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Identification of CCL4 as an Immune-Related Prognostic Biomarker Associated With Tumor Proliferation and the Tumor Microenvironment in Clear Cell Renal Cell Carcinoma.

ABSTRACT: The last decade has witnessed revolutionary advances taken in immunotherapy for various malignant tumors. However, immune-related molecules and their characteristics in the prediction of clinical outcomes and immunotherapy response in clear cell renal cell carcinoma (ccRCC) remain largely unclear. C-C Motif Chemokine Ligand 4 (CCL4) was extracted from the intersection analysis of common differentially expressed genes (DEGs) of four microarray datasets from the Gene Expression Omnibus database and immune-related gene lists in the ImmPort database using Cytoscape plug-ins and univariate Cox regression analysis. Subsequential analysis revealed that CCL4 was highly expressed in ccRCC patients, and positively correlated with multiple clinicopathological characteristics, such as grade, stage and metastasis, while negatively with overall survival (OS). We performed gene set enrichment analysis (GSEA) and gene set variant analysis (GSVA) with gene sets coexpressed with CCL4, and observed that gene sets positively related to CCL4 were enriched in tumor proliferation and immune-related pathways while metabolic activities in the negatively one. To further explore the correlation between CCL4 and immune-related biological process, the CIBERSORT algorithm, ESTIMATE method, and tumor mutational burden (TMB) score were employed to evaluate the tumor microenvironment (TME) characteristics of each sample and confirmed that high CCL4 expression might give rise to high immune cell infiltration. Moreover, correlation analysis revealed that CCL4 was positively correlated with common immune checkpoint genes, such as programmed cell death protein 1 (PD-1), cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and lymphocyte activating 3 (LAG3). Overall, this study demonstrated that CCL4 might serve as a potential immune-related prognostic biomarker to predict clinical outcomes and immunotherapy response in ccRCC. Moreover, CCL4 might contribute to TME modulation, indicating the mechanism CCL4 involved in tumor proliferation and metastasis, which could provide novel therapeutic perceptions for ccRCC patients.

SUBMITTER: Zhang L

PROVIDER: S-EPMC8652234 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The tumor microenvironment (TME) plays an important part in the initiation and development of clear cell renal cell carcinoma (ccRCC). However, an understanding of the immune infiltration in TME is still unknown. Our study aims to explore the correlation between the TME and the clinical features, as well as the prognosis of ccRCC. In the present study, ESTIMATE and CIBERSORT computational methods were applied to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal fractions in the ccRCC form The Cancer Genome Atlas (TCGA) database. Then, we sought to find out those immune cell types and genes which may play a significant role and validated them in the GEO database. Furthermore, an immunohistochemical analysis of our external validation dataset was used to detect SAA1 and PDL1 expression in the ccRCC cancer tissues and corresponding normal tissues. Statistical analysis was performed to study the relationship between SAA1 and clinical characteristics, as well as PDL1 expression. Furthermore, a ccRCC cell model with SAA1 knockdown was constructed, which was used for cell proliferation and the migration test. The intersection analysis of the univariate COX and PPI analysis were performed to imply Serum Amyloid A1 (SAA1) as a predictive factor. The expression of SAA1 was significantly negatively correlated to OS and positively correlated to the clinical TMN stage system. The genes in the high-expression SAA1 group were basically enriched in immune-related activities. The proportion of mast cells resting was negatively correlated with SAA1 expression, indicating that SAA1 may be involved in the maintenance of the immune status for the TME. Moreover, the PDL1 expression was positively related to the SAA1 expression and negatively correlated with the patients' prognosis. Further experiments revealed that the knockdown of SAA1 inhibited ccRCC development through suppressing cell proliferation and migration. SAA1 may be a novel marker for the prognosis prediction of ccRCC patients and may play a vital role in the TME by mast cell resting and PDL1 expression. SAA1 has the potential to become a therapeutic target and indicator for immune target therapy in ccRCC treatment.

Project description:Background: The tumor microenvironment affects the occurrence and development of cancers, including clear cell renal cell carcinoma (ccRCC). However, how the immune contexture interacts with the cancer phenotype remains unclear. Methods: We identified and evaluated immunophenotyping clusters in ccRCC using machine-learning algorithms. Analyses for functional enrichment, DNA variation, immune cell distribution, association with independent clinicopathological features, and predictive responses for immune checkpoint therapies were performed and validated. Results: Three immunophenotyping clusters with gradual levels of immune infiltration were identified. The intermediate and high immune infiltration clusters (Clusters B and C) were associated with a worse prognosis for ccRCC patients. Tumors in the immune-hot Clusters B and C showed pro-tumorigenic immune infiltration, and these patients showed significantly worse survival compared with patients in the immune-cold Cluster A in the training and testing cohorts (n = 422). In addition to distinct immune cell infiltrations of immunophenotyping, we detected significant differences in DNA variation among clusters, suggesting a high degree of genetic heterogeneity. Furthermore, expressions of multiple immune checkpoint molecules were significantly increased. Clusters B and C predicted favorable outcomes in 64 ccRCC patients receiving immune checkpoint therapies from the FUSCC cohort. In 360 ccRCC patients from the FUSCC validation cohort, Clusters B and C significantly predicted worse prognosis compared with Cluster A. After immunophenotyping of ccRCC was confirmed, significantly increased tertiary lymphatic structures, aggressive phenotype, elevated glycolysis and PD-L1 expression, higher abundance of CD8+ T cells, and TCRn cell infiltration were found in the immune-hot Clusters B and C. Conclusion: This study described immunophenotyping clusters that improved the prognostic accuracy of the immune contexture in the ccRCC microenvironment. Our discovery of the novel independent prognostic indicators in ccRCC highlights the relationship between tumor phenotype and immune microenvironment.

Project description:Background Kidney renal clear cell carcinoma (KIRC) is a malignant tumor with a high degree of immune infiltration. Identifying immune biomarkers is essential for the treatment of KIRC. Studies have identified the potential of NEIL3 to modulate the immune microenvironment and promote tumor progression. However, the role of NEIL3 in KIRC remains uncertain. This study was to investigate the effect of NEIL3 on the prognosis and immune infiltration of patients with KIRC. Methods TCGA and GEO databases were used to study the expression of NEIL3 in KIRC. Cox regression analysis was used to examine the relationship between the expression of NEIL3 and clinicopathological variables and survival. Furthermore, Gene Set Cancer Analysis (GSCA) was applied to study the impact of NEIL3 methylation on outcomes of KIRC. Through gene ontology (GO) and Gene set enrichment (GSEA) analysis, the biological processes and signal pathways related to NEIL3 expression were identified. In addition, immune infiltration analysis was conducted via CIBERSORT analysis, ssGSEA analysis and TISIDB database. Results NEIL3 was overexpressed in KIRC, and it was significantly related with histologic grade, pathologic stage, T stage, M stage, and vital status of KIRC patients (P < 0.001). The expression of NEIL3 was associated with worse outcomes. Univariate and multivariate Cox analysis showed that NEIL3 may be an indicator of adverse outcomes in KIRC. GSEA analysis revealed that NEIL3 may be involved in signal pathways including cell cycle, DNA replication, mismatch repair, P53 signal pathway, and antigen processing and presentation. In addition, immune infiltration analysis showed a positive correlation between NEIL3 expression and multiple immune cells (activated CD8 T cells, activated dendritic cells, myeloid-derived suppressor cells, follicular helper T cells, and regulatory T cells) and immunoinhibitors (PD1, CTLA4, LAG3, TIGHT, IL10, and CD96). Conclusion NEIL3 is a potential independent biomarker of KIRC, which is relevant to immune infiltration.

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Identification of CCL4 as an Immune-Related Prognostic Biomarker Associated With Tumor Proliferation and the Tumor Microenvironment in Clear Cell Renal Cell Carcinoma.

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