Abstract

   Emerging studies demonstrated that miRNAs played fundamental roles in
   lung cancer. In this study, we attempted to explore the clinical
   significance of the miRNA signature in different histological subtypes
   of non-small cell lung cancer (NSCLC). Three miRNome profiling datasets
   ([32]GSE19945, [33]GSE25508 and [34]GSE51853) containing lung squamous
   cell carcinoma (SCC), lung adenocarcinoma (ADC) and large cell lung
   cancer (LCLC) samples were obtained for bioinformatics and survival
   analysis. Moreover, pathway enrichment and coexpression network were
   performed to explore underlying molecular mechanism. MicroRNA-375
   (miR-375), miR-203 and miR-205 were identified as differentially
   expressed miRNAs (DEmiRNAs) which distinguished SCC from other NSCLC
   subtypes. Pathway enrichment analysis suggested that Hippo signaling
   pathway was combinatorically affected by above mentioned three miRNAs.
   Coexpression analysis of three miRNAs and the Hippo signaling pathway
   related genes were conducted based on another dataset, [35]GSE51852.
   Four hub genes (TP63, RERE, TJP1 and YWHAE) were identified as the
   candidate targets of three miRNAs, and three of them (TP63, TJP1 and
   YWHAE) were validated to be downregulated by miR-203 and miR-375,
   respectively. Finally, survival analysis further suggested the
   prognostic value of three-miRNA signature in SCC patients. Taken
   together, our study compared the miRNA profiles among three
   histological subtypes of NSCLC, and suggested that a three-miRNA
   signature might be potential diagnostic and prognostic biomarkers for
   SCC patients.

   Keywords: non-small cell lung cancer, histological subtype,
   differentially expressed miRNAs

INTRODUCTION

   Lung cancer remains the leading cause of cancer-related death both
   worldwide and in China [[36]1, [37]2]. Non-small cell lung cancer
   (NSCLC) including three main histological subtypes, lung squamous cell
   carcinoma (SCC), lung adenocarcinoma (ADC) and large cell lung cancer
   (LCLC), accounts for over 80% cases of lung cancer [[38]3]. Increasing
   studies demonstrated that therapeutic effect varied in different
   histological subtypes of NSCLC, suggesting that early confirmation of
   histology is of critical importance [[39]4-[40]6]. Nevertheless, there
   are emerging researches into the biological features of different
   histological subtypes of NSCLC, but the fundamental molecular
   mechanisms remain elusive. For instance, smoking is a much greater risk
   of SCC than of ADC [[41]7]. Thus, a clear understanding of the
   differences from morphological to genetic levels between them will
   definitely have a positive effect on precise therapy.

   MicroRNAs (miRNAs), comprising 19-23 nucleotides or so, are a class of
   endogenously expressed RNAs belonging to non-coding RNA family [[42]8].
   In the last decade, miRNAs have attracted so much attention as
   biomarkers with great potential in the early diagnosis, and clinical
   outcome prediction of multiple malignancies [[43]9-[44]11]. In NSCLC,
   emerging evidence suggested miRNAs as potential diagnostic and
   prognostic biomarkers [[45]12-[46]14]. Several miRNAs were reported to
   be capable of distinguishing different subtypes of NSCLC [[47]15,
   [48]16]. Therefore, exploration of miRNA-based biomarkers provides
   opportunities for investigate the molecular changes among different
   histological subtypes of NSCLC.

   Recently, high-throughput platforms have been utilized as a fundamental
   method dealing with massive genetic data. Data from high-throughput
   platforms including microarrays and next-generation sequence, if
   analyzed by bioinformatics methods, can provide amounts of useful
   information for screening cancer biomarkers and therapeutic targets
   [[49]17]. Gene Expression Omnibus (GEO) database
   ([50]https://www.ncbi.nlm.nih.gov/geo/), hosted by the National Center
   for Biotechnology Information (NCBI), serves as a public genetic
   expression profile repository for a wide range of high-throughput
   experimental data. Therefore, in the present study, we adopted
   high-throughput data from GEO database for bioinformatics analysis to
   identify the differentially diagnostic miRNA signature between three
   subtypes of NSCLC.

RESULTS

Identification of differentially expressed miRNAs (DEmiRNAs) in NSCLC

   The workflow of this study was presented in Figure [51]1. Firstly, we
   compared the miRNA expression profiles in three miRNA datasets
   ([52]GSE19945, [53]GSE25508 and [54]GSE51852). DEmiRNAs between cancer
   and normal tissue samples were identified in each dataset. In
   [55]GSE19945, 39 up-regulated and 52 down-regulated miRNAs were
   obtained (Figure [56]2A). Based on [57]GSE51853, a total of 53
   dysregulated miRNAs were identified including 14 up-regulated and 31
   down-regulated miRNAs (Figure [58]2B). In [59]GSE25508, there were 29
   up-regulated and 7 down-regulated miRNAs (Figure [60]2C). When
   comparing the list of dysregulated miRNAs among three datasets, six
   upregulated miRNAs (miR-130b, miR-210, miR-183, miR-21, miR-200b and
   miR-96) and five down-regulated miRNAs (miR-30a, miR-638, miR-486,
   miR-30d and miR-145) (Figure [61]2D).

Figure 1. Schematic of workflow depicting the eligible datasets and analysis
procedure.

   [62]Figure 1
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Figure 2. Identification of common DEmiRNAs in NSCLC.

   [64]Figure 2
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   Heat map of the significantly dysregulated miRNAs between the cancer
   and normal tissue samples in [66]GSE19945 (A), [67]GSE51852 (B) and
   [68]GSE25508 (C). (D) (Upper) Venn diagram between the up-regulated and
   down-regulated miRNAs from each dataset. (Lower) the lists of the
   common dysregulated miRNAs. Red: up-regulation; blue: down-regulation.

Identification of DEmiRNAs in different histological subtypes of NSCLC

   We further compared miRNA expression profiles between cancer and normal
   tissue samples in different histological subtypes of NSCLC (ADC, SCC
   and LCLC) of each dataset. A Venn diagram was conducted to examine the
   overlap of resulting DEmiRNAs lists from three datasets in each
   histological type of NSCLC. In ADC, there were 12 up- and 11
   down-regulated miRNAs listed in more than two datasets (Figure [69]3A).
   Robust rank aggregation (RRA) analysis showed miR-210 and miR-130b was
   significantly up-regulated with a Bonferroni-corrected p-value less
   than 0.05 (Figure [70]3B), but no downregulated miRNA reached
   statistical significance after Bonferroni correction (Figure [71]3C).
   Similarly, a total of 11 up- and 26 down-regulated miRNAs commonly
   listed in SCC datasets (Figure [72]3D). Integrated meta-analysis
   confirmed that miR-205, miR-210 and miR-130b was up-regulated, and
   miR-486 and miR-30a were down-regulated (Figure [73]3E and [74]3F). In
   LCLC, there were 14 up- and 27 down-regulated miRNAs were selected
   (Figure [75]3G), and miR-30a*, miR-30a, miR-497, miR-144 and miR-145
   were identified as commonly down-regulated miRNAs in LCLC (Figure
   [76]3H and [77]3I).

Figure 3. Identification of DEmiRNAs in three histological subtype of NSCLC.

   [78]Figure 3
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   (A) Overlapped DEmiRNAs in ADC were presented as Venn diagram. RRA
   analysis of up-regulated (B) and down-regulated miRNAs (C) to identify
   common DEmiRNAs in ADC. (D) Overlapped DEmiRNAs in SCC were presented.
   RRA analysis of up-regulated (E) and down-regulated miRNAs (F) in SCC
   were listed. (G) Overlapped DEmiRNAs in LCLC were presented. RRA
   analysis of up-regulated (H) and down-regulated miRNAs (I) in LCLC were
   listed. For the RRA column diagram, red: crude p-value (up-regulation);
   Dark red: corrected p-value (up-regulation). Blue: crude p-value
   (down-regulation); Dark blue: corrected p-value (down-regulation).

MiR-203, miR-205 and miR-375 distinguished SCC from other NSCLC subtypes

   We also explored the DEmiRNAs between SCC and other two histological
   subtypes of NSCLC, LCLC and ADC. It was found that a total of 6 miRNAs
   were significantly dysregulated from the comparison of SCC versus LCLC
   (Figure [80]4A). The corresponding RRA approach revealed that miR-205,
   miR-375 and miR-203 were the commonly dysregulated miRNAs (Figure
   [81]4B). When comparing SCC with ADC, four significantly dysregulated
   miRNAs were selected (Figure [82]4C). The comprehensive meta-analysis
   showed that miR-375 and miR-203 were significantly dysregulated (Figure
   [83]4D). Although miR-205 was not significant, it was the third
   dysregulated miRNA collectively. In addition, we did not obtain
   positive result from comparison between ADC and LCLC (data not shown).
   Interestingly, miR-375, miR-203 and miR-205 were listed in both SCC
   versus ADC and SCC versus LCLC. Therefore, miR-375, miR-203 and miR-205
   were selected for further analysis.

Figure 4. Identification of DEmiRNAs among histological subtypes of NSCLC.

   [84]Figure 4
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   Venn diagram of overlapped dysregulated miRNAs of SCC vs. LCLC (A) and
   SCC vs. ADC (C). Common DEmiRNAs of SCC vs. LCLC (B) and SCC vs. ADC
   (D) were identified using RRA method. For the RRA column diagram, red:
   rude p-value; blue: corrected p-value.

Function prediction of miR-203, miR-205 and miR-375 in SCC

   Target genes of miR-203, miR-205 and miR-375 were predicted by
   TargetScan and mapped in Cytoscape (Figure [86]5A). Kyoto Encyclopedia
   of Genes and Genomes (KEGG) pathway enrichments of target genes were
   carried out, respectively (Figure [87]5B-5D). “Pathway in cancer
   (KEGG_05200)”, “Focal adhesion (KEGG_04510)” and “Adherens junction
   (KEGG_04520)” were commonly top pathways by function prediction of
   three miRNAs. Moreover, the combinatorial effects of three miRNAs in
   pathways were predicted by the DIANA tools
   ([88]http://www.microrna.gr/miRPathv2). Hippo signaling pathway was the
   most enriched KEGG pathway (Figure [89]5E). Recently, increasing
   evidence suggested that Hippo pathway, which was demonstrated to
   regulate cell proliferation, differentiation and migration, played a
   critical role in lung cancer, particularly in NSCLC [[90]23-[91]25]. We
   therefore employed another GEO dataset, [92]GSE51852, to further
   analysis the expression correlations between three miRNAs and genes in
   Hippo signaling pathway (Figure [93]6A). Pearson correlation was
   calculated and yielded three sets of genes that were significantly
   correlated with miR-375, miR-203 and miR-205 (Figure [94]6B). We then
   conducted interaction network based on three miRNAs and their targets
   which reached statistical significance. From the comprehensive network,
   4 genes (TP63, RERE, TJP1 and YWHAE) were found to be interacted with
   all three miRNAs, and 6 genes (YAP1, AMOT, WWC1, PTPN14, TSHZ2 and
   STK4) were interacted with two miRNAs (Figure [95]6C). In addition, we
   also validated the regulationship between three miRNAs and above Hippo
   signaling genes. Our data revealed that TP63 and TJP1 were
   downregulated by miR-203 (Figure [96]7A and [97]7B), meanwhile, YWHAE
   was downregulated by miR-375 (Figure [98]7C).

Figure 5. MiRNA-gene network and KEGG pathway enrichment.

   [99]Figure 5
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   (A) The interaction between three miRNAs (miR-203, miR-205 and miR-375)
   and of their predicted target genes. The central yellow circles
   represented miRNAs. The color of the circles with the names of the
   target genes on it varies according to their cumulative weighted
   context++ score. KEGG pathway enrichments of predicted target genes of
   miR-203 (B), miR-205 (C) and miR-375 (D), were presented, respectively.
   (E) The combinatorial effects of three miRNAs in KEGG pathways were
   predicted.

Figure 6. Coexpression analysis of three miRNAs and Hippo signaling related
genes.

   [101]Figure 6
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   (A) The expression profile of 3 miRNAs (miR-203, miR-205 and miR-375)
   and 90 Hippo signaling related genes was presented as heatmap. (B)
   Pearson correlation for each miRNA and 90 Hippo signaling related
   genes. The genes which considered to have statistically significant
   relationship with the miRNA were marked red. (C) The interaction
   network of the significant couples derived from Pearson correction.

Figure 7. Comparison of target gene expression levels between control and
miRNA mimics -transfected HCC cells.

   [103]Figure 7
   [104]Open in a new tab

   (A) Downregulation of TP63 by miR-203 mimics transfection; (B)
   Downregulation of TJP1 by miR-203 mimics transfection; (C)
   Downregulation of YWHAE by miR-375 mimics transfection.

Prognostic values of miR-203, miR-205 and miR-375 in SCC

   Kaplan-Meier curve was conducted to analyze the association between
   three miRNAs (miR-203, miR-205 and miR-375) expression and overall
   survival in SCC patients. To reduce inter-lab variability, prognostic
   data were obtained from two independent datasets based on sequencing
   (TCGA-LUSC dataset, n=134) and microarray platforms ([105]GSE16025
   dataset, n=61), respectively. By using the SurvMicro web tool, we
   compared the overall survival between the SCC patients with high risk
   and low risk groups, according to their expression levels of three
   miRNAs. As shown in Figure [106]8, combination of miR-203, miR-205 and
   miR-375 was significantly associated with overall survival of SCC in
   both two datasets, with Log-Rank p-values being 0.0032 and 0.0286,
   respectively. The Cox regression model suggested that SCC patients in
   high risk groups showed shorter overall survival time, compared with
   those in low risk groups. The corresponding Hazard ratio (HR) values
   were 2.57 (95% confidence interval (CI), 1.37-4.84) and 2.15 (95%CI,
   1.08-4.25) in TCGA and [107]GSE16025 datasets, respectively, suggesting
   that three-miRNA signature was related to the overall survival in SCC
   patients.

Figure 8. Survival analysis of three miRNAs.

   [108]Figure 8
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   Kaplan-Meier survival curves were conducted to compare overall survival
   of SCC patients in high or low risk groups. (A) TCGA; (B)
   [110]GSE16025. Green: low risk group. Red: high risk group.

DISCUSSION

   Lung cancer is initiated by various genetic alterations, and
   investigation of cancer genome landscapes have a profound effect on
   clinical practice [[111]26]. Recently, high-throughput methods have
   been widely used to screen thousands of genes simultaneously which
   makes genomic study much more efficiently [[112]27]. Bioinformatic
   analysis based on high-throughput data will definitely provide more
   evidence for understanding molecular mechanisms of carcinogenesis.
   Here, we employed three miRNA expression profiles ([113]GSE19945,
   [114]GSE25508 and [115]GSE51853) to search for the key miRNAs as
   candidate clinical biomarkers in different histological subtypes of
   NSCLC. After the comprehensive analysis, miR-375, miR-203 and miR-205
   were found to distinguish SCC from other subtypes of NSCLC. Moreover,
   function prediction and coexpression analysis suggested that above
   three miRNAs might affect cancer progression and Hippo signaling
   pathway. Finally, the prognostic values of three-miRNA signature were
   confirmed by two independent datasets.

   Recent studies showed the clinical features varied among different
   NSCLC subtypes [[116]29, [117]30]. SCC, accounting for some 30% of lung
   cancer cases, is more closely associated with tobacco smoking than
   ADC/LCLC [[118]26, [119]28, [120]29]. Most SCCs are centrally located
   and tend to originate from proximal bronchi whereas ADC/LCLC are often
   peripheral [[121]30]. ADC arises most frequently from bronchial mucosal
   glands and are highly vascular [[122]31]. And the least common subtype,
   LCLC was highly malignant, one subtype of which has neuroendocrine
   properties [[123]30]. ADC/LCLC was easier and earlier to metastasize
   than SCC, the latter always grows slowly and are more likely to remain
   in the central area of lung [[124]32, [125]33]. Furthermore, the
   responses to targeted therapy are also different in patients with
   different histological subtypes [[126]34, [127]35]. Therefore,
   exploration the underlying molecular changes among different
   histological subtypes might provide evidence for understanding
   mechanisms of NSCLC. In this study, we compared the miRNA expression
   profiles of SCC/ADC/LCLC samples with normal lung samples using three
   GEO datasets. From ADC vs. normal, there were two miRNAs significantly
   up-regulated (miR-210 and miR-130b) and none down-regulated. And from
   SCC vs. normal, 3 miRNAs (miR-205, miR-210 and miR-130b) were
   significantly up-regulated and 2 (miR-486 and miR-30a) were
   significantly down-regulated. As for LCLC vs. normal, 5 (miR-30a*,
   miR-30a, miR-497, miR-144 and miR-145) were significantly
   down-regulated and none up-regulated. Moreover, we compared different
   subtypes of NSCLC (SCC vs. ADC, SCC vs. LCLC and ADC vs LCLC) using the
   similar method. Interestingly, we found miR-203, miR-205 and miR-375
   distinguished SCC from other NSCLC subtypes, consisting with previous
   studies [[128]36-[129]38]. Moreover, we also confirmed that the
   signature of three-above mentioned miRNAs was also associated with
   overall survival of SCC patients. Canueto et al. reported the
   prognostic values of miR-205 and miR-203 in cutaneous squamous cell
   carcinoma [[130]39]. Additionally, miR-375 was also found to be
   negatively associated with clinical outcomes in NSCLC [[131]40,
   [132]41]. Therefore, our above findings provide evidence for
   investigating mechanisms and exploring therapeutic target for NSCLC,
   especially for SCC patients.

   In order to further explore the function of three identified miRNAs,
   pathway enrichment analysis of their target genes was performed. Hippo
   signaling pathway was the most predicted pathway affected by miR-203,
   miR-205 and miR-375. Hippo signaling pathway was reported to be closely
   related to the occurrence of multiple malignancies, including NSCLC
   [[133]42-[134]44]. Therefore, we further performed coexpression
   analysis of three miRNAs and Hippo signaling pathway related genes.
   Four hub genes (TP63, RERE, TJP1 and YWHAE) were identified as the
   pivotal genes that might be affected by three miRNAs. Furthermore, our
   validation data confirmed the association between miRNAs and three
   Hippo signaling pathway related genes (TP63, TJP1 and YWHAE). TP63
   (tumor protein p63), a member of p53 family, was considered as one of
   “squamous markers” [[135]45], and its copy number variation was
   reported to be associated with SCC [[136]46]. TJP1 (tight junction
   protein 1), also known as ZO-1 (Zonula Occludens-1), participated in
   cell-cell adhesion and is reported to be associated with skin SCC
   [[137]47]. Recent studies suggested that TJP1 might be a useful
   prognostic predictor of NSCLC [[138]48, [139]49]. YWHAE, a gene
   encoding 14-3-3 protein, was found to involve in carcinogenesis and
   progression of SCC [[140]50]. Taken together, both our findings and
   previous reports suggested the critical roles of three miRNAs (miR-203,
   miR-205 and miR-375) in regulating Hippo signaling pathway. Therefore,
   investigation the underlying interaction between above mentioned miRNA
   and genes will be of particular interest to study the molecular
   mechanisms of NSCLC.

   In conclusion, we identified differentially expressed miRNAs and their
   possible target genes involved in different histological subtypes of
   NSCLC. The novel findings in the present study were that miR-203,
   miR-205 and miR-375 could distinguish SCC from other subtypes of NSCLC,
   and were associated with overall survival in SCC patients. Hippo
   signaling pathway might be affected by above-mentioned three miRNAs.
   Our results provide evidence to better understand the molecular changes
   underlying distinctive biological properties of different NSCLC
   subtypes.

MATERIALS AND METHODS

Microarray datasets

   The miRNA datasets were searched from the GEO database
   ([141]https://www.ncbi.nlm.nih.gov/geo/). The search strategy was
   (“lung” OR “pulmonary” OR “respiratory” OR “bronchi” [Mesh]) AND
   (“cancer” OR “carcinoma” OR “tumor” OR neoplas* OR malignan* OR
   “squamous Cell Carcinoma” OR “adenocarcinoma” OR “large cell lung
   cancer” [Mesh]) AND (MicroRNA OR miRNA [Mesh]). Finally, three datasets
   ([142]GSE19945, [143]GSE25508 and [144]GSE51853) were selected for
   further analysis. These three datasets contained the histological
   information of NSLC (ADC, SCC and LCLC).

   [145]GSE19945 was conducted through [146]GPL9948 platform (Agilent
   Human 0.6K miRNA Microarray G4471) and consisted of 4 ADC, 5 SCC, 11
   LCLC and 8 normal lung tissue samples. Meanwhile, [147]GSE51853, based
   on [148]GPL6480 platform (Agilent-014850 Whole Human Genome Microarray
   4x44K G4112F), consisted of 76 ADC, 29 SCC, 17 LCLC and 5 normal lung
   tissue samples [[149]18]. [150]GSE25508 was conducted by [151]GPL7731
   platform (Agilent-019118 Human miRNA Microarray 2.0 G4470B) to explore
   miRNA expression panel with or without asbestos exposure [[152]19].
   From [153]GSE25508 dataset, miRNA profiling data of 10 ADC, 8 SCC, 4
   LCLC and 22 non-cancer lung tissue samples were extracted for analysis.

Identification of DEmiRNAs

   DEmiRNAs between different groups (cancer vs normal, or comparison
   among different histological subtypes) were screened based on three GSE
   datasets, respectively. Only fold change (FC)>=1.5 and p-value for
   t-test <0.05 was considered statistically significant. The heatmap of
   DEmiRNAs was conducted by a web-based tool, Morpheus
   ([154]https://software.broadinstitute.org/morpheus/). Common DEmiRNAs
   among three GSE datasets was conducted by Venny 2.1.0
   ([155]http://bioinfogp.cnb.csic.es/tools/venny/). Moreover, we carried
   out a comprehensive meta-analysis of the expression of the miRNA
   screened above through a RRA method to identify DEmiRNAs [[156]17].
   This approach requires ranked miRNA lists and the total probe number of
   each miRNome profiling study. MiRNAs were re-ranked by corrected
   p-value.

Prediction of target genes

   Target genes of the DEmiRNAs was predicted by a web tool,
   TargetScanHuman 7.1 ([157]http://www.targetscan.org/vert_71/). The
   cumulative weighted context++ score <-0.4 (-0.2 for miR-375) was
   adopted as the cut-off value. These genes were intended for pathway
   enrichment. [158]GSE51852, another subseries of [159]GSE51855, was a
   gene expression profile based on the same sample as [160]GSE51853. The
   interaction between the DEmiRNAs and the significantly genes was mapped
   in Cytoscape [[161]20].

Pathway enrichment analysis

   Gene Ontology (GO) and KEGG pathway enrichment was performed by
   Database for Annotation, Visualization and Integrated Discovery (DAVID)
   ([162]https://david-d.ncifcrf.gov/summary.jsp), a web tool for gene
   functional annotation [[163]21]. Identifier of official gene symbol and
   homo sapiens were selected.

Cell culture and transfection

   The HCC cell line, SMMC-7721, used in this study were obtained from the
   Liver Cancer Institute, Fudan University (Shanghai, China), and
   maintained in DMEM containing 10% fetal bovine serum at 37°C with 5%
   CO[2]. The miRNA mimics were synthesized by GenePharma (Shanghai,
   China). The transfections of miR-375 and miR-203 mimics were performed
   using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) according to
   the procedure recommended by the manufacturer.

Reverse transcription-polymerase chain reaction (RT-PCR)

   Total RNA was isolated from the different cell groups using the
   RNAfast200 Total RNA Extract Kit (Fastgene, Shanghai, China), and 2 μg
   RNA was reverse transcribed to cDNA by the RevertAid™ First Strand cDNA
   Synthesis Kit (Fermentas, MBI, Lithuania). Quantitative RT-PCR was
   carried out using the SYBR^® PrimeScriptTM miRNA RT-PCR Kit and SYBR^®
   Premix Ex TaqTM (TaKaRa Biotechnology, Dalian, China). The Fold change
   of gene expression was calculated based on the threshold cycle (Ct) as
   relative to β-actin using a 2-Δ(ΔCt) method. The sequences of PCR
   primers for target genes are listed as followings. TP63: forward
   primer, 5’-GGACCAGCAGATTCAGAACGG-3’; reverse primer, 5’-AGGACACG
   TCGAAACTGTGC-3’; TJP1: forward primer, 5’-CAACATACAGTGACGCTTCACA-3’;
   reverse primer, 5’- CACTATTGACGTTTCCCCACTC-3’; YWHAE: forward primer,
   5’- GATTCGGGAATATCGGCAAATGG-3’; reverse primer, 5’-GCTGGAATGAGGTGTTTG
   TCC-3’. All primer pairs were synthesized by TaKaRa.

Survival analysis

   The prognostic value of DEmiRNAs was evaluated by SurvMicro
   ([164]http://bioinformatica.mty.itesm.mx:8080/Biomatec/Survmicro.jsp)
   [[165]22]. Kaplan-Meier curves were conducted based on TCGA-LUSC and
   [166]GSE16025 datasets. MiRNA profile of TCGA-LUSC dataset was
   sequenced using Illumina Genoma Analyzer IIx Equipment. [167]GSE16025
   dataset contained 61 SCC samples and 10 matched normal lung samples and
   profiled on MirVana miRNA Bioarrays (version 2, Ambio). HR and 95% CI
   of the association between miRNAs and overall survival was estimated
   using a Cox proportional hazards analysis. Statistical assessment was
   performed using the Log Rank test.

Statistical analysis

   Statistical analyses were performed using STATA software, version 12.0.
   The paired student t-test was used to compare the differences in gene
   expression between control and miRNA mimics -transfected HCC cells.
   Pearson correlation was applied to evaluate the expression correlation
   between the DEmiRNAs and the selected genes. p-value less than 0.05 was
   considered statistically different.

Acknowledgments