Abstract Purpose Cervical cancer (CC) is one of the most common malignant tumors among women. The present study aimed at integrating two expression profile datasets to identify critical genes and potential drugs in CC. Materials and methods Expression profiles, [37]GSE7803 and [38]GSE9750, were integrated using bioinformatics methods, including differentially expressed genes analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and protein–protein interaction (PPI) network construction. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis websites. Connectivity Map (CMap) was used to query potential drugs for CC. Results A total of 145 upregulated genes and 135 downregulated genes in CC were identified. The functional changes of these differentially expressed genes related to CC were mainly associated with cell cycle, DNA replication, p53 signaling pathway, and oocyte meiosis. A PPI network was identified by STRING with 220 nodes and 2,111 edges. Thirteen key genes were identified as the intersecting genes of the enrichment pathways and the top 20 nodes in PPI network. Survival analysis revealed that high mRNA expression of MCM2, PCNA, and RFC4 was significantly associated with longer overall survival, and the survival was significantly better in the low-expression RRM2 group. Moreover, CMap predicted nine small molecules as possible adjuvant drugs to treat CC. Conclusion Our study found key dysregulated genes involved in CC and potential drugs to combat it, which might provide insights into CC pathogenesis and might shed light on potential CC treatments. Keywords: cervical cancer, bioinformatics, cell cycle, biomarker, drug Introduction Cervical cancer (CC) is the second most common malignant tumor among women, responsible for ~527,600 new cases and >265,700 deaths annually.[39]^1 Despite advances in screening detection and new treatment strategies, CC is one of the leading causes of cancer death among females in many developing countries.[40]^2^,[41]^3 Although most patients can be cured if diagnosed at an early stage, poor prognosis is observed with secondary metastatic cancer and tumor relapse. Although human papillomavirus (HPV) is a prerequisite for CC, only a small number of women infected by this virus develop cancer. Thus, other risk factors should be considered as cofactors contributing to the progression of CC.[42]^4 Dysregulated genes play important roles in CC development.[43]^5 Several studies have used gene expression profiling to identify key genes between CC samples and normal cervix.[44]^6^–[45]^9 Hundreds of differentially expressed genes (DEGs) were detected. However, DEGs reported in different studies vary enormously with only some of them consistently detected. Therefore, the discovery of novel effective therapeutic targets against CC is urgently required. A number of chemotherapeutic agents have shown activity against CC, including cisplatin,[46]^10 bevacizumab,[47]^11 carboplatin,[48]^12 paclitaxel,[49]^13 ifosfamide,[50]^14 and topotecan.[51]^15 Various combinations of these agents are recommended as therapies.[52]^16 A recent systematic literature review found that carboplatin–paclitaxel is equally effective and less toxic than cisplatin–paclitaxel as the first-line therapy for metastatic CC.[53]^17 However, patients overall survival (OS) times remains short, indicating an urgent need to discover some molecular drugs that are more efficient and selective. Based on bioinformatics approaches, several studies found small molecules as potential anticancer agents.[54]^18^–[55]^20 In this study, we selected the following microarray datasets [56]GSE7803 and [57]GSE9750 from the Gene Expression Omnibus (GEO) database to identify DEGs. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the identified DEGs was investigated. A protein–protein interaction (PPI) network was constructed to elucidate the significant relationships among DEGs and to identify key genes. Furthermore, the Kaplan–Meier estimator was used on the Gene Expression Profiling Interactive Analysis (GEPIA) website. Candidate small molecules were identified for their potential use in the treatment of CC. Materials and methods Data collection Two CC microarray datasets were downloaded from the GEO website ([58]http://www.ncbi.nlm.nih.gov/geo/). [59]GSE7803 microarray data contained 21 CC tissues and 10 normal cervical epithelia tissues.[60]^6 [61]GSE9750 included 33 tumors samples and 24 healthy cervical samples.[62]^7 Both the profile datasets were based on the Affymetrix [63]GPL96 platform (Affymetrix Human Genome U133A Array). Because Connectivity Map (CMap) strictly required all probesets obtained from the Affymetrix Human Genome U133A Array,[64]^21 we predicted the drugs for the DEGs measured only in this platform with high accuracy. [65]GSE63514 data included 28 cancer cases and 24 normal cases[66]^8 and were chosen to validate RRM2 mRNA expression in our analysis. Data preprocessing and DEGs screening The raw data were standardized and transformed into expression values using the affy package of Bioconductor ([67]http://www.bioconductor.org/).[68]^22 DEGs between cancer and normal samples were selected by significance analysis using the empirical Bayes methods within limma package.[69]^23 False discovery rate (FDR) <0.05 and |log2 (fold change)| >1 were set as the cutoff criteria for the identification of DEGs. Common dysregulated probesets between [70]GSE7803 and [71]GSE9750 were selected for subsequent analyzes. KEGG pathway analysis Pathway enrichment analysis was performed using the clusterProfiler package and a pathway with an adjusted P-value <0.05 was considered significantly enriched.[72]^24 DEGs that we identified could be involved in multiple pathways, Thus, some overlap was observed among the pathways. We identified the significant pathways that shared the same DEGs and used Cytoscape (version 3.5.1) to construct graphical representations of the interactive relationships among the pathways.[73]^25 PPI network construction and analysis The PPI pairs of the screened DEGs were analyzed using the online database STRING version 10.5 ([74]https://string-db.org/).[75]^26 The pairs with combined scores >0.4 were used for the PPI network construction, then the Cytoscape software was used to construct the network and analyze the interaction relationship of the candidate DEGs encoding proteins in CC. Validation of key genes Key genes were identified as the intersecting genes of the enrichment pathways and top 20 nodes in PPI network. To confirm the reliability of these genes from our detection, we analyzed their prognostic and expression in CC using GEPIA.[76]^27 GEPIA is an interactive web application for gene expression analysis based on 9,736 tumors and 8,587 normal samples from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression databases.[77]^28^,[78]^29 We evaluated the expression of key genes in CC tissues and normal tissues. Then the survival curve and boxplot were performed to visualize the relationships. Identification of candidate small molecules The CC gene signature was used to query CMap to find potential drugs for use in patients.[79]^21 CMap is an in silico method to predict potential drugs that could possibly reverse, or induce, the biological state encoded in particular gene expression signatures. The common differently expressed probesets in [80]GSE7803 and [81]GSE9750 between CC samples and healthy controls were divided into upregulated and downregulated groups. Then, these probesets were used to query the CMap database. Finally, the enrichment score representing similarity was calculated, ranging from −1 to 1. A positive connectivity score indicates that a drug is able to induce the input signature in human cell lines. Conversely, a negative connectivity score indicates that a drug is able to reverse the input signature. Negative connectivity scores were investigated, which indicate potential therapeutic value. After rank ordering all instances, the connectivity score of various instances were filter by the number of instances (N>10) and P-value (<0.05). Results DEGs identification The two mRNA expression profiles, including 54 patients with CC and 34 healthy individuals, were included in our study. Using a FDR <0.05 and |logFC| >1 as cutoff criteria, we extracted 443 and 848 differentially expressed probesets from the expression profile datasets [82]GSE7803 and [83]GSE9750, respectively. In [84]GSE7803, 212 unregulated probes and 231 downregulated probes were identified. A total of 376 unregulated probes and 472 downregulated probes were identified in [85]GSE9750. After being overlapped, the common 149 upregulated and 146 downregulated probesets corresponding to 145 upregulated and 135 downregulated genes were identified from the two profile datasets ([86]Table S1). CC significant pathways evaluation A total of 16 pathways with adjusted P-value <0.05 were found enriched including 10 upregulated and 6 downregulated pathways ([87]Table 1). The most significant upregulated pathway was cell cycle; the other significant pathways included DNA replication, oocyte meiosis, p53 signaling pathway, microRNAs in cancer, and cellular senescence. The downregulated pathways included arachidonic acid metabolism, serotonergic synapse, gap junction, estrogen signaling pathway, signaling pathways regulating pluripotency of stem cells, and proteoglycans in cancer ([88]Figure 1A). In order to consider the potentially biological complexities in which a gene may belong to multiple pathways and provide information of numeric changes, we constructed pathway–gene networks to extract the complex association ([89]Figure 1B, C). Cell cycle pathway contained the most significant genes in the network. Table 1. Pathway enrichment analysis of DEGs function in CC ID Description Adjusted P-value Count Gene symbol Upregulated hsa04110 Cell cycle 2.02E−19 21 BUB1B, CCNB1, CCNB2, CCNE2, CDC7, CDK1, CDKN2A, CDKN2C, E2F3, MAD2L1, MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, ORC6, PCNA, PTTG1, SMC1A, TTK hsa03030 DNA replication 3.59E−11 10 FEN1, MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, PCNA, RFC4, RFC5 hsa04114 Oocyte meiosis 7.63E−04 8 AURKA, CCNB1, CCNB2, CCNE2, CDK1, MAD2L1, PTTG1, SMC1A hsa04115 p53 signaling pathway 1.16E−03 6 CCNB1, CCNB2, CCNE2, CDK1, CDKN2A, RRM2 hsa05206 MicroRNAs in cancer 1.07E−02 10 CCNE2, CDKN2A, DDIT4, DNMT1, E2F3, EZH2, MIR106B, MIR25, PLAU, STMN1 hsa04218 Cellular senescence 1.44E−02 7 CCNB1, CCNB2, CCNE2, CDK1, CDKN2A, CXCL8, E2F3 hsa03430 Mismatch repair 2.09E−02 3 PCNA, RFC4, RFC5 hsa04914 Progesterone-mediated oocyte maturation 3.43E−02 5 AURKA, CCNB1, CCNB2, CDK1, MAD2L1 hsa05166 HTLV-I infection 3.43E–02 8 BUB1B, CCNB2, CDKN2A, CDKN2C, E2F3, MAD2L1, PCNA, PTTG1 hsa03410 Base excision repair 4.22E−02 3 FEN1, MBD4, PCNA Downregulated hsa00590 Arachidonic acid metabolism 2.61E−02 5 ALOX12, ALOX12B, ALOX15B, GPX3, PTGDS hsa04726 Serotonergic synapse 2.79E−02 6 ALOX12, ALOX12B, ALOX15B, CYP2C18, DUSP1, ITPR2 hsa04540 Gap junction 3.37E−02 5 GJA1, ITPR2, PDGFD, TUBA1A, TUBB2A hsa04915 Estrogen signaling pathway 3.37E−02 6 CALML3, ESR1, FOS, ITPR2, KRT10, KRT13 hsa04550 Signaling pathways regulating pluripotency of stem cells 3.37E−02 6 FGFR2, FZD1, ID4, IGF1, ISL1, KLF4 hsa05205 Proteoglycans in cancer 3.70E−02 7 CCND1, DCN, ESR1, FZD1, IGF1, ITPR2, PDCD4 [90]Open in a new tab Abbreviations: CC, cervical cancer; DEGs, differentially expressed genes; HTLV-I, human T-lymphotropic virus type 1. Figure 1. [91]Figure 1 [92]Open in a new tab Significantly enriched pathway terms associated to DEGs in CC. Notes: (A) KEGG pathways in CC DEGs enrichment analysis. (B) Upregulated pathway–gene network including 35 upregulated genes and 10 pathways. (C) Downregulated pathway–gene network including 26 downregulated genes and 6 pathways. Abbreviations: CC, cervical cancer; DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes; HTLV-I, human T-lymphotropic virus type 1. PPI network construction STRING was used for mining proteins expressed by DEGs which can interact with others. At a combined score >0.4, a total of 222 DEGs (118 upregulated and 104 downregulated genes) among the 280 commonly altered DEGs were filtered into the DEGs PPI network, containing 222 nodes and 2,111 edges ([93]Figure 2A). NetworkAnalyzer app in Cyto-scape was used to calculate the node degree.[94]^25 The genes CDK1, PCNA, TOP2A, CCNB1, RFC4, MAD2L1, NDC80, CCNB2, AURKA, TYMS, MCM2, FEN1, RRM2, NCAPG, TTK, PRC1, MCM4, ZWINT, DTL, and MCM6 were the most significant 20 node degree genes and were selected as the hub nodes, since they might play important roles in CC progression ([95]Figure 2B). Figure 2. [96]Figure 2 [97]Open in a new tab PPI network analysis. Notes: (A) Using the STRING online database, a total of 222 DEGs (118 upregulated in red standing for upregulation and 104 downregulated genes in green standing for downregulation) were filtered into the DEGs PPI network. Bigger nodes represent genes with more links. (B) Degree of the top 20 nodes in the PPI network. All these nodes are upregulated genes. Abbreviations: DEGS, differentially expressed genes; PPI, protein–protein interaction. Key gene signatures identification in CC Compared with KEGG enrichment genes, 13 of the top 20 nodes in the PPI network, including AURKA, CCNB1, CCNB2, CDK1, FEN1, MAD2L1, MCM2, MCM4, MCM6, PCNA, RFC4, RRM2, and TTK were found as key genes. Further survival analyses on these key genes were employed to evaluate their effects on CC patients’ survival using GEPIA. Expression levels of MCM2, PCNA, RFC4, and RRM2 were significantly related to the OS of patients with cervical squamous cancer (P<0.05). High expression of MCM2, PCNA, and RFC4 could result in a high survival rate, and increased RRM2 expression in CC was significantly associated with shorter patients’ survival ([98]Figure 3A–D). The expression of these four genes was significantly higher in CC tissues compared to that of normal tissues (P<0.01; [99]Figure 3E–H). Together, the high level of these four genes might represent the important prognostic factor to predict the survival of CC. [100]GSE63514 was used to validate RRM2 mRNA expression. The results showed that RRM2 expression was significantly higher in CC compared to that of normal tissues (P<0.01; [101]Figure 4A). The PPI network based on RRM2 found that PCNA and RFC4 have a close relationship with RRM2, and most of the proteins in the network were related to cell cycle ([102]Figure 4B). Figure 3. [103]Figure 3 [104]Open in a new tab Survival curves and expression boxplots of key genes using GEPIA website. Notes: (A–D) Expression level of MCM2, PCNA, RFC4, and RRM2 was significantly related to the overall survival of patients with cervical squamous cancer (P<0.05). (E–H) MCM2, PCNA, RFC4, and RRM2 were significantly upregulated in cervical squamous cancer compared with normal tissues (P<0.01). Abbreviations: CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; GEPIA, Gene Expression Profiling Interactive Analysis; TPM, transcripts per million. Figure 4. [105]Figure 4 [106]Open in a new tab RRM2 validation using [107]GSE63514 and PPI network. Notes: (A) [108]GSE63514 showed higher expression of RRM2 in CC tissues compared with normal cervical tissues (P<0.01). (B) RRM2 PPI network based on STRING. Abbreviations: CC, cervical cancer; PPI, protein–protein interaction. Related small molecule drugs screening In order to screen out small molecule drugs, consistent differently expressed probesets between CC samples and healthy controls were analyzed with CMap. The related small molecules with highly significant correlations are listed in [109]Table 2. Among these molecules, trichostatin A (TSA), tanespimycin, vorinostat, trifluoperazine, prochlorperazine, and thioridazine showed higher negative correlation and the potential to treat CC. Table 2. Results of CMap analysis Rank CMap name Mean N Enrichment P-value 1 Trichostatin A −0.480 182 −0.419 0 2 Tanespimycin −0.372 62 −0.301 0.00002 3 Vorinostat −0.551 12 −0.571 0.00034 4 Trifluoperazine −0.511 16 −0.488 0.00054 5 Prochlorperazine −0.461 16 −0.436 0.00277 6 Thioridazine −0.407 20 −0.375 0.00526 7 Alpha-estradiol −0.367 16 −0.365 0.02104 8 Fluphenazine −0.403 18 −0.326 0.03608 9 Chlorpromazine −0.366 19 −0.310 0.04109 [110]Open in a new tab Abbreviation: CMap, Connectivity Map. Discussion Driver genes play vital roles during stages of cancer progression. Although many studies on CC development are available, more efforts are needed to identify driver genes and candidate drugs that may shed light on CC treatments. This study integrated two gene profile datasets based on Affymetrix Human Genome U133A Array, utilized bioinformatics methods to analyze these datasets, and identified 280 commonly changed DEGs (145 upregulated and 135 downregulated). Pathway enrichment analysis indicated that cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway, cellular senescence, and DNA repair-relevant biological pathways were overrepresented among the upregulated genes. The PPI network was constructed including 222 nodes/DEGs and 2,111 edges. Thirteen key genes were identified and chosen for survival analysis. MCM2, PCNA, RFC4, and RRM2 were clearly related to the prognosis of patients. In addition, small molecules that can provide new insights in CC therapeutic studies were identified. Many researchers have found that four key genes were involved in cell cycle, participating in tumorigenesis and tumor proliferation. MCM2 has been studied in a wide range of human malignancies and is associated with tumor histopathological grade in several malignancies, including colon, oral cavity, ovarian, urothelial, and non-small cell lung carcinoma.[111]^30^–[112]^34 In cervical carcinoma and precancerous lesions, MCM2 is overexpressed and positively correlated with high risk types of HPV.[113]^35 Amaro Filho et al also reported an increasing expression of MCM2 in invasive CC compared to control, but they suggested that MCM2 is not a good biomarker when comparing the different clinical stages of CC.[114]^36 PCNA acts as a central coordinator of DNA transactions by providing a multivalent interaction surface for factors involved in DNA replication and cell cycle regulation. Owing to its function, PCNA has been widely used as a tumor marker for cancer cell progression and patient prognosis.[115]^37^–[116]^39 A recent systematic literature review found that the expression of PCNA is significantly associated with poor 5-year survival, International Federation of Gynecology and Obstetrics stage, or WHO grade, suggesting its use as a valuable prognostic and diagnostic biomarker in CC and gliomas.[117]^40 RFC4 is involved in cancer. Knockdown of RFC4 in HepG2 cells induces apoptosis.[118]^41 Similar results were discovered in breast carcinoma.[119]^42 In colorectal cancer, overexpression of RFC4 is associated with tumor progression and poor survival outcome.[120]^43 Additionally, with gene network reconstruction, RFC4 is regarded as one of the main drivers in cell cycle network in CC.[121]^44 Together with our results, MCM2, PCNA, and RFC4 were significantly upregulated in CC compared with normal samples, and in CC patients, the survival rate was positively correlated with the high expression of these genes. RRM2 is markedly upregulated in many patients’ cancer types and indeed acts as an oncogene.[122]^45 RRM2 knockdown reduces cell proliferation and invasive ability in gastric cancer and pancreatic adenocarcinoma.[123]^46^,[124]^47 Wang et al reported that RRM2 expression inhibition significantly increases apoptosis, promotes cell cycle arrest at the G1 phase, and inhibits tumor formation in CC nude mice transplant models.[125]^48 Several studies showed that RRM2 is an independent prognostic factor and may predict poor survival in ovarian cancer, bladder cancer, breast cancer, and CC.[126]^49^–[127]^52 In this study, according to the PPI network, RRM2 closely interacts with PCNA and RFC4 involved in CC progression. Therefore, a further exploration of cell cycle and related genes was of enormous significance. Consistent with our results, recent studies have also reported the identification of DEGs in CC. van Dam et al used three publicly available Affymetrix gene expression datasets ([128]GSE5787, [129]GSE7803, and [130]GSE9750) and identified five cancer hallmarks enriched pathways in CC, showing that cell cycle deregulation is the major component of CC biology. They also identified seven probesets that were highly expressed in both CIN3 samples compared to normal samples and in cancer samples compared to CIN3 samples. From these probesets, six genes (AURKA, DTL, HMGB3, KIF2C, NEK2, and RFC4) were overexpressed in CC cell lines compared to cancer samples, suggesting their potential role as biomarkers in CC early diagnosis.[131]^53 One of these genes, such as RFC4, was also identified in our study. Furthermore, our conclusion generated from both expression and survival analysis suggested that RFC4 might have a prognostic value. Another report from Li et al was based on TCGA data.[132]^54 They found that MCM2, MCM4, MCM5, PCNA, and RNASEH2A participating in DNA replication pathway might be prognostic biomarkers in CC patients. MCM2 and PCNA were also found in our results. Several small molecules with potential therapeutic efficacy against CC were identified. The most significant small molecules in our result have been reported to display anticancer activity. TSA, as a histone deacetylase (HDAC) inhibitor, shows a potential therapeutic effect in various types of cancer cells, when combined with radiotherapy or chemotherapy.[133]^55^,[134]^56 In particular, TSA and its hydroxamate analogs can effectively and selectively induce tumor growth arrest at very low concentrations.[135]^57 Additionally, TSA can inhibit HeLa cells growth via Bcl-2-mediated and caspase-dependent apoptosis.[136]^58 Vorinostat is a hydroxamate-based pan-HDAC inhibitor also known as suberoylanilide hydroxamic acid used for the treatment of cutaneous T-cell lymphoma.[137]^59 In HeLa cell, both mRNA and protein levels of HPV18 E6 and E7 were reduced after vorinostat treatment.[138]^60 Furthermore, vorinostat promotes SiHa apoptosis through upregulation of p21 and Bax mRNA and protein, leading to cell cycle arrest in G0/G1 phase.[139]^61 Thioridazine, a derivative of phenothiazine, displays anticancer abilities in a variety of cancer types and can reverse multidrug resistance.[140]^62^–[141]^64 Kang et al found that thioridazine can inhibit the PI3K/Akt/mTOR/p70S6K signaling pathway and exert cytotoxic effect on CC cells by inducing cell cycle arrest and apoptosis.[142]^65 Thus, we might suppose that these identified drugs could play certain roles to combat CC. Conclusion Using bioinformatics analysis, 280 DEGs were identified, which were significantly enriched in several pathways, mainly associated with cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway, and cellular senescence. We also identified key genes including MCM2, PCNA, RFC4, and RRM2 that might play important roles in CC and that might represent novel biomarkers in CC diagnosis, prognosis, and therapy. Additionally, a group of small molecules was identified that might be exploited as adjuvant drugs for improved therapeutics for CC. However, further investigations are required to validate the predicted drugs. Supplementary material Table S1. Common dysregulated probes identified in [143]GSE7803 and [144]GSE9750 Number Probe name Gene symbol logFC __________________________________________________________________ Adjusted P-value __________________________________________________________________ [145]GSE7803 [146]GSE9750 [147]GSE7803 [148]GSE9750 Upregulated 1 200783_s_at STMN1 1.0912 1.0985 4.11E–05 1.25E–04 2 201202_at PCNA 1.8714 1.3752 4.41E–09 2.87E–05 3 201291_s_at TOP2A 2.4680 2.4862 1.05E–08 5.16E–06 4 201292_at TOP2A 1.2403 2.0680 1.71E–06 2.30E–04 5 201506_at TGFBI 1.1157 1.1297 2.21E–02 1.60E–02 6 201555_at MCM3 1.0145 1.2759 3.14E–07 5.41E–07 7 201589_at SMC1A 1.3907 1.0445 5.43E–06 8.52E–05 8 201650_at KRT19 1.5763 2.3797 2.89E–02 8.95E–05 9 201663_s_at SMC4 1.4923 1.5673 1.60E–06 4.31E–05 10 201664_at SMC4 1.7038 1.8408 9.71E–06 7.08E–06 11 201697_s_at DNMT1 1.0219 1.2128 1.05E–08 4.10E–09 12 201761_at MTHFD2 1.6111 1.3783 4.73E–05 4.37E–05 13 201839_s_at EPCAM 1.6182 2.2101 3.36E–04 1.82E–04 14 201890_at RRM2 1.7698 2.3342 1.05E–05 1.44E–06 15 201897_s_at CKS1B 1.3398 1.2246 1.04E–06 4.26E–05 16 201930_at MCM6 1.5487 1.4628 6.91E–09 1.25E–08 17 201970_s_at NASP 1.3464 1.1522 1.38E–08 9.44E–09 18 202107_s_at MCM2 1.7296 2.2864 1.91E–08 7.29E–10 19 202219_at SLC6A8 1.4325 1.8698 5.38E–03 2.75E–05 20 202234_s_at SLC16A1 1.3561 1.2683 4.70E–03 7.50E–04 21 202338_at TK1 1.1038 1.2846 2.40E–06 2.91E–06 22 202412_s_at USP1 1.0775 1.2056 3.24E–04 3.54E–04 23 202430_s_at PLSCR1 1.2889 1.1148 1.29E–04 7.10E–05 24 202446_s_at PLSCR1 1.6150 1.5934 1.16E–06 1.41E–06 25 202503_s_at PCLAF 2.0118 1.9842 2.33E–10 1.18E–04 26 202589_at TYMS 1.5263 2.2375 1.95E–04 8.52E–08 27 202619_s_at PLOD2 1.8339 2.0812 1.44E–06 1.41E–07 28 202620_s_at PLOD2 2.8767 2.2084 3.54E–09 5.09E–07 29 202625_at LYN 1.1767 1.4027 1.20E–03 2.09E–03 30 202626_s_at LYN 1.5737 1.7521 1.07E–03 8.51E–05 31 202633_at TOPBP1 1.6677 1.4670 3.67E–07 7.76E–07 32 202666_s_at ACTL6A 1.5525 1.1292 9.46E–07 1.46E–03 33 202688_at TNFSF10 1.0878 1.1141 3.63E–02 3.00E–02 34 202705_at CCNB2 1.0405 1.7114 7.17E–05 8.58E–06 35 202854_at HPRT1 1.0911 1.2618 2.75E–04 7.59E–05 36 202859_x_at CXCL8 1.5100 2.7989 1.66E–02 3.86E–04 37 202887_s_at DDIT4 1.0791 2.1221 4.05E–02 3.21E–04 38 202983_at HLTF 2.0242 1.2271 4.08E–07 2.36E–03 39 203046_s_at TIMELESS 1.1641 1.4554 1.96E–08 1.55E–07 40 203209_at RFC5 1.5612 1.2914 1.93E–07 7.95E–06 41 203213_at CDK1 1.5427 1.9989 1.11E–05 9.35E–06 42 203358_s_at EZH2 1.6144 1.6656 3.31E–07 3.91E–05 43 203362_s_at MAD2L1 1.0749 1.2051 4.94E–03 1.40E–02 44 203554_x_at PTTG1 1.2746 1.3424 1.32E–05 3.11E–03 45 203693_s_at E2F3 1.2182 1.2321 1.16E–06 1.28E–04 46 203744_at HMGB3 1.1938 1.6479 3.01E–04 1.41E–07 47 203755_at BUB1B 1.0860 1.8703 3.34E–07 1.96E–06 48 203764_at DLGAP5 1.4536 2.2334 3.73E–05 9.69E–05 49 203819_s_at IGF2BP3 1.2319 1.4909 2.75E–02 2.22E–02 50 203856_at VRK1 1.1485 1.1641 4.00E–06 2.15E–04 51 204023_at RFC4 1.5116 2.1996 7.47E–08 2.54E–07 52 204026_s_at ZWINT 1.5906 1.8649 2.05E–04 2.07E–05 53 204092_s_at AURKA 1.1672 1.0470 1.39E–07 2.36E–05 54 204146_at RAD51AP1 1.6216 1.6478 4.26E–07 8.74E–05 55 204159_at CDKN2C 1.5231 1.2248 2.07E–03 3.73E–03 56 204162_at NDC80 1.5684 1.3280 9.05E–05 1.03E–03 57 204170_s_at CKS2 1.4786 1.5687 6.24E–05 5.32E–03 58 204416_x_at APOC1 1.0620 1.3204 8.58E–03 9.07E–04 59 204439_at IFI44L 1.5993 1.4595 4.00E–02 5.25E–02 60 204510_at CDC7 1.3374 1.6750 1.88E–06 2.38E–06 61 204580_at MMP12 1.6409 2.9620 2.22E–03 2.97E–05 62 204641_at NEK2 1.3957 2.1694 6.79E–08 2.33E–07 63 204698_at ISG20 1.0250 1.3923 6.87E–04 1.78E–05 64 204767_s_at FEN1 1.4080 1.7083 1.31E–09 3.37E–07 65 204784_s_at MLF1 1.6420 1.6891 6.24E–05 3.01E–04 66 204822_at TTK 1.4460 1.4235 7.52E–08 2.45E–03 67 204825_at MELK 1.8745 1.9957 2.90E–07 3.60E–07 68 205034_at CCNE2 1.3408 1.7091 4.18E–04 5.08E–04 69 205157_s_at KRT17 1.5725 3.3509 2.32E–02 1.54E–05 70 205339_at STIL 1.0641 1.5078 1.16E–06 9.67E–08 71 205449_at SAC3D1 1.3483 1.1408 4.86E–05 2.80E–04 72 205479_s_at PLAU 1.2262 1.4554 1.65E–03 4.38E–04 73 205483_s_at ISG15 1.3717 2.0505 1.65E–02 5.73E–04 74 205569_at LAMP3 1.7550 1.2873 4.09E–04 2.57E–02 75 205691_at SYNGR3 1.2198 1.4950 5.72E–04 3.29E–04 76 205910_s_at CEL 1.8737 1.2510 1.62E–02 3.08E–02 77 206102_at GINS1 1.6544 1.9896 6.95E–05 4.57E–06 78 206332_s_at IFI16 1.5947 1.2876 8.99E–07 2.56E–04 79 206513_at AIM2 2.0306 2.3769 1.22E–03 2.74E–03 80 206546_at SYCP2 1.3491 2.3512 2.17E–03 5.99E–05 81 206632_s_at APOBEC3A, APOBEC3B 2.9688 1.9572 1.68E–08 2.46E–04 82 206858_s_at HOXC6 2.1365 1.4749 1.66E–05 1.02E–03 83 207039_at CDKN2A 4.6085 4.0377 3.50E–14 1.62E–14 84 207165_at HMMR 1.4593 1.0523 2.62E–06 1.30E–02 85 207332_s_at TFRC 1.2833 1.3954 4.66E–04 5.87E–03 86 207828_s_at CENPF 1.4100 1.9877 1.36E–07 9.05E–08 87 208079_s_at AURKA 2.2857 2.0803 1.85E–09 5.77E–08 88 208691_at TFRC 1.5192 1.4901 6.53E–06 5.22E–04 89 208795_s_at MCM7, MIR25, MIR93, MIR106B 1.1151 1.3206 1.55E–07 2.44E–05 90 208808_s_at HMGB2 1.5215 1.1577 8.56E–07 7.41E–04 91 208965_s_at IFI16 1.4542 1.3939 2.56E–05 6.32E–04 92 208966_x_at IFI16 1.7717 1.3388 4.13E–07 6.92E–05 93 208998_at UCP2 1.9521 1.1262 2.31E–05 2.83E–03 94 209398_at HIST1H1C 1.1786 1.2497 5.37E–03 7.31E–03 95 209408_at KIF2C 1.4768 1.5638 1.85E–09 4.49E–09 96 209579_s_at MBD4 1.2877 1.1884 6.26E–07 6.34E–05 97 209773_s_at RRM2 1.2805 1.9504 2.40E–03 6.29E–05 98 209875_s_at SPP1 2.5457 3.4037 3.80E–04 3.18E–06 99 209900_s_at SLC16A1 1.5149 1.2504 1.45E–03 1.73E–03 100 209969_s_at STAT1 1.8886 2.1349 6.82E–04 4.07E–04 101 210580_x_at SLX1A-SULT1A3, SLX1B-SULT1A4, SULT1A3, SULT1A4 1.0598 1.0491 1.80E–03 1.19E–03 102 212022_s_at MKI67 1.4831 1.5781 8.11E–07 1.81E–06 103 212236_x_at KRT17 1.3508 2.7588 3.84E–02 7.95E–06 104 212255_s_at ATP2C1 1.0824 1.0588 1.60E–04 8.54E–05 105 212297_at ATP13A3 1.4423 1.1315 1.69E–06 6.46E–05 106 212621_at NEMP1 1.4685 1.2100 1.63E–07 2.29E–07 107 212840_at UBXN7 1.0252 1.0213 1.25E–04 1.36E–03 108 212977_at ACKR3 2.0327 1.2298 4.91E–03 5.31E–02 109 213007_at FANCI 1.3983 1.6034 2.79E–07 2.80E–07 110 213008_at FANCI 1.0861 1.6205 4.34E–05 4.91E–08 111 213164_at SLC5A3 1.0329 1.0596 2.97E–05 4.04E–04 112 213457_at MFHAS1 1.0606 1.0186 3.06E–02 2.25E–03 113 213693_s_at MUC1 1.2200 1.9901 3.72E–02 4.37E–04 114 213951_s_at PSMC3IP 1.2274 1.2423 1.92E–07 2.52E–07 115 213988_s_at SAT1 1.1293 1.0600 2.52E–03 7.87E–04 116 214329_x_at TNFSF10 2.3354 1.2571 6.72E–06 1.12E–02 117 214710_s_at CCNB1 1.2879 1.7956 3.73E–05 1.97E–04 118 215388_s_at CFH, CFHR1 1.4348 1.0577 1.61E–02 4.50E–02 119 216237_s_at MCM5 1.5683 2.0746 1.77E–08 8.15E–11 120 217885_at IPO9 1.1126 1.0339 1.76E–07 1.89E–05 121 217901_at DSG2 1.3065 2.5311 6.24E–05 3.60E–07 122 218009_s_at PRC1 1.6401 2.1259 5.88E–08 1.78E–06 123 218039_at NUSAP1 2.1401 2.3735 7.34E–09 5.69E–06 124 218350_s_at GMNN 1.7878 1.8225 3.13E–07 2.66E–06 125 218355_at KIF4A 1.0153 1.7434 2.38E–06 2.89E–07 126 218542_at CEP55 1.3865 2.4903 2.46E–06 2.29E–07 127 218585_s_at DTL 1.3800 2.8428 2.32E–06 4.81E–09 128 218662_s_at NCAPG 1.6736 1.5539 4.30E–06 1.30E–04 129 218757_s_at UPF3B 1.3448 1.0318 6.72E–05 2.68E–05 130 218883_s_at CENPU 1.2494 1.5454 7.55E–04 3.91E–03 131 219014_at PLAC8 1.2330 1.4057 2.84E–02 3.55E–02 132 219105_x_at ORC6 1.0780 1.0908 2.10E–04 5.03E–07 133 219258_at TIPIN 1.1365 1.4832 3.91E–07 1.27E–06 134 219306_at KIF15 1.0990 1.0348 1.84E–04 1.03E–03 135 219507_at RSRC1 1.3864 1.2573 3.27E–05 3.86E–04 136 219787_s_at ECT2 2.8139 2.5551 1.00E–08 1.37E–06 137 219918_s_at ASPM 1.2168 1.9490 4.36E–05 2.25E–04 138 219959_at MOCOS 1.0344 1.9971 3.59E–03 1.67E–05 139 219978_s_at NUSAP1 1.1780 1.6455 8.94E–04 6.82E–05 140 219990_at E2F8 1.4188 1.1301 1.17E–04 1.10E–03 141 220239_at KLHL7 1.0503 1.0053 3.05E–03 1.40E–03 142 221046_s_at GTPBP8 1.0722 1.0602 1.44E–06 4.04E–04 143 221521_s_at GINS2 1.4631 1.8407 8.85E–06 3.27E–06 144 222036_s_at MCM4 1.0134 1.8445 1.71E–06 1.67E–06 145 222039_at KIF18B 1.5126 1.0619 7.17E–09 2.91E–06 146 222077_s_at RACGAP1 1.5482 1.6939 2.69E–06 5.07E–05 147 222380_s_at PDCD6 1.0922 1.0579 3.96E–04 1.18E–02 148 31845_at ELF4 1.2212 1.0448 2.96E–05 1.00E–05 149 33304_at ISG20 1.1281 1.1279 1.62E–04 7.59E–05 Downregulated 1 200795_at SPARCL1 −2.6933 −2.5139 3.39E–04 2.05E–04 2 201012_at ANXA1 −1.6637 −1.2447 1.15E–03 1.28E–03 3 201041_s_at DUSP1 −1.7177 −1.2448 7.16E–03 3.26E–02 4 201201_at CSTB −1.6745 −1.0817 6.75E–04 2.05E–04 5 201312_s_at SH3BGRL −1.0735 −2.0334 1.43E–02 3.28E–04 6 201324_at EMP1 −2.5006 −2.0144 1.69E–05 7.39E–05 7 201325_s_at EMP1 −2.8729 −2.3264 2.29E–08 3.96E–06 8 201348_at GPX3 −1.6408 −2.9297 1.32E–05 9.66E–08 9 201667_at GJA1 −2.1421 −2.0359 3.83E–03 6.21E–03 10 201735_s_at CLCN3 −1.1179 −1.0850 1.09E–03 1.89E–03 11 201811_x_at SH3BP5 −1.2423 −1.3642 3.25E–03 5.04E–03 12 201893_x_at DCN −1.2951 −2.2495 1.17E–03 7.34E–04 13 202539_s_at HMGCR −1.0673 −1.3111 2.12E–03 1.05E–02 14 202575_at CRABP2 −1.1350 −1.9855 2.03E–07 1.63E–03 15 202660_at ITPR2 −1.2894 −1.0723 6.71E–07 2.03E–04 16 202668_at EFNB2 −1.0648 −1.0430 2.83E–03 1.00E–02 17 202768_at FOSB −1.6730 −2.2309 6.70E–03 5.21E–03 18 202967_at GSTA4 −1.4779 −1.8389 4.26E–07 2.57E–04 19 203407_at PPL −1.5681 −1.8813 3.47E–05 3.27E–06 20 203535_at S100A9 −1.9766 −1.2926 8.14E–03 1.86E–02 21 203585_at ZNF185 −1.3599 −1.3991 1.52E–03 4.20E–05 22 203638_s_at FGFR2 −1.3240 −1.5564 3.23E–04 7.27E–04 23 203700_s_at DIO2 −1.3828 −1.1342 1.94E–03 4.10E–02 24 203913_s_at HPGD −1.7678 −2.7646 5.41E–05 3.98E–05 25 203914_x_at HPGD −2.4427 −2.7244 1.11E–05 2.65E–05 26 203961_at NEBL −1.4367 −1.5881 2.34E–03 5.38E–03 27 204141_at TUBB2A −1.7240 −1.5928 1.18E–03 1.89E–04 28 204256_at ELOVL6 −1.3219 −1.1355 4.57E–03 4.75E–02 29 204284_at PPP1R3C −2.6784 −3.7692 4.26E–07 2.33E–09 30 204359_at FLRT2 −1.1097 −2.2141 5.71E–03 4.36E–05 31 204451_at FZD1 −1.1135 −1.0997 1.15E–05 1.01E–04 32 204731_at TGFBR3 −1.3493 −1.8325 7.17E–03 4.04E–04 33 204750_s_at DSC2 −2.0225 −1.1059 2.83E–04 4.70E–02 34 204751_x_at DSC2 −1.9548 −2.2472 2.98E–04 1.15E–04 35 204777_s_at MAL −4.8179 −5.7789 9.50E–07 1.62E–14 36 204952_at LYPD3 −1.5886 −1.7448 1.08E–04 4.43E–04 37 205064_at SPRR1B −2.2769 −2.7744 1.39E–03 1.20E–02 38 205185_at SPINK5 −3.8683 −3.6665 3.46E–07 3.15E–05 39 205225_at ESR1 −3.0458 −2.7160 9.37E–06 1.54E–05 40 205239_at AREG −1.8099 −1.5361 3.96E–02 1.14E–03 41 205363_at BBOX1 −1.8640 −2.8822 2.54E–09 1.27E–05 42 205382_s_at CFD −2.1856 −2.5747 9.39E–07 1.20E–06 43 205470_s_at KLK11 −1.9196 −2.3007 8.84E–08 1.78E–03 44 205726_at DIAPH2 −1.0814 −1.4450 1.17E–03 1.34E–04 45 205759_s_at SULT2B1 −1.2047 −1.2198 1.04E–06 5.73E–03 46 205765_at CYP3A5 −1.8262 −1.1731 3.95E–06 5.05E–03 47 205767_at EREG −1.6854 −2.1525 1.38E–04 8.11E–05 48 205778_at KLK7 −1.3998 −1.7611 4.35E–03 1.13E–02 49 205862_at GREB1 −1.5579 −1.8147 1.16E–03 1.94E–06 50 205863_at S100A12 −1.2720 −2.0771 4.51E–03 6.18E–04 51 205900_at KRT1 −4.8450 −5.1604 9.91E–10 1.22E–06 52 206008_at TGM1 −1.3988 −1.4672 3.53E–03 2.12E–02 53 206104_at ISL1 −1.8146 −1.8069 4.46E–05 4.23E–06 54 206295_at IL18 −1.8318 −1.1817 9.99E–08 1.78E–02 55 206400_at LGALS7, LGALS7B −1.2508 −1.7496 1.50E–02 2.32E–02 56 206605_at ENDOU −2.0623 −3.5113 1.38E–10 3.61E–09 57 206642_at DSG1 −3.6072 −4.3758 2.39E–09 6.70E–07 58 206714_at ALOX15B −1.2685 −1.0664 8.35E–04 2.03E–02 59 206884_s_at SCEL −2.4970 −3.2369 4.84E–05 3.50E–06 60 207002_s_at PLAGL1 −1.1346 −1.2887 9.02E–03 5.44E–04 61 207023_x_at KRT10 −1.6054 −1.6701 5.50E–03 1.32E–03 62 207057_at SLC16A7 −1.5167 −1.0148 2.07E–06 2.86E–02 63 207206_s_at ALOX12 −2.4692 −2.9129 2.60E–07 5.89E–06 64 207381_at ALOX12B −1.8250 −1.5189 8.81E–07 1.92E–02 65 207463_x_at PRSS3 −1.6595 −2.2675 1.69E–05 7.14E–04 66 207480_s_at MEIS2 −1.1053 −1.4587 8.12E–03 3.86E–03 67 207602_at TMPRSS11D −1.7796 −2.2185 1.94E–04 1.24E–03 68 207720_at LOR −1.5659 −1.7321 9.03E–03 5.54E–03 69 207761_s_at METTL7A −1.4377 −1.7274 1.64E–02 1.54E–03 70 207802_at CRISP3 −3.5353 −4.9186 8.56E–07 2.03E–08 71 207908_at KRT2 −1.0700 −1.7438 7.77E–06 2.44E–04 72 207935_s_at KRT13 −3.3723 −3.4606 6.80E–04 5.75E–03 73 208126_s_at CYP2C18 −1.0287 −1.2034 3.17E–04 2.53E–02 74 208228_s_at FGFR2 −1.1180 −1.5103 5.59E–03 2.23E–03 75 208399_s_at EDN3 −1.7159 −2.7146 2.90E–07 1.83E–07 76 208539_x_at SPRR2A, SPRR2B, SPRR2D −1.0258 −3.4094 4.88E–03 2.74E–04 77 208650_s_at CD24 −1.6380 −1.0461 5.52E–03 9.70E–03 78 208712_at CCND1 −1.7584 −1.3400 1.85E–09 1.70E–04 79 209118_s_at TUBA1A −1.1814 −1.6618 3.70E–03 8.97E–04 80 209126_x_at KRT6A, KRT6B −1.0109 −1.7619 7.83E–03 3.16E–03 81 209189_at FOS −1.2550 −1.7443 6.40E–03 1.23E–02 82 209242_at PEG3 −1.1185 −1.7051 1.60E–04 4.77E–05 83 209250_at DEGS1 −1.6475 −1.0716 1.68E–04 4.35E–03 84 209283_at CRYAB −1.4519 −2.9331 4.29E–07 1.48E–09 85 209291_at ID4 −2.2617 −1.7203 2.32E–06 1.04E–03 86 209318_x_at PLAGL1 −1.0631 −1.5734 2.80E–02 1.87E–03 87 209335_at DCN −1.6677 −2.5536 2.57E–03 3.04E–04 88 209540_at IGF1 −1.1470 −2.0332 1.72E–02 5.42E–03 89 209541_at IGF1 −1.4871 −2.5855 1.91E–03 2.83E–03 90 209550_at NDN −1.1674 −1.7010 4.80E–05 3.81E–04 91 209569_x_at NSG1 −1.3910 −1.9295 8.88E–08 1.28E–04 92 209570_s_at NSG1 −1.6985 −1.3952 1.95E–04 2.86E–05 93 209605_at TST −1.5512 −1.0193 1.42E–07 1.02E–02 94 209687_at CXCL12 −1.6878 −3.5983 1.05E–03 1.57E–05 95 210020_x_at CALML3 −1.2936 −1.5506 2.22E–03 2.33E–02 96 211423_s_at SC5D −1.0341 −1.1407 2.63E–04 2.14E–02 97 211548_s_at HPGD −2.2811 −2.9565 9.06E–06 1.77E–05 98 211549_s_at HPGD −1.5563 −1.6731 1.97E–05 2.01E–05 99 211597_s_at HOPX −3.4727 −3.8543 1.85E–09 6.16E–10 100 211748_x_at PTGDS −1.1759 −2.8281 6.75E–04 3.00E–05 101 211813_x_at DCN −1.0371 −2.4546 5.80E–03 8.88E–05 102 211896_s_at DCN −1.7222 −2.8559 4.39E–05 6.65E–04 103 212099_at RHOB −1.2852 −1.4439 1.59E–02 9.09E–03 104 212187_x_at PTGDS −1.0762 −2.8519 1.63E–03 2.66E–05 105 212230_at PLPP3 −1.2267 −1.8056 4.59E–03 2.70E–03 106 212268_at SERPINB1 −2.0739 −1.0263 1.49E–06 1.91E–02 107 212593_s_at PDCD4, MIR4680 −1.0203 −1.0249 1.71E–06 6.53E–04 108 213005_s_at KANK1 −1.3237 −1.4490 2.32E–06 1.01E–04 109 213240_s_at KRT4 −4.3954 −3.6606 1.22E–05 4.69E–03 110 213287_s_at KRT10 −1.4603 −1.5958 6.51E–03 7.27E–04 111 213421_x_at PRSS3 −1.3548 −1.8542 1.74E–05 2.02E–03 112 213680_at KRT6B −1.9134 −1.6133 1.08E–02 4.60E–02 113 213796_at SPRR1A −2.5895 −3.5348 1.67E–02 2.67E–03 114 213895_at EMP1 −1.5169 −1.8942 7.08E–07 1.20E–06 115 214091_s_at GPX3 −1.6787 −3.0400 6.10E–06 7.67E–08 116 214247_s_at DKK3 −1.7619 −1.4292 4.50E–04 1.56E–02 117 214549_x_at SPRR1A −2.7653 −3.2505 1.35E–04 6.67E–04 118 214599_at IVL −2.6075 −2.2661 3.08E–05 4.70E–05 119 214621_at GYS2 −1.6288 −1.5121 1.62E–04 9.55E–06 120 214624_at UPK1A −3.4453 −2.3695 1.95E–11 4.10E–09 121 214696_at MIR22, MIR22HG −1.2503 −1.0092 9.91E–04 6.55E–04 122 217845_x_at HIGD1A −1.1969 −1.1395 4.79E–04 1.22E–03 123 218002_s_at CXCL14 −2.3615 −2.3685 5.25E–03 2.29E–03 124 218312_s_at ZSCAN18 −1.5418 −1.8495 2.07E–06 4.65E–06 125 218502_s_at TRPS1 −1.0972 −1.8370 2.27E–04 1.42E–06 126 218677_at S100A14 −1.2332 −1.0900 4.46E–05 2.11E–03 127 218990_s_at SPRR3 −3.8102 −4.0176 5.84E–05 5.09E–04 128 219090_at SLC24A3 −1.4945 −1.8096 2.33E–05 3.51E–05 129 219267_at GLTP −1.9362 −1.2742 5.88E–07 2.51E–03 130 219304_s_at PDGFD −1.2399 −2.5374 1.71E–05 1.89E–09 131 219554_at RHCG −2.3009 −3.4518 1.07E–05 3.81E–05 132 219648_at MREG −1.1721 −1.0151 3.34E–06 2.70E–02 133 219836_at ZBED2 −1.8152 −1.6920 4.01E–07 4.37E–04 134 219995_s_at ZNF750 −1.3202 −1.4628 3.99E–03 6.24E–03 135 220026_at CLCA4 −1.7727 −2.3287 2.21E–02 2.67E–03 136 220066_at NOD2 −1.2609 −1.3621 1.70E–05 8.88E–05 137 220090_at CRNN −4.8078 −6.4682 1.02E–11 7.05E–15 138 220266_s_at KLF4 −1.1657 −2.2531 4.46E–05 1.27E–04 139 220403_s_at TP53AIP1 −1.2074 −1.2307 1.17E–03 1.28E–03 140 220431_at TMPRSS11E −1.1214 −2.9572 3.59E–04 5.14E–04 141 220620_at CRCT1 −2.6450 −4.1521 1.60E–06 6.43E–05 142 220723_s_at CWH43 −1.7748 −2.6534 4.41E–09 7.02E–07 143 221667_s_at HSPB8 −1.6989 −1.8139 7.42E–06 5.09E–07 144 221841_s_at KLF4 −2.1774 −2.2756 3.11E–05 2.90E–04 145 221896_s_at HIGD1A −1.1638 −1.1978 7.06E–04 9.97E–04 146 57588_at SLC24A3 −1.1619 −1.5407 2.29E–05 1.32E–05 [149]Open in a new tab Abbreviation: FC, fold change. Acknowledgments