Exosome-related lncRNA score: A value-based individual treatment strategy for predicting the response to immunotherapy in clear cell renal cell carcinoma.

Exosomes play a crucial role in intercellular communication in clear cell renal cell carcinoma (ccRCC), while the long non-coding RNAs (lncRNAs) are implicated in tumorigenesis and progression. The purpose of this study is to construction a exosomes-related lncRNA score and a ceRNA network to predict the response to immunotherapy and potential targeted drug in ccRCC. Data of ccRCC patients were obtained from the TCGA database. Pearson correlation analysis was used to identify eExosomes-related lncRNAs (ERLRs) from Top10 exosomes-related genes that have been screened. The entire cohort was randomly divided into a training cohort and a validation cohort in equal scale. LASSO regression and multivariate cox regression was used to construct the ERLRs-based score. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and drug susceptibility between the high- and low-risk groups were also investigated. Finally, the relevant ceRNA network was constructed by machine learning to analyze their potential targets in immunotherapy and drug use of ccRCC patients. A score consisting of 4ERLRs was identified, and patients with higher ERLRs-based score tended to have a worse prognosis than those with lower ERLRs-based score. ROC curves and multivariate Cox regression analysis demonstrated that the score could be considered as a risk factor for prognosis in both training and validation cohorts. Moreover, patients with high scores are predisposed to experience poor overall survival, a larger prevalence of advanced stage (III-IV), a greater tumor mutational burden, a higher infiltration of immunosuppressive cells, and a greater likelihood of responding favorably to immunotherapy. The importance of EMX2OS was determined by mechanical learning, and the ceRNA network was constructed, and EMX2OS may be a potential therapeutic target, possibly exerting its function through the EMX2OS/hsa-miR-31-5p/TLN2 axis. Based on machine learning, a novel ERLRs-based score was constructed for predicting the survival of ccRCC patients. The ERLRs-based score is a promising potential independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics. Meanwhile, we screened out key lncRNAEMX2OS and identified the EMX2OS/hsa-miR-31-5p/TLN2 axis, which may provide new clues for the targeted therapy of ccRCC.

Yang Z ，Zhang X ，Zhan N ，Lin L ，Zhang J ，Peng L ，Qiu T ，Luo Y ，Liu C ，Pan C ，Hu J ，Ye Y ，Jiang Z ，Liu X ，Sun M ，Zhang Y ... -  《Cancer Medicine》

Construction of a Novel Immune-Related lncRNA Pair Signature with Prognostic Significance for Kidney Clear Cell Renal Cell Carcinoma.

Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in the urinary system, among which the clear cell renal cell carcinoma (ccRCC) is the most common subtype. The immune-related long noncoding ribonucleic acids (irlncRNAs) which are abundant in immune cells and immune microenvironment (IME) have potential significance in evaluating the prognosis and effects of immunotherapy. The signature based on irlncRNA pairs and independent of the exact expression level seems to have a latent predictive significance for the prognosis of patients with malignant tumors but has not been applied in ccRCC yet. In this article, we retrieved The Cancer Genome Atlas (TCGA) database for the transcriptome profiling data of the ccRCC and performed coexpression analysis between known immune-related genes (ir-genes) and lncRNAs to find differently expressed irlncRNA (DEirlncRNA). Then, we adopted a single-factor test and a modified LASSO regression analysis to screen out ideal DEirlncRNAs and constructed a Cox proportional hazard model. We have sifted 28 DEirlncRNA pairs, 12 of which were included in this model. Next, we compared the area under the curve (AUC), found the cutoff point by using the Akaike information criterion (AIC) value, and distinguished the patients with ccRCC into a high-risk group and a low-risk group using this value. Finally, we tested this model by investigating the relationship between risk score and survival, clinical pathological characteristics, cells in tumor immune microenvironment, chemotherapy, and targeted checkpoint biomarkers. A novel immune-related lncRNA pair signature consisting of 12 DEirlncRNA pairs was successfully constructed and tightly associated with overall survival, clinical pathological characteristics, cells in tumor immune microenvironment, and reactiveness to immunotherapy and chemotherapy in patients with ccRCC. Besides, the efficacy of this signature was verified in some commonly used clinicopathological subgroups and could serve as an independent prognostic factor in patients with ccRCC. This signature was proven to have a potential predictive significance for the prognosis of patients with ccRCC and the efficacy of immunotherapy.

Sun JX ，Xia QD ，Liu CQ ，Xu JZ ，Xun Y ，Lu JL ，Hu J ，Wang SG ... -  《-》

A novel prognostic model based on immunogenomics for clear cell renal cell carcinoma.

Immune cell infiltration into tumor tissue is closely related to the clinical outcomes of patients with clear cell renal cell carcinoma (ccRCC). This study aimed to screen out potential immune genes associated with ccRCC, analyze their relationships with clinical outcomes, and construct a signature to predict ccRCC. The transcriptome RNA-sequencing data in 539 ccRCC and 72 adjacent normal tissues were obtained from TCGA database. Biomedical computational algorithms were conducted to identify immune-related differential expressed genes (IRDGs) and enriched pathways. Then, LASSO Cox and multivariate Cox analyses were performed to screen out genes that were then used to construct the prognostic model. A total of 116 down-regulated and 565 up-regulated IRDGs were identified. Pathway enrichment analysis suggested that IRDGs was mainly enriched in the pathway of "cytokines and cytokine receptors". The entire data of ccRCC were randomly divided into the training set and the test set with a ratio of 1:1. A 4-gene signature was then constructed using LASSO Cox analysis and multivariate Cox analysis in the training set. This prognostic signature could stratify patients into high- and low-risk groups successfully, and serve as an independent predictor when adjusted with clinical factors by univariate and multivariate Cox regression analysis. These results were verified in the test set and the entire set. Besides, the abundance of CD4 + T cells and dendritic cells increased in the high-risk group. Finally, we built a nomogram incorporating risk score and clinical factors to predict the overall survival of ccRCC patients. These findings may contribute to the research of ccRCC in immunization part.

Wu Z ，Shen Y ，Fan D ，Liu J ，Chen D ，Wang K ，Xu X ... -  《-》

Computational construction of TME-related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma.

The tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio-informative analysis was adopted to construct a TME-related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients. The TME mRNAs were screened following the immune and stromal scores with the data from GSE15641, GSE29609, GSE36895, GSE46699, GSE53757, and The Cancer Genome Atlas (TCGA)-kidney renal clear cell carcinoma (KIRC). And the TME-related lncRNAs were recognized using correlation analysis. The TME-related lncRNAs prognostic model was constructed using the training dataset. Kaplan-Meier analysis, principal-component analysis, and time-dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single-sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model. In the current study, 364 TME-related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME-related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high-risk group exhibited poor overall survival and disease-free survival than those in the low-risk group. The 1-, 3-, and 5-year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high-risk group was concentrated on immune-related pathways. The high-risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS-PD-1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high-risk group. The TME-related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients' therapy.

Zhou L ，Fang H ，Guo F ，Yin M ，Long H ，Weng G ... -  《-》

Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning.

Bai Z ，Lu J ，Chen A ，Zheng X ，Wu M ，Tan Z ，Xie J ... -  《Biomolecules》