing to cisplatin
   resistance in ovarian cancer have not been completely understood. It
   seems that several regulatory genes and signaling pathways are
   associated with the emergence of the chemo‐resistant phenotype.

Aims

   Here, a meta‐analysis approach was adopted to assess deregulated genes
   involved in relapse after the first line of chemotherapy (cisplatin).

Methods and Results

   To do so, six ovarian cancer libraries were gathered from GEO
   repository. Batch effect removal and quality assessment, and boxplots
   and PCA were performed using SVA and ggplot2 packages in R,
   respectively. Cisplatin‐resistant and ‐sensitive ovarian cancer groups
   were compared with find genes with significant expression changes using
   linear regression models in the LIMMA R package. The significance
   threshold for DEGs was taken as adj p‐value < .05 and − 1 > logFC > 1.
   A total of 261 genes were identified to have significant differential
   expression levels in the cisplatin‐resistant versus cisplatin‐sensitive
   group. Among the 10 top up‐regulated and down‐regulated genes, PITX2,
   SNCA, and EPHA7 (up), as well as TMEM98 (down) are indirect upstream
   regulators of PI3K/AKT signaling pathway, contributing greatly to the
   development of chemo‐resistance in cancer via promoting cell
   proliferation, survival, and cell cycle progression as well as
   inhibiting apoptosis. Moreover, a comprehensive assessment of DEGs
   revealed the dysregulation of not only membrane ion channels KCa1.1,
   Kv4, and CACNB4, affecting cell excitability, proliferation, and
   apoptosis but also cell adhesion proteins COL4A6, EPHA3, and CD9,
   affecting the attachment of normal cells to ECM and apoptosis,
   introducing good options to reverse cisplatin resistance.

Conclusion

   Our results predict and suggest that upstream regulators of PI3K/AKT
   signaling pathway, ion channels, and cell adhesion proteins play
   important roles in cisplatin resistance development in ovarian cancer.

   Keywords: cisplatin resistance, meta‐analysis, ovarian cancer

1. INTRODUCTION

   Ovarian cancer (OC) is ranked as the ninth most prevalent malignancy
   worldwide among the female population and the eighth major cause of
   cancer‐related mortality. In 2022, around 19 880 new OC cases and
   12 810 related deaths were reported in the United States alone.[36] ^1
   Epithelial ovarian carcinoma (EOC) has the highest frequency and
   comprises 90%–98% of ovarian tumors.[37] ^2 EOC is often referred to as
   the “silent killer” since it demonstrates a few symptoms until its
   metastasis within the peritoneal cavity, which corresponds to a
   significantly reduced chance of curing.[38] ^3

   The usual treatment procedure for OC patients includes cytoreductive
   surgery and then chemotherapy with paclitaxel, carboplatin, or
   cisplatin. Nevertheless, ~70% of patients receiving this treatment will
   experience tumor relapse.[39] ^4 , [40]^5 This necessitates
   understanding the molecular mechanisms of platinum resistance.
   Cisplatin serves as a highly effective chemotherapy drug for the
   treatment of a wide variety of cancers such as OC. Cisplatin targets
   cancerous cells by forming adducts/crosslinks with DNA purine bases,
   preferably with guanine. These crosslinks damage DNA; this in turn
   suppresses proper gene replication and transcription and induces cell
   apoptosis.[41] ^6 Despite proper early response rate, the majority of
   patients treated with cisplatin will suffer later relapse after
   6 months. Tumor recurrence more than 6 months following front‐line
   platinum treatment is known as sensitivity to platinum, while
   recurrence before 6 months is regarded as resistance. Progression of OC
   during the 6‐month period after the end of platinum‐based chemotherapy
   is normally related to platinum resistance.[42] ^7 Due to its
   significant impact on patient survival time and quality, improving the
   response to cisplatin through discovering key genes and signaling
   pathways involved in cisplatin resistance is an important subject and
   paves the way for cancer treatment in the future.

   According to the literature, resistance to platinum‐based drugs is
   associated with a number of cellular events including decreased
   concentration of the platinum compounds by either active
   efflux/sequestration/secretion or impaired influx, detoxification by
   GSH conjugates, high DNA damage repair levels (nucleotide excision
   repair and mismatch repair), changed DNA methylation status, altered
   membrane protein trafficking due to faulty distribution and
   organization of the cytoskeleton, overexpression of chaperones,
   modification of microRNA (miRNA), long non‐coding RNA (lncRNA)
   expression, transcription factors, and small GTPases, inactivated
   apoptosis pathway, and activated EMT pathway.[43] ^7 , [44]^8 , [45]^9
   Nevertheless, insufficient information regarding molecular mechanisms
   involved in the cisplatin‐resistance in OC prevents the development of
   efficient targeted therapy for eliminating cancer cells.[46] ^10 Hence,
   identifying new target genes is of particular interest in terms of
   discovering molecular mechanisms involved in the cisplatin resistance
   in OC and finally obtaining molecular biomarkers capable of indicating
   pharmacological response in the body and novel efficient treatment
   strategies for patients with advanced OC.[47] ^11

   There has been increasing interest in the use of integrated analysis
   for examining multiple independent microarray data sets.[48] ^12 Based
   on increasing findings, meta‐analysis enhances the statistical power of
   expression profiling and allows for an investigation of cross‐study
   heterogeneity; this can result in more reliable and robust gene
   signatures.[49] ^13 Hence, in this work, a meta‐analysis was carried
   out on multiple public microarray‐based data sets regarding
   chemotherapeutic response in OC to discover strong gene expression
   signatures and pathways related to the cisplatin‐resistance.

2. METHODS

2.1. Data set selection approach

   Gene Expression Omnibus (GEO) ([50]https://www.ncbi.nlm.nih.gov/gds)
   repository was explored, and corresponding ovarian drug resistance
   published array expression data sets were retrieved. The MeSH terms
   searched for the current study included “Ovarian neoplasms,
   Carcinoma/Ovarian Epithelial” and “Drug resistance, Recurrence” and
   “Cisplatin.” In order to limit the results, two filters, namely “Homo
   sapiens” and “Expression profiling by array” were selected. Then,
   inconsistent data sets and duplicates were removed by including several
   criteria, such as cells (epithelial) or OC patients, treatment (a
   minimum of one chemotherapy session using cisplatin), and platforms of
   microarray tests. Afterward, six microarray data sets were selected
   with two different platforms, namely Affymetrix and Agilent
   single‐channel arrays. Furthermore, exclusion criteria for studies
   included the unavailable raw data and low quality. Here, the considered
   chemotherapy regimen consisted of only cisplatin. Thus, data sets that
   included cisplatin regimen combined with other anti‐cancer drugs with
   DNA‐damaging effects including carboplatin, taxol, or doxorubicin were
   removed. According to the above‐mentioned criteria, samples were
   arranged in two distinct groups: cisplatin‐sensitive (total
   disappearance after the first treatment) and cisplatin‐resistance
   (recurrence). The results considered here were derived from epithelial
   OC cell lines as well as OC patients' samples. All data sets of cell
   lines were combined and analyzed together and patient data sets were
   analyzed separately. In addition, only the A2780 cell line was selected
   for further analysis. For each case, a different cross‐integration
   analysis was carried out, and gene set enrichment analysis (GSEA) was
   conducted for common differentially expressed genes (DEGs).
   Figure [51]1 shows the general research workflow.

FIGURE 1.

   FIGURE 1
   [52]Open in a new tab

   Meta‐analysis workflow of microarray data sets.

2.2. Data collection and filtering

   This analysis was carried out on five data sets [53]GSE73935
   ([54]GPL13667), [55]GSE51683 ([56]GPL6244), [57]GSE23553 ([58]GPL570),
   [59]GSE15372 ([60]GPL570), and [61]GSE131978 (GLP96, [62]GPL570) with
   Affymetrix platforms and one data set, [63]GSE33482 with Agilent‐014850
   ([64]GPL6480) platform. These data sets were as follows: (1)
   [65]GSE73935, which included the gene expression profiles of nine
   biological replica of cisplatin resistance OC cell line, A2780CR. (2)
   [66]GSE51683, which involved the gene expression profiles of human OC
   cell lines A2780 and A2780CP20. Profiles included two data sets that
   were sensitive and two data sets that were resistant to cisplatin. Four
   expression profiles were excluded from this work due to being treated
   with temsirolimus; (3) [67]GSE23553, which included RNA expression
   profiles of one cisplatin‐sensitive, A2780S, and one
   cisplatin‐resistant A2780CP cell line. (4) [68]GSE15372, which
   consisted of the gene expression profiles of five biological copies of
   A2780 cell line (cisplatin‐sensitive) together with round5 A2780 cell
   line (cisplatin‐resistant), the latter obtained by culturing A2780
   cells and exposing them to several cycles (treatment rounds) with
   incremental cisplatin doses. (5) [69]GSE33482, which involved the
   expression profiles of six biological copies of A2780
   (cisplatin‐sensitive) together with six biological copies of A2780cis
   (cisplatin‐resistant) OC cell line; and (6) [70]GSE131978, which
   comprised of the expression profiles of 39 OC patients who had received
   cisplatin as a chemotherapy drug. These profiles included 16 samples
   that were sensitive and 7 samples that were resistant to cisplatin.
   Sixteen expression profiles were excluded from this work due to being
   unclear.

2.3. Normalization of raw data and quality control

   Preprocessing, quality control, and human GRCh38 alignment were carried
   out for all data sets, and raw read counts were downloaded for
   additional study. Removal of potential background noise, non‐specific
   binding, and log2 transformation were conducted for each selected data
   set. In the following, batch effect removal and data normalization
   after data integration were performed using surrogate variable analysis
   (SVA) package. The normalization quality and data distribution were
   assessed by drawing the pre‐ and post‐normalization boxplots. Other
   quality assessment approaches included the pre‐ and post‐normalization
   evaluation and bath effect removal between array data sets by principal
   component analysis (PCA) using ggplot2 package in R.

2.4. Integrative meta‐analysis

   PCA plots were developed on the integrated data to examine the level of
   batch effect (or undesirable variation) on the data. A comparison was
   made between OC groups resistant to cisplatin and those sensitive to
   it, and differentially expressed genes were identified with no regard
   to French‐American‐British (FAB) classification, drug dose, and sample
   source. Moreover, another meta‐analysis was carried out with arrays of
   the samples of patients (cisplatin‐resistant and cisplatin‐sensitive
   patients) to obtain a more precise set of DEGs participating in the
   cisplatin‐resistance in OC.

2.5. Gene set enrichment analysis

   Gene Ontology (GO) and pathway enrichment analysis were performed for
   DEGs through the web‐based enrichment analysis tool, Enrichr
   ([71]https://maayanlab.cloud/Enrichr/), containing >180 000 curated
   gene sets in multiple categories from >100 gene set libraries.
   Moreover, the human protein atlas ([72]https://www.proteinatlas.org)
   and GeneCards ([73]https://www.genecards.org) were used for further
   investigation of DEGs. To better illustrate the key DEGs that
   contributed to OC drug resistance, heatmap graph was pictured using
   pheatmap and viridis packages in R.

2.6. Statistical analysis

   The R statistical computer program was used to perform all statistical
   analyses. Linear regression models in linear models for microarray data
   (LIMMA) R program were used to analyze differential gene expression
   obtained from comparing cisplatin‐resistant cell line to cisplatin
   sensitive cell line. The significance thresholds for DEGs were taken as
   adj p‐value < .05 and logFC > 1 or log FC < −1. In addition, via the
   Benjamini–Hochberg (B–H) method, the t‐statistics test results were
   analyzed and false positive results were reduced.

3. RESULTS

3.1. Data collection and filtering

   We matched six publicly available microarray data sets comprising a
   total of 57 arrays to our pre‐specified criteria for inclusion.
   Table [74]1 gives the details of the aforementioned six data sets. By
   eliminating outliers and inconsistent sample arrays, data sets were
   normalized with 57 sample arrays, which were considered for more
   downstream analyses. Afterward, we divided the samples into two
   subgroups when conducting the meta‐analysis: chemo‐sensitive and
   chemo‐resistant. Table [75]2 shows brief information of cell line
   samples used in this meta‐analysis.

TABLE 1.

   Characteristics of the gene expression data sets applied in the
   meta‐analysis.
   Accession no Platform Drugs Tissue Sample Number of samples
   Selected/total Sensitive Resistance
   [76]GSE73935 [77]GPL13667, affymetrix Cisplatin, doxorubicin,
   topotecan, paclitaxel Ovarian cancer A2780 cell line 6/48 ‐ 6
   [78]GSE51683 [79]GPL6244, affymetrix Cisplatin, temsirolimus Ovarian
   cancer A2780 cell line 4/8 2 2
   [80]GSE23553 [81]GPL570, affymetrix Cisplatin Ovarian cancer 2/56 1 1
   [82]GSE15372 [83]GPL570, affymetrix Cisplatin Ovarian cancer A2780 cell
   line 10/10 5 5
   [84]GSE33482 [85]GPL6480, agilent Cisplatin Ovarian cancer A2780 cell
   line 12/12 6 6
   [86]GSE131978 [87]GPL96, [88]GPL570 affymetrix Cisplatin Ovarian cancer
   Patients 23/39 16 7
   [89]Open in a new tab

TABLE 2.

   Characteristics of the samples applied in the meta‐analysis and further
   investigation.
   Accession       Group     Study         Platform
   [90]GSM1906460  Resistant [91]GSE73935  HG‐U219
   [92]GSM1906461  Resistant [93]GSE73935  HG‐U219
   [94]GSM1906462  Resistant [95]GSE73935  HG‐U219
   [96]GSM1906463  Resistant [97]GSE73935  HG‐U219
   [98]GSM1906464  Resistant [99]GSE73935  HG‐U219
   [100]GSM1906465 Resistant [101]GSE73935 HG‐U219
   [102]GSM1250444 Sensitive [103]GSE51683 HuGene‐1_0‐st
   [104]GSM1250445 Sensitive [105]GSE51683 HuGene‐1_0‐st
   [106]GSM1250446 Resistant [107]GSE51683 HuGene‐1_0‐st
   [108]GSM1250447 Resistant [109]GSE51683 HuGene‐1_0‐st
   [110]GSM577774  Resistant [111]GSE23553 HG‐U133_Plus_2
   [112]GSM577781  Sensitive [113]GSE23553 HG‐U133_Plus_2
   [114]GSM385721  Sensitive [115]GSE15372 HG‐U133_Plus_2
   [116]GSM385722  Sensitive [117]GSE15372 HG‐U133_Plus_2
   [118]GSM385723  Sensitive [119]GSE15372 HG‐U133_Plus_2
   [120]GSM385724  Sensitive [121]GSE15372 HG‐U133_Plus_2
   [122]GSM385725  Sensitive [123]GSE15372 HG‐U133_Plus_2
   [124]GSM385726  Resistant [125]GSE15372 HG‐U133_Plus_2
   [126]GSM385727  Resistant [127]GSE15372 HG‐U133_Plus_2
   [128]GSM385728  Resistant [129]GSE15372 HG‐U133_Plus_2
   [130]GSM385729  Resistant [131]GSE15372 HG‐U133_Plus_2
   [132]GSM385730  Resistant [133]GSE15372 HG‐U133_Plus_2
   [134]GSM828379  Sensitive [135]GSE33482 Agilent
   [136]GSM828380  Sensitive [137]GSE33482 Agilent
   [138]GSM828381  Sensitive [139]GSE33482 Agilent
   [140]GSM828382  Sensitive [141]GSE33482 Agilent
   [142]GSM828383  Sensitive [143]GSE33482 Agilent
   [144]GSM828384  Sensitive [145]GSE33482 Agilent
   [146]GSM828385  Resistant [147]GSE33482 Agilent
   [148]GSM828386  Resistant [149]GSE33482 Agilent
   [150]GSM828387  Resistant [151]GSE33482 Agilent
   [152]GSM828388  Resistant [153]GSE33482 Agilent
   [154]GSM828389  Resistant [155]GSE33482 Agilent
   [156]GSM828390  Resistant [157]GSE33482 Agilent
   [158]Open in a new tab

3.2. Quality evaluation of calibrated data

   The batch effect was reduced by applying the LIMMA, which is a
   well‐established procedure (Figure [159]2). As can be seen in
   Figure [160]2, in most cases, chips are mainly distributed around zero.
   Our analysis showed no significant differences representing a bias.
   Furthermore, the clustering patterns of the sample were assessed by
   presenting the results by PCA plots. This plot shows clustering
   primarily according to cisplatin‐resistant and ‐sensitive series
   (Figure [161]3).

FIGURE 2.

   FIGURE 2
   [162]Open in a new tab

   Boxplot of ovarian cancer sample arrays (A) before and (B) after batch
   correction. The concentrated distribution of chips around zero
   indicates that undesired variation has almost been eliminated. R
   language program was used to develop the plot.

FIGURE 3.

   FIGURE 3
   [163]Open in a new tab

   PCA plots of data summarized via batch correction. PCA scatter plots
   give PC1 versus PC2 outputs for each calibrated ovarian cancer sample
   of data sets (A) before and (B) after batch correction. PCA plots
   developed using ggplot2 package version 3.3.3 in R indicate that gene
   expression profiles of samples are similar.

3.3. Statistical meta‐analysis

   Identifying DEG signatures in chemo‐resistant OC samples.

   When all data sets were normalized, integration analysis was conducted
   in two stages on (1) arrays from OC cell lines (34 arrays) and (2) all
   normalized arrays from samples taken from patients (23 arrays).
   Employing the LIMMA method and taking cutoff criterion of adj p‐value
   < .05 led to the identification of 261 DEGs in chemo‐resistant group in
   comparison with sensitive group for the combination of cell line
   samples (Figure [164]4). Figures [165]5 and [166]6 demonstrate the
   interactions between genes with significant up‐ or down‐regulation
   levels in the first meta‐analysis. Analysis of patients' samples showed
   that none of the discovered DEGs was statistically significant, thus
   they were removed from further steps.

FIGURE 4.

   FIGURE 4
   [167]Open in a new tab

   Volcano plots of DEGs. Ggplot2 package in R74 was used to visualize
   DEGs discovered via meta‐analysis. Cut‐off for p‐value was considered
   .05. It is seen that 30 genes were revealed as common DEGs with a
   change greater than two folds in chemo‐resistant relative to
   chemo‐sensitive group. Data are provided as log2 fold change. Ggplot2 R
   package version 3.3.363 was utilized to create the plots.

FIGURE 5.

   FIGURE 5
   [168]Open in a new tab

   Interaction between genes that are up‐regulated in ovarian cancer
   cells, cisplatin resistance cell line, A2780CP, relative to
   cisplatin‐sensitive cell line, A2780S, according to DEGs. Darker color
   and bigger circles represents higher degree.

FIGURE 6.

   FIGURE 6
   [169]Open in a new tab

   Interaction between genes that are down‐regulated in ovarian cancer
   cells, cisplatin resistance cell line, A2780CP, relative to
   cisplatin‐sensitive cell line, A2780S according to DEGs. Darker color
   and bigger circles represents higher degree.

   Thirty genes out of the 261 DEGs in cell lines with the most
   significant up‐ or down‐regulation levels (logFC > 2 or log FC < −2)
   were suggested for further analyses. These genes include PITX2, FIBIN,
   LRRTM4, ANO3, SNCA, FOLH1, KANK4, FGF18, POSTN, KANK1, GALNT14, EPHA7,
   ECEL1, and EPHX2 (Figures [170]5 and [171]6). This means that these 30
   DEGs, consisting of four up‐regulated and 26 down‐regulated genes in
   cisplatin‐resistant relative to cisplatin‐sensitive OC group play
   potential roles in drug resistance development in OC. Of these DEGs,
   PITX2 gene was found to have the most significant deregulation level
   with a notable decrease in expression in the chemo‐resistant samples
   (with logFC, −3.27 and p‐value =.0015255). On the contrary, the KANK1
   gene was found to have the most significant increase in the expression
   (with logFC, 2.45 and p‐value = .0011569).

3.4. Functional gene enrichment analysis

   Biological functions of DEGs in cisplatin resistance development in OC
   were revealed through functional gene enrichment analysis using the
   Enrichr for top up‐ and down‐regulated genes. Enrichr presents GO
   enrichment comprising categories of molecular function (MF), biological
   process (BP) and cellular component (CC). Further web‐based pathway
   analysis was conducted for mapping genes to pathways generated by Kyoto
   Encyclopedia of Genes and Genomes (KEGG) and Reactome online
   resources.[172] ^14 , [173]^15 Our results did not identify any
   significant signaling pathway related to cisplatin resistance in OC
   (p ≥ .05). In the following, the human protein atlas and GeneCards were
   used to clarify the biological functions of DEGs. It was revealed that
   several top up‐regulated genes such as KANK1, GALNT14, TMEM98, and
   PTPN3 and top down‐regulated genes such as PITX2, SNCA, and EPHA7 play
   key roles in cancer progression and chemotherapy response. A
   comprehensive assessment of DEGs revealed significant changes in the
   expression of several ion transporters such as KCa1.1 (KCNMA1), Kv4
   (KCND2), Kv10.1 (KCNH1), GRIN3A, SLC7A3, and CLIC3 and ion transporter
   regulators such as NRXN1, CNRIP1, and CACHD1. Moreover, the expression
   of some genes involved in cell adhesion including MFAP4, PCDH19, CDH2,
   CDH10, NRXN1, COL4A6, COL15A1, PIP5K1B, EPHA3, BOC, and CD36 showed
   significant changes during cisplatin resistance in OC. To better
   understand, a heatmap graph of the above‐mentioned genes based on Log
   Fold change values available in obtained DEGs was depicted
   (Figure [174]7).

FIGURE 7.

   FIGURE 7
   [175]Open in a new tab

   Heatmap of Log fold variations for DEGs with most significant
   regulation level. Heatmap indicates the normalized relative expression
   values of DEGs with most significant regulation level involved in drug
   resistance between cisplatin‐resistant and cisplatin‐sensitive ovarian
   samples. Each column indicates ovarian samples. All the samples were
   separated into two clusters according to their characteristics
   (resistant and sensitive to chemotherapy). Higher gene expression based
   on microarray data shows with darker color.

4. DISCUSSION

   This study was conducted to investigate deregulated genes and signaling
   pathways involved in cisplatin resistance development in OC. A total of
   261 DEGs were identified. Given that the most significant up‐ and
   down‐regulated genes in our meta‐analysis have potential roles in drug
   resistance development, we tried to find the biological roles of 30
   annotated genes, the most significant up‐ and down genes, and their
   relation with drug resistance emergence. Regarding the fact that
   several molecular mechanisms such as decreased drug uptake and
   increased drug efflux are involved in cancer drug resistance,[176] ^7
   we focused on membrane ion channels, pumps, and transmembrane proteins
   as well as their regulators in obtained DEGs. Astonishingly, among top
   up‐regulated genes in the current meta‐analysis, the role of GALNT14
   and TMEM98 genes in the development of drug resistance have been
   confirmed previously which was in line with our results. GALNT14 is a
   mediator of O‐glycosylation whose relation with different tumors are
   known.[177] ^16 Overexpression of GALNT14 is correlated with poor
   prognosis and disease progression in high‐grade serous OC.[178] ^17
   GALNT14 regulates the stability of P‐gp, an efflux pump in the cell
   membrane which leads to an increase in the efflux of anticancer drugs
   outside the cell and the development of multidrug resistance in breast
   cancer.[179] ^18 In addition, TMEM98 is a member of single‐pass
   transmembrane proteins of the endoplasmic reticulum that shows
   dysregulation in various cancers. The contribution of this family
   members to drug resistance has been reported in different cancers. For
   example, the hypomethylation of the promoter region of TMEM88 gene in
   OC leads to an increase in its protein expression and development of
   cisplatin‐resistance.[180] ^19

   Besides, a comprehensive investigation of DEGs obtained in this study
   revealed changes in the expression of some ionic channels KCal.1, Kv4,
   Kv10.1, GRIN3A, SLC7A3, and CLIC3 as well as their regulating molecules
   including NRXN1, CNRIP1, and CACHD1. Ionic channels have recently been
   revealed as key players in drug resistance. The participation of KCal.1
   in drug resistance has been established. The expression of this gene is
   inversely related with drug resistance, which confirms the results of
   our study. The knockdown of KCal.1 using the specific siRNA leads to
   increased sensitivity to cisplatin in drug‐resistant ovarian cell
   line.[181] ^20 A decrease in the expression of Kv4 was revealed in the
   results of our study. Although the participation of this channel in
   drug resistance has not been explored, the dysregulation of other
   members of this family in developing drug resistance has been
   established in different studies. For example, the suppression of kv1.5
   via potassium channel blockers or specialized siRNA leads to an
   increase in doxorubicin resistance in gastric cancer.[182] ^21 In
   addition, Kv1.1 and Kv1.3 lead to the sensitivity of tumor cells and
   induction of cell death by cisplatin.[183] ^22 An increase in CACNB4
   expression encoding the β subunit of voltage‐gated calcium channels was
   confirmed in our study. Different studies have established the
   participation of T‐type and L‐type Ca2+ channels in drug
   resistance.[184] ^23 Since ionic channels in theory can be easily
   modulated, they serve as attractive therapeutic targets and pave the
   way for the development of personalized therapies aimed at stopping or
   slowing drug resistance process through ionic channel
   modifications.[185] ^24

   Contribution of the PI3K/AKT/mTOR pathway to chemotherapy resistance
   has been confirmed in various cancers.[186] ^25 , [187]^26 For example,
   Ng et al. (2014) observed that the TMEM98 gene played a critical role
   in the emergence and development of chemotherapy‐resistance in
   hepatocellular carcinoma through activating AKT signaling pathway and
   deactivating p53.[188] ^27 Furthermore, among top‐down‐regulated genes,
   the role of PITX2, SNCA, EPHA7 genes in the development of drug
   resistance have been confirmed previously which was in line with our
   results. Based on the results of the current report, the PITX2 gene has
   the most significant decrease in the expression. PITX2 plays diverse
   and complex roles in carcinogenesis. The reduced expression of this
   gene due to hypermethylation is established in patients with breast,
   prostate, and clone cancers.[189] ^28 However, in some tumors, this
   gene plays an oncogenic role and induces cell growth and
   proliferation.[190] ^29 Lee et al. (2019) established that this gene
   increases drug resistance in clone and kidney cancers.[191] ^30
   Decreased expression of PITX2 leads to a decrease in B55α production
   and subsequently prevents the inhibition of AKT, and it may lead to the
   emergence of cisplatin resistance in OC through the activation of the
   PI3K/AKT pathway.[192] ^28 Moreover, SNCA (α‐Synuclein) is involved the
   development of various tumors. The reduced expression of this gene has
   been established in breast, bladder, kidney, lung, ovarian, brain, and
   CNS cancers. By reducing the activity of PI3K/AKT, this gene inhibits
   the proliferation of lung adenocarcinoma cells.[193] ^31 EPHA7 is a
   member of the protein‐tyrosine kinase family and plays a dual part of
   an oncogene or tumor suppressor in intercellular signal transduction
   and the regulation of cell proliferation and differentiation as well as
   resistance to chemotherapy drugs.[194] ^32 , [195]^33 Besides,
   down‐regulation of SNCA and EPHA7 genes has been associated with
   multidrug resistance in OC.[196] ^34 The PI3K/AKT signaling pathway is
   the most common changed signaling pathway in OC and dysregulation of
   this pathway has been identified in 70% of OC patients.[197] ^35 The
   activation of this pathway is associated with aggressive phenotype,
   poor prognosis, and drug resistance in OC.[198] ^36 The level of
   phospho‐Akt and its kinase activity is significantly higher in OC cells
   resistant to cisplatin than those sensitive to it. A decrease in PTEN
   and increase in PIK3CA lead to the development of cisplatin resistance
   in OC OVCAR‐3/CDDP cell line. Through inhibiting the release of
   cytochrome c and the transcription of pro‐apoptotic Bad and Bax genes,
   Akt inhibits apoptosis and develops cisplatin resistance in
   cancer.[199] ^37 Using the specialized inhibitors of this pathway is an
   efficient and safe strategy to overcome drug resistance without
   affecting the normal cell performance in the cancer tissue.[200] ^38
   Therefore, the use of the specialized suppressors of the genes of this
   pathway and related proteins, in combination with chemotherapy drugs is
   proposed as a highly efficient therapeutic strategy to combat drug
   resistance.

   Furthermore, the expression of some genes involved in cell adhesion was
   found to be dysregulated in the current meta‐analysis. Cell
   adhesion‐mediated drug resistance inhibits chemotherapy‐induced
   apoptosis in OC and plays an important role in drug resistance in this
   cancer.[201] ^39 The findings revealed expression variation in several
   proteins participating in cell adhesion including MFAP4, PCDH19, CDH2,
   CDH10, NRXN1, COL4A6, COL15A1, PIP5K1B, EPHA3, BOC, and CD36.
   Downregulation of COL4A6 and EPHA3 genes was found in DEGs obtained in
   our study. COL4A6 is involved in ECM‐receptor interaction. The
   expression of this gene decreased in drug‐resistant cell lines and
   contributed to the development of drug resistance by preventing the
   penetration of the drug into the cancerous tissue.[202] ^40 The
   knockdown of EPHA3 gene led to the appearance of the resistant SCLC
   phenotype due to apoptosis reduction and induced G2/M phase arrest, and
   the re‐expression of this gene led to the inversion of the resistant
   phenotype in the above cells.[203] ^41 Preclinical studies show that
   targeting molecules participating in cellular attachments including
   integrins, adapter proteins, and associated kinases can be proposed as
   a promising strategy to increase the sensitivity of cancer cells to
   chemotherapy and radiotherapy as well as development of synthetic
   lethal approaches by multi‐targeting in combination with
   radio‐chemotherapy in future.[204] ^42

   Cumulatively, the results of this study were obtained via investigating
   high‐throughput studies using in silico tools to predict molecular
   mechanisms of cisplatin resistance, therefore, using the results,
   researchers can develop hypotheses for future experimental studies in
   the area of drug resistance and targeted therapy. The limitations of
   this study are as follow: absence of independent in vitro and/or in
   vivo validations, dependency on low‐resolution microarray data, and few
   numbers of assessed data sets.

5. CONCLUSION

   In summary, the identification of genes related to drug resistance
   using bioinformatics tools can be suggested as an important approach
   for the detection of therapeutic targets for future experimental and
   preclinical studies aimed at overcoming drug resistance. In this study,
   we identified some of important mechanisms of drug resistance such as
   change in drug uptake and increased drug efflux via dysregulation of
   pumps, ion‐channels and transporters, dysregulation of the PI3K/AKT
   signaling pathway via upstream up‐regulators and miss‐expression of
   genes involved in cellular attachments. These mechanisms are proposed
   as important candidates for future in vitro/ in vivo and targeted
   therapies.

AUTHOR CONTRIBUTIONS

   Somayeh Hashemi Sheikhshabani: Conceptualization (equal); investigation
   (equal); methodology (equal); validation (equal); visualization
   (equal); writing – original draft (equal). Zeinab Amini‐farsani:
   Conceptualization (equal); data curation (equal); investigation
   (equal); methodology (equal); writing – original draft (equal). Nesa
   Kazemifard: Formal analysis (equal); methodology (equal); software
   (equal); validation (equal); visualization (equal). Parastoo Modarres:
   Conceptualization (equal); investigation (equal); methodology (equal);
   resources (equal); software (equal). Zahra Amini‐farsani: Formal
   analysis (equal); methodology (equal); resources (equal); software
   (equal); validation (equal). Mir Davood Omrani: Conceptualization
   (equal); investigation (equal); writing – review and editing (equal).
   Soudeh Ghafouri‐Fard: Conceptualization (equal); project administration
   (equal); supervision (equal); validation (equal); writing – review and
   editing (equal).

CONFLICT OF INTEREST STATEMENT

   The authors have stated explicitly that there are no conflicts of
   interest in connection with this article.

ACKNOWLEDGMENTS