DNA methylation profiling distinguishes histological subtypes of renal cell carcinoma

Renal cell carcinoma (RCC) accounts for around 3% of cancers in the UK, and both incidence and mortality are increasing with the aging population. RCC can be divided into several subtypes: conventional RCC (the most common, comprising 75% of all cases), papillary RCC (15%) and chromophobe RCC (5%). Renal oncocytoma is a benign tumor and accounts for 5% of RCC. Cancer and epigenetics are closely associated, with DNA hypermethylation being widely accepted as a feature of many cancers. In this study the DNA methylation profiles of chromophobe RCC and renal oncocytomas were investigated by utilizing the Infinium HumanMethylation450 BeadChips. Cancer-specific hypermethylation was identified in 9.4% and 5.2% of loci in chromophobe RCC and renal oncocytoma samples, respectively, while the majority of the genome was hypomethylated. Thirty (hypermethylated) and 41 (hypomethylated) genes were identified as differentially methylated between chromophobe RCC and renal oncocytomas (p < 0.05). Pathway analysis identified some of the differentially hypermethylated genes to be involved in Wnt (EN2), MAPK (CACNG7) and TGFβ (AMH) signaling, Hippo pathway (NPHP4), and cell death and apoptosis (SPG20, NKX6-2, PAX3 and BAG2). In addition, we analyzed ccRCC and papillary RCC data available from The Cancer Genome Atlas portal to identify differentially methylated loci in chromophobe RCC and renal oncocytoma in relation to the other histological subtypes, providing insight into the pathology of RCC subtypes and classification of renal tumors.     

嫌色性肾细胞癌与嗜酸细胞瘤的临床病理分析与比较

目的探讨嫌色性肾细胞癌(chromophobe renal cell carcinoma,CRCC)和嗜酸细胞瘤(oncocytoma)的临床病理学特征,提高对二者的认识和诊疗水平。方法对手术切除的5例CRCC和2例Oncocytoma进行肉眼和光镜观察、免疫组织化学染色,分析其临床病理学差异。结果 CRCC以实体结构为主、胞质半透明或嗜酸性颗粒状、核皱缩且核周有空晕;Oncocytoma以巢状结构为主,胞质嗜酸颗粒状、核圆形、核仁小。免疫组化染色显示两者均呈E-cadherin、EMA、CK20、vimentin阳性;CRCC瘤细胞CD10和CK7强阳性而S-100蛋白阴性,5名患者随访8~32个月,其中1例手术12个月后由于转移死亡;Oncocytoma瘤细胞S-100蛋白强阳性、CD10和CK7阴性,2名患者随访41个月,均未出现复发和转移。结论 CRCC和oncocytoma二者形态学各有特征,免疫表型相似,二者鉴别诊断主要在于生长方式和细胞学特征。综上所述:CRCC为惰性肿瘤,Oncocytoma为良性肿瘤,预后较好,手术切除后极少发生复发和转移。     

A two-CpG-based prognostic signature for oral squamous cell carcinoma overall survival

Oral squamous cell carcinoma (OSCC) represents one of the most common head and neck cancer that with dire prognosis due partly to the lack of reliable prognostic biomarker. Here, we aimed to develop a CpG site–based prognostic signature through which we could accurately predict overall survival (OS) of patients with OSCC. We obtained OSCC-related DNA methylation and gene expression data sets from the public accessible Gene Expression Omnibus. Correlations between methylation level of CpG sites and OS of patients with OSCC were assessed by univariate Cox regression analysis followed by robust likelihood-based survival analysis on those CpG sites with permutation P < 0.05 for further screening the optimal CpG sites for OSCC OS prediction based on the risk score formula that composed of the methylation level of optimal CpG sites weighted by their regression coefficients. Besides, differential expression genes (DEGs) and differential methylation genes (DMGs) in OSCC samples compared with normal samples were obtained and shared genes were considered as vital genes in OSCC tumorgenesis and progression. As a result, two CpG sites including cg17892178 and cg17378966 that located in NID2 and IDO1, respectively, were identified as the optimal prognostic signatures for OSCC OS. In addition, 12 overlapping genes between DEGs and DMGs that closely associated with inflammation or blood and tissue development–related biological processes were obtained. In conclusions, this study should provide valuable signatures for OSCC diagnosis and treatment.     

Gene expression profiling of chromophobe renal cell carcinomas and renal oncocytomas by Affymetrix GeneChip using pooled and individual tumours

Due to overlapping morphology, malignant chromophobe renal cell carcinomas (RCC) and benign renal oncocytomas (RO) may pose a diagnostic problem. In the present study, we have applied different algorithms to evaluate the data sets obtained by hybridisation of pooled and also individual samples of renal cell tumours (RCT) onto two different gene expression platforms. The two approaches revealed high similarities in the gene expression profiles of chromophobe RCCs and ROs but also some differences. After identifying the differentially expressed genes by statistic analyses, the candidate genes were further selected by a real time and normal RT-PCR and their products were analysed by immunohistochemistry. We have identified CD82 and S100A1 as valuable markers for chromophobe RCC as well as AQP6 for ROs. However, these genes are expressed at the protein level in other types of RCTs as well albeit at a low frequency and low intensity. As none of the selected genes marks exclusively one type of RCTs, for the differential diagnosis of chromophobe RCCs and ROs, a set of markers such as CD82, S100A1 and AQP6 as well as some others would be an option in routine histological laboratories.     

Expression signature of six‐snoRNA serves as novel non‐invasive biomarker for diagnosis and prognosis prediction of renal clear cell carcinoma

Increasing evidence has verified that small nucleolar RNAs (snoRNAs) play significant roles in tumorigenesis and exhibit prognostic value in clinical practice. In the study, we analysed the expression profile and clinical relevance of snoRNAs from TCGA database including 530 ccRCC (clear cell renal cell carcinoma) and 72 control cases. By using univariate and multivariate Cox analysis, we established a six‐snoRNA signature and divided patients into high‐risk or low‐risk groups. We found patients in high‐risk group had significantly shorter overall survival and recurrence‐free survival than those in low‐risk group in test series, validation series and entire series by Kaplan‐Meier analysis. We also confirmed this signature had a great accuracy and specificity in 64 clinical tissue cases and 50 serum samples. Then, depending on receiver operating characteristic curve analysis we found the six‐snoRNA signature was an superior indicator better than conventional clinical factors (AUC = 0.732). Furthermore, combining the signature with TNM stage or Fuhrman grade were the optimal indicators (AUC = 0.792; AUC = 0.800) and processed the clinical applied value for ccRCC. Finally, we found the SNORA70B and its hose gene USP34 might directly regulate Wnt signalling pathway to promote tumorigenesis in ccRCC. In general, our study established a six‐snoRNA signature as an independent and superior diagnosis and prognosis indicator for ccRCC.     

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

ABSTRACT

Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.     

Prognostic value of a gene signature in clear cell renal cell carcinoma

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients’ prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509–3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601–4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113–8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243–4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.     

Development and validation of a nomogram to predict overall survival of T1 esophageal squamous cell carcinoma patients with lymph node metastasis

PurposeTo develop a nomogram for predicting the prognosis of T1 esophageal squamous cell carcinoma (ESCC) patients with positive lymph node.MethodsT1 ESCC patients with lymph node metastasis diagnosed between 2010 and 2015 were selected from the Surveillance, Epidemiology, and Final Results (SEER) database. The entire cohort was randomly divided in the ratio of 7:3 into a training group (n=457) and validation group (n=192), respectively. Prognostic factors were identified by univariate and multivariate Cox regression models. Harrell''s concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to evaluate the discrimination and calibration of the nomogram. The accuracy and clinical net benefit of the nomogram compared with the 7^th AJCC staging system were evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).ResultsThe nomogram consisted of eight factors: insurance, T stage, summary stage, primary site, radiation code, chemotherapy, surgery, and radiation sequence with surgery. In the training and validation cohorts, the AUCs exceeded 0.700, and the C-index scores were 0.749 and 0.751, respectively, indicating that the nomogram had good discrimination. The consistency between the survival probability predicted by the nomogram and the actual observed probability was indicated by the calibration curve in the training and validation cohorts. For NRI>0 and IDI>0, the predictive power of the nomogram was more accurate than that of the 7^th AJCC staging system. Furthermore, the DCA curve indicated that the nomogram achieved better clinical utility than the traditional system.ConclusionsUnlike the 7^th AJCC staging system, the developed and validated nomogram can help clinical staff to more accurately, personally and comprehensively predict the 1-year and 3-year OS probability of T1 ESCC patients with lymph node metastasis.     

Systematic analysis of survival-associated alternative splicing signatures in clear cell renal cell carcinoma

Alternative splicing (AS) constitutes a major reason for messenger RNA (mRNA) and protein diversity. Increasing studies have shown a link to splicing dysfunction associated with malignant neoplasia. Systematic analysis of AS events in kidney cancer remains poorly reported. Therefore, we generated AS profiles in 533 kidney renal clear cell carcinoma (KIRC) patients in The Cancer Genome Atlas (TCGA) database using RNA-seq data. Then, prognostic models were developed in a primary cohort (N = 351) and validated in a validation cohort (N = 182). In addition, splicing networks were built by integrating bioinformatics analyses. A total of 11 268 and 8083 AS variants were significantly associated with patient overall survival time in the primary and validation KIRC cohorts, respectively, including STAT1, DAZAP1, IDS, NUDT7, and KLHDC4. The AS events in the primary KIRC cohorts served as candidate AS events to screen the independent risk factors associated with survival in the primary cohort and to develop prognostic models. The area under the curve of the receiver-operator characteristic curve for prognostic prediction in the primary and validation KIRC cohorts was 0.84 and 0.82 at 2500 days of overall survival, respectively. In addition, splicing correlation networks revealed key splicing factors (SFs) in KIRC, such as HNRNPH1, HNRNPU, KHDBS1, KHDBS3, SRSF9, RBMX, SFQ, SRP54, HNRNPA0, and SRSF6. In this study, we analyzed the AS landscape in the TCGA KIRC cohort and detected predictors (prognostic) based on AS variants with high performance for risk stratification of the KIRC cohort and revealed key SFs in splicing networks, which could act as underlying mechanisms.     

Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma

Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features (P = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.     

Identification of a universal 6-lncRNA prognostic signature for three pathologic subtypes of renal cell carcinoma

Renal cell carcinoma (RCC) is the most common adult renal epithelial cancer susceptible to metastasis and patients with irresectable RCC always have a poor prognosis. Long noncoding RNAs (lncRNAs) have recently been documented as having critical roles in the etiology of RCC. Nevertheless, the prognostic significance of lncRNA-based signature for outcome prediction in patients with RCC has not been well investigated. Therefore, it is essential to identify a lncRNA-based signature for predicting RCC prognosis. In the current study, we comprehensively analyzed the RNA sequencing data of the three main pathological subtypes of RCC (kidney renal clear cell carcinoma [KIRC], kidney renal papillary cell carcinoma [KIRP], and kidney chromophobe carcinoma [KICH]) from The Cancer Genome Atlas (TCGA) database, and identified a 6-lncRNA prognostic signature with the help of a step-wise multivariate Cox regression model. The 6-lncRNA signature stratified the patients into low- and high-risk groups with significantly different prognosis. Multivariate Cox regression analysis showed that predictive value of the 6-lncRNA signature was independent of other clinical or pathological factors in the entire cohort and in each cohort of RCC subtypes. In addition, the three independent prognostic clinical factors (including age, pathologic stage III, and stage IV) was also stratified into low- and high-risk groups basis on the risk score, and the stratification analyses demonstrated that the high-risk score was a poor prognostic factor. In conclusion, these findings indicate that the 6-lncRNA signature is a novel prognostic biomarker for all three subtypes of RCC, and can increase the accuracy of predicting overall survival.     

Genome-wide screening and cohorts validation identifying novel lncRNAs as prognostic biomarkers for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma with varied prognosis. We aimed to identify and assess the possible prognostic long noncoding RNA (lncRNA) biomarkers. LncRNAs expression data and corresponding clinical information of 619 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes analysis, univariate Cox regression, the least absolute shrinkage and selection operator Cox regression model were utilized to identify hub lncRNAs. Multivariate Cox regression was used to establish the risk model. Statistical analysis was performed using R 3.5.3. The expression value of five lncRNAs and the risk-score levels were significantly associated with a survival prognosis of ccRCC patients (all P < .001). In the TCGA validation cohort, the area under the curve (AUC) for the integrated nomogram was 0.905 and 0.91 for 3-, 5-year prediction separately. The AUC reached up to 0.757 in an independent ICGC cohort. Besides, the calibration plots also illustrated well curve-fitting between observation values and predictive values. Weighted gene co-expression network analysis and subsequent pathway analysis revealed that the PI3K-Akt-mTOR and hypoxia-inducible factor signaling crosstalk might function as the most essential mechanisms related to the five-lncRNAs signature. Our study suggested that lncRNA AC009654.1, AC092490.2, LINC00524, LINC01234, and LINC01885 were significantly associated with ccRCC prognosis. The prognostic model based on this five lncRNA may predict the overall survival of ccRCC.     

IDUA,NDST1, SAP30L,CRYBA4, and SI as novel prognostic signatures clear cell renal cell carcinoma

Carcinoma of the kidney is one of the most prevalent carcinoma worldwide. The majority types of carcinoma are clear cell renal cell carcinoma (CCRCC), which consist more than 80% of the cases. As a genetically diverse disease, identification of prognosis-related genes has utmost importance in the early diagnosis and prognosis of the CCRCC. In this study, we performed gene expression profiling to identify prognosis-related genes for CCRCC. In addition, we developed and validated a gene signature-based risk score to comprehensively assess the prognostic function of differentially expressed genes. Furthermore, we performed a ROC analysis to identify the optimal cut-off point for classification risk level of the patients. Univariate Cox regression models were used to assess the association between differentially expressed genes in relation to the prognosis of patients with different stages of CCRCC. Five genes were identified significantly differentially expressed in CCRCC and associated with their survival time, namely: IDUA, NDST1, SAP30L, CRYBA4, and SI. A 5-gene signature-based risk score was developed based on the Cox coefficient of the individual genes. The prognostic value of this risk score was validated in an internal testing data set. In summary, a gene-based risk score was identified and validated, which can predict CCRCC patient survival. The potential functions of this gene expression signature and individual differentially expressed gene as prognostic targets of CCRCC were revealed by this study. Furthermore, these findings may have important implications in the understanding of the potential therapeutic method for the CCRCC patients.     

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma

Background: Ferroptosis is associated with cancer initiation and progression. However, the molecular mechanism and prognostic value of ferroptosis-related genes in lung squamous cell carcinoma (LUSC) are poorly understood.Methods: The mRNA expression profiles, methylation data, and clinical information of patients with LUSC were downloaded from TCGA and GEO database. Ferroptosis-related differentially expressed genes (DEGs) were identified between cancerous and non-cancerous tissues, and their prognostic value was systemically investigated by bioinformatic analyses.Results: A ferroptosis-related gene signature (ALOX5, TFRC, PHKG2, FADS2, NOX1) was constructed using multivariate Cox regression analysis and represented as a risk score. Overall survival (OS) probability was significantly lower in the high-risk group than in the low-risk group (P<0.001), and receiver operating characteristic curve showed a good predictive capacity (AUC = 0.739). The risk score was an independent prognostic factor for LUSC. A nomogram was constructed to predict the OS probabilities at 1, 3, and 5 years. High-risk score was associated with increased immune infiltration, lower methylation levels, higher immune checkpoint genes expression levels, and better chemotherapy response. Cell adhesion molecules, focal adhesion, and extracellular matrix receptor interaction were the main pathways in the high-risk group. The signature was validated using the TCGA test cohort, entire TCGA cohort, {"type":"entrez-geo","attrs":{"text":"GSE30219","term_id":"30219"}}GSE30219, {"type":"entrez-geo","attrs":{"text":"GSE157010","term_id":"157010"}}GSE157010, {"type":"entrez-geo","attrs":{"text":"GSE73403","term_id":"73403"}}GSE73403, and {"type":"entrez-geo","attrs":{"text":"GSE4573","term_id":"4573"}}GSE4573 datasets. The gene disorders in patients with LUSC were validated using real-time PCR and single-cell RNA sequencing analysis.Conclusions: A ferroptosis-related gene signature was constructed to predict OS probability in LUSC. This could facilitate novel therapeutic methods and guide individualized therapy.     

A large-scale transcriptome analysis identified ELANE and PRTN3 as novel methylation prognostic signatures for clear cell renal cell carcinoma

DNA methylation was involved in the progress of many types of cancer including clear cell renal cell carcinomas (ccRCCs). This study aimed to identify the prognostic DNA methylation biomarkers for the ccRCCs by a large-scale RNA-seq analysis. The DNA methylation data and the corresponding clinical information of the patients with ccRCCs were extracted from TCGA database and randomly divided into the training group and the validation group. The differentially expressed CpG sites and the survival-related CpG sites were further identified, which was combined into CpG sites pair and followed by screening the survival-related pairs. The C-index and the forward search algorithms were constructed to identify the prognostic signatures for the patients with ccRCCs. The prognostic signatures were verified by the validation dataset and the protein–protein interactions (PPI) network analysis was performed on the CPG sites of the signature. A total of 9,861 differentially expressed CPG sites were identified and 567 CpG sites were found to relate to the overall survival (OS) of the patients with ccRCCs. Besides, 1,146 CPG sites pairs were found to be related to the OS of the ccRCCs samples and the signature composed of seven CpG sites pairs were obtained to predict the prognosis of patients with ccRCCs and the results were verified in the validation dataset. Besides, the PPI network analysis showed that ELANE and PRTN3 gene may be associated with the invasion and metastasis of ccRCCs and could function as potential prognostic and therapeutic signatures for ccRCCs.     

嫌色性肾细胞癌17例临床病理学分析

目的探讨嫌色性肾细胞癌的临床病理特征、诊断与鉴别诊断要点。方法对17例嫌色性肾细胞癌进行组织形态学、免疫组化染色及Hale’s胶样铁染色观察,结合文献对其临床表现、病理形态特点及鉴别诊断进行探讨。结果嫌色性肾细胞癌17例,大体肿瘤直径3-10.5cm。镜下肿瘤由嫌色细胞和嗜酸细胞构成,呈片状、梁状和腺泡状分布。嫌色细胞体积较大,多角形,胞膜清晰,胞质半透明细网状,胞核皱缩,可见核沟及核异型,核仁不明显;而嗜酸细胞胞质嗜酸,可见明显的核周空晕。免疫组化:EMA 100%阳性,CD10 52.9%阳性,Vimentin阴性,CK7 88.2%阳性,P504S29.4%阳性,CD11794.1%阳性。Hale’s胶样铁染色100%阳性。17例中12例随访6个月到3年,仅1例在术后15个月发现肝脏转移,其余均未发现复发及转移。结论嫌色性肾细胞癌是一种少见的肾肿瘤,恶性程度相对较低,预后良好。掌握该肿瘤独特的病理学特征,对鉴别其他肾上皮性肿瘤有重要帮助。     

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.     

Identification of a four immune-related genes signature based on an immunogenomic landscape analysis of clear cell renal cell carcinoma

Renal clear cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, which has strong immunogenicity. A comprehensive study of the role of immune-related genes (IRGs) in ccRCC is of great significance in finding ccRCC treatment targets and improving patient prognosis. In this study, we comprehensively analyzed the expression of IRGs in ccRCC based on The Cancer Genome Atlas datasets. The mechanism of differentially expressed IRGs in ccRCC was analyzed by bioinformatics. In addition, Cox regression analysis was used to screen prognostic related IRGs from differentially expressed IRGs. We also identified a four IRGs signature consisting of four IRGs (CXCL2, SEMA3G, PDGFD, and UCN) through lasso regression and multivariate Cox regression analysis. Further analysis results showed that the four IRGs signature could effectively predict the prognosis of patients with ccRCC, and its predictive power is independent of other clinical factors. In addition, the correlation analysis of immune cell infiltration showed that this four IRGs signature could effectively reflect the level of immune cell infiltration of ccRCC. We also found that the expression of immune checkpoint genes CTLA-4, LAG3, and PD-1 in the high-risk group was higher than that in the low-risk group. Our research revealed the role of IRGs in ccRCC, and developed a four IRGs signature that could be used to evaluate the prognosis of patients with ccRCC, which will help to develop personalized treatment strategies for patients with ccRCC and improve their prognosis. In addition, these four IRGs may be effective therapeutic targets for ccRCC.