Background: Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients. Methods: Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples. Results: By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints. Conclusion: In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

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Background: Bladder cancer (BLCA) is one of the most common human cancers while its incidence is on the rise, especially in female. The tumor microenvironment (TME) acts a significant part in the development and progression of the tumor. We surveyed the expression profiles of BLCA in the Cancer Genome Atlas Program database and investigated genes that might affect the composition of tumor microenvironment (TME). We then analyzed the relationship between genes and tumor prognosis, looking for immune prognostic markers which might lead to new styles for the diagnosis and treatment of BLCA. Methods We got the RNA-seq data of BLCA from The Cancer Genome Atlas (TCGA); utilized the Estimation of Stromal and Immune Cells in the Malignant Tissues Expression (ESTIMATE) data algorithm to compute the immune and stromal score. The differentially expressed genes were performed to COX regression analysis and used to construct a protein-protein interaction (PPI) network. Results Intersection analysis demonstrated that Matrix Metalloproteinase-9 (MMP9) is the only crossover gene. We computed the percent of tumor infiltrating immune cells (TICs) in BLCA through the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The differences in the high and low expression group of MMP9 in TICs were established. The expression levels of MMP9 in each kind of TICs were then correlated. Via survival, clinical, and enrichment analyses, MMP9 was found to be an immune prognostic factor for BLCA and act a significant part in the tumor microenvironment. Ultimately, the correlation analysis between MMP9 and the immune checkpoint genes indicated that MMP9 can forecast the effect of immunotherapy on BLCA. Conclusion MMP9 is an immune prognostic marker gene. MMP9 is involved in regulating the immunological process of BLCA through multiple pathways and is closely related to PD-1 and CTLA4 immune targets. The expression of MMP9 may impact the prognosis of BLCA, indicating that it may be a prognostic-related marker of BLCA.