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Background: Chromobox (CBX) proteins are essential components of polycomb group proteins and perform essential functions in bladder cancer (BLCA). However, research on CBX proteins is still limited, and the function of CBXs in BLCA has not been well illustrated. Methods and Results: We analyzed the expression of CBX family members in BLCA patients from The Cancer Genome Atlas database. By Cox regression analysis and survival analysis, CBX6 and CBX7 were identified as potential prognostic factors. Subsequently, we identified genes associated with CBX6/7 and performed enrichment analysis, and they were enriched in urothelial carcinoma and transitional carcinoma. Mutation rates of TP53 and TTN correlate with expression of CBX6/7. In addition, differential analysis indicated that the roles played by CBX6 and CBX7 may be related to immune checkpoints. The CIBERSORT algorithm was used to screen out immune cells that play a role in the prognosis of bladder cancer patients. Multiplex immunohistochemistry staining confirmed a negative correlation between CBX6 and M1 macrophages, as well as a consistent alteration in CBX6 and regulatory T cells (Tregs), a positive correlation between CBX7 and resting mast cells, and a negative correlation between CBX7 and M0 macrophages. Conclusions: CBX6 and CBX7 expression levels may assist in predicting the prognosis of BLCA patients. CBX6 may contribute to a poor prognosis in patients by inhibiting M1 polarization and promoting Treg recruitment in the tumor microenvironment, while CBX7 may contribute to a better prognosis in patients by increasing resting mast cell numbers and decreasing macrophage M0 content.

BackgroundRenal cell carcinoma (RCC) is the most common kidney cancer in adults. According to the histological features, it could be divided into several subtypes, of which the most common one is kidney renal clear cell carcinoma (KIRC), which contributed to more than 90% of cases for RCC and usually ends with a dismal outcome. Previous studies suggested that basement membrane genes (BMGs) play a pivotal role in tumor development. However, the significance and prognostic value of BMGs in KIRC still wrap in the mist.MethodsKIRC data were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A prognostic risk score (PRS) model based on BMGs was established using univariate and least absolute shrinkage and selection operator (LASSO) and the Cox regression analysis was performed for prognostic prediction. The Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curves, nomogram, and calibration curves were utilized to evaluate and validate the PRS model. All KIRC cases were divided into the high-risk score (HRS) group and the low-risk score (LRS) group according to the median risk scores. In addition, single-sample gene set enrichment analysis (ssGSEA), immune analysis, tumor microenvironment (TME) analysis, principal component analysis (PCA), and half-maximal inhibitory concentration (IC50) were also applied. Expression levels of BMGs were confirmed by qRT-PCR in both human renal cancer cell lines and tissues.ResultsWe established the BMGs-based prognostic model according to the following steps. Within the TCGA cohort, patients’ prognosis of the HRS group was significantly worse than that of the LRS group, which was consistent with the analysis results of the GEO cohort. PCA patterns were significantly distinct for LRS and HRS groups and pathological features of the HRS group were more malignant compared with the LRS group. Correlation analysis of the PRS model and TME features, such as immune cell scores, stromal cell scores, and ESTIMATE values, revealed a higher immune infiltration in the HRS group compared with the LRS group. The chemotherapeutic response was also evaluated in KIRC treatment. It showed that the HRS group exhibited stronger chemoresistance to chemotherapeutics like FR-180204, GSK1904529A, KIN001-102, and YM201636. The therapeutic reactivity of the other 27 chemotherapeutic agents was summarized as well. Furthermore, the FREM2 level was measured in both human kidney tissues and associated cell lines, which suggested that lower FREM2 expression prompts a severer pathology and clinical ending.ConclusionsOur study showed that KIRC is associated with a unique BMG expression pattern. The risk scores related to the expression levels of 10 BMGs were assessed by survival status, TME, pathological features, and chemotherapeutic resistance. All results suggested that FREM2 could be a potential candidate for KIRC prognosis prediction. In this study, we established a valid model and presented new therapeutic targets for the KIRC prognosis prediction as well as the clinical treatment recommendation, and finally, facilitated precision tumor therapy for every single individual.

PurposeBladder cancer (BLCA) is one of the most frequently diagnosed urological malignancies and is the 4th most common cancer in men worldwide. Molecular targets expressed in bladder cancer (BLCA) are usually used for developing targeted drug treatments. However, poor prognosis and poor immunotherapy efficacy remain major challenges for BLCA. Numerous studies have shown that long non-coding RNAs (LncRNAs) play an important role in the development of cancer. However, the role of lncRNAs related to inflammation in BLCA and their prognostic value remain unclear. Therefore, this study is aimed to explore new potential biomarkers that can predict cancer prognosis.MethodsWe downloaded BLCA-related RNA sequencing data from The Cancer Genome Atlas (TCGA) and searched for inflammation-related prognostic long non-coding RNAs (lncRNAs) by univariate Cox (uniCox) regression and co-expression analysis. We used the least absolute shrinkage and selection operator (LASSO) analysis to construct an inflammation-related lncRNA prognosis risk model. Samples were divided into high-risk score (HRS) group and low-risk score (LRS) group based on the median value of risk scores. The independent variable factors were identified by univariate Cox (uni-Cox) and multivariate Cox (multi-Cox) regression analyses, and receiver operating characteristic (ROC) curves were used to compare the role of different factors in predicting outcomes. Nomogram and Calibration Plot were generated by the R package rms to analyze whether the prediction results are correct and show good consistency. Correlation coefficients were calculated by Pearson analysis. The Kaplan-Meier method was used to assess the prognostic value. The expression of 7 lncRNAs related with inflammation was also confirmed by qRT-PCR in BLCA cell lines. Kyoto Encyclopedia of Gene and Genome (KEGG) pathways that were significantly enriched (P < 0.05) in each risk group were identified by the GSEA software. The R package pRRophetic was used to predict the IC50 of common chemotherapeutic agents. TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC and CIBERSORT were applied to quantify the relative proportions of infiltrating immune cells. We also used package ggpubr to evaluate TME scores and immune checkpoint activation in LRS and HRS populations. R package GSEABase was used to analyze the activity of immune cells or immune function. Different clusters of principal component analysis (PCA), t-distribution random neighborhood embedding (t-SNE), and Kaplan-Meier survival were analyzed using R package Rtsne’s. The R package ConsensesClusterPlus was used to class the inflammation-related lncRNAs.ResultsIn this study, a model containing 7 inflammation-related lncRNAs was constructed. The calibration plot of the model was consistent with the prognosis prediction outcomes. The 1-, 3-, and 5-year ROC curve (AUC) were 0.699, 0.689, and 0.699, respectively. High-risk patients were enriched in lncRNAs related with tumor invasion and immunity, and had higher levels of immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 2 and 3 and cluster 1, respectively, which indicated that hot tumors are more susceptible to immunotherapy.ConclusionOur study showed that inflammation-related LncRNAs are closely related with BLCA, and inflammation-related lncRNA can accurately predict patient prognosis and effectively differentiate between hot and cold tumors, thus improving individualized immunotherapy for BLCA patients. Therefore, this study provides an effective predictive model and a new therapeutic target for the prognosis and clinical treatment of BLCA, thus facilitating the development of individualized tumor therapy.

Background: Renal fibrosis is a widely used pathological indicator of progressive chronic kidney disease (CKD), and renal fibrosis mediates most progressive renal diseases as a final pathway. Nevertheless, the key genes related to the host response are still unclear. In this study, the potential gene network, signaling pathways, and key genes under unilateral ureteral obstruction (UUO) model in mouse kidneys were investigated by integrating two transcriptional data profiles.Methods: The mice were exposed to UUO surgery in two independent experiments. After 7 days, two datasets were sequenced from mice kidney tissues, respectively, and the transcriptome data were analyzed to identify the differentially expressed genes (DEGs). Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were executed. A Protein-Protein Interaction (PPI) network was constructed based on an online database STRING. Additionally, hub genes were identified and shown, and their expression levels were investigated in a public dataset and confirmed by quantitative real time-PCR (qRT-PCR) in vivo.Results: A total of 537 DEGs were shared by the two datasets. GO and the KEGG analysis showed that DEGs were typically enriched in seven pathways. Specifically, five hub genes (Bmp1, CD74, Fcer1g, Icam1, H2-Eb1) were identified by performing the 12 scoring methods in cytoHubba, and the receiver operating characteristic (ROC) curve indicated that the hub genes could be served as biomarkers.Conclusion: A gene network reflecting the transcriptome signature in CKD was established. The five hub genes identified in this study are potentially useful for the treatment and/or diagnosis CKD as biomarkers.

Background: The m6A mutation may have a significant effect on non-coding RNA metabolism, ultimately contributing to tumor growth and metastasis. However, the mechanism of m6A-related lncRNAs in Kidney Renal Clear Cell Carcinoma (KIRC) has not yet been elucidated. Methods: Herein, we used transcriptional and clinical data from the TCGA to conduct the comprehensive analysis of m6A-related prognostic lncRNAs in KIRC. Twelve lncRNAs correlated with m6A were shown to be strongly associated with clinical prognosis. We utilized consensus clustering to classify these prognostic lncRNAs into two groups based on their expression levels in tumor tissue. The cluster B was significantly associated with higher expression levels of m6A-related prognostic lncRNAs and shorter patient survival. Subsequently, seven m6A-related lncRNAs were recognized to structure a predictive model, which were strongly correlated with tumor prognosis and could be employed as the independent prognostic factor. In the analysis of clinical correlation, it was shown that risk score was remarkably related with tumor metastasis. Finally, our analysis established correlation between infiltrating immune cells and m6A-related lncRNAs in KIRC patients. Results: As a result, we investigated m6A-related lncRNAs and the prognosis of KIRC to reveal the relationship between the 530 clinical samples and 611 transcriptome data showed that the prognostic model established by 12 high-risk lncRNAs can predict KIRC. The prognosis of patients, among which seven key m6A-related lncRNAs: AC005261.3, AC024060.2, AC079174.2, AL139123.1, AL355388.1, CD27-AS1, and DGUOK-AS1, were significantly associated with the overall survival and prognosis of KIRC. Conclusion: Overall, our results imply that m6A-related prognostic lncRNAs could be underlying biomarkers for immunotherapy in KIRC.