Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (−0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.     

Circulating Concentrations of Vitamin B6 and Kidney Cancer Prognosis: A Prospective Case-Cohort Study

Prospective cohort studies have found that prediagnostic circulating vitamin B6 is inversely associated with both risk of kidney cancer and kidney cancer prognosis. We investigated whether circulating concentrations of vitamin B6 at kidney cancer diagnosis are associated with risk of death using a case-cohort study of 630 renal cell carcinoma (RCC) patients. Blood was collected at the time of diagnosis, and vitamin B6 concentrations were quantified using LC-MS/MS. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox regression models. After adjusting for stage, age, and sex, the hazard was 3 times lower among those in the highest compared to the lowest fourth of B6 concentration (HR_4vs1 0.33, 95% CI [0.18, 0.60]). This inverse association was solely driven by death from RCC (HR_4vs1 0.22, 95% CI [0.11, 0.46]), and not death from other causes (HR_4vs1 0.89, 95% CI [0.35, 2.28], p-interaction = 0.008). These results suggest that circulating vitamin B6 could provide additional prognostic information for kidney cancer patients beyond that afforded by tumour stage.     

Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.     

A panel of eight autophagy-related long non-coding RNAs is a good predictive parameter for clear cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) carries a variable prognosis. Prognostic biomarkers can stratify patients according to risk, and can provide crucial information for clinical decision-making. We screened for an autophagy-related long non-coding lncRNA (lncRNA) signature to improve postoperative risk stratification in The Cancer Genome Atlas (TCGA) database. We confirmed this model in ICGC and SYSU cohorts as a significant and independent prognostic signature. Western blotting, autophagic-flux assay and transmission electron microscopy were used to verify that regulation of expression of 8 lncRNAs related to autophagy affected changes in autophagic flow in vitro. Our data suggest that 8-lncRNA signature related to autophagy is a promising prognostic tool in predicting the survival of patients with ccRCC. Combination of this signature with clinical and pathologic parameters could aid accurate risk assessment to guide clinical management, and this 8-lncRNAs signature related to autophagy may serve as a therapeutic target.     

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 novel prognostic alternative splicing signature in papillary renal cell carcinoma

Papillary renal cell carcinoma (pRCC) is a heterogeneous disease containing multifocal or solitary tumors with an aggressive phenotype. Increasing evidence has indicated the involvement of aberrant splicing variants in renal cell cancer, while systematic profiling of aberrant alternative splicing (AS) in pRCC was lacking and largely unknown. In the current study, comprehensive profiling of AS events were performed based on the integration of pRCC cohort from the Cancer Genome Atlas database and SpliceSeq software. With rigorous screening and univariate Cox analysis, a total of 2077 prognoses AS events from 1642 parent genes were identified. Then, stepwise least absolute shrinkage and selection operator method-penalized Cox regression analyses with 10-fold cross-validation followed by multivariate Cox regression were used to construct the prognostic AS signatures within each AS type. And a final 21 AS event-based signature was proposed which showed potent prognostic capability in stratifying patients into low- and high-risk subgroups (P < .0001). Furthermore, time-dependent receiver operating characteristics curves confirmed that the final AS signature was effective and robust in predicting overall survival for pRCC patients with the area under the curve above 0.9 from 1 to 5 years. In addition, splicing correlation network was built to uncover the potential regulatory pattern among prognostic splicing factors and candidate AS events. Besides, gene set enrichment analysis revealed the involvement of these candidates AS events in tumor-related pathways including extracellular matrix organization, oxidative phosphorylation, and P53 signaling pathways. Taken together, our results could contribute to elucidating the underlying mechanism of AS in the oncogenesis process and broaden the novel field of prognostic and clinical application of molecule-targeted approaches in pRCC.     

Advances in molecular classification of renal neoplasms

Kidney neoplasms are classified by light microscopy using the World Health Organization (WHO) system. The WHO system defines histopathologic tumor subtypes with distinct clinical behavior and underlying genetic mutations. In adults, the common malignant subtypes are variants of renal cell carcinoma (RCC). Histopathologic classification is critical for clinical management of RCC, but is becoming more complex with recognition of novel tumor subtypes, development of procedures yielding small diagnostic biopsies, and emergence of molecular therapies directed at tumor gene activity. Therefore, classification systems based on gene expression are likely to become essential for diagnosis, prognosis and treatment of kidney tumors. Recent DNA microarray studies have shown that clinically relevant renal tumor subtypes are characterized by distinct gene expression profiles, which are useful for discovery of novel diagnostic and prognostic biomarkers. In this review, we summarize the WHO classification system for renal tumors, general applications of microarray technology in cancer research, and specific microarray studies that have advanced knowledge of renal tumor diagnosis, prognosis, therapy and pathobiology.     

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.     

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

A 7-lncRNA signature predict prognosis of Uterine corpus endometrial carcinoma

Current research indicate that long noncoding RNAs (lncRNAs) are associated with the progression of various cancers and can be used as prognostic biomarkers. This study aims to construct a prognostic lncRNA signature for the risk assessment of Uterine corpus endometrial carcinoma (UCEC). The RNA-Seq expression profile and corresponding clinical data of UCEC patients obtained from The Cancer Genome Atlas database. First, some prognosis-related lncRNAs were obtained by univariate Cox analysis. The minimum absolute contraction and selection operator (LASSO) regression and the Cox proportional hazard regression method were used to further identify the lncRNA prognostic model. Finally, seven lncRNAs (AC110491.1, AL451137.1, AC005381.1, AC103563.2, AC007422.2, AC108025.2, and MIR7-3HG) were identified as potential prognostic factors. According to the model constructed by the above analysis, the risk score of each UCEC patient was calculated, and the patients were classified into high and low-risk groups. The low-risk group had significant survival benefits. Moreover, we constructed a nomogram that incorporated independent prognostic factors (age, tumor stage, tumor grade, and risk score). The c-index value for evaluating the predictive nomogram model was 0.801. The area under the curve was 0.797 (3-year survival). The calibration curve also showed that there was a satisfactory agreement between the predicted and observed values in the probability of 1-, 3-, and 5-year overall survival. On the basis of the coexpression relationship, we established a coexpression network of lncRNA-messenger RNA (mRNA) of the 7-lncRNA. The Kyoto Encyclopedia of Genes and Genomes analysis of the coexpressing mRNAs showed that the main pathways related to the 7-lncRNA signature were neuroactive ligand-receptor interaction, serotonergic synapse, and gastric cancer pathway. Therefore, our study revealed that the 7-lncRNA could be used to predict the prognosis of UCEC and for postoperative treatment and follow-up.     

Identification of a 4-microRNA signature for clear cell renal cell carcinoma metastasis and prognosis

Renal cell carcinoma (RCC) metastasis portends a poor prognosis and cannot be reliably predicted. Early determination of the metastatic potential of RCC may help guide proper treatment. We analyzed microRNA (miRNA) expression in clear cell RCC (ccRCC) for the purpose of developing a miRNA expression signature to determine the risk of metastasis and prognosis. We used the microarray technology to profile miRNA expression of 78 benign kidney and ccRCC samples. Using 28 localized and metastatic ccRCC specimens as the training cohort and the univariate logistic regression and risk score methods, we developed a miRNA signature model in which the expression levels of miR-10b, miR-139-5p, miR-130b and miR-199b-5p were used to determine the status of ccRCC metastasis. We validated the signature in an independent 40-sample testing cohort of different stages of primary ccRCCs using the microarray data. Within the testing cohort patients who had at least 5 years follow-up if no metastasis developed, the signature showed a high sensitivity and specificity. The risk status was proven to be associated with the cancer-specific survival. Using the most stably expressed miRNA among benign and tumorous kidney tissue as the internal reference for normalization, we successfully converted his signature to be a quantitative PCR (qPCR)-based assay, which showed the same high sensitivity and specificity. The 4-miRNA is associated with ccRCC metastasis and prognosis. The signature is ready for and will benefit from further large clinical cohort validation and has the potential for clinical application.     

A prognostic five long–noncoding RNA signature for patients with rectal cancer

This study aimed to identify prognostic long noncoding RNAs (lncRNAs) signature for predicting the prognosis of patients with rectal cancer. LncRNA-sequencing data and clinicopathological data of patients with rectal cancer were retrieved from The Cancer Genome Atlas database. Univariate and multivariate Cox proportional hazards regression analysis, the least absolute shrinkage, and selection operator analysis and the Kaplan-Meier curve method were employed to identify prognostic lncRNAs and construct multi-lncRNA signature. Finally, five lncRNAs (AC079789.1, AC106900.2, AL121987.1, AP004609.1, and LINC02163) were identified to construct a five-lncRNA signature. According to the five-lncRNA signature, patients with rectal cancer were divided into a high-risk group and low-risk group. Patients with rectal cancer had significantly poorer overall survival in the high-risk group than in the low-risk group. We used a time-dependent receiver operating characteristic curve to assess the power of the five-lncRNA signature by calculating the area under the curve (AUC). The AUCs for predicting 3-year survival and 5-year survival were 0.742 and 0.935, respectively, which indicated a good performance of the five-lncRNA signature. The five-lncRNA signature was independently associated with the prognosis of patients with rectal cancer through using univariate and multivariate Cox regression analysis. The biological function of the five lncRNAs was enriched in some cancer-related biological processes and pathways by performing functional enrichment analysis of their correlated protein-coding genes. In conclusion, we developed a five-lncRNA signature as a potential indicator for rectal cancer.     

An eight-long noncoding RNA expression signature for colorectal cancer patients’ prognosis

Long noncoding RNAs (lncRNAs) have recently emerged as important biomarkers of cancer progression. Here, we proposed to develop a lncRNA-based signature with a prognostic value for colorectal cancer (CRC) overall survival (OS). Through mining microarray datasets, we analyzed the lncRNA expression profiles of 122 patients with CRC from Gene Expression Omnibus. Associations between lncRNA and CRC OS were firstly evaluated through univariate Cox regression analysis. A random survival forest method was applied for further screening of the lncRNA signature, which resulted in eight lncRNAs, including PEG3-AS1, LOC100505715, MINCR, DBH-AS1, LINC00664, FAM224A, LOC642852, and LINC00662. Combination of the eight lncRNAs weighted by their multivariate Cox regression coefficients formed a prognostic signature, through which, we could divide the 122 patients with CRC into two subgroups with significantly different OS. Good robustness of the lncRNA signature's prognostic value was verified through an independent data set consisting of 55 patients with CRC. In addition, gene set enrichment analysis indicated the potential association between high prognostic value and oxygen metabolism-related processes. This result should indicate that lncRNAs could be a useful signature for CRC prognosis.     

A robust six-miRNA prognostic signature for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) remains a major health problem worldwide. We aimed to identify a robust microRNA (miRNA)-based signature for predicting HNSCC prognosis. The miRNA expression profiles of HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. The TCGA HNSCC cohort was randomly divided into the discovery and validation cohort. A miRNA-based prognostic signature was built up based on TGCA discovery cohort, and then further validated. The downstream targets of prognostic miRNAs were subjected to functional enrichment analyses. The role of miR-1229-3p, a prognosis-related miRNA, in tumorigenesis of HNSCC was further evaluated. A total of 305 significantly differentially expressed miRNAs were found between HNSCC samples and normal tissues. A six-miRNA prognostic signature was constructed, which exhibited a strong association with overall survival (OS) in the TCGA discovery cohort. In addition, these findings were successfully confirmed in TCGA validation cohort and our own independent cohort. The miRNA-based signature was demonstrated as an independent prognostic indicator for HNSCC. A risk signature-based nomogram model was constructed and showed good performance for predicting the OS for HNSCC. The functional analyses revealed that the downstream targets of these prognostic miRNAs were closely linked to cancer progression. Mechanistically, in vitro analysis revealed that miR-1229-3p played a tumor promoting role in HNSCC. In conclusion, our study has developed a robust miRNA-based signature for predicting the prognosis of HNSCC with high accuracy, which will contribute to improve the therapeutic outcome.     

Prediction of head and neck squamous cell carcinoma survival based on the expression of 15 lncRNAs

Recent evidence suggests that long noncoding RNAs (lncRNAs) are essential regulators of many cancer-related processes, including cancer cell proliferation, invasion, and migration. There is thus a reason to believe that the detection of lncRNAs may be useful as a diagnostic and prognostic strategy for cancer detection, however, at present no effective genome-wide tests are available for clinical use, constraining the use of such a strategy. In this study, we performed a comprehensive assessment of lncRNAs expressed in samples in the head and neck squamous cell carcinoma (HNSCC) cohort available in The Cancer Genome Atlas database. A risk score (RS) model was constructed based on the expression data of these 15 lncRNAs in the validation data set of HNSCC patients and was subsequently validated in validation data set and the entire data set. We were able to stratify patients into high- and low-risk categories, using our lncRNA expression panel to determine an RS, with significant differences in overall survival (OS) between these two groups in our test set (median survival, 1.863 vs. 5.484 years; log-rank test, p < 0.001). We were able to confirm the predictive value of our 15-lncRNA signature using both a validation data set and a full data set, finding our signature to be reproducible and effective as a means of predicting HNSCC patient OS. Through the multivariate Cox regression and stratified analyses, we were further able to confirm that the predictive value of this RS was independent of other predictive factors such as clinicopathological parameters. The Gene set enrichment analysis revealed potential functional roles for these 15 lncRNAs in tumor progression. Our findings indicate that an RS established based on a panel of lncRNA expression signatures can effectively predict OS and facilitate patient stratification in HNSCC.     

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.     

肾癌预后分子生物标记物研究进展

肾癌发病率约占全身恶性肿瘤的3%。肾癌组织学行为多变,预后有不确定性。外科手术可以治疗局限性肾癌,但有将近20%的局限性肾癌患者原发肿瘤切除后出现转移,而且肾癌对化疗及放疗均不敏感。基于此临床上开展了许多辅助试验的研究,并建立了许多模型来研究肾癌术后的预后,而模型的精准度一般都需要依据肾癌的生物标记物监测。有很多分子生物标记物已经证实和肾癌预后相关,如VHL、P53、Ki-67等,本文综述了肾癌预后的分子生物标记物的最新进展。     

Identification of a three-miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Current studies suggest that some microRNAs (miRNAs) are associated with prognosis in clear cell renal cell carcinoma (ccRCC). In this paper, we aimed to identify a miRNAs signature to improve prognostic prediction for ccRCC patients. Using ccRCC RNA-Seq data of The Cancer Genome Atlas (TCGA) database, we identified 177 differentially expressed miRNAs between ccRCC and paracancerous tissue. Then all the ccRCC tumor samples were divided into training set and validation set randomly. Three-miRNA signature including miR130b, miR-18a, and miR-223 were constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression model in training set. According to optimal cut-off value of three-miRNA signature risk score, all the patients could be classified into high-risk group and low-risk group significantly. Survival of patients was significantly different between two groups (hazard ratio, 5.58, 95% confidence interval, 3.17-9.80; P < 0.0001), and three-miRNA signature performed favorably prognostic and predictive accuracy. The results were further validated in the validation set and total set. Multivariate Cox regression analyses and subgroup analyses showed that three-miRNA signature was an independent prognostic factor. Two nomograms that integrated three-miRNA signature and three clinicopathological risk factors were constructed to predict overall survival and disease-free survival after surgery for ccRCC patients. Functional enrichment analysis showed the possible roles of three-miRNA signature in some cancer-associated biological processes and pathways. In conclusion, we developed a novel three-miRNA signature that performed reliable prognostic for patient survival with ccRCC, it might facilitate ccRCC patients counseling and individualize management.