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Background. FXYD2, a gene coding for the γ subunit of Na+/K+-ATPase, was demonstrated to involve in carcinogenesis recently. However, the specific role of FXYD2 in clear cell renal cell carcinoma (ccRCC) remains unknown. The current study was conducted to investigate the expression, biological function, and potentially immune-related mechanisms of FXYD2 in ccRCC. Materials and methods. The data from TCGA-KIRC, ICGC, GEO, Oncomine, ArrayExpress, TIMER, HPA datasets, and our clinical samples were used to determine and validate the expression level, prognostic roles, and potentially immune-related mechanisms in ccRCC. Cell function assays were performed to investigate the biological role of FXYD2 in vitro. Results. FXYD2 was identified to be downregulated in ccRCC tissue compared to normal tissue, which was confirmed by our RT-PCR, WB, and IHC analyses. Kaplan-Meier survival analysis and Cox regression analysis suggested that downregulated FXYD2 could independently predict poor survival of ccRCC patients. Through the ESTIMATE algorithm, ssGSEA algorithm, CIBERSORT algorithm, TIMER database, and our laboratory experiment, FXYD2 was found to correlate with the immune landscape, especially regulatory T cells (Treg), in ccRCC. Gain-of-function experiment revealed that FXYD2 could restrain cell proliferation, migration, and invasion in vitro. Functional enrichment analysis illustrated that TGF-β-SMAD2/3, Notch, and PI3K-Akt-mTOR signaling pathways may be potential signaling pathways of FXYD2 in ccRCC. Conclusions. Downregulation of FXYD2 is associated with ccRCC tumorigenesis, poor prognosis, and increased Treg infiltration in ccRCC, which may be related to TGF-β-SMAD2/3, Notch, and PI3K-Akt-mTOR signaling pathways. This will probably provide a novel prognostic marker and potential therapeutic target for ccRCC.

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction. Methods. The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature. Results. Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8+ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature. Conclusion. Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

Acute rejection (AR) after kidney transplant is one of the major obstacles to obtain ideal graft survival. Reliable molecular biomarkers for AR and renal allograft loss are lacking. This study was performed to identify novel long noncoding RNAs (lncRNAs) for diagnosing AR and predicting the risk of graft loss. The several microarray datasets with AR and nonrejection specimens of renal allograft downloaded from Gene Expression Omnibus database were analyzed to screen differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). Univariate and multivariate Cox regression analyses were used to identify optimal prognosis-related DElncRNAs for constructing a risk score model. 39 common DElncRNAs and 185 common DEmRNAs were identified to construct a lncRNA-mRNA regulatory relationship network. DElncRNAs were revealed to regulate immune cell activation and proliferation. Then, 4 optimal DElncRNAs, ATP1A1-AS1, CTD-3080P12.3, EMX2OS, and LINC00645, were selected from 17 prognostic DElncRNAs to establish the 4-lncRNA risk score model. In the training set, the high-risk patients were more inclined to graft loss than the low-risk patients. Time-dependent receiver operating characteristics analysis revealed the model had good sensitivity and specificity in prediction of 1-, 2-, and 3-year graft survival after biopsy (AUC=0.891,0.836,and 0.733, respectively). The internal testing set verified the result well. Gene set enrichment analysis which expounded NOD-like receptor, the Toll-like receptor signaling pathways, and other else playing important role in immune response was enriched by the 4 lncRNAs. Allograft-infiltrating immune cells analysis elucidated the expression of 4 lncRNAs correlated with gamma delta T cells and eosinophils, etc. Our study identified 4 novel lncRNAs as potential biomarkers for AR of renal allograft and constructed a lncRNA-based model for predicting the risk of graft loss, which would provide new insights into mechanisms of AR.