Abstract Background Accumulating evidence suggests long noncoding RNAs (lncRNAs) play important roles in the initiation and progression of cancers. However, their functions in chromophobe renal cell carcinoma (chRCC) are not fully understood. Methods We analyzed the expression profiles of lncRNA, microRNA, and protein-coding RNA, along with the clinical information of 59 primary chRCC patients collected from The Cancer Genome Atlas database to identify lncRNA biomarkers for prognosis. We also constructed an lncRNA–microRNA–mRNA coexpression network (competitive endogenous RNAs network) by bioinformational approach. Results One hundred and forty-two lncRNAs were found to be differentially expressed between the cancer and normal tissues (fold change ≥1.5, P<0.001). Among them, 12 lncRNAs were also differentially expressed with the corresponding clinical characteristics (fold change ≥1.5, P<0.01). Besides, 7 lncRNAs (COL18A1-AS, BRE-AS1, SNHG7, TMEM51-AS1, C21orf62-AS1, LINC00336, and LINC00882) were identified to be significantly correlated with overall survival (log-rank P<0.05). A competitive endogenous RNA network in chRCC containing 16 lncRNAs, 18 miRNAs, and 168 protein-coding RNAs was constructed. Conclusion Our results identified specific lncRNAs associated with chRCC progression and prognosis, and presented competing endogenous RNA potential of lncRNAs in the tumor. Keywords: long noncoding RNA, chromophobe renal cell carcinoma, biomarker, competing endogenous RNA network Introduction Renal cell carcinoma (RCC) is one of the most common genitourinary cancers worldwide.[35]^1 An estimated 61,560 new cases of RCC were expected in the US in 2015.[36]^2 Chromophobe renal cell carcinoma (chRCC) is a relatively rare subtype of RCC, accounting for approximately 5% of all patients.[37]^3 Compared to other RCC subtypes, chRCC has significantly higher cancer-specific survival probabilities. Prognosis for patients with chRCC has improved in past decades due to technological advances in early detection and intervention.[38]^4 Even so, the clinical behavior and long-term outcomes of chRCC are still highly variable. Hence, identifying novel molecular biomarkers and studying the detailed molecular mechanism of chRCC are necessary. Noncoding RNAs with length greater than 200 nucleotides are cataloged as long noncoding RNAs (lncRNAs).[39]^5 LncRNAs are usually short of meaningful open reading frames (ORFs) and not translated into proteins, but they can regulate the gene expression in the form of RNA in many aspects.[40]^6^,[41]^7 Competitive endogenous RNA (ceRNA) hypothesis was proposed by Salmena et al in 2011. They pointed out that some messenger RNAs and noncoding RNAs such as pseudogene, lncRNAs, and circular RNAs can regulate the target genes by competitive binding to the same microRNA (miRNA)-binding sites through miRNA response elements (MREs), so the inhibition of target genes by miRNA can be released or lessened.[42]^8 This is to say that lncRNA–miRNA–mRNA may form a large and subtle regulatory RNA network in tumors. To date, various lncRNA and miRNA interactions with significant functions have been identified in many cancers.[43]^9^–[44]^11 In RCC, lncRNA MALAT1 was found to function as a competing endogenous RNA to regulate epithelial–mesenchymal transition-related proteins by sponging miR-200s and miR-205, and HOTAIR was proved to promote the proliferation and invasion of renal clear cell adenocarcinoma cells 786-O by interacting with miR-141.[45]^12^–[46]^15 However, more functions of lncRNA in chRCC remain to be elucidated. In this study, we analyzed the expression data of lncRNA, miRNA, and protein-coding RNA and the corresponding clinical information of 59 chRCC patients selected from The Cancer Genome Atlas (TCGA) database to explore the differential expression profiles of lncRNAs in different clinical statuses and to identify tumor-specific lncRNAs’ competing endogenous RNA potential in the tumor. Methods Data collection Fifty-nine chRCC patients selected from the TCGA database were enrolled in our study. The inclusion criteria were set as follows: 1) the tumor histological type was chRCC; 2) the patient did not have a history of other malignancies; 3) the patient had not received neoadjuvant therapy; and 4) the clinical information was complete. Among these 59 patients (Cohort T), 23 patients provided the adjacent nontumor tissues (Cohort M). Their corresponding RNA expression data (level 3) were downloaded from TCGA data portal ([47]http://cancergenome.nih.gov, up to Jan 20, 2016). These gene expression profiles were produced by using Illumina HiSeq 2000 sequencer platforms (Illumina Inc., San Diego, CA, USA). The raw expression data of lncRNAs and mRNAs which were generated from RNA sequencing raw reads by RNASeqV2 postprocessing pipelines were normalized as RNA-Seq by Expectation-Maximization. The raw expression data of miRNAs were standardized as reads per million by the TCGA project. Patient data were collected and processed following the data access policies approved by the Ethics Committee of The Cancer Genome Atlas Program. The authors downloaded all the data from the TCGA database and performed this study in line with the TCGA publication guidelines ([48]http://cancergenome.nih.gov/publications/publicationguidelines). All patients enrolled in the program were well informed. Therefore, no further ethical approval was required for this study. We analyzed these expression profiles with BRB-Array tools (version 4.4.0) developed by Dr Richard Simon and the BRB-Array Tools Development Team.[49]^16 Construction of lncRNA-associated ceRNA network LncRNA-associated ceRNA network was constructed based on the “ceRNA hypothesis” that lncRNAs can regulate the expression of mRNAs which contain common MREs by combining the miRNAs competitively. We identified differentially expressed lncRNAs and miRNAs (fold change ≥5.0, P<0.001) in the tumor. Predicted human miRNA–lncRNA interactions were collected from starBase v2.0[50]^17 and miRcode.[51]^18 Experimentally validated miRNA–target mRNA interactions were retrieved from the miRTarBase.[52]^19 Differentially expressed miRNAs were set as hub nodes. The lncRNAs and mRNAs were connected with these hub nodes according to their interactions. Maximal information coefficient (MIC) algorithm was used to identify the robustness of pair-wise relationships of miRNA–lncRNA and miRNA–mRNA (MIC >0.15, MIC-ρ2 >0.15).[53]^20 Cytoscape v3.0[54]^21 was applied to construct and visualize the network graph. Functional enrichment analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the coding RNAs involved in the ceRNA network was conducted using Database for Annotation, Visualization, and Integrated Discovery.[55]^22 We did the analysis with default parameters. The whole human genome was set as background; functional categories with P-value <0.05 were regarded as statistically significant. Statistical analysis Clinical category variables were presented as counts and percentages. The chi-square test was applied to analyze differences of distribution between Cohort M and Cohort T. RNA expression data were presented as mean ± standard deviation. Paired sample t-test was used to examine differences in lncRNA and miRNA expression between cancerous and matched adjacent tissues (significant P-value was set as 0.001). Unpaired t-test was conducted to find out the difference in lncRNA expression levels between different clinicopathological groups (significant P-value was set as 0.01). Unsupervised hierarchical cluster analysis was used to generate tree clusters for the separation of different classes with lncRNA expression profiles. Univariate Cox proportional hazards regression was applied to identify the lncRNAs associated with overall survival; Kaplan–Meier survival analyses and log-rank test were performed to study the relations of lncRNA expression states (cutoff point: median value) and survival time (significant P-value was set as 0.05). All statistical analyses were performed by the SPSS 19 (IBM Corporation, Armonk, NY, USA) and BRB-Array Tools 4.0. Results Patient characteristics A total of 59 chRCC patients were enrolled in our study. Among them (Cohort T), 23 patients provided adjacent tissues (Cohort M). Their demographic characteristics and clinical information are summarized in [56]Table 1. Table 1. Clinical characteristics of patients with chromophobe renal cell carcinoma Category Cohort M __________________________________________________________________ Cohort T __________________________________________________________________ P-value (n=23) (%) (n=59) (%) Age, mean ± SD 52.6±13.3 51.0±14.2 0.647 Gender, n (%) 0.623  Female 12 (52.2) 26 (44.1)  Male 11 (47.8) 33 (55.9) AJCC stages, n (%) 0.594  Stage I 9 (39.2) 17 (28.9)  Stage II 8 (34.8) 23 (39.0)  Stage III 3 (13.0) 14 (23.7)  Stage IV 3 (13.0) 5 (8.4) Tumor size, n (%) 0.790  T1 9 (39.1) 17 (28.8)  T2 8 (34.8) 23 (39.0)  T3 5 (21.7) 14 (23.7)  T4 1 (4.4) 5 (8.5) Lymph node, n (%) 0.382  N0 11 (47.8) 38 (64.4)  N1+N2 2 (8.7) 4 (6.8)  NX 10 (43.5) 17 (28.8) Metastasis status, n (%) 0.947  M0 18 (78.3) 48 (81.4)  M1 1 (4.4) 2 (3.4)  MX 4 (17.3) 9 (15.2) Tumor status, n (%) 0.783  Tumor free 19 (82.6) 50 (84.7)  With tumor 3 (13.0) 8 (13.6)  NA 1 (4.4) 1 (1.7) [57]Open in a new tab Abbreviations: AJCC, American Joint Committee on Cancer; NA, not applicable; SD, standard deviation. Differential expression analysis of lncRNAs We identified 605 lncRNAs from the TCGA level 3 RNASeqV2 data according to the classification of HUGO Gene Nomenclature Committee (HGNC) ([58]http://www.genenames.org). A total of 143 lncRNAs were found to be expressed differentially between the cancer and the paired adjacent tissues (fold change ≥1.5, P<0.001) ([59]Table S1). Unsupervised hierarchical clustering could clearly discriminate cancer and normal class with these differentially expressed lncRNAs ([60]Figures S1 and [61]S2). In consideration of the fold change, 43 of them had an absolute fold change ≥5.0, and they were selected to build the ceRNA network ([62]Table 2). Furthermore, among these 143 differentially expressed lncRNAs, 12 cancer-specific lncRNAs were also identified to be differentially expressed in different clinical features (fold change ≥1.5, P<0.01) with 3 for gender, 1 for age, 5 for tumor status, and 3 for American Joint Committee on Cancer stage and tumor size ([63]Table 3). Because the number of patients with metastasis status M1 and lymph node status N1+N2 was too small, class comparison analyses were not conducted for them. Table 2. Forty-three cancer specific lncRNAs in ceRNA network construction LncRNA Entrez ID Chromosome Expression change (T vs N) LINC00588 26138 Chr8 Upregulation SLC26A4-AS1 286002 Chr7 Upregulation BAALC-AS2 157556 Chr8 Upregulation LINC00265 349114 Chr7 Upregulation UCKL1-AS1 100113386 Chr20 Upregulation LINC00239 145200 Chr14 Upregulation PART1 25859 Chr5 Upregulation PACRG-AS1 285796 Chr6 Upregulation KRTAP5-AS1 338651 Chr11 Upregulation CDKN2B-AS1 100048912 Chr9 Upregulation LINC00889 158696 ChrX Upregulation LINC00669 647946 Chr18 Upregulation LINC00930 100144604 Chr15 Upregulation LINC00598 646982 Chr13 Upregulation NR2F1-AS1 441094 Chr5 Downregulation LINC00882 100302640 Chr3 Downregulation LINC00242 401288 Chr6 Downregulation LINC01554 202299 Chr5 Downregulation CASC2 255082 Chr10 Downregulation LINC00312 29931 Chr3 Downregulation TINCR 257000 Chr19 Downregulation LINC00092 100188953 Chr9 Downregulation HCG4 54435 Chr6 Downregulation HNF1A-AS1 283460 Chr12 Downregulation LOC145837 145837 Chr15 Downregulation MEG3 55384 Chr14 Downregulation LINC00839 84856 Chr10 Downregulation LOC285768 285768 Chr6 Downregulation ADORA2A-AS1 646023 Chr22 Downregulation GATA3-AS1 399717 Chr10 Downregulation LINC00924 145820 Chr15 Downregulation BRE-AS1 100302650 Chr2 Downregulation UCA1 652995 Chr19 Downregulation EGOT 100126791 Chr3 Downregulation LINC00908 284276 Chr18 Downregulation LINC00671 388387 Chr17 Downregulation LINC00271 100131814 Chr6 Downregulation COL18A1-AS1 378832 Chr21 Downregulation LINC01550 388011 Chr14 Downregulation WT1-AS 51352 Chr11 Downregulation LINC01139 339535 Chr1 Downregulation LINC00473 90632 Chr6 Downregulation LHFPL3-AS2 723809 Chr7 Downregulation [64]Open in a new tab Notes: The names, Entrez IDs and chromosomal locations of theses lncRNAs were obtained from the Entrez Gene database [65]http://www.ncbi.nlm.nih.gov/gene.[66]^38 Abbreviations: ceRNA, competing endogenous RNA; lncRNA, long noncoding RNA; N, normal; T, tumor. Table 3. LncRNAs associated with the progression of chromophobe renal cell carcinoma Comparisons Downregulated Upregulated Gender (female vs male) CHKB-AS1, LOC285768 XIST Age at diagnosis (≥51 vs <51) LINC01119 AJCC stage (III+IV vs I+II) TMEM51-AS1 LINC00242, CHKB-AS1 AJCC T (T3+T4 vs T1+T2) TMEM51-AS1 LINC00242, CHKB-AS1 Tumor status (with tumor vs tumor free) PSMD5-AS1, ADORA2A-AS1, INE2 CDKN2B-AS1, LINC00669 [67]Open in a new tab Abbreviations: AJCC, American Joint Committee on Cancer; lncRNA, long noncoding RNA. LncRNAs in relation to patient prognosis Among differentially expressed lncRNAs, 7 lncRNAs (COL18A1-AS, BRE-AS1, SNHG7, TMEM51-AS1, C21orf62-AS1, LINC00336, and LINC00882) were identified to be associated with the overall survival of chRCC by univariate Cox regression analysis. Kaplan–Meier survival curves indicated that COL18A1-AS1 (P=0.009), BRE-AS1 (P=0.011), SNHG7 (P=0.014), TMEM51-AS1 (P=0.024), C21orf62-AS1 (P=0.027), and LINC00336 (P=0.037) were positively correlated with overall survival, while the remaining LINC00882 (P=0.047) was negatively associated with overall survival ([68]Figure 1). Figure 1. [69]Figure 1 [70]Open in a new tab Kaplan–Meier survival curves for 7 prognosis-related lncRNAs. Notes: Horizontal axis: overall survival time; vertical axis: survival function; cutoff point: median value. Abbreviation: LncRNA, long noncoding RNA. LncRNA-associated ceRNA network Thirty-one miRNAs identified to be expressed differentially between the cancer and adjacent tissues with absolute fold change higher than 5 (P<0.001) ([71]Table S2) were selected to construct the ceRNA network. In a ceRNA network, miRNAs interact with lncRNAs through MREs, and we used miRcode and starBase v2.0 to find the potential MREs of these miRNAs in tumor-specific lncRNAs, as described in [72]Table 2. The result demonstrated that 18 of 31 cancer-specific miRNAs might interact with 16 of 43 cancer-specific lncRNAs ([73]Table 4). Subsequently, 167 experimentally validated target genes of miRNAs described in [74]Table 4 were identified by using miRTarBase ([75]Table 5), and all these miRNA–mRNA interactions were validated by reporter assay, Western blot, and qPCR. Then, an lncRNA–miRNA–mRNA network was established based on the above-mentioned data ([76]Tables 4 and [77]5). The MIC algorithm was applied to test pair-wise correlations based on their expression levels. To enhance the robustness of the ceRNA network, only those pair-wise interactions with MIC >0.15 and MIC-ρ2 >0.15 were included in the ceRNA network ([78]Figure 2). Table 4. Putative miRNAs that may target cancer-specific lncRNAs by MREs lncRNA miRNAs LINC00473 hsa-mir-199a-1/2, hsa-mir-199b WT1-AS hsa-mir-199a-1/2, hsa-mir-199b, hsa-mir-221, hsa-mir-9-1, hsa-mir-96 COL18A1-AS1 hsa-mir-187, hsa-mir-196a-1 LINC00271 hsa-mir-192 EGOT hsa-mir-183 UCA1 hsa-mir-182, hsa-mir-190, hsa-mir-455, hsa-mir-96 LINC00839 hsa-mir-130a MEG3 hsa-mir-182, hsa-mir-192, hsa-mir-199a-1/2, hsa-mir-199b, hsa-mir-204, hsa-mir-217, hsa-mir-221, hsa-mir-455, hsa-mir-9-1, hsa-mir-96 HNF1A-AS1 hsa-mir-183, hsa-mir-194-1/2, hsa-mir-199a-1/2, hsa-mir-199b, hsa-mir-217, hsa-mir-455, hsa-mir-9-1 HCG4 hsa-mir-217, hsa-mir-96 LINC00312 hsa-mir-190, hsa-mir-192, hsa-mir-9-1 CASC2 hsa-mir-130a, hsa-mir-192, hsa-mir-194-1/2 LINC00242 hsa-mir-204, hsa-mir-217, hsa-mir-221, hsa-mir-222 PART1 hsa-mir-9-1 LINC00265 hsa-mir-182, hsa-mir-217 SLC26A4-AS1 hsa-mir-130a [79]Open in a new tab Abbreviations: lncRNA, long noncoding RNA; miRNA, microRNA; MREs, microRNA response elements. Table 5. Experimentally validated miRNA targets miRNA mRNAs targeted by miRNA hsa-mir-130a HOXA5, ATXN1, MEOX2, PPARG, GJA1, TNF hsa-mir-182 FOXO1, CDKN1A, MITF, RECK, FLOT1, PTEN, GSK3B, ANUBL1, CYLD, BCL2, CCND2, PDCD4, SATB2, CHL1, CADM1, TP53INP1, TCEAL7, ULBP2 hsa-mir-183 FOXO1, EZR, PDCD4, AKAP12, GSK3B, SMAD4, ZFPM1, DKK3, BMI1, ZEB1, SNAI2, PPP2CB, PPP2R4 hsa-mir-187 TNF, CD276 hsa-mir-190 IGF1, PHLPP1, MARK2, KCNQ5 hsa-mir-192 ALCAM, CDC7, CUL5, ERCC3, LMNB2, MAD2L1, ERCC4, RB1, WNK1, DICER1, CAV1 hsa-mir-194-1/2 IGF1R, CDH2, RAC1, HBEGF, PTPN12, PTPN13, ITGA9, SOCS2, DNMT3A, SOX5, BMI1, RBX1, BMP1 hsa-mir-199a-1/2 MET, MTOR, CAV1, GSK3B, FZD4, WNT2, JAG1, CD44, IKBKB, KL, CDH1, HIF1A, SMARCA2, MAPK1, DDR1, MAP3K11, FUT4, CAV2, ERBB2, SIRT1, PTGS2, HSPA5, ATF6, ERN1, HGF, WNK1, NFKB1, ACVR1B hsa-mir-199b HES1, SET, PODXL, JAG1, DDR1, ERBB2, SETD2 hsa-mir-204 BCL2, THRB, BIRC2, EZR, M6PR, RAB22A, RAB40B, SERP1, TCF12, SOX4, CDC42, RUNX2, EFNB2, SIRT1, NTRK2, USP47, ANKRD13A, TMPRSS3, CDH1, VIM, BDNF, HMX1 hsa-mir-217 SIRT1, ROBO1, EZH2, DACH1, FOXO3, GPC5 hsa-mir-221 CDKN1B, DDIT4, KIT, CDKN1C, BBC3, BNIP3L, FOS, BNIP3, MBD2, BMF, FOXO3, TMED7, ESR1, TICAM1, PTEN, TRPS1, WEE1, HECTD2, ASZ1, MDM2, ETS1, IMP3, DIRAS3, CERS2, ZEB2, RB1, APAF1, ANXA1, CTCF, RAB1A, RECK, SIRT1 hsa-mir-222 CDKN1B, SOD2, MMP1, KIT, FOS, PTEN, STAT5A, FOXO3, CDKN1C, ESR1, BBC3, TRPS1, VGLL4, ETS1, TIMP3, DIRAS3, CERS2, DKK2 hsa-mir-455 MUC1, NCSTN hsa-mir-9-1 RAB34, ONECUT2, FOXO1, NFKB1, NR2E1, AP3B1, CCNG1, DICER1, SIRT1, STMN1, CREB1, NF1, ELAVL1, CXCR4, FOXP1, PRTG, ACAT1, MTHFD1, BCL2L11 hsa-mir-96 FOXO1, CDKN1A, KRAS, FOXO3, GSK3B, RECK, REV1, RAD51, ALK, ZEB1, SNAI2 [80]Open in a new tab Abbreviation: miRNA, microRNA. Figure 2. [81]Figure 2 [82]Open in a new tab Cancer-specific lncRNA associated ceRNA network presented by Cytoscape.[83]^21 Abbreviations: lncRNA, long noncoding RNA; miRNA, microRNA. KEGG pathway enrichment analysis To explore the biological functions of these protein-coding RNAs involved in the ceRNA network, KEGG pathway enrichment analysis was conducted using Database for Annotation, Visualization, and Integrated Discovery. As summarized in [84]Table 6, 12 cancer-related pathways were enriched, including those for prostate cancer, melanoma, pancreatic cancer, chronic myeloid leukemia, colorectal cancer, bladder cancer, glioma, RCC, small cell lung cancer, endometrial cancer, and acute myeloid leukemia, and 6 non-cancer-related pathways were enriched, including those for focal adhesion, adherens junction, cell cycle, neurotrophin signaling pathway, ErbB signaling pathway, and p53 signaling pathway. Table 6. KEEG pathways enriched by the protein-coding genes involved in ceRNA network with P<0.001 Pathway type KEGG pathways Number of genes P-value Cancer-related pathways Pathways in cancer 37 1.25571E–19 Prostate cancer 17 3.53254E–12 Melanoma 12 5.47354E–08 Pancreatic cancer 11 6.62018E–07 Chronic myeloidleukemia 11 9.7726E–07 Colorectal cancer 11 2.83419E–06 Bladder cancer 8 1.00607E–05 Glioma 9 1.83661E–05 Renal cell carcinoma 9 4.02163E–05 Small cell lung cancer 9 0.000149891 Endometrial cancer 7 0.000369377 Acute myeloid leukemia 7 0.000671143 Noncancer-related pathways Focal adhesion 16 3.59367E–06 Adherens junction 10 1.07149E–05 Cell cycle 12 1.72817E–05 Neurotrophin signaling pathway 11 9.0498E–05 ErbB signaling pathway 9 0.000191896 p53 signaling pathway 8 0.000243417 [85]Open in a new tab Note: The P-value is corrected for multiple hypothesis testing using the Benjamini–Hochberg method. Abbreviations: ceRNA, competing endogenous RNA; KEGG, Kyoto Encyclopedia of Genes and Genomes. Discussion RCC has various histological subtypes, of which clear-cell RCC (about 70%), papillary RCC (about 10%–15%), and chRCC (about 5%) are the most prevalent.[86]^3 These subtypes have diverse genetic and clinical features, and the identification of molecular mechanisms behind their oncogenesis and progression comprises an important area of cancer research.[87]^4^,[88]^23 In the present study, we focused on exploring the prognostic roles and the competing endogenous RNA potential of lncRNAs in chRCC. By analyzing the clinical information and large-scale sequencing data pertaining to a chRCC patient cohort, we identified tumor-specific lncRNAs in chRCC and investigated their distribution in different clinical features and prognoses. Besides, we constructed an lncRNA-related ceRNA network of chRCC consisting of lncRNAs, miRNAs, and protein-coding RNAs. As a highly heterogeneous group of noncoding RNAs, lncRNAs can regulate the gene expression by means of diverse mechanisms and are involved in various biological processes.[89]^5^,[90]^24 Mounting evidences suggest lncRNAs have key roles in regulation of tumor development and progression.[91]^10^,[92]^25 These aberrantly expressed lncRNAs could be tracked in the migration, apoptosis, proliferation, and drug resistance patterns of tumor cells, which implies that lncRNAs could serve as potential therapeutic targets and biomarkers.[93]^26^–[94]^29 Numerous studies have documented that lncRNAs could affect the expression of cancer-related proteins by interacting with miRNAs, somewhat validating the ceRNA hypothesis.[95]^14^,[96]^15 In order to gain more insight about their effects in tumors, lncRNA profiling has become a major method to study the widespread dysregulated lncRNAs, and their coexpression networks with mRNAs and miRNAs have been constructed in various tumors.[97]^30^–[98]^32 However, such lncRNAs-related ceRNA networks in RCC are still poorly explored. Hence, we conducted the present study with the aim to identify lncRNA biomarkers of prognosis and construct an lncRNA–miRNA–mRNA coexpression network in chRCC. By analyzing the lncRNA expression profiles of 59 primary chRCC patients, we identified 142 differentially expressed lncRNAs between cancer and adjacent tissues, 43 of which had a more than a fivefold change in expression levels. In those upregulated lncRNAs, CDKN2B-AS1 has previously been reported to be able to promote cell proliferation. Furthermore, its high expression has been linked to poor prognosis in prostate and gastric cancer.[99]^33^,[100]^34 The expression level of SLC26A4-AS1 was found to be significantly associated with overall higher survival of gastric cancer patients, but the mechanism was not elaborated.[101]^35 In those downregulated lncRNAs, CASC2 was found to be aberrantly expressed in glioma and non-small-cell lung cancer. Increase in CASC2 expression could inhibit cell proliferation of the 2 tumors, and CASC2 was proved to be an independent predictor of overall survival for non-small-cell lung cancer patients.[102]^36 In addition, 12 tumor-specific lncRNAs were found to be abnormally expressed in different clinical features. Dysregulated lncRNAs identified by tumor stage or size are identical because patients’ distributions in their different groups are common. As the number of patients with metastasis status M1 and lymph node status N1 + N2 was too small, class comparison analyses were not conducted. In consideration of the relationship between cancer-specific lncRNAs and prognosis, we identified 7 lncRNAs that were associated with chRCC overall survival, and they may serve as prognosis prediction tools or candidate drug targets for chRCC management. Among the 6 protective lncRNAs, SNHG7 was reported to be involved in the cellular response to radiation-induced oxidative stress.[103]^37 The functions of the other 5 protective and 1 risky lncRNA are still unknown. For further analyzing the interactions between lncRNA, miRNA, and mRNA in chRCCs, we constructed a ceRNA network by bioinformational methods. This ceRNA network contained 16 tumor-specific lncRNAs, 18 tumor-specific miRNAs, and 168 protein-coding RNAs. To improve the prediction accuracy of the coexpression network, pair-wise relationships of lncRNA–miRNA–mRNA were filtered based on their expression levels by the MIC algorithm which could detect novel associations in complex datasets. Through KEGG analysis, we found that those ceRNA network-involved genes were mainly enriched in cancer-related pathways, further indicating that lncRNAs may play a vital role in tumor molecular regulatory networks. The ceRNA network we constructed reveals an unknown ceRNA regulatory network in chRCC and gives some new perspectives of lncRNAs’ functions in gene regulation. However, some issues should be acknowledged in interpreting this ceRNA network. The network was constructed in silico and could serve as a reference for further research. For validation of the lncRNA/miRNA/mRNA pathway, additional biological experiments need to be conducted. Conclusion By analyzing an independent chRCC patient cohort extracted from the TCGA database, we screened differentially expressed lncRNAs under different clinical features and constructed an lncRNA-related ceRNA network. Our study suggests that some lncRNAs are associated with chRCC progression and prognosis, and they may function as ceRNAs in a complex ceRNA network. Acknowledgments