Gene expression-based immune infiltration analyses of renal cancer and their associations with survival outcome

A novel cancer-associated fibroblast signature for kidney renal clear cell carcinoma via integrated analysis of single-cell and bulk RNA-sequencing

Cancer-associated fibroblasts (CAFs), integral components of the tumor microenvironment, play a pivotal role in tumor proliferation, metastasis, and clinical outcomes. However, its specific roles in Kidney Renal Clear Cell Carcinoma (KIRC) remain poorly understood. Employing the established Seurat single-cell analysis pipeline, we identified 21 CAFs marker genes. Subsequently, a prognostic signature consisting of 6 CAFs marker genes (RGS5, PGF, TPM2, GJA4, SEPT4, and PLXDC1) was developed in a cohort through univariate and LASSO Cox regression analyses. The model's efficacy was then validated in an external cohort, with a remarkable predictive performance in 1-, 3-, and 5-year. Patients in the high-risk group exhibited significantly inferior survival outcomes (p < 0.001), and the risk score was an independent prognostic factor (p < 0.05). Distinct differences in immune cell profiles and drug susceptibility were observed between the two risk groups. In KIRC, the PGF-VEGFR1 signaling pathway displayed a notable increase. PGF expression was significantly elevated in tumor tissues, as demonstrated by quantitative real-time polymerase chain reaction. In vitro, transwell assays and CCK8 revealed that recombinant-PGF could enhance the capability of cell proliferation, migration, and invasion in 769P and 786-O cells. This study firstly developed a novel predictive model based on 6 CAFs genes for KIRC. Additionally, PGF may present a potential therapeutic target to enhance KIRC treatment.

Low OGDHL expression affects the prognosis and immune infiltration of kidney renal clear cell carcinoma

Background

Oxoglutarate dehydrogenase-like (OGDHL) modulates glutamine metabolism to influence tumor progression. Therefore, we aimed to explore the potential role of OGDHL in the prognosis of kidney renal clear cell carcinoma (KIRC) and its effect on immune infiltration.

Methods

The Cancer Genome Atlas, Tumor Immune Estimation Resource, Gene Expression Profiling Interactive Analysis, Human Protein Atlas, and The University of Alabama at Birmingham Cancer databases and the GSE53757 dataset were utilized to analyze expression difference and prognosis of OGDHL in tumor and normal tissue; diagnostic value was assessed using receiver operating characteristic curves. Correlations with clinical features and survival prognosis were analyzed. Independent prognostic factors were identified using univariate and multifactorial Cox regression analysis. We used the CIBERSORT analysis tool to discover the proportion of tumor-infiltrating immune cells (TIICs) in KIRC patients. Next, the differences in the proportion of TIICs under different OGDHL expression were analyzed. Finally, we explored the potential mechanisms by which OGDHL expression affects patient survival using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA).

Results

OGDHL expression was markedly downregulated in KIRC tissues compared to in normal tissues, and the downregulation of OGDHL expression was significantly associated with tumor progression (including tumor stage and grade) and poor prognosis. Cox regression analyses revealed OGDHL to be an independent prognostic factor for KIRC. CIBERSORT analysis showed that OGDHL expression is associated with differences in the proportion of several TIICs, particularly resting mast cells. Finally, GO and KEGG analysis showed that OGDHL was associated with extracellular matrix and epithelial cell differentiation involved in kidney development. GSEA indicated that low OGDHL was closely related to the activation of carcinogenic signaling pathways, including epithelial mesenchymal transition, tumor necrosis factor alpha and nuclear factor kappa B signaling pathway, negative regulation of apoptotic signaling, collagen formation, etc.

Conclusions

OGDHL level can be monitored for diagnosing KIRC. Reduced expression is associated with poor prognosis and immune infiltration of KIRC. OGDHL is expected to become a new target for the treatment of KIRC.

Metabolic reprogramming involves in transition of activated/resting CD4+ memory T cells and prognosis of gastric cancer

Background

Little is known on how metabolic reprogramming potentially prompts transition of activated and resting CD4+ memory T cells infiltration in tumor microenvironment of gastric cancer (GC). The study aimed to evaluate their interactions and develop a risk model for predicting prognosis in GC.

Methods

Expression profiles were obtained from TCGA and GEO databases. An immunotherapeutic IMvigor210 cohort was also enrolled. CIBERSORT algorithm was used to evaluate the infiltration of immune cells. The ssGSEA method was performed to assess levels of 114 metabolism pathways. Prognosis and correlation analysis were conducted to identify metabolism pathways and genes correlated with activated CD4+ memory T cells ratio (AR) and prognosis. An AR-related metabolism gene (ARMG) risk model was constructed and validated in different cohorts. Flow cytometry was applied to validate the effect of all-trans retinoic acid (ATRA) on CD4+ memory T cells.

Results

Since significantly inverse prognostic value and negative correlation of resting and activated CD4+ memory T cells, high AR level was associated with favorable overall survival (OS) in GC. Meanwhile, 15 metabolism pathways including retinoic acid metabolism pathway were significantly correlated with AR and prognosis. The ARMG risk model could classify GC patients with different outcomes, treatment responses, genomic and immune landscape. The prognostic value of the model was also confirmed in the additional validation, immunotherapy and pan-cancer cohorts. Functional analyses revealed that the ARMG model was positively correlated with pro-tumorigenic pathways. In vitro experiments showed that ATRA could inhibit levels of activated CD4+ memory T cells and AR.

Conclusion

Our study showed that metabolic reprogramming including retinoic acid metabolism could contribute to transition of activated and resting CD4+ memory T cells, and affect prognosis of GC patients. The ARMG risk model could serve as a new tool for GC patients by accurately predicting prognosis and response to treatment.