Abstract Purpose The prognostic role of miR-204-5p (previous ID: miR-204) is varied and inconclusive in diverse types of malignant neoplasm. Therefore, the purposes of the study comprehensively explore the overall prognostic role of miR-204-5p based on high-throughput microRNA sequencing data, and to investigate the potential role of miR-204-5p via bioinformatics approaches. Materials and Methods The data of microRNA sequencing and survival were downloaded from The Cancer Genome Atlas (TCGA), and the prognostic value of miR-204-5p was analyzed by using Kaplan-Meier and univariate cox regressions. Then a meta-analysis was conducted with all TCGA data and relevant studies collected from literature. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. The prospective molecular mechanism of miR-204-5p was also assessed at a functional level with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-to-protein interactions (PPI) network. Results From TCGA data, the prognostic value of miR-204-5p obviously varied among 20 types of cancers. The pooled HR was 0.928 (95% CI: 0.774–1.113, P = 0.386, 6203 cases of malignancies). For the meta-analysis based on 15 studies from literature, the pooled HR was 0.420 (95% CI: 0.306–0.576, P < 0.001, 1783 cases of malignancies) for overall survival (OS). Furthermore, the combined HR from both TCGA and literature was 0.708 (95% CI: 0.600–0.834, P < 0.001, 7986 cases of malignancies). Subgroup analyses revealed that miR-204-5p could act as a prognostic marker in cancers of respiratory system and digestive system. Functional analysis was conducted on genes predicted as targets (n = 2057) after the overlay genes from six out of twelve software were extracted. Two significant KEGG pathways were enriched (hsa04360: Axon guidance and hsa04722: Neurotrophin signaling pathway). PPI network revealed some hub genes/proteins (CDC42, SOS1, PIK3R1, MAPK1, PLCG1, ESR1, MAPK11, and AR). Conclusions The current study demonstrates that over-expression of miR-204-5p could be a protective factor for a certain group of cancers. Clinically, the low miR-204-5p level could gain a predictive value for a poor survival in cancers of respiratory system and digestive system. The detailed molecular mechanisms of miR-204-5p remain to be verified. Keywords: miR-204-5p, TCGA, prognostic, malignancies, bioinformatics INTRODUCTION MicroRNAs (miRNAs) are a class of non-coding RNAs with about 18–25 nucleotides. One of the mechanisms through which miRNAs regulate gene expression involves the base-pairing primarily with the 3′-untranslated regions (3′-UTRs), and more rarely with 5′-end of their target mRNAs through the “seed” sequences of the miRNAs [[40]1]. The base-pairing can cause translational inhibition, mRNA destabilization and/or degradation. Hence, miRNAs can function as a switch and a fine-tuner of the gene regulatory network, which may consist of hundreds, even thousands of potential target genes [[41]2]. Since the first miRNA Lin-4 was discovered, it has become increasingly clear that miRNAs exert its functions via various biological processes. Increasing evidence has also shown that imbalance of miRNA expression can lead to a wide variety of diseases, including inheriting diseases, obesity, cardiovascular diseases, anxiety disorder and cancers. Previous scientific evidence indicates that aberrant miRNAs expression exists in numerous cancers, and miRNAs participate in tumorigenesis and progression, functioning as oncogenes or tumor suppressors [[42]3, [43]4]. Moreover, aberrant expression of miRNAs, being tissue-specific, can be detected both in tissue and body fluid. And the abnormal expression of miRNAs is apparently related to deviant cancerous outcomes, suggesting that miRNAs are potential substantial biomarkers of cancers [[44]5–[45]13]. MiR-204-5p (previous ID: miR-204), one of the most promising miRNAs, has a considerable impact on the tumorigenesis and progression of cancers through different mechanisms [[46]14–[47]16]. In recent years, a large number of studies have reported that the reduced expression of miR-204-5p correlated with worse survival of numerous cancers, including nasopharyngeal carcinoma (NPC), gastric cancer (GC), non-small cell lung cancer (NSCLC), breast cancer (BC), neuroblastoma, acute myeloid leukemia (AML), hepatocellular carcinoma (HCC) and oral squamous cell carcinomas(OSCC), which indicates that miR-204-5p serves as a tumor suppressor [[48]17–[49]26]. However, one study focusing on uterine corpus endometroid carcinoma (UCEC) revealed no significant association between miR-204-5p down-regulation and overall survival (OS) [[50]16]. The correlation between miR-204-5p expression and OS of pancreatic cancer in the non-gemcitabine and gemcitabine groups was contrary [[51]27]. And the prognostic value of miR-204-5p for colorectal cancer (CRC) was controversial [[52]28–[53]30]. So, opinions were split on the prognostic role of miR-204-5p based on all publications. The aim of The Cancer Genome Atlas (TCGA) pilot project is to evaluate the value of large-scale multidimensional analysis of molecular characteristics in TCGA can deepen the understanding of the molecular foundation of different malignancies, including the information of the prognostic value of a certain marker in various cancers [[54]31]. Therefore, we investigated the prognostic value of miR-204-5p in all cancers available based on TCGA data. We also conducted a meta-analysis and systematic review to clarify the comprehensive prognostic significance of miR-204-5p with a comparatively refined result by combining data of TCGA and literatures. RESULTS The expression and prognostic value of miR-204-5p of various types of cancer based on TCGA data MiR-204-5p expression was analyzed in a total of 17 categories of malignancies and decreased miR-204-5p was detected in 13 cancers compared with corresponding non-cancerous tissues (P < 0.05, Figure [55]1, [56]Supplementary Figures 1, 2). Figure 1. The expression of miR-204-5p in cancers in TCGA. [57]Figure 1 [58]Open in a new tab Down-regulation of miR-204-5p was detected in LUSC, LUAD, COAD and READ compared with corresponding non-cancerous tissues. LUSC (lung squamous cell carcinoma); LUAD (lung adenocarcinoma); COAD (colon adenocarcinoma); READ (rectum adenocarcinoma). Altogether, the survival data could be achieved from 20 categories of malignancies (Table [59]1). The Kaplan-Meier curves were shown in Figure [60]2 ([61]Supplementary Figure 3). Furthermore, HR of miR-204-5p in each tumor was calculated with univariate cox regression analysis (Table [62]1). Distinct HRs were noted for various cancers. For instance, in kidney renal clear cell carcinoma (KIRC), the HR was 0.435 (95% CI: 0.318–0.596, P < 0.001, n = 477), and in colon adenocarcinoma (COAD), the HR was 2.089 (95% CI: 1.196–3.648, P = 0.01, n = 223). We intended to use a meta-analysis to pool the HR to probe an overall prognostic value of miR-204-5p in all cancers and the summarized HR was 0.928 (95% CI: 0.774–1.113, P = 0.422, 6203 cases of malignancies, Figure [63]3) with random-effects model (P < 0.001, I^2 = 76.8%). Table 1. Characteristics of the included studies for the overall survival (OS) analysis in TCGA. Cancers Sample size Sampling site Detection Cut off Follow-up (days) Outcome Risk evaluation method HR 95% CI P KIRC 477 Tissue MicroRNA sequencing Median 90–5925 OS univariate 0.435 0.318 0.596 < 0.001 KIRP 270 Tissue MicroRNA sequencing Median 90–4537 OS univariate 0.438 0.239 0.8 0.007 LIHC 324 Tissue MicroRNA sequencing Median 90–6408 OS univariate 0.606 0.4 0.918 0.018 SKCM 400 Tissue MicroRNA sequencing Median 90–11252 OS univariate 0.661 0.498 0.878 0.004 CESC 146 Tissue MicroRNA sequencing Median 90–3675 OS univariate 0.692 0.328 1.459 0.333 LUSC 338 Tissue MicroRNA sequencing Median 90–4765 OS univariate 0.717 0.507 1.014 0.06 PAAD 163 Tissue MicroRNA sequencing Median 90–3720 OS univariate 0.869 0.569 1.327 0.516 UCEC 443 Tissue MicroRNA sequencing Median 90–7428 OS univariate 0.885 0.56 1.397 0.599 LUAD 363 Tissue MicroRNA sequencing Median 90–2741 OS univariate 0.894 0.62 1.29 0.549 STAD 249 Tissue MicroRNA sequencing Median 90–5651 OS univariate 0.904 0.608 1.343 0.616 OV 422 Tissue MicroRNA sequencing Median 90–5481 OS univariate 0.911 0.715 1.161 0.452 SARC 222 Tissue MicroRNA sequencing Median 90–5723 OS univariate 0.961 0.627 1.471 0.854 BRCA 563 Tissue MicroRNA sequencing Median 90–3846 OS univariate 1.065 0.669 1.695 0.79 READ 99 Tissue MicroRNA sequencing Median 90–7106 OS univariate 1.102 0.407 2.985 0.849 BLCA 237 Tissue MicroRNA sequencing Median 90–5480 OS univariate 1.158 0.801 1.674 0.436 HNSC 246 Tissue MicroRNA sequencing Median 90–5050 OS univariate 1.178 0.8 1.734 0.407 GBM 500 Tissue MicroRNA sequencing Median 90–3881 OS univariate 1.298 1.069 1.576 0.009 ESCA 64 Tissue MicroRNA sequencing Median 90–2532 OS univariate 1.557 0.738 3.282 0.245 LGG 454 Tissue MicroRNA sequencing Median 90–6423 OS univariate 1.952 1.353 2.814 < 0.001 COAD 223 Tissue MicroRNA sequencing Median 90–4270 OS univariate 2.089 1.196 3.648 0.01 [64]Open in a new tab The patients from TCGA were selected with survival more than 90 days. HR: hazard ratio; CI: confidence interval; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LIHC: liver hepatocellular carcinoma; SKCM: skin cutaneous melanoma; CESC: cervical squamous cell carcinama and endocervical adenocarcinoma; LUSC: lung squamous cell carcinoma; PAAD: pancreatic adenocarcinoma; UCEC: uterine corpus endometrial carcinoma; LUAD: lung adenocarcinoma; STAD: stomach adenocarcinoma; OV: ovarian serous cystadenocarcinoma; SARC: sarcoma; BR CA: breast invasive carcinoma; READ: rectum adenocarcinoma; BLCA: bladder urothelial carcinoma; HNSC: head and neck squamous cell carcinoma; GBM: glioblastoma multiforme; ESCA: esophageal carcinoma; LGG: brain lower grade glioma; COAD: colon adenocarcinoma. Figure 2. The survival curves of miR-204-5p in cancers in TCGA. [65]Figure 2 [66]Open in a new tab HR: hazard ratio; CI: confidence interval; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LIHC: liver hepatocellular carcinoma; SKCM: skin cutaneous melanoma. Figure 3. Forest plots of miR-204-5p expression and OS rate in 20 cancers in TCGA. [67]Figure 3 [68]Open in a new tab HR: hazard ratio; CI: confidence interval; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LIHC: liver hepatocellular carcinoma; SKCM: skin cutaneous melanoma; CESC: cervical squamous cell carcinama and endocervical adenocarcinoma; LUSC: lung squamous ce ll carcinoma; PAAD: pancreatic adenocarcinoma; UCEC: uterine corpus endometrial carcinoma; LUAD: lung adenocarcinoma; STAD: stomach adenocarcinoma; OV: ovarian serous cystadenocarcinoma; SARC: sarcoma; BRCA: breast invasive carcinoma; READ: rectum adenocarcinoma; BLCA: bladder urothelial carcinoma; HNSC: head and neck squamous cell carcinoma; GBM: glioblastoma multiforme; ESCA: esophageal carcinoma; LGG: brain lower grade glioma; COAD: colon adenocarcinoma. Meta-analysis results of literature Eligible studies of the meta-analysis based on literature A total of 841 records were preliminarily identified according to the search strategy, and 91 articles remained candidates after titles and abstracts were screened. After the full text of the rest 91 studies was checked, 74 records were excluded due to absence of necessary information. Consequently, a total of 14 papers conformed to our selection criteria and one study, which was performed by our group to investigate the relationship between miR-204-5p and survival of HCC, were finally accepted in this research, and the publication time were between 2012 and 2016 [[69]17–[70]26, [71]32]. Papers evaluating the risk of survival according to different clinical stages and different populations were listed twice in Table [72]2 [[73]20, [74]28], which were regarded as two individual studies. The process of study selection was presented in a flow diagram ([75]Supplementary Figure 4). Table 2. Characteristics of the included studies of literatures for the overall survival (OS) analysis. Study Year patient origin Tumor type N Sample Detection Cut off Follow-up (months) Outcome HR (95% CI) Risk evaluation method NOS score Sümbül 2014 Turkey CRC 66 Tissue qRT-PCR Median 0–80 OS 1.326 (0.625–2.815) univariate 7 Yin 2014 China CRC 94 Tissue qRT-PCR Quarter 0–80 OS 0.303 (0.147–0.622) multivariate 8 Boisen 2014 USA CRC 276 Tissue TaqMan Human MicroRNA array Mean 0–75 OS 0.850 (0.720–1.010) multivariate 8 2014 USA CRC 127 Tissue TaqMan Human MicroRNA array Mean 0–75 OS 1.010 (0.850–1.210) multivariate 8 Ma 2014 China NPC 275 Tissue qRT-PCR Median 0–55 OS 0.323 (0.202–0.515) survival curve 6 Peng 2014 China NPC 50 Tissue qRT-PCR Mean 0–60 OS 0.271 (0.077–0.962) survival curve 6 Shi 2014 China NSCLC(I/II) 47 Tissue qRT-PCR Median 0–60 OS 0.348 (0.123–0.990) survival curve 7 7 2014 China NSCLC(III/IV) 48 Tissue qRT-PCR Median 0–60 OS 0.169 (0.030–0.935) survival curve Guo 2015 China NSCLC 126 Plasma qRT-PCR Median 0–60 OS, DFS OS0.584 (0.354–0.963) multivariate 8 DFS0.476 (0.233–0.980) survival curve Sacconi 2012 Italy GC 69 Tissue qRT-PCR Median 0–80 OS 0.256 (0.085–0.769) multivariate 8 Chen 2016 China GC 115 Serum qRT-PCR Median 0–60 OS 0.276 (0.123–0.354) multivariate 8 Ge 2015 China HCC 48 Tissue qRT-PCR Median 0–80 OS 0.427 (0.193–0.943) survival curve 7 Our data 2016 China HCC 70 Tissue qRT-PCR Median 0–68 OS 0.355 (0.093–1.357) univariate 7 Li 2014 China BC 129 Tissue qRT-PCR Median 0–55 OS, DFS OS 0.418 (0.198–0.885) DFS 0.465 (0.221–0.980) survival curve survival curve 6 Yu 2016 Taiwan OSCC 60 Tissue qRT-PCR NA 0–60 OS 0.204 (0.062–0.667) survival curve 7 Ryan 2012 USA Neuroblastoma 143 Tissue qRT-PCR Median 0–60 OS 0.179 (0.075–0.426) univariate 8 Butrym 2015 Poland AML 40 Plasma qRT-PCR Median 0–55 OS 0.159 (0.028–0.917) survival curve 6 [76]Open in a new tab NA not available; HR: hazard ratio; OS: overall survival; DFS: disease free survival; NOS: Newcastle-Ottawa Scale; CRC: colorectal cancer; NPC: Nasopharyngeal carcinoma; NSCLC: non-small cell lung cancer; GC: gastric cancer; HCC: hepatocellular carcinoma; BC: breast cancer; OSCC: oral squamous cell carcinomas; AML: acute myeloid leukemia. TaqMan Human MicroRNA array: The TaqMan Human MicroRNA array A and B Cards Set v3.0 (Applied Biosystems) was used to quantify expression of human miRNAs with single determinations in the study reported by Boisen et al. The primary characteristics of the 15 studies were shown in Table [77]2. A total of 1783 participants were recruited in this meta-analysis, from Turkey, China, Italy, USA and Poland, respectively. Cancer types covered CRC, NPC, NSCLC, GC, HCC, Neuroblastoma, AML, OSCC and BC. Among the 15 articles, there were 3 studies (n = 563) for CRC; two studies (n = 325) for NPC, three studies (n = 221) for NSCLC, two studies for GC (n = 184) and two studies for HCC (n = 118). In the rest of studies, BC, GC, OSCC, neuroblastoma, HCC and AML were mentioned once only. All included studies measured miR-204-5p in tissues except two studies in plasma [[78]21, [79]24]. Quantitative real-time PCR (qRT-PCR) was performed for 14 studies, and TaqMan Human MicroRNA array was used for only one study by Boisen et al. [[80]28]. Association of miR-204-5p level with survival based on literature In total, 15 studies were included for OS and a random-effects model was applied due to the high level of heterogeneity (P < 0.001, I^2 = 81%). The pooled HR of OS was 0.420 (95% CI: 0.306–0.576, P < 0.001, 1783 cases of malignancies, Figure [81]4). Moreover, two studies were analyzed for disease free survival (DFS) and the combined HR was 0.471 (95% CI: 0.281–0.789, P = 0.004, 255 cases of malignancies, Figure [82]5). Therefore, miR-204-5p could act as a protective factor for various cancers in general. Figure 4. Forest plots of miR-204-5p expression and OS in cancers of literatures. [83]Figure 4 [84]Open in a new tab The mete-analysis of the 15 studies (17 cohorts, 1783 cases) showed a significant result that miR-204-5p was considered to be a protective factor (HR = 0.420, 95% CI: 0.306–0.576, P < 0.001). Figure 5. Forest plots of miR-204-5p expression and DFS in cancers of literatures. [85]Figure 5 [86]Open in a new tab Two studies (255 cases) were included for DFS, and the pooled HR was 0.471 (95% CI: 0.281–0.789, P = 0.004). Sensitivity analysis and publication bias The sensitivity analysis revealed a stable combined HR after the removal of certain studies ([87]Supplementary Figure 5). Begg's test and Egger's test revealed apparent publication bias among all the included studies for OS (Begg's test: P = 0.434, Egger's test: P < 0.001, [88]Supplementary Figure 5). The result did not change at all after the adjustment of the trim and filling method, which suggested that the publication bias did not affect the conclusions obviously. Therefore, the HR of 0.420 indicated a significant correlation of poorer OS with low expression levels of miR-204-5p. Comprehensive meta-analysis combing TCGA and literature sources To better understand the comprehensive prognostic value of miR-204-5p based on both TCGA and literature data, we further performed a meta-analysis by combining all available information. The combined HR from both TCGA and literature was 0.708 (95% CI: 0.600–0.834, P < 0.001, 7986 cases of malignancies, Figure [89]6). This was calculated by using random-effects model (I^2 = 80%, P < 0.001). The result further confirmed the predictive value of low miR-204-5p for a poor survival of various cancers. Figure 6. Forest plots of miR-204-5p expression and OS rate in TCGA and literatures. [90]Figure 6 [91]Open in a new tab KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LIHC: liver hepatocellular carcinoma; SKCM: skin cutaneous melanoma; CESC: cervical squamous cell carcinama and endocervical adenocarcinoma; LUSC: lung squamous cell carcinoma; PAAD: pancreatic adenocarcinoma; UCEC: uterine corpus endometrial carcinoma; LUAD: lung adenocarcinoma; STAD: stomach adenocarcinoma; OV: ovarian s erous cystadenocarcinoma; SARC: sarcoma; BRCA: breast invasive carcinoma; READ: rectum adenocarcinoma; BLCA: bladder urothelial carcinoma; HNSC: head and neck squamous cell carcinoma; GBM: glioblastoma multiforme; ESCA: esophageal carcinoma; LGG: brain lower grade glioma; COAD: colon adenocarcinoma. Different subgroup analyses were further performed on the basis of countries of the studies and types of cancers (Table [92]3). For the subgroup analysis for patient origin, only 15 published studies were analyzed. Eleven studies were from China (HR = 0.357, 95% CI: 0.286–0.445, P < 0.001, n = 1062, Figure [93]7A), and six studies were not from China (HR = 0.605, 95% CI = 0.410–0.895, P = 0.012, n = 721, Figure [94]7B). These included studies were split up into five groups: nervous system (HR = 0.888, 95% CI: 0.399–1.974, P = 0.771, n = 1097, Figure [95]8A), head and neck neoplasms (HR = 0.504, 95% CI: 0.177–1.400, P = 0.201, n = 571, Figure [96]8B), respiratory system (HR = 0.666, 95% CI: 0.557–0.797, P < 0.001, n = 1051, Figure [97]8C), digestive system disease (HR = 0.674, 95% CI: 0.510–0.892, P = 0.006, n = 1798, Figure [98]8D) and urinary and reproductive system (HR = 0.720, 95% CI: 0.505–1.025, P = 0.068, n = 1552, Figure [99]8E). Table 3. Subgroup analysis of HR in overall survival (OS). Analysis No. of studies No.of patients HR(95% CI) P value Model Heterogeneity I^2 (%) P Subgroup1: patient origin China 11 1062 0.357 (0.286–0.445) < 0.001 Fixed-effect 0.0% 0.713 Non-China 6 721 0.605 (0.410–0.895) 0.012 Random-effect 79% < 0.001 Subgroup 2 tumor types Nervous system 3 1097 0.888 (0.399–1.974) 0.771 Random-effect 92% < 0.001 Head and neck squamous cell carcinoma 3 571 0.504 (0.177–1.440) 0.206 Random-effect 90% < 0.001 Respiratory system 6 1051 0.666 (0.557–0.797) < 0.001 Fixed-effect 25% 0.249 Gastrointestinal system 14 1798 0.674 (0.510–0.892) 0.006 Random-effect 78% < 0.001 Urinary and reproductive system 6 1995 0.720 (0.505–1.025) 0.068 Random-effect 78% < 0.001 [100]Open in a new tab Figure 7. Subgroup analyses of region of miR-204-5p expression and OS in cancers. [101]Figure 7 [102]Open in a new tab (A) the included studies of China; (B) the included studies of non-China. Figure 8. Subgroup analyses of cancer types of miR-204-5p expression and OS in cancers. [103]Figure 8 [104]Open in a new tab (A) nervous system; (B) head and neck squamous cell carcinoma; (C) respiratory system; (D) gastrointestinal system; (E) urinary and reproductive system. KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LIHC: liver hepatocellular carcinoma; SKCM: skin cutaneous melanoma; CESC: cervical squamous cell carcinama and endocervical adenocarcinoma; LUSC: lung squamous cell carcinoma; PAAD: pancreatic adenocarcinoma; UCEC: uterine corpus endometrial carcinoma; LUAD: lung adenocarcinoma; STAD: stomach adenocarcinoma; OV: ovarian serous cystadenocarcinoma; SARC: sarcoma; BRCA: breast invasive carcinoma; READ: rectum adenocarcinoma; BLCA: bladder urothelial carcinoma; HNSC: head and neck squamous cell carcinoma; GB M: glioblastoma multiforme; ESCA: esophageal carcinoma; LGG: brain lower grade glioma; COAD: colon adenocarcinoma. Targets prediction, functional enrichment analysis and PPI network To gain insights into the prophetic role and molecular mechanism of miR-204-5p in various cancers, we predicted the target genes of miR-204-5p with 12 individual programs. Then, functional analysis was conducted on genes predicted as targets (n = 2057) after the overlay genes from six software were extracted. Gene ontology (GO) analysis unveiled that 29 pathways were associated with biological process (BP) (Figure [105]9), nine pathways with cellular component (CC) (Figure [106]10), and four pathways with molecular function (MF) (Figure [107]11), respectively (FDR < 0.05). The top four pathways of BP, CC and MF enriched functional analyses were listed in Table [108]4. Furthermore, two KEGG pathways were enriched (hsa04360:Axon guidance, hsa04722: Neurotrophin signaling pathway, FDR < 0.05, Table [109]5, Figure [110]12). No Panther pathway was achieved with FDR < 0.05. At last, PPI network of the target genes was shown in Figure [111]13. In this network, eight hub gene/proteins with more than 30 interactions with other gene/proteins were determined, including cell division CDC42, SOS1, PIK3R1, MAPK1, PLCG1, ESR1, MAPK11 and AR. Figure 9. The network of enriched gene ontology (GO) terms of biological process. [112]Figure 9 [113]Open in a new tab The intensity of the color indicates p-value size (a smaller P value owns a deeper color), node refers to pathways and the node size is representative of the number of genes (the larger node owns more genes). The GO terms of biological process were presented with P < 0.0001. Figure 10. The network of enriched gene ontology terms of cellular component. [114]Figure 10 [115]Open in a new tab The intensity of the color indicates p-value size (a smaller P value owns a deeper color), node refers to pathways and the node size is representative of the number of genes (the larger node owns more genes). The GO terms of cellular component was showed with P < 0.01. Figure 11. The network of enriched gene ontology terms of molecular function. [116]Figure 11 [117]Open in a new tab The intensity of the color indicates p-value size (a smaller P value owns a deeper color), node refers to pathways and the node size is representative of the number of genes (the larger node owns more genes). The GO terms of molecular function was showed with P < 0.05. Table 4. The gene ontology (GO) analysis of the potential targets of miR-204-5p. ID Term FDR Genes BP GO:0030182 Neuron differentiation 3.21E-07 ALS2, NRP2, NRP1, CNP, GRIN3A, RORA, PAX2, PRKG1, GDNF, CXCL12 GO:0048666 neuron development 5.90E-07 ALS2, NRP2, NRP1, CNP, GRIN3A, PAX2, PRKG1, GDNF, CXCL12, ZFP91 GO:0031175 neuron projection development 2.27E-06 NRP2, ALS2, NRP1, CNP, GRIN3A, PAX2, PRKG1, CXCL12, GDNF, BDNF GO:0048812 neuron projection morphogenesis 4.34E-05 NRP2, ALS2, NRP1, LPPR4, ADORA2A, ERBB3, CNP, PAX2, CXCL12, EPHB1 MF GO:0003700 transcription factor activity 1.02E-05 MEF2C, MEF2A, HMX2, HMX1, THRB, STAT5B, RORA, PGR, TAF5L, HOXC8 GO:0030528 transcription regulator activity 2.00E-05 MEF2C, MEF2A, STAT5B, MED22, RORA, MXI1, PGR, TAF5L, CREB3L2, ZNF396 GO:0004714 transmembrane receptor protein tyrosine kinase activity 0.002602197 NRP2, RET, NRP1, ERBB4, EFNB3, ERBB3, EFNA3, EPHA10, EPHA1, EPHB1 GO:0043565 sequence-specific DNA binding 0.022779507 ISX, MEF2C, PPARA, HMX2, MEF2A, BACH2, ELF2, HMX1, FOXK1, THRB CC GO:0044459 plasma membrane part 4.98E-06 VAPA, IL6ST, RP2, EFNA3, SYT6, GRIN3A, ZNRF1, AQP2, ATP2B1, ATP2B4 GO:0005887 integral to plasma membrane 2.50E-04 MPZL1, KCNC4, IL6ST, SLC6A20, EFNA3, GRIN3A, TLR6, VIPR2, ATP2B1, ATP2B4 GO:0031226 intrinsic to plasma membrane 3.67E-04 MPZL1, KCNC4, IL6ST, SLC6A20, EFNA3, GRIN3A, TLR6, VIPR2, ATP2B1, ATP2B4 GO:0005626 insoluble fraction 0.00195022 ALS2, SEPT3, VAPA, HMGCR, VAPB, ADCY6, SYNCRIP, LEMD3, CNP, RAB1A [118]Open in a new tab Three parts of GO analysis was included (BP:biological process; MF: molecular function; CC: cellular component). The enriched terms with FDR less than 0.05 of the top 4 were presented. Table 5. KEGG pathway: the top 10 FDR from small to large order of KEGG pathway. ID Term FDR Genes hsa04360 Axon guidance 6.66E-04 NRP1, PLXNA2, EFNA3, PPP3R1, CXCL12, EPHB1, SEMA5A, CDC42, PAK7, EPHB6 hsa04722 Neurotrophin signaling pathway 0.008788241 YWHAZ, ZNF274, NFKBIE, CDC42, IRAK3, BDNF, MAP3K3, BCL2, SOS1, SOS2 hsa04010 MAPK signaling pathway 1.087388423 MEF2C, FGF5, FGF18, IL1R1, PPP3R1, CACNB1, ATF2, CDC42, BDNF, MAP3K3 hsa04012 ErbB signaling pathway 3.053736293 PRKCA, ERBB4, ERBB3, STAT5B, PRKCB, MAPK1, PAK7, CRKL, PLCG1, PAK2 hsa05214 Glioma 5.792493391 PRKCA, E2F3, PRKCB, MAPK1, CCND1, PLCG1, SOS1, SOS2, CAMK2D, PDGFRB hsa04020 Calcium signaling pathway 7.877057658 ADCY1, GNA15, ADCY2, ERBB4, ADORA2A, ERBB3, PPP3R1, ATP2B1, ATP2B4, GRPR hsa05220 Chronic myeloid leukemia 10.40115864 E2F3, TGFBR1, STAT5B, TGFBR2, SMAD4, BCL2L1, MAPK1, CCND1, CRKL, GAB2 hsa04720 Long-term potentiation 10.9789348 PRKCA, MAPK1, RPS6KA3, ADCY1, GRIN2B, CAMK2D, PPP3R1, GRIN2A, PRKACB, PPP1CC hsa04910 Insulin signaling pathway 15.39204299 SOCS3, PRKAB2, HK2, PRKCI, ACACA, PDE3A, SOCS4, PPP1CC, PPARGC1A, PCK1 hsa04210 Apoptosis 16.17575426 IL1R1, PPP3R1, ENDOD1, BCL2L1, BIRC2, CASP10, IRAK3, TNFRSF10D, BCL2, RIPK1 [119]Open in a new tab Figure 12. Top 10 KEGG pathways of miR-204a-5p prospective targets. [120]Figure 12 [121]Open in a new tab The bar schematic was drawn with R ggplot. High P-value was in blue and low P-value in green. Horizontal axis indicated the gene number in each pathway. MAPK signaling pathway was the most significant one among all 10 pathways, which contained 68 genes. Figure 13. The network of protein-protein interaction (PPI) of miR-204a-5p prospective target genes. [122]Figure 13 [123]Open in a new tab Cytoscape (version 3.4.0) was used for the analysis of PPI network. The size of nodes expressed feature with a degree of connectivity. The color of nodes showed their clustering coefficient, representing the connectedness with high level in red and low level in green. High value to large size of edge was in red. DISCUSSION In the current study, we investigated the prognostic role of miR-204-5p based on TCGA data for individual cancers and achieved heterogeneous results. The pooled HR indicated that miR-204-5p could not predict the survival, which could be due to the small size of cases and simple detecting method of microRNA-sequencing. To explore the prognostic role of miR-204-5p in cancers, we further performed meta-analyses with literature, as well as the combination of literature and TCGA. Interestingly, low miR-204-5p level could have predictive value for a poor survival of specific types of cancers. To have a better comprehension of the potential mechanism of miR-204-5p, we performed functional analysis with predicted target genes and found that the signaling pathways of Axon guidance and Neurotrophin pathway were closely related to the function of miR-204-5p. Finally, PPI network also revealed several hub gene/proteins among all the predicted targets. From TCGA, we analyzed the prognostic value of miR-204-5p in 20 classes of cancers with Kaplan-Meier and univariate cox regression analysis. The results showed that miR-204-5p might play apparently different roles in various malignancies. To acquire overall prognostic significance of miR-204-5p for all cancers, we performed a meta-analysis and found that the summarized HR was 0.928 (P > 0.05), which suggested that miR-204-5p could not act as a prognostic marker for all the cancers based on TCGA. Since TCGA data were obtained only from one single technique of microRNA sequencing, we further added information from literature to further explore the prognostic value of miR-204-5p. The pooled HR from literature was 0.420 (95% CI: 0.306–0.576, P < 0.001) for OS and 0.471 (95% CI: 0.281–0.789, P = 0.004) for DFS. Thus, down-regulation of miR-204-5p could have the potential to predict poorer survival in cancers. Interestingly, when we combined the data from both TCGA and the literature, the final HR was 0.708 (95% CI = 0.600–0.834, P < 0.001), which indicated that miR-204-5p could act as a protective factor for various cancers in general. Considering the heterogeneity among different cancers, we conducted subgroup analyses according to cancer types. The subgroup analyses revealed that low miR-204-5p could predict shorter survival in cancers of respiratory system and digestive system. Meanwhile, in cancers of respiratory system, we observed that miR-204-5p (miR-204) level was significantly decreased both in LUAD and LUSC tissues compared with non-cancerous tissues (both P < 0.05). The aberrant expression of miR-204-5p in cancers of respiratory system suggests that miR-204-5p participates in carcinogenesis and progression, which supports its prognostic role. Thus, significant down-regulatory expression of miR-204-5p contributes to its predictive value of prognosis in cancers of respiratory system. As for caners of digestive system, we observed similar trend of miR-204-5p expression in cancer tissues (COAD, ESCA, LIHC, PAAD and READ). But, the HRs of COAD, ESCA and READ are not as expected, which might be resulted from limited sample size and the other confounding factors, such as age, different treatments, etc. The confounding factors should be further investigated in future to better clarify the prognostic role of miR-204-5p. In a word, as a biomarker of prognosis, miR-204-5p could be clinically employed in cancers of respiratory system and digestive system based on the subgroup analyses. In the subgroup analyses of the meta-analysis, we observed that low miR-204-5p expression predicted a worse survival in cancers of respiratory system, digestive system, but not in nervous system, head and neck neoplasms or urinary and reproductive system. The reasons may be that 1) miR-204-5p expressed heterogeneously in different cancers, 2) the regulatory mechanisms which miR-204-5p participated in may be cancer-specific, 3) the aberrant genes in different cancers may be inconsistent, since APC in colorectal cancer and p16 in glioma, which may lead to a different status of miR-204-5p in cancers. After finding miR-204-5p gained the clinical significance to predict the prognosis of cancers, we wonder how miR-204-5p performs its function. A qualitative study reviewed the role of miR-204-5p in cancers, including its expression, regulation and biological functions, especially focusing on its role in tumor development and progression. In this review, Li et al. summarized the targets identified in previous studies and showed the regulatory mechanisms of miR-204-5p to promote tumor development and progression, such as induction of cell apoptosis, inhibition of epithelial-mesenchymal transition, control of long non-coding RNA, acting as a suppressor in cancer stem cell and increasing sensitivity of chemotherapy. Several targets of miR-204-5p have been confirmed in various cancers, such as EZRIN [[124]33, [125]34], BCL-2 [[126]23], FOXC1 [[127]35], MEIS1 [[128]36], RAB22A [[129]37], NAUKI [[130]20], SIRT1 [[131]38], LC3B [[132]39], PRKR-Like ER Kinase (PERK) [[133]40], MX1 and TXNIP [[134]41], snai1 [[135]42], FOXM1 [[136]43], ATF2 [[137]44], JAK2 [[138]45], etc. Notwithstanding, a single miRNA always functions through numerous targets, even an extremely large gene network. For a better comprehension of the potential signaling pathway and gene network of miR-204-5p, target genes were predicted by 12 softwares. Next, the bioinformatics studies of the functional pathway category and integrated network were performed, including GO, KEGG, Panther enrichment analysis and PPI. Interestingly, the top three terms in KEGG analysis were all closely related to tumorigenesis and tumor progression, including “Axon guidance” [[139]46, [140]47], “Neurotrophin signaling pathway” [[141]48, [142]49] and “MAPK signaling pathway” [[143]50]. Among all the potential targets, PPI revealed that several hub genes were strongly associated with different process of malignancies, including CDC42 [[144]51], SOS1 [[145]52], PIK3R1 [[146]53], MAPK1 [[147]54], PLCG1 [[148]55], ESR1 [[149]56], MAPK11 [[150]57] and AR [[151]58]. In tumors, differentially-expressed microRNAs were obvious in plasma; thus, it was non-invasive and economical to detect the plasma rather than the tissue [[152]59]. Unfortunately, few studies, to date, have evaluated the prognostic value of miR-204-5p in plasma [[153]21, [154]24]. More studies focusing on other cancers are needed to test plasma detection of miR-204-5p efficiency. Furthermore, experimental studies as well as clinical researches have been launched to investigate the relationship of miR-204-5p expression difference with chemotherapy sensitivity [[155]19, [156]22–[157]24]. Inspiringly, it was reported that miR-204-5p might increase sensitivity of some chemotherapy drugs in gastric cancer, neuroblastoma, and colorectal cancer [[158]19, [159]23, [160]29]. However, our results should be interpreted meticulously due to the following limitations. Firstly, the publication bias of OS existed as the value of Egger's test pointed out. But the publication bias did not lead to different conclusion according to adjusted results by trim and fill method, which suggested our meta-analysis results were relatively robust. Secondly, the number of eligible studies, especially from literatures (n = 15), was relatively small, which prevented us from drawing convincing conclusions. Thirdly, the inconsistency of the cut off values may influence our findings. Fourthly, HRs were extracted from the Kaplan-Meier survival curves, which could unavoidably result in slight statistical errors. Fifthly, another possible bias could result from the fact that some negative findings could not be accepted for publication in a journal. Sixthly, we did not perform any experiments to validate the clinical role of miR-204-5p, including real-time quantitative polymerase chain reaction (RT-qPCR) or fluorescence in situ hybridization (FISH) with tissue or blood samples, especially for those cancers only containing miRNA-sequencing data from TCGA. Seventhly, no in vitro or in vivo functional experiments have been performed to test the predicting target genes and the possible molecular mechanism of miR-204-5p. Finally, different detecting methods of miR-204-5p may lead to potential heterogeneity among the combined studies, even though the random effect model was used to abate the effect of heterogeneity. Most recently, a hypothesis termed competing endogenous RNA (ceRNA), which is closely related to miRNA, has grabbed researchers’ attention. The core of this hypothesis is that mRNA, pseudogenes, long noncoding RNA (lncRNA), or other molecules, which have the same miRNA response element (MRE), could ‘sponge’ the miRNA competitively, then perform their biological function [[161]60, [162]61]. MiR-204-5p has also been studied in this hypothesis. For instance, MALAT1 could upregulate the expression of miR-204-5p target gene SLUG through competitively ‘sponging’ miR-204-5p to form a branch of the MALAT1/miR-204/SLUG pathway to regulate the progression of lung adenocarcinoma [[163]60]. Similarly, another lncRNA, UCA1 could sponge endogenous miR-204-5p and suppress its activity via targeting CREB1 in CRC [[164]62]. Furthermore, the axis of NEAT1/miR-204-5p/ZEB1 was confirmed in nasopharyngeal carcinoma [[165]63]. In the future, further researches into the ceRNA network with miR-204-5p are required to be conducted in various cancers. MATERIALS AND METHODS TCGA data extraction From the TCGA ([166]http://cancergenome.nih.gov/), we downloaded and extracted the data of miR-204 expression from RNASeqV2 (level 3), as well as all clinical parameters, including survival status of all cancers in January 2016, through bulk download mode. MiR-204 expression data were presented as upper quartile normalized RSEM count estimates [[167]39]. The miR-204 is immature pri-miRNA in TCGA data. Extracted data were used without further transformation. Meta-analyses for both TCGA data and literature All TCGA data were used for meta-analysis and the detail was described below. We also performed a traditional meta-analysis with the information from publications. Search strategy and study selection of literature Relevant studies were selected by comprehensive searching the online databases PubMed, Embase, web of science, Wiley Online Library, Cochrane library, Science Direct, Wan Fang, Chinese VIP, Chinese Biomedical Literature Database and CNKI up to January 1, 2017, independently. To achieve the maximum recall and precision of the articles, the keywords and entry words were as follows: (1) (miR-204 OR miRNA-204 OR microRNA-204 OR miR204 OR miRNA204 OR “miR 204” OR “miRNA 204” OR “microRNA 204” OR miR-204-5p OR miRNA-204-5p OR microRNA-204-5p); (2) (cancer OR tumor OR tumour OR neoplas* OR carcinoma OR sarcoma OR malignan*). In addition, some references of