Literature DB >> 29068474

The PI3K/AKT signaling pathway: Associations of miRNAs with dysregulated gene expression in colorectal cancer.

Martha L Slattery1, Lila E Mullany1, Lori C Sakoda2, Roger K Wolff1, John R Stevens3, Wade S Samowitz4, Jennifer S Herrick1.   

Abstract

The PI3K/AKT-signaling pathway is one of the most frequently activated signal-transduction pathways in cancer. We examined how dysregulated gene expression is associated with miRNA expression in this pathway in colorectal cancer (CRC). We used data from 217 CRC cases to evaluate differential pathway gene expression between paired carcinoma and normal mucosa and identify miRNAs that are associated with these genes. Gene expression data from RNA-Seq and miRNA expression data from Agilent Human miRNA Microarray V19.0 were analyzed. We focused on genes most associated with CRC (fold change (FC) of >1.5 or <0.67) that were statistically significant after adjustment for multiple comparisons. Of the 304 genes evaluated, 76 had a FC of <0.67, and 57 had a FC of >1.50; 47 of these genes were associated with miRNA differential expression. There were 145 mRNA:miRNA seed-region matches of which 26 were inversely associated suggesting a greater likelihood of a direct association. Most miRNA:mRNA associations were with factors that stimulated the pathway. For instance, both IL6R and PDGFRA had inverse seed-region matches with seven miRNAs, suggesting that these miRNAs have a direct effect on these genes and may be key elements in activation of the pathway. Other miRNA:mRNA associations with similar impact on the pathway were miR-203a with ITGA4, miR-6071 with ITGAV, and miR-375 with THBS2, all genes involved in extracellular matrix function that activate PI3Ks. Gene expression in the PI3K/Akt-signaling pathway is dysregulated in CRC. MiRNAs were associated with many of these dysregulated genes either directly or in an indirect manner.
© 2017 The Authors. Molecular Carcinogenesis Published by Wiley Periodicals, Inc.

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Keywords:  PI3K/AKT signaling; colorectal cancer; mRNA; miRNA

Mesh：

Substances：

Year:  2017        PMID： 29068474      PMCID： PMC5760356          DOI： 10.1002/mc.22752

Source DB:  PubMed          Journal:  Mol Carcinog        ISSN： 0899-1987            Impact factor:   4.784


INTRODUCTION

Phosphoinositide 3‐kinases (PI3K) are a family of enzymes, first discovered in 1985 that have been classified into three groups.1 Class I PI3Ks are the best characterized and have been implicated in cancer given their role in various cellular processes including cell differentiation, metabolism, inflammation, cell motility, and cancer progression.2 The PI3Ks can be activated by an array of stimuli, including growth factors, cytokines, and hormones through various membrane receptors such as receptor tyrosine kinases (RTKs) that include EGFR (epidermal growth factor receptor) and PDGFR (platelet‐derived growth factor receptor).3 Downstream of PI3K, AKT (alias protein kinase B or PKB) is activated by PI3K. AKT, initially identified as a component of insulin intracellular signaling, is involved in the regulation of key factors related to cell growth, survival, and proliferation, including activation of mammalian target of rapamycin complex 1 (mTORC made up of mTOR, Raptor mLST8, and PRAS40)4; ribosomal S6 kinases; pro‐apoptotic FOXO proteins; IKK which induces NFκB transcriptional activity; CREB (cAMP response element‐binding protein) a cellular transcription factor that influences cell survival; and BAD (Bcl‐2‐associated death promoter) involved in the initiation of apoptosis.2, 5, 6 PTEN (phosphatase and tensin homolog), a tumor suppressor gene, can inactivate this pathway and has been associated with multiple cancers and alterations in cell cycle regulation and apoptosis.7 We have shown that genetic variation in PIK3CA and AKT1 is associated with a strong increased risk of colon cancer.8 Akt has been shown to be a key regulator in cell survival and proliferation.9 PIK3CA, the human gene encoding PI3K, also was importantly related to survival after diagnosis with colon cancer, suggesting that genetic variation in the PI3K/Akt pathway influences survival.10 The PI3K/AKT signaling‐pathway is recognized as being one of the most frequently activated signal transduction pathways in cancer.2 MiRNAs can directly influence gene expression by binding to the 3'un‐translated region (UTR) of the target mRNA and causing mRNA transcript degradation or repression of the target gene translation.11, 12 There is evidence that through these mechanisms miRNAs may influence genes that are central to the PI3K/Akt‐signaling pathway. Among the miRNAs reported to influence this pathway are miR‐133a, let‐7, miR‐21, miR‐205, miR‐200, miR‐130b, miR‐155, and miR‐106b.13, 14 In this paper we ascertained 342 genes in the PI3K/Akt‐signaling pathway using Kyoto Encyclopedia of Genes and Genomes (KEGG) and evaluated 304 for which we have adequate expression data in our CRC samples. We identified genes in this pathway that are dysregulated in that the colorectal tumor mRNA expression levels are significantly different than mRNA expression in normal colorectal mucosa. For those genes that were dysregulated, we further evaluated their associations with expression levels of miRNAs. We hypothesized that genes in the PI3K/Akt‐signaling pathway are dysregulated in in colorectal cancer (CRC) and that miRNAs are linked to this dysregulation.

METHODS

Study participants

Study participants come from two population‐based case‐control studies that included all incident colon and rectal cancer between 30 and 79 years of age in Utah or were members of Kaiser Permanente in Northern California (KPNC). Participants were non‐Hispanic white, Hispanic, or black for the colon cancer study; the rectal cancer study also included people of Asian race.15, 16 Case diagnosis was verified by tumor registry data as a first primary adenocarcinoma of the colon or rectum and were diagnosed between October 1991 and September 1994 (colon study) and between May 1997 and May 2001 (rectal study).17 The Institutional Review Boards at the University of Utah and at KPNC approved the study.

RNA processing

Formalin‐fixed paraffin embedded tissue from the initial biopsy or surgery was used to extract RNA. RNA was extracted, isolated, and purified from carcinoma tissue and adjacent normal mucosa as previously described.18 We observed no differences in RNA quality based on age of the tissue. The same RNA preparation was used to generate both the mRNA and miRNA data.

mRNA: RNA‐Seq sequencing library preparation and data processing

Total RNA from 245 colorectal carcinoma and normal mucosa pairs was chosen for sequencing based on availability of RNA and high quality miRNA data; 217 pairs passed quality control (QC) and were used in these analyses. RNA library construction was done with the Illumina TruSeq Stranded Total RNA Sample Preparation Kit with Ribo‐Zero. The samples were then fragmented and primed for cDNA synthesis, adapters were then ligated onto the cDNA, and the resulting samples were then amplified using PCR; the amplified library was then purified using Agencount AMPure XP beads. A more detailed description of the methods can be found in our previous work.19 Illumina TruSeq v3 single read flow cell and a 50 cycle single‐read sequence run was performed on an Illumina HiSeq instrument. Reads were aligned to a sequence database containing the human genome (build GRCh37/hg19, February 2009 from genome.ucsc.edu) and alignment was performed using novoalign v2.08.01. Total gene counts were calculated for each exon and UTR of the genes using a list of gene coordinates obtained from http://genome.ucsc.edu. We disregarded genes that were not expressed in our RNA‐Seq data or for which the expression was missing for the majority of samples.19

miRNA

The Agilent Human miRNA Microarray V19.0 was used. Data were required to pass stringent QC parameters established by Agilent that included tests for excessive background fluorescence, excessive variation among probe sequence replicates on the array, and measures of the total gene signal on the array to assess low signal. Samples failing to meet quality standards were re‐labeled, hybridized to arrays, and re‐scanned. If a sample failed QC assessment a second time, the sample was excluded from analysis. The repeatability associated with this microarray was extremely high (r = 0.98)17; comparison of miRNA expression levels obtained from the Agilent microarray to those obtained from qPCR had an agreement of 100% in terms of directionality of findings and the FCs were almost identical.20 To normalize differences in miRNA expression that could be attributed to the array, amount of RNA, location on array, or factors that could erroneously influence miRNA expression levels, total gene signal was normalized by multiplying each sample by a scaling factor which was the median of the 75th percentiles of all the samples divided by the individual 75th percentile of each sample.21

PI3K/Akt‐signaling genes

The Kyoto Encyclopedia of Genes and Genomes (KEGG) (www.genome.jp/kegg-bin/show_pathway?hsa04151) pathway map for PI3K‐AKT‐signaling was used to identify genes associated with this pathway. Using this map, we identified 342 genes, 304 of which had sufficient expression in CRC tissue for inclusion in the study (Supplemental Table S1).

Statistical methods

We utilized a negative binomial mixed effects model in SAS (accounting for carcinoma/normal status as well as for subject effect) to determine genes in the PI3K/Akt‐signaling pathway that had a significant difference in expression between individually paired colorectal carcinoma and normal mucosa (ie, differentially expressed) and their related fold changes (FC). In this test we offset the overall exposure as the log expression of all identified protein‐coding genes (n = 17,461). The Benjamini and Hochberg22 procedure was used to control the false discovery rate (FDR) using a value of 0.05 or less. A FC greater than one indicates a positive differential expression (ie, up‐regulated in carcinoma), while a FC between zero and one indicates a negative differential expression (ie, down‐regulated in carcinoma). We calculated level of expression of each gene by dividing the total expression for that gene in an individual by the total expression of all protein‐coding genes per million transcripts (RPMPCG or reads per million protein‐coding genes). We focused on those genes with FC of >1.50 or <0.67 for analysis with differential miRNA expression as these levels of FC may have a greater biological significance than smaller FCs. There were 814 miRNAs expressed in greater than 20% of normal colorectal mucosa that were analyzed; differential expression was calculated using subject‐level paired data as the expression in the carcinoma tissue minus the expression in the normal mucosa. In these analyses, we fit a least squares linear regression model to the RPMPCG differential expression levels and miRNA differential expression levels. P‐values were generated using the bootstrap method by creating a distribution of 10 000 F statistics derived by resampling the residuals from the null hypothesis model of no association between gene expression and miRNA expression using the boot package in R. Linear models were adjusted for age and sex. Multiplicity adjustments for gene/miRNA associations were made at the gene level using the FDR by Benjamini and Hochberg.22

Bioinformatics analysis

We analyzed miRNAs and targeted mRNAs for seed region matches. The mRNA 3′ UTR FASTA as well as the seed region sequence of the associated miRNA were analyzed to determine seed region pairings between miRNA and mRNA. MiRNA seed regions were calculated as described in our previous work23; we calculated and included seeds of six, seven, and eight nucleotides in length. A seed match between the miRNA and the mRNA would increase the likelihood that identified genes associated with a specific miRNA were more likely to have a direct association, especially if there was an inverse association, given a higher propensity for binding and thus mRNA degradation. As miRTarBase24 uses findings from many different investigations spanning across years and alignments, we used FASTA sequences generated from both GRCh37 and GRCh38 Homo sapiens alignments, using UCSC Table Browser (https://genome.ucsc.edu/cgi-bin/hgTables).25 We downloaded FASTA sequences that matched our Ensembl IDs and had a consensus coding sequences (CCDS) available. Analysis was done using scripts in R 3.2.3 and in perl 5.018002.

RESULTS

Of the 217 study participants, 169 had been diagnosed with colon cancer, 118 were male, and 161 were non‐Hispanic white (Table 1). The mean age of the study population was 64.8 years. The majority of study participants (55.6%) were diagnosed at either AJCC Stage 1 or 2. TP53 mutations were the most common tumor molecular phenotype of cases (47.5%) and BRAF‐mutated tumors were the least common tumor molecular phenotype observed (10.1%).
Table 1

Description of Study Population

N %
Site
Colon16977.9
Rectal4822.1
Sex
Male11854.4
Female9945.6
Age
Mean (SD)64.810.1
Race
Non‐Hispanic White16174.2
Hispanic146.5
Non‐Hispanic Black83.7
Unknown3415.7
AJCC stage
15827.1
26128.5
37233.6
42310.8
Tumor phenotype
TP53 mutated10347.5
KRAS mutated6931.8
BRAF mutated2110.1
CIMP high4520.7
MSI2913.4
Vital status
Dead9242.6
Alive12457.4
Description of Study Population Of the 304 genes evaluated, 254 (83.6%) were statistically significantly dysregulated after adjustment for multiple comparisons. Of the 254 significantly dysregulated genes, 76 had a FC of <0.67, and 57 had a FC of >1.50 (Table 2) (Supplemental Table S2 lists expression results for all genes in the pathway). Among the most dysregulated genes (both up and down‐regulated) were genes involved in extracellular matrix (ECM) functions including collagen type genes (such as COL4A6, COL4A4, COL2A1, COL4A3, and COL6A5), tenascin genes (TNC and TNXB), thrombospondin genes (THBS2), and integrin subunit alpha and subunit beta genes. The cytokine‐related genes, IL3RA, IL6R, and IL7R were significantly down‐regulated. Several growth factor genes, including IGF1, FGFR2, FGF9, FGF10, EGF, and PDGFD were down‐regulated in CRC tumor tissue. PIK3CG and PIK3CD also were significantly down‐regulated (FC 0.40 and 0.59 respectively), which could influence down‐stream genes including BRCA1, CCND1, MYC, BCL2, and CREB. Figure 1 highlights those genes that are down‐regulated (yellow) and up‐regulated (green) in the PI3K/Akt‐signaling pathway.
Table 2

Statistically significant differentially expressed PI3K/AKT pathway genes whose fold change is <0.67 or >1.5

GeneTumor meanNormal meanFold change P‐valueAdjusted P‐value
TCL1A 0.193.210.061.46E‐185.40E‐18
PCK1 42.44305.320.141.11E‐461.98E‐45
GNG7 3.6622.760.162.58E‐495.59E‐48
TNXB 23.48134.340.171.24E‐631.26E‐61
FGF9 1.669.080.184.27E‐303.17E‐29
COL4A6 3.8016.610.231.94E‐372.10E‐36
COL6A5 0.973.970.241.34E‐133.85E‐13
CREB3L3 0.692.760.251.88E‐104.22E‐10
IL6R 25.93101.240.262.77E‐581.40E‐56
ITGA8 9.8437.300.262.42E‐464.09E‐45
COL4A5 10.7040.450.264.50E‐252.21E‐24
NGFR 2.107.670.279.76E‐265.30E‐25
LPAR1 12.9146.290.286.42E‐521.63E‐50
COL4A4 13.5847.420.296.88E‐335.98E‐32
PDGFD 5.9019.580.308.17E‐285.10E‐27
TNR 0.531.750.304.45E‐111.06E‐10
FIGF 0.361.140.314.89E‐089.23E‐08
CHRM1 1.103.430.324.01E‐121.03E‐11
GHR 6.6820.210.333.79E‐302.88E‐29
PIK3R6 3.8911.430.341.35E‐268.02E‐26
G6PC 0.250.740.348.92E‐041.27E‐03
COL2A1 1.474.170.356.37E‐111.49E‐10
G6PC2 0.330.920.361.63E‐042.52E‐04
IGF1 13.8538.800.365.43E‐283.59E‐27
RELN 5.6815.850.368.90E‐183.15E‐17
SGK1 50.97137.980.374.36E‐385.10E‐37
EIF4E1B 0.160.420.387.63E‐039.81E‐03
FGFR2 26.5168.820.392.32E‐219.93E‐21
COL4A3 12.2631.400.391.96E‐238.77E‐23
BCL2 24.4262.110.393.73E‐394.72E‐38
GYS2 0.230.580.405.32E‐047.82E‐04
PIK3CG 13.9935.040.404.52E‐283.06E‐27
CHAD 8.6521.440.402.76E‐251.42E‐24
PRKAA2 8.3320.320.411.41E‐082.84E‐08
EFNA5 10.4125.330.412.50E‐157.92E‐15
FGF10 1.704.060.421.85E‐104.21E‐10
KIT 14.5833.840.436.08E‐172.10E‐16
LAMC3 4.069.430.432.41E‐147.17E‐14
IL7R 35.0379.980.448.00E‐295.65E‐28
EGF 2.836.340.451.71E‐052.87E‐05
PPP2R3A 28.1062.550.453.67E‐373.84E‐36
PPP2R2B 1.723.780.469.70E‐102.12E‐09
GNG2 13.9329.330.474.15E‐241.91E‐23
GNGT2 0.711.460.491.10E‐051.87E‐05
CREB3L1 96.13195.060.491.51E‐165.15E‐16
CD19 3.787.590.502.65E‐085.17E‐08
ITGA4 36.2771.100.511.71E‐258.99E‐25
FGFR3 36.1570.780.511.13E‐184.28E‐18
ITGB7 11.1321.510.526.05E‐182.17E‐17
GNGT1 0.480.910.532.33E‐033.15E‐03
PDGFRA 88.47166.610.535.08E‐262.81E‐25
COL9A2 26.5349.910.534.06E‐121.03E‐11
LAMB4 0.791.470.542.64E‐044.05E‐04
CHRM2 2.324.200.551.89E‐032.61E‐03
PHLPP2 79.32139.850.571.77E‐281.22E‐27
FGF2 10.0417.660.572.79E‐137.86E‐13
TEK 8.9215.470.585.93E‐121.49E‐11
LPAR4 0.400.680.582.33E‐022.87E‐02
FGF13 2.424.180.582.84E‐054.69E‐05
PIK3CD 27.3346.480.599.96E‐224.33E‐21
CSF1 28.4947.900.593.95E‐211.67E‐20
FGF7 8.1713.700.601.49E‐082.97E‐08
ITGA7 21.3335.690.601.74E‐165.89E‐16
LPAR3 1.362.260.601.41E‐031.98E‐03
IL3RA 2.213.590.622.82E‐033.80E‐03
ITGA2B 0.540.860.629.90E‐031.26E‐02
CSF3 1.031.660.628.63E‐031.10E‐02
EPOR 6.8110.870.632.99E‐085.72E‐08
NR4A1 201.17319.660.635.51E‐111.31E‐10
EFNA2 5.048.000.631.35E‐062.42E‐06
FASLG 1.071.700.634.96E‐036.48E‐03
BCL2L11 56.1988.470.641.23E‐235.58E‐23
LPAR5 25.3739.840.645.44E‐202.15E‐19
SGK2 42.1865.060.651.12E‐092.42E‐09
PHLPP1 42.5665.010.651.03E‐204.16E‐20
THBS1 585.51881.120.663.69E‐181.34E‐17
NOS3 23.6715.661.511.27E‐092.72E‐09
PDGFC 28.5518.831.521.45E‐093.08E‐09
YWHAE 214.66141.141.528.22E‐285.10E‐27
ITGB8 74.1747.901.551.88E‐156.02E‐15
YWHAH 102.5966.001.551.54E‐258.22E‐25
EIF4B 299.92192.201.561.47E‐391.94E‐38
LAMA5 242.34155.241.563.52E‐141.03E‐13
ITGB1 328.08209.701.562.06E‐341.90E‐33
EFNA4 15.369.821.571.17E‐143.55E‐14
GNG10 6.374.001.592.92E‐085.62E‐08
CCND2 773.45483.061.603.23E‐161.08E‐15
ITGB5 110.3768.021.624.02E‐231.77E‐22
COL6A3 893.58541.261.652.92E‐181.07E‐17
VEGFA 532.25316.771.685.74E‐324.71E‐31
ITGAV 191.03113.431.682.91E‐302.27E‐29
TNC 156.3992.441.697.51E‐111.73E‐10
FGF1 4.772.791.719.30E‐061.59E‐05
CDK6 289.15166.121.741.71E‐351.63E‐34
YWHAB 389.26223.041.751.32E‐371.49E‐36
TP53 105.0759.631.763.25E‐241.54E‐23
RHEB 46.5126.011.791.22E‐309.79E‐30
PPP2R3B 4.342.421.791.09E‐082.20E‐08
YWHAQ 130.4772.741.798.52E‐378.63E‐36
CCNE1 8.704.801.812.93E‐106.49E‐10
CDK2 45.7024.961.838.37E‐337.07E‐32
PDGFRB 166.2487.761.898.03E‐285.10E‐27
RPS6 850.00447.371.905.64E‐459.02E‐44
LAMC2 138.1170.951.953.56E‐241.67E‐23
DDIT4 66.2932.772.023.01E‐251.53E‐24
YWHAG 241.75119.302.031.69E‐524.68E‐51
F2R 51.1925.162.032.84E‐261.63E‐25
COL4A1 509.38243.492.091.17E‐431.78E‐42
FGF18 1.900.852.221.27E‐052.14E‐05
BCL2L1 144.2864.052.251.06E‐564.02E‐55
COL9A1 10.414.592.273.34E‐096.90E‐09
EIF4EBP1 20.838.842.363.54E‐251.76E‐24
COL9A3 17.747.512.361.74E‐124.68E‐12
BRCA1 87.4635.352.472.77E‐413.83E‐40
CDK4 66.6526.902.489.86E‐584.28E‐56
ITGA2 206.6782.692.501.27E‐482.41E‐47
HSP90AA1 627.50246.842.547.12E‐594.33E‐57
CCND1 317.79122.642.591.53E‐591.16E‐57
FN1 1461.37554.512.648.52E‐347.62E‐33
HSP90AB1 593.22213.902.776.27E‐751.91E‐72
EFNA3 24.988.692.885.14E‐293.72E‐28
OSM 6.682.312.904.35E‐161.44E‐15
COL1A2 1838.76588.953.129.45E‐491.91E‐47
ITGA11 64.2319.893.231.31E‐361.29E‐35
MET 302.6084.883.561.25E‐681.89E‐66
MYC 181.1149.003.705.76E‐551.95E‐53
COL1A1 3190.86754.734.231.19E‐543.61E‐53
FGF20 0.720.145.292.29E‐053.83E‐05
THBS2 212.1232.956.441.67E‐513.91E‐50
COMP 9.531.456.578.75E‐203.41E‐19
GNG4 35.424.398.072.36E‐382.87E‐37
FGF19 1.390.159.426.08E‐131.68E‐12
SPP1 38.033.2811.605.67E‐428.21E‐41
Figure 1

Kegg PI3K/Akt‐Signaling pathway with highlighted dysregulated mRNA and their associated seed‐region matched miRNA. Bold items depict negative beta coefficient between miRNA and mRNA

Statistically significant differentially expressed PI3K/AKT pathway genes whose fold change is <0.67 or >1.5 Kegg PI3K/Akt‐Signaling pathway with highlighted dysregulated mRNA and their associated seed‐region matched miRNA. Bold items depict negative beta coefficient between miRNA and mRNA Differential mRNA expression of 47 of the 133 genes with a FC of <0.67 or >1.50 were associated with differential miRNA expression. Of these 47 genes, 36 genes had seed‐region matches with their associated miRNAs (Table 3). Of the 147 mRNA:miRNA associations with seed‐region matches, 26 were inversely associated meaning that mRNA was down‐regulated when the miRNA was up‐regulated or the mRNA was up‐regulated when the miRNA was down‐regulated, suggesting a greater likelihood of a direct effect. While some genes only had a seed‐region match with one miRNA, such as IGF1, PHLPP2, SGK2, ITGA4, CDK4, THBS1, ITGA11, CREB3L1, and GNG2, most had seed‐region matches with multiple miRNAs. IL6R had a seed‐region match with 11 miRNAs with the majority being inversely associated, ITGA2 had a seed‐region match with 14 miRNAs, and YWHAB had a seed‐region match with 19 miRNAs. Several miRNAs had seed‐region matches with multiple genes: miR‐1246, miR‐21‐3p, miR‐21‐5p, miR‐23a‐3p, miR‐375, and miR‐934 with two genes; let‐7i‐5p, miR193b‐3p, miR‐196‐5p, miR‐19b‐3p, miR‐221‐3p, and miR‐24‐3p with three genes; miR‐106b‐5p, miR199a‐5p, miR‐199b‐5p, miR‐29b‐3p, miR‐3651, and miR‐501‐3p with four genes; miR‐145‐5p and miR‐93‐5p with five genes; miR‐199a‐3p, miR‐203a, and miR‐27a‐3p with six genes; miR‐150‐5p, miR‐214‐3p and miR‐650 with seven genes; miR‐17‐5p, miR‐20a‐5p, and miR‐20b‐5p with eight genes. Expression levels of all of the genes associated with miR‐203a expression were inversely associated, suggesting direct associations. Figure 1 highlights the miRNAs with seed‐region matches associated with various components of the PI3K/Akt‐signaling pathway.
Table 3

Associations between PI3K/AKT pathway genes with miRNAs that have a seed match

Gene nameTumor meanNormal meanFold changemiRNATumor meanNormal meanFold changeBetaRaw P‐valueFDR P‐value
BRCA1 a 87.4635.352.47hsa‐miR‐1246629.21412.811.520.250.00080.0383
hsa‐miR‐145‐5p 132.97223.140.60−0.280.00040.025
hsa‐miR‐17‐5p61.0416.383.730.28<0.00010.0116
hsa‐miR‐20a‐5p70.7817.614.020.250.00060.0383
hsa‐miR‐20b‐5p17.653.305.350.250.00040.0326
hsa‐miR‐93‐5p41.7215.202.740.30<0.00010.0116
IGF1 13.8538.800.36hsa‐miR‐145‐5p132.97223.140.600.28<0.00010.0407
PHLPP2 79.32139.850.57hsa‐miR‐150‐5p14.9039.170.380.29<0.00010.0271
TNC 156.3992.441.69hsa‐miR‐199a‐5p20.189.282.170.250.00020.0163
hsa‐miR‐199b‐5p4.691.533.070.270.00020.0203
PHLPP1 42.5665.010.65 hsa‐miR‐214‐3p 13.246.132.16−0.260.00020.0488
hsa‐miR‐663b 65.5032.212.03−0.26<0.00010.0488
ITGB5 110.3768.021.62hsa‐miR‐199a‐3p44.8322.531.990.32<0.00010.0163
hsa‐miR‐199a‐5p20.189.282.170.36<0.00010.0163
hsa‐miR‐199b‐5p4.691.533.070.280.00020.0233
hsa‐miR‐24‐3p106.7562.391.710.250.00060.0444
SGK2 42.1865.060.65hsa‐miR‐196b‐5p17.895.533.240.37<0.00010.0271
PIK3CG 13.9935.040.40 hsa‐miR‐203a 12.523.703.38−0.34<0.00010.0203
hsa‐miR‐6504.5116.600.270.36<0.00010.0203
COL1A1 3190.86754.734.23hsa‐miR‐199a‐5p20.189.282.170.29<0.00010.0163
hsa‐miR‐199b‐5p4.691.533.070.30<0.00010.0163
hsa‐miR‐214‐3p13.246.132.160.34<0.00010.0163
hsa‐miR‐9344.360.944.660.58<0.00010.0163
CCND1 317.79122.642.59hsa‐miR‐106b‐5p15.905.193.060.250.00060.0349
hsa‐miR‐17‐5p61.0416.383.730.30<0.00010.0244
hsa‐miR‐19b‐3p29.8010.422.860.280.00030.0244
hsa‐miR‐20a‐5p70.7817.614.020.28<0.00010.0244
hsa‐miR‐20b‐5p17.653.305.350.29<0.00010.0244
hsa‐miR‐221‐3p13.534.123.280.260.00030.0349
hsa‐miR‐27a‐3p56.2623.292.420.270.00020.0244
hsa‐miR‐29b‐3p24.319.832.470.270.00030.0244
hsa‐miR‐93‐5p41.7215.202.740.260.00020.0244
PDGFRB 166.2487.761.89hsa‐miR‐193b‐3p9.125.421.680.270.00020.0181
hsa‐miR‐214‐3p13.246.132.160.38<0.00010.0102
ITGA4 36.2771.100.51 hsa‐miR‐203a 12.523.703.38−0.30<0.00010.0116
YWHAQ 130.4772.741.79hsa‐miR‐106b‐5p15.905.193.060.29<0.00010.0116
hsa‐miR‐17‐5p61.0416.383.730.30<0.00010.0116
hsa‐miR‐20a‐5p70.7817.614.020.30<0.00010.0116
hsa‐miR‐20b‐5p17.653.305.350.31<0.00010.0116
hsa‐miR‐27a‐3p56.2623.292.420.240.0010.0471
hsa‐miR‐425‐5p11.766.971.690.230.00110.0471
hsa‐miR‐93‐5p41.7215.202.740.33<0.00010.0116
PDGFRA 88.47166.610.53hsa‐miR‐145‐5p132.97223.140.600.260.00020.0163
hsa‐miR‐150‐5p14.9039.170.380.240.00040.0233
hsa‐miR‐17‐5p 61.0416.383.73−0.28<0.00010.0136
hsa‐miR‐19b‐3p 29.8010.422.86−0.29<0.00010.0136
hsa‐miR‐203a 12.523.703.38−0.30<0.00010.0136
hsa‐miR‐20a‐5p 70.7817.614.02−0.280.00020.0163
hsa‐miR‐20b‐5p 17.653.305.35−0.30<0.00010.0136
hsa‐miR‐29b‐3p 24.319.832.47−0.270.00030.0222
hsa‐miR‐501‐3p 7.072.952.39−0.250.00040.0233
hsa‐miR‐6504.5116.600.270.32<0.00010.0136
CDK4 66.6526.902.48hsa‐miR‐365158.6625.922.260.260.00030.0488
THBS1 585.51881.120.66hsa‐miR‐145‐5p132.97223.140.600.29<0.00010.0407
ITGA11 64.2319.893.23hsa‐miR‐193b‐3p9.125.421.680.28<0.00010.0102
ITGAV 191.03113.431.68hsa‐let‐7i‐5p62.1639.971.560.310.00040.0271
hsa‐miR‐199a‐3p44.8322.531.990.30<0.00010.0136
hsa‐miR‐214‐3p13.246.132.160.29<0.00010.0136
hsa‐miR‐23a‐3p174.6887.532.000.270.00030.0244
hsa‐miR‐27a‐3p56.2623.292.420.250.00120.0425
hsa‐miR‐6071 0.971.700.57−0.230.00110.0425
PDGFC 28.5518.831.52hsa‐miR‐199b‐5p4.691.533.070.280.00020.0488
hsa‐miR‐9344.360.944.660.27<0.00010.0407
CREB3L1 96.13195.060.49 hsa‐miR‐196b‐5p 17.895.533.24−0.280.00020.0203
IL6R 25.93101.240.26hsa‐miR‐150‐5p14.9039.170.380.30<0.00010.0136
hsa‐miR‐17‐5p 61.0416.383.73−0.240.00080.0362
hsa‐miR‐20a‐5p 70.7817.614.02−0.240.00030.0163
hsa‐miR‐20b‐5p 17.653.305.35−0.290.00020.0136
hsa‐miR‐21171.504.090.370.220.0010.0388
hsa‐miR‐21‐5p 463.11167.372.77−0.29<0.00010.0136
hsa‐miR‐23a‐3p 174.6887.532.00−0.250.00070.0335
hsa‐miR‐27a‐3p 56.2623.292.42−0.28<0.00010.0136
hsa‐miR‐3651 58.6625.922.26−0.250.00030.0163
hsa‐miR‐518c‐5p1.762.900.610.220.00140.0475
hsa‐miR‐6504.5116.600.270.290.00020.0136
COL6A3 893.58541.261.65hsa‐let‐7i‐5p62.1639.971.560.26<0.00010.0081
hsa‐miR‐214‐3p13.246.132.160.43<0.00010.0081
ITGA2 206.6782.692.50hsa‐miR‐106b‐5p15.905.193.060.230.0010.0339
hsa‐miR‐10a‐5p44.6426.741.670.250.00060.0257
hsa‐miR‐17‐5p61.0416.383.730.28<0.00010.0081
hsa‐miR‐203a12.523.703.380.34<0.00010.0081
hsa‐miR‐20a‐5p70.7817.614.020.27<0.00010.0081
hsa‐miR‐20b‐5p17.653.305.350.33<0.00010.0081
hsa‐miR‐21‐5p463.11167.372.770.280.00020.0116
hsa‐miR‐221‐3p13.534.123.280.37<0.00010.0081
hsa‐miR‐24‐3p106.7562.391.710.230.00160.0432
hsa‐miR‐29a‐3p110.2951.042.160.240.00060.0257
hsa‐miR‐29b‐3p24.319.832.470.220.00220.0497
hsa‐miR‐365158.6625.922.260.220.0020.0479
hsa‐miR‐501‐3p7.072.952.390.260.00030.0163
hsa‐miR‐93‐5p41.7215.202.740.260.00050.0239
COL1A2 1838.76588.953.12hsa‐let‐7i‐5p62.1639.971.560.240.00020.0239
hsa‐miR‐193b‐3p9.125.421.680.29<0.00010.0148
hsa‐miR‐199a‐3p44.8322.531.990.36<0.00010.0116
YWHAB 389.26223.041.75hsa‐miR‐106b‐5p15.905.193.060.30<0.00010.0029
hsa‐miR‐1246629.21412.811.520.240.00080.0099
hsa‐miR‐130b‐3p8.744.891.790.180.0120.0485
hsa‐miR‐17‐5p61.0416.383.730.44<0.00010.0029
hsa‐miR‐196a‐5p6.704.211.590.230.00070.0234
hsa‐miR‐196b‐5p17.895.533.240.39<0.00010.0095
hsa‐miR‐199a‐3p44.8322.531.990.220.0010.0029
hsa‐miR‐19b‐3p29.8010.422.860.37<0.00010.0029
hsa‐miR‐20a‐5p70.7817.614.020.45<0.00010.0029
hsa‐miR‐20b‐5p17.653.305.350.42<0.00010.0029
hsa‐miR‐21‐3p22.689.892.290.33<0.00010.0029
hsa‐miR‐24‐3p106.7562.391.710.200.00670.035
hsa‐miR‐27a‐3p56.2623.292.420.28<0.00010.0029
hsa‐miR‐32‐3p4.742.811.680.180.01150.0468
hsa‐miR‐365158.6625.922.260.29<0.00010.0029
hsa‐miR‐375 20.5054.530.38−0.260.00020.0048
hsa‐miR‐501‐3p7.072.952.390.190.00780.0376
hsa‐miR‐6515‐5p 1.204.410.27−0.250.00050.0083
hsa‐miR‐93‐5p41.7215.202.740.30<0.00010.0029
GNG4 35.424.398.07hsa‐miR‐17‐5p61.0416.383.730.230.0010.0388
hsa‐miR‐20a‐5p70.7817.614.020.240.00040.025
hsa‐miR‐20b‐5p17.653.305.350.220.00140.0407
hsa‐miR‐424‐3p39.8125.371.570.35<0.00010.009
hsa‐miR‐501‐3p7.072.952.390.230.00080.0326
hsa‐miR‐663a374.83234.911.600.250.00080.0326
hsa‐miR‐92a‐3p121.6041.182.950.34<0.00010.009
TNXB 23.48134.340.17hsa‐miR‐30a‐5p2.384.610.520.250.00040.0444
hsa‐miR‐497‐5p1.777.120.250.30<0.00010.0271
IL7R 35.0379.980.44hsa‐miR‐150‐5p14.9039.170.380.32<0.00010.0271
hsa‐miR‐6504.5116.600.270.26<0.00010.0271
YWHAG 241.75119.302.03hsa‐miR‐21‐3p22.689.892.290.27<0.00010.0116
hsa‐miR‐221‐3p13.534.123.280.29<0.00010.0116
hsa‐miR‐27a‐3p56.2623.292.420.26<0.00010.0116
hsa‐miR‐29b‐3p24.319.832.470.240.00070.0438
PIK3CD 27.3346.480.59hsa‐miR‐150‐5p14.9039.170.380.35<0.00010.0271
hsa‐miR‐6504.5116.600.270.31<0.00010.0271
BCL2 24.4262.110.39hsa‐miR‐150‐5p14.9039.170.380.34<0.00010.0203
hsa‐miR‐195‐5p3.5912.180.290.240.00030.0488
hsa‐miR‐203a 12.523.703.38−0.27<0.00010.0203
hsa‐miR‐6504.5116.600.270.38<0.00010.0203
GNG7 3.6622.760.16hsa‐miR‐150‐5p14.9039.170.380.28<0.00010.0407
hsa‐miR‐6504.5116.600.270.30<0.00010.0407
CHRM2 2.324.200.55hsa‐miR‐133b1.716.940.250.36<0.00010.0271
hsa‐miR‐145‐5p132.97223.140.600.35<0.00010.0271
F2R 51.1925.162.03hsa‐miR‐199a‐3p44.8322.531.990.280.00020.0326
hsa‐miR‐199a‐5p20.189.282.170.270.00040.0407
hsa‐miR‐214‐3p13.246.132.160.30<0.00010.0271
THBS2 212.1232.956.44hsa‐miR‐199a‐3p44.8322.531.990.35<0.00010.0081
hsa‐miR‐214‐3p13.246.132.160.42<0.00010.0081
hsa‐miR‐375 20.5054.530.38−0.240.00050.0271
GNG2 13.9329.330.47 hsa‐miR‐203a 12.523.703.38−0.28<0.00010.0271
hsa‐miR‐6504.5116.600.270.34<0.00010.0271

Bold text highlights negative beta coefficient between differential expression of miRNA and mRNA.

Associations between PI3K/AKT pathway genes with miRNAs that have a seed match Bold text highlights negative beta coefficient between differential expression of miRNA and mRNA. Table 4 shows associations between mRNA and miRNAs without seed‐region match, suggesting an indirect association between the miRNA and the mRNA, in that while miRNA and mRNA are associated, it is through the binding with other mRNA that influence the signaling pathway. In total there were 165 associations without seed‐region matches. Like those miRNA and mRNA with seed‐region matches, differential expression of most genes were associated with multiple miRNAs and miRNAs were associated with multiple genes. Several of the miRNAs that had seed‐region matches to the mRNA, such as let‐7i‐5p, were also associated with other genes, but without seed‐region matches. A summary of the miRNA and gene expression associations by seed‐region match can be seen in Table 5. Most miRNAs have both direct associations, supported by seed‐region matches, and indirect associations with pathway genes.
Table 4

Associations between PI3K/AKT pathway genes with miRNAs that do not have a seed region match

Gene nameTumor meanNormal meanFold changemiRNATumor meanNormal meanFold changeRaw P‐valueFDR P‐value
BRCA 87.4635.352.47hsa‐miR‐19b‐3p29.8010.422.860.00080.0383
hsa‐miR‐25‐3p30.0512.782.350.00020.0163
hsa‐miR‐425‐5p11.766.971.690.0010.0452
hsa‐miR‐6504.5116.600.270.00020.0163
hsa‐miR‐92a‐3p121.6041.182.950.00050.0291
PHLPP2 79.32139.850.57hsa‐miR‐196b‐5p17.895.533.24<0.00010.0271
TNC 156.3992.441.69hsa‐let‐7i‐5p62.1639.971.56<0.00010.0163
hsa‐miR‐146b‐5p4.462.671.670.00040.0296
hsa‐miR‐199a‐3p44.8322.531.99<0.00010.0163
hsa‐miR‐214‐3p13.246.132.16<0.00010.0163
hsa‐miR‐21‐5p463.11167.372.770.00040.0296
hsa‐miR‐9344.360.944.66<0.00010.0163
HSP90AA1 627.50246.842.54hsa‐miR‐203a12.523.703.38<0.00010.0271
ITGB5 110.3768.021.62hsa‐miR‐193b‐3p9.125.421.68<0.00010.0163
hsa‐miR‐214‐3p13.246.132.16<0.00010.0163
hsa‐miR‐365a‐3p8.434.331.940.00070.0475
hsa‐miR‐9344.360.944.66<0.00010.0163
hsa‐miR‐99a‐5p6.303.701.710.00060.0444
HSP90AB1 593.22213.902.77hsa‐miR‐203a12.523.703.38<0.00010.0271
hsa‐miR‐20a‐5p70.7817.614.020.00020.0326
hsa‐miR‐221‐3p13.534.123.280.00020.0326
hsa‐miR‐6515‐5p1.204.410.27<0.00010.0271
hsa‐miR‐663b65.5032.212.03<0.00010.0271
OSM 6.682.312.90hsa‐miR‐424‐3p39.8125.371.57<0.00010.0407
hsa‐miR‐9344.360.944.66<0.00010.0407
COMP 9.531.456.57hsa‐miR‐425‐5p11.766.971.69<0.00010.0407
hsa‐miR‐9344.360.944.66<0.00010.0407
PIK3CG 13.9935.040.40hsa‐miR‐150‐5p14.9039.170.38<0.00010.0203
COL1A1 3190.86754.734.23hsa‐miR‐193b‐3p9.125.421.680.00020.0233
hsa‐miR‐199a‐3p44.8322.531.990.00020.0233
hsa‐miR‐21549.5377.270.640.00070.0438
hsa‐miR‐37520.5054.530.380.00070.0438
YWHAE 214.66141.141.52hsa‐miR‐424‐3p39.8125.371.570.00020.0407
CCND1 317.79122.642.59hsa‐miR‐203a12.523.703.380.00020.0244
hsa‐miR‐21‐5p463.11167.372.770.00060.0349
PDGFRB 166.2487.761.89hsa‐miR‐199a‐3p44.8322.531.99<0.00010.0102
hsa‐miR‐199a‐5p20.189.282.17<0.00010.0102
hsa‐miR‐199b‐5p4.691.533.07<0.00010.0102
hsa‐miR‐9344.360.944.66<0.00010.0102
ITGA4 36.2771.100.51hsa‐miR‐150‐5p14.9039.170.38<0.00010.0116
hsa‐miR‐6504.5116.600.27<0.00010.0116
YWHAH 102.5966.001.55hsa‐miR‐6504.5116.600.27<0.00010.0163
LAMA5 242.34155.241.56hsa‐miR‐17‐5p61.0416.383.73<0.00010.0203
hsa‐miR‐193b‐3p9.125.421.68<0.00010.0203
hsa‐miR‐331‐3p14.649.301.570.00020.0271
YWHAQ 130.4772.741.79hsa‐miR‐19b‐3p29.8010.422.860.00020.0181
hsa‐miR‐21‐3p22.689.892.290.00090.0471
hsa‐miR‐221‐3p13.534.123.280.00020.0181
hsa‐miR‐25‐3p30.0512.782.35<0.00010.0116
hsa‐miR‐29a‐3p110.2951.042.160.00110.0471
hsa‐miR‐29b‐3p24.319.832.470.0010.0471
hsa‐miR‐365158.6625.922.260.00060.0444
hsa‐miR‐92a‐3p121.6041.182.950.00110.0471
CDK4 66.6526.902.48hsa‐miR‐17‐5p61.0416.383.73<0.00010.0271
hsa‐miR‐20a‐5p70.7817.614.02<0.00010.0271
hsa‐miR‐20b‐5p17.653.305.350.00020.0407
hsa‐miR‐93‐5p41.7215.202.74<0.00010.0271
MYC 181.1149.003.70hsa‐miR‐1246629.21412.811.520.00020.0163
hsa‐miR‐17‐5p61.0416.383.73<0.00010.0136
hsa‐miR‐19b‐3p29.8010.422.860.00020.0163
hsa‐miR‐20a‐5p70.7817.614.02<0.00010.0136
hsa‐miR‐20b‐5p17.653.305.350.00020.0163
hsa‐miR‐365158.6625.922.260.00030.0188
hsa‐miR‐37520.5054.530.38<0.00010.0136
hsa‐miR‐501‐3p7.072.952.390.00030.0188
hsa‐miR‐5836.613.222.050.00040.0233
hsa‐miR‐663a374.83234.911.600.00030.0188
hsa‐miR‐663b65.5032.212.03<0.00010.0136
hsa‐miR‐92a‐3p121.6041.182.95<0.00010.0136
ITGA11 64.2319.893.23hsa‐miR‐199a‐3p44.8322.531.99<0.00010.0102
hsa‐miR‐199a‐5p20.189.282.17<0.00010.0102
hsa‐miR‐199b‐5p4.691.533.07<0.00010.0102
hsa‐miR‐214‐3p13.246.132.16<0.00010.0102
hsa‐miR‐9344.360.944.66<0.00010.0102
hsa‐miR‐99a‐5p6.303.701.710.00060.0136
ITGAV 191.03113.431.68hsa‐miR‐12031.762.830.62<0.00010.0136
hsa‐miR‐12431.483.200.460.00050.0291
hsa‐miR‐199a‐5p20.189.282.170.00030.0244
hsa‐miR‐204‐3p0.922.370.390.00110.0244
hsa‐miR‐21‐3p22.689.892.290.00090.0425
hsa‐miR‐21‐5p463.11167.372.770.00020.0203
hsa‐miR‐24‐3p106.7562.391.710.0010.0425
hsa‐miR‐34a‐5p25.1512.322.040.00070.038
hsa‐miR‐42513.311.771.860.0010.0425
hsa‐miR‐9344.360.944.66<0.00010.0136
PDGFC 28.5518.831.52hsa‐miR‐99a‐5p6.303.701.71<0.00010.0407
CREB3L1 96.13195.060.49hsa‐miR‐193b‐3p9.125.421.68<0.00010.0136
hsa‐miR‐37520.5054.530.38<0.00010.0136
hsa‐miR‐424‐3p39.8125.371.570.00050.0407
hsa‐miR‐6515‐5p1.204.410.270.00020.0203
hsa‐miR‐92a‐3p121.6041.182.95<0.00010.0136
IL6R 25.93101.240.26hsa‐miR‐19b‐3p29.8010.422.860.00020.0136
hsa‐miR‐203a12.523.703.380.00070.0335
hsa‐miR‐221‐3p13.534.123.280.00150.0488
hsa‐miR‐92a‐3p121.6041.182.95<0.00010.0136
COL6A3 893.58541.261.65hsa‐miR‐193b‐3p9.125.421.68<0.00010.0081
hsa‐miR‐199a‐3p44.8322.531.99<0.00010.0081
hsa‐miR‐199a‐5p20.189.282.17<0.00010.0081
hsa‐miR‐199b‐5p4.691.533.07<0.00010.0081
hsa‐miR‐9344.360.944.66<0.00010.0081
ITGA2 206.6782.692.50hsa‐miR‐12915.523.671.510.00090.0318
hsa‐miR‐19b‐3p29.8010.422.86<0.00010.0081
hsa‐miR‐23a‐3p174.6887.532.000.00020.0116
hsa‐miR‐27a‐3p56.2623.292.42<0.00010.0081
hsa‐miR‐34a‐5p25.1512.322.04<0.00010.0081
hsa‐miR‐6504.5116.600.270.00020.0116
hsa‐miR‐663b65.5032.212.030.00190.0116
hsa‐miR‐92a‐3p121.6041.182.950.00040.0116
COL1A2 1838.76588.953.12hsa‐miR‐199a‐5p20.189.282.17<0.00010.0116
hsa‐miR‐199b‐5p4.691.533.07<0.00010.0116
hsa‐miR‐214‐3p13.246.132.16<0.00010.0116
hsa‐miR‐9344.360.944.66<0.00010.0116
YWHAB 389.26223.041.75hsa‐let‐7i‐5p62.1639.971.560.00220.0176
hsa‐miR‐151a‐3p5.151.563.31<0.00010.0029
hsa‐miR‐15a‐5p7.695.071.520.00020.0048
hsa‐miR‐193b‐3p9.125.421.680.00360.0234
hsa‐miR‐199a‐5p20.189.282.170.00570.0113
hsa‐miR‐199b‐5p4.691.533.070.00230.0311
hsa‐miR‐21‐5p463.11167.372.770.00020.0048
hsa‐miR‐221‐3p13.534.123.28<0.00010.0029
hsa‐miR‐23a‐3p174.6887.532.000.00050.0083
hsa‐miR‐25‐3p30.0512.782.35<0.00010.0029
hsa‐miR‐29a‐3p110.2951.042.16<0.00010.0029
hsa‐miR‐29b‐3p24.319.832.47<0.00010.0029
hsa‐miR‐34a‐5p25.1512.322.040.00050.0083
hsa‐miR‐361‐5p11.626.201.870.0010.0113
hsa‐miR‐424‐3p39.8125.371.57<0.00010.0029
hsa‐miR‐425‐5p11.766.971.690.00070.0095
hsa‐miR‐4749‐3p8.0112.040.670.00270.0191
hsa‐miR‐663a374.83234.911.600.00560.0311
hsa‐miR‐663b65.5032.212.03<0.00010.0029
hsa‐miR‐92a‐3p121.6041.182.95<0.00010.0029
GNG4 35.424.398.07hsa‐miR‐365158.6625.922.260.00080.0326
hsa‐miR‐663b65.5032.212.03<0.00010.009
TNXB 23.48134.340.17hsa‐miR‐133b1.716.940.25<0.00010.0271
hsa‐miR‐145‐5p132.97223.140.60<0.00010.0271
hsa‐miR‐365a‐3p8.434.331.940.00080.0458
hsa‐miR‐39762.971.242.390.00060.0444
hsa‐miR‐99a‐5p6.303.701.710.00090.0458
IL7R 35.0379.980.44hsa‐miR‐203a12.523.703.38<0.00010.0271
YWHAG 241.75119.302.03hsa‐miR‐106b‐5p15.905.193.06<0.00010.0116
hsa‐miR‐17‐5p61.0416.383.73<0.00010.0116
hsa‐miR‐19b‐3p29.8010.422.860.00040.0362
hsa‐miR‐20a‐5p70.7817.614.020.00020.0203
hsa‐miR‐20b‐5p17.653.305.350.00080.0465
hsa‐miR‐21‐5p463.11167.372.770.00060.0116
hsa‐miR‐25‐3p30.0512.782.35<0.00010.0438
hsa‐miR‐365158.6625.922.260.00060.0438
hsa‐miR‐6504.5116.600.270.00070.0438
hsa‐miR‐93‐5p41.7215.202.74<0.00010.0116
BCL2L1 144.2864.052.25hsa‐miR‐92a‐3p121.6041.182.95<0.00010.0407
CD19 3.787.590.50hsa‐miR‐150‐5p14.9039.170.38<0.00010.0407
F2R 51.1925.162.03hsa‐let‐7i‐5p62.1639.971.560.00030.0407
hsa‐miR‐146b‐5p4.462.671.670.00040.0407
hsa‐miR‐193b‐3p9.125.421.680.00020.0326
hsa‐miR‐199b‐5p4.691.533.07<0.00010.0271
hsa‐miR‐9344.360.944.66<0.00010.0271
THBS2 212.1232.956.44hsa‐let‐7i‐5p62.1639.971.560.00030.0203
hsa‐miR‐193b‐3p9.125.421.68<0.00010.0081
hsa‐miR‐199a‐5p20.189.282.17<0.00010.0081
hsa‐miR‐199b‐5p4.691.533.07<0.00010.0081
hsa‐miR‐9344.360.944.66<0.00010.0081
GNG2 13.9329.330.47hsa‐miR‐150‐5p14.9039.170.38<0.00010.0271
LPAR1 12.9146.290.28hsa‐miR‐21‐5p463.11167.372.77<0.00010.0407
hsa‐miR‐497‐5p1.777.120.25<0.00010.0407
Table 5

Associations between miRNAs and mRNA expression based on seed region matches

miRNAmRNA associated with a seed matchmRNA associated without seed region match
hsa‐let‐7i‐5p ITGAV, COL6A3, COL1A2 TNC, YWHAB, F2R, THBS2
hsa‐miR‐10a‐5p ITGA2
hsa‐miR‐106b‐5p CCND1, YWHAQ, ITGA2, YWHAB YWHAG
hsa‐miR‐1203 ITGAV
hsa‐miR‐1243 ITGAV
hsa‐miR‐1246 BRCA1, YWHAB MYC
hsa‐miR‐1291 ITGA2
hsa‐miR‐130b‐3p YWHAB
hsa‐miR‐133b CHRM2 TNXB
hsa‐miR‐145‐5p BRCA1 a IGF1, PDGFRA, THBS1, CHRM2 TNXB
hsa‐miR‐146b‐5p TNC, F2R
hsa‐miR‐150‐5p PHLPP2, PDGFRA, IL6R, IL7R, PIK3CD, BCL2, GNG7 PIK3CG, ITGA4, CD19, GNG2
hsa‐miR‐151a‐3p YWHAB
hsa‐miR‐15a‐5p YWHAB
hsa‐miR‐17‐5p BRCA1, CCND1, YWHAQ, PDGFRA, IL6R, ITGA2, YWHAB, LAMA5, CDK4, MYC, YWHAG
hsa‐miR‐193b‐3p PDGFRB, ITGA11, COL1A2 ITGB5, COL1A1, LAMA5, CREB3L1, COL6A3, YWHAB, F2R, THBS2
hsa‐miR‐195‐5p BCL2
hsa‐miR‐196a‐5p YWHAB
hsa‐miR‐196b‐5p SGK2, CREB3L1, YWHAB PHLPP2
hsa‐miR‐199a‐3p ITGB5, ITGAV, COL1A2, YWHAB, F2R, THBS2 TNC,COL1A1, PDGFRB, ITGA11, COL6A3
hsa‐miR‐199a‐5p TNC, ITGB5, COL1A1, F2R PDGFRB, ITGA11, ITGAV, COL6A3, COL1A2, YWHAB, THBS2
hsa‐miR‐199b‐5p TNC, ITGB5, COL1A1, PDGFC PDGFRB, ITGA11, COL6A3, COL1A2, YWHAB, F2R, THBS2
hsa‐miR‐19b‐3p CCND1, PDGFRA, YWHAB BRCA1, YWHAQ, MYC, IL6R, ITGA2, YWHAG
hsa‐miR‐203a PIK3CG, ITGA4, PDGFRA, ITGA2, BCL2, GNG2 HSP90AA1, HSP90AB1, CCND1, IL6R, IL7R
hsa‐miR‐204‐3p ITGAV
hsa‐miR‐20a‐5p BRCA1, CCND1, YWHAQ, PDGFRA, IL6R, ITGA2, YWHAB, GNG4 HSP90AB1, CDK4, MYC, YWHAG
hsa‐miR‐20b‐5p BRCA1, CCND1, YWHAQ, PDGFRA,IL6R, ITGA2, YWHAB, GNG4 CDK4, MYC, YWHAG
hsa‐miR‐2117 IL6R
hsa‐miR‐21‐3p YWHAB, YWHAG YWHAQ, ITGAV
hsa‐miR‐21‐5p IL6R, ITGA2 TNC, CCND1, ITGAV, YWHAB, YWHAG, LPAR1‘
hsa‐miR‐214‐3p PHLPP1, COL1A1, PDGFRB, ITGAV, COL6A3, F2R, THBS2 TNC, ITGB5, ITGA11, COL1A2
hsa‐miR‐215 COL1A1
hsa‐miR‐221‐3p CCND1, ITGA2, YWHAG HSP90AB1, YWHAQ, IL6R, YWHAB
hsa‐miR‐23a‐3p ITGAV, IL6R ITGA2, YWHAB
hsa‐miR‐24‐3p ITGB5, ITGA2, YWHAB ITGAV
hsa‐miR‐25‐3p BRCA1, YWHAQ, YWHAB
hsa‐miR‐27a‐3p CCND1, YWHAQ, ITGAV, IL6R, YWHAB, YWHAG ITGA2
hsa‐miR‐29a‐3p ITGA2 YWHAQ, YWHAB
hsa‐miR‐29b‐3p CCND1, PDGFRA, ITGA2, YWHAG YWHAQ, YWHAB
hsa‐miR‐30a‐5p TNXB
hsa‐miR‐32‐3p YWHAB
hsa‐miR‐331‐3p LAMA5
hsa‐miR‐34a‐5p ITGAV, ITGA2, YWHAB
hsa‐miR‐361‐5p YWHAB
hsa‐miR‐365a‐3p ITGB5, TNXB
hsa‐miR‐3651 CDK4, IL6R, ITGA2, YWHAB YWHAQ, MYC, GNG4, YWHAG
hsa‐miR‐375 YWHAB, THBS2 COL1A1, MYC, CREB3L1
hsa‐miR‐3976 TNXB
hsa‐miR‐424‐3p GNG4 YWHAB,OSM, YWHAE, CREB3L1,
hsa‐miR‐425‐5p YWHAQ BRCA1, YWHAB
hsa‐miR‐4251 ITGAV
hsa‐miR‐4749‐3p YWHAB
hsa‐miR‐497‐5p TNXB LPAR1
hsa‐miR‐501‐3p PDGFRA, ITGA2, YWHAB, GNG4 MYC
hsa‐miR‐518c‐5p IL6R
hsa‐miR‐583 MYC
hsa‐miR‐6071 ITGAV
hsa‐miR‐650 PIK3CG, PDGFRA, IL6R, IL7R, PIK3CD, BCL2, GNG7, GNG2 BRCA1, ITGA4, YWHAH, ITGA2, YWHAG
hsa‐miR‐6515‐5p YWHAB HSP90AB1, CREB3L1
hsa‐miR‐663a GNG4 MYC, ITGA2, YWHAB
hsa‐miR‐663b PHLPP1 HSP90AB1, MYC, YWHAB, GNG4
hsa‐miR‐92a‐3p GNG4 BRCA1, YWHAQ, MYC, CREB3L1, IL6R, ITGA2, YWHAB, BCL2L1
hsa‐miR‐934 COL1A1, PDGFC TNC, OSM, PDGFRB, ITGA11, COL6A3, COL1A2, F2R, THBS2
hsa‐miR‐93‐5p BRCA1, CCND1,YWHAQ, ITGA2, YWHAB ITGB5, CDK4, YWHAG
hsa‐miR‐99a‐5p ITGB5, ITGA11, PDGFC, TNXB

Bold text indicates an inverse direction of association.

Associations between PI3K/AKT pathway genes with miRNAs that do not have a seed region match Associations between miRNAs and mRNA expression based on seed region matches Bold text indicates an inverse direction of association.

DISCUSSION

The PI3K/Akt‐signaling pathway is frequently activated in cancer. Of the 304 genes evaluated 133 (43.8%) had FC differences that were <0.67 or >1.50, which is slightly higher than the rate of dysregulated genes for all protein‐coding genes (37.5%). Forty‐seven of the 133 dysregulated genes were associated with miRNA expression. For those mRNA and miRNAs with a seed region match, 26 were inversely associated suggesting a greater likelihood of a direct association. Several genes within this signaling pathway have become therapeutic targets, thus a better understanding of the PI3K/Akt‐signaling pathway can provide insight into the underlying biology associated with these targets. During cellular stress, as in oncogenesis, the PI3K/Akt‐signaling pathway is a key regulator of cell survival.26 PI3K can be divided into two major subclasses that have been associated with cancer.4 Subclass IA is activated by receptors with a protein tyrosine kinase (RPTK) activity; hormones, cytokines, growth factors, insulin receptors activate the subclass IA. The IB subclass of PI3Ks is activated by receptors coupled with G proteins (GPCR). In this signaling pathway AKTs are key intermediates. There are three AKTs, amplification of AKT2 has been detected in colorectal tumors and has been positively associated with tumor aggressiveness.2, 27 In our data AKT2 was significantly up‐regulated, although the FC was modest (FC = 1.15). Amplification of AKTs, alters several downstream molecules including mTOR, MYC, eukaryotic initiation factor of translation (eIF4E), IKK, cyclin D1, CREB, CASP9, and BAD. Both BAD and CASP9 were down‐regulated in our data (FC of 0.71 and 0.74, respectively) and MYC and cyclin D1 were up‐regulated (FC 3.70 and 2.59, respectively) as could be expected when AKT2 would be up‐regulated.28 PTEN is a negative regulator of the PI3K/Akt‐signaling pathway and was significantly down‐regulated, although the FC was modest (FC = 0.83). Many of the expression levels that we observed are consistent with an activated PI3K/Akt‐signaling pathway that would promote tumorigenesis. Several parts of the PI3K/Akt‐signaling pathway appear to have dysregulated genes. Most major junctures in the pathway had both up and down‐regulated genes. One example are the genes and receptors associated with extracellular matrix integrity including collagen genes COL1A1, COL1A2, and COL4A1 that were significantly up‐regulated with strong FCs (FC 4.32, 3.12, and 2.09, respectively) while other collagen genes were down‐regulated (Supplemental Table S2 list all genes analyzed and FCs). Tenascin genes involved in cell to matrix interaction and extracellular matrix formation were down‐regulated (TNN FC 0.67, TNXB FC 0.17, and TNC FC 1.69) as were the integrin genes, which are extracellular matrix receptors for collagens and related proteins (ITGA11 FC 3.23, ITGA2 FC 2.50, ITGA2B FC 0.62, ITGA4 FC 0.51, ITGA8 FC 0.26, and ITGAV FC 1.68). The PI3K genes also showed both up and down‐regulation, such as PIK3CA with a FC of 1.22, PIK3CD FC 0.59, and PIK3CG FC 0.40. The ultimate impact of these genes on tumorigenesis, may depend on the balance of up‐ and down‐regulation and how it enables cells to survive and grow. Several miRNAs have previously been associated with genes in the PI3K/Akt‐signaling pathway.13 In the study by Josse and colleagues, the PI3K/Akt‐signaling pathway was one of the top pathways associated with dysregulated miRNA expression levels.13 MiRNAs identified as being important to this signaling pathway were miR‐143, miR‐145, miR‐133a, and miR‐223. We only identified miR‐145‐5p as being associated with BRCA1, IGF1, PDGFRA, THBS1, CHRM2, and TNXB. Other miRNAs associated previously with genes in PI3K/Akt‐signaling pathway include miR‐590‐5p, 106b, and miR‐93 with PTEN,29, 30 miR‐497 with IGF1R,31 miR‐451 with LKB1, AKT, PI3K, and BCL2,32 and miR‐126 with AKT.33 We observed associations between miR‐497 and miR‐106b‐5p with various genes in the pathway. Differences between our study and the literature in detection of associations result from our focusing on those genes with higher FCs. Thus we did not assess genes with lower FC, such as AKT and PTEN, with miRNAs. We utilized seed‐region matches to identify miRNAs that were more likely to have a direct effect on the mRNA, leading to mRNA degradation from the miRNA binding with the 3′UTR of the mRNA. Those matches that were most likely to have a direct effect were the 26 matches where the expression of the miRNA was opposite that of the mRNA expression. Indirect effects are most likely operating feedback and feed‐forward loops.34, 35, 36 In feedback loops, regulators such as miRNAs and transcription factors (TFs) can have either the same effect (repression of expression) or opposite effects where the TF enhances the mRNA on each other.35 In feed‐forward loops, TF regulates the miRNA as well as a target gene (TG), which is in turn also regulated by the miRNA. In this instance, the miRNA may regulate the TG directly, through seed region binding leading to mRNA degradation or translational repression, or indirectly, through repression of the TF that is influencing transcription of the same TG. It has been suggested that regulatory paths involving miRNAs are prevalent mechanisms of gene expression.35 Studies have shown that genes can share a regulatory network with miR‐150‐5p via feed forward loops.34 As this miRNA was only associated with mRNAs with a positive beta coefficient, even when seed‐region matches were identified, it is possible that this miRNA is involved in feed‐forward loops in CRC. Of the 26 mRNA:miRNA significant inverse association, both IL6R and PDGFRA were associated with seven miRNAs. Both of these genes stimulate Class IA PI3Ks that in turn alter AKT. Both of these genes were associated with multiple miRNAs in the miR‐17 to miR‐92 cluster, including miR‐17‐5p, miR‐19b‐3p, miR‐20a‐5p, miR‐20b‐5p, miR‐29b, miR‐27a‐3p, and miR‐23a‐3p. Overexpression of miR‐17‐5p has been shown to inhibit proliferation and trigger apoptosis.37 MiR‐20 has previously been associated with cyclin D138 and has been shown to be associated with vascular endothelial growth factor, a cytokine, via activation of Akt signaling.39 MiR‐27a‐3p has been shown to promote cell proliferation and invasion possibly through its association with the cytokine TRAIL (TNF‐related apoptosis‐inducing ligand)40 or through its effects on insulin.41 IL6R differential expression also was inversely associated with miR‐21‐5p suggesting a direct association between this mRNA and miRNA. MiR‐21 has been previously linked to increased inflammation and has been shown to promote Akt signaling and exert an anti‐apoptotic effect.42 Other miRNA:miRNAs that had a greater potential for a direct effect given the inverse association between the miRNA and the mRNAs that stimulated Class IA PI3Ks were miR‐203a, miR‐375 and miR‐607a through their inverse association with ITGA4, ITGAV, and THBS2. These direct associations also would impact the Class IA PI3Ks that could lead to indirect effects with other miRNA:mRNA associations. MiR‐203a was inversely association with both PIK3CG and GNG2 expression, again suggesting a direct association that would operate at the stimulate component of the pathway. Up‐regulation of miR‐203 has been shown to inhibit proliferation and metastasis in CRC.43 These associations could serve as key targets for future research. Several miRNAs may be important given the frequent number of associated genes both with and without seed‐region matches although associations do not any miRNA:mRNAs with inverse associations. These include miR‐150‐5p, miR‐199a‐3p, miR‐199a‐5p, and miR‐199b‐5p with 11 mRNAs, miR‐650 with 13 mRNAs, and miR‐93‐5p with 7 mRNAs. MiR‐150 has been shown to impair inflammatory cytokine production44; ITGA3 has previously been associated with the anti‐tumor miR‐199 family. Over‐expression of miR‐93 has been shown to promote tumorigenesis and metastasis by activating the PI3K/Akt‐signaling pathway.45 YWHAB (14‐3‐3 proteinβ) was associated with 19 miRNA seed‐region matches, two of which had inverse associations, miR‐375 and miR‐6515‐5p. Although our sample size is small, it is one of the largest available containing samples with paired carcinoma and normal mucosa data. While normal colonic mucosa may not be truly “normal,” it is the closest normal mucosa that can be used for a matched‐paired analysis. Normal colonic mucosa was taken from the same colonic site as the tumor to prevent differences in expression from being the result of tumor location. The same RNA prep was used to generate both miRNA and mRNA data, which could minimize differences in tissue samples that could contribute to associations. We focused only on those genes that were statistically significant and also had a FC of 1.5 or greater or 0.67 or less. Using these criteria, we did not examine all statistically significant genes and miRNAs that were differentially expressed. Thus, genes like PIK3CA, which were statistically significantly dysregulated but the FC was 1.22 and PTEN with a FC of 0.83, were not examined further with miRNAs. A biologically important FC is not well defined, and by using set values for further follow‐up we could have missed genes associated with miRNAs. Additionally, in our current analysis, we utilized a negative binomial model with a random subject effect. Other statistical methods based on fixed effect models such as DESEQ2 could have slightly different results. We exclusively used the KEGG pathway database to identify signaling pathway genes. Thus, other genes not identified in KEGG may importantly alter PI3K/Akt‐signaling as well as influence miRNA expression. When evaluating miRNA with mRNAs, we could have further missed important associations since miRNAs have their impact post‐transcriptionally. However, much of the current information on miRNA target genes comes from gene expression data and associations observed may have important biological meaning, but must be acknowledged as being incomplete.24, 46 In conclusion, we find the PI3K/Akt‐signaling pathway is dysregulated in CRC and that several of the dysregulated genes are associated with miRNAs. Several of these associations involve negative beta coefficients and seed‐region matches between the miRNA and mRNA, suggesting that these miRNAs directly repress gene expression, and thus alter PI3K/Akt signaling; other associations we identified may be downstream effects resulting from the pathways disruption. These results need to be confirmed in other CRC studies, including laboratory‐based setting. Given the prominence of the PI3K/Akt‐signaling pathway in the carcinogenic process, it is important to obtain a better understanding of the underlying genomics of this pathway in an effort to help identify and refine therapeutic agents.

AUTHORS’ CONTRIBUTION

MS obtained funding, planned study, oversaw study data collection and analysis, and wrote the manuscript. JRS provided input into the statistical analysis. LEM conducted bioinformatics analysis and helped write manuscript. LCS provided data and input into the manuscript. RKW oversaw laboratory analysis and gave input into data interpretation. WS reviewed and edited the manuscript and did pathology overview for the study. JSH conducted statistical analysis and managed data. All authors approved final manuscript.

CONFLICTS OF INTEREST

All the authors declare that they have no conflict of interest. Additional Supporting Information may be found online in the supporting information tab for this article. Table S1. Genes in the KEGG PI3K/AKT signaling pathway. Table S1. Differential expression of all genes in PI3K/AKT signaling pathway. Click here for additional data file.
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