RCC has been recognized as an immunotherapy-responsive tumor due to its high immune cell infiltration(26). However, immunological checkpoint molecules and other immune suppressive factors are frequently upregulated in response to immune cell infiltration, which suppresses anti-tumor immune responses. Consequently, immunotherapy which focus on the immune suppressive microenvironment have revolutionized RCC treatment(27). Immune checkpoint inhibitors only help a small percentage of patients over the long term, which has hampered their ability to be widely used in clinical settings(28). Improved prognostic models are required to guide clinical decision-making and enable more accurate survival prediction for RCC patients. However, previous prognostic models have been limited by biases stemming from the use of absolute gene expression values and dependence on tissue-level rather than single-cell sequencing data, which overlooks tumor microenvironment complexity. Here, we developed a gene pair index that utilizes relative within-sample gene expression ordering, eliminating the need for data normalization and enabling capture of tumor heterogeneity. This approach enhances the reliability and generalizability of our findings. Significantly, our integration of scRNA seq data has enabled a more precise focus on immune cell characteristics(29).
In this work, we leveraged bulk RNA sequencing data from an immunotherapy-treated cohort to systematically investigate associations between T cell populations and clinical outcomes in patients who underwent immunotherapy. The findings revealed that poor outcomes and treatment effectiveness were associated with significant levels of T cell infiltration in RCC. Our results align with prior studies indicting that CD8 + cytotoxic T lymphocytes are highly infiltrated in major renal masses but correlate with poorer prognosis(27). The heterogeneity of the RCC tumor microenvironment was then examined using the GSE145281 scRNA-seq dataset. Through a meticulous annotation process, we identified eight distinct cell types present in the dataset, namely B cells, CD4 + T cells, CD8 + T cells, dentritic cells, mast cells, monocyte/macrophages, natural killer cells, and plasma cells.
To comprehensively elucidate the relationship between T cells and clinical outcome in RCC patients, we developed a prognostic gene pair index (GPI) specifically focusing on CD4 + T cell and CD8 + T cell marker genes within the immunotherapy cohort. The prognosis of RCC is highly influenced by the biological characteristics of T cells. Therefore, we conducted a more in-depth investigation into the correlation between the GPI and the biological attributes of T cells by employing GSVA, GSEA, pseudo-time analysis and cell-cell interaction analysis. Concerning GSVA and GSEA analysis of the GPI in T cells based on single-cell sequencing data, low GPI enriched immune-related signaling pathways, while high GPI were connected to the activation of metabolism-related signaling pathways that were associated with to tumor genesis and development((30). Furthermore, analysis of the pseudotime trajectory of the T cells revealed that genes that fluctuate with developmental time were divided into four clusters and the the clusters associated with immune-related pathways are activated with developmental time (corresponding to a decrease in GPI). Regarding the cell-cell interaction analysis, our results from intercellular communication analysis provided compelling evidence of direct and intense interactions between T cells and other cell subtypes within TIME. Particularly, the MIF signaling pathways were identified as crucial mediators of these intercellular communications. In the MIF signaling pathways, T cells with high GPI exhibited intensive interactions with other cells, potentially contributing to tumor aggressiveness and poor survival outcomes in patients (31).
Subsequently, the TCGA-KIRC and E-MATB-1980 cohorts were used to further confirm the performance of GPI. In both cohorts, we observed consistent findings, which points to the strong resilience and repeatability of GPI. More strikingly, the analysis of GPI and immune-related features revealed that immune infiltration was more intense in the low GPI group, with activated CD4 + T cell, activated CD8 + T cell, effector memory CD8 + T cell, endothelia cell, immature dendritic cell, mast cell, memory B cell, natural killer cell, natural killer T cell, neutrophil, regulatory T cell, T follicular helper cell, and type 2 T helper cell being particularly dominant. It has become clearly obvious that all lymphoid immune system cells, including CD8 + T cells, CD4 + T cells, B cells, and innate lymphoid cells, must be present, activated, and costimulated for an effective antitumor immune response(32).The immune checkpoint pathway is a crucial component of traits associated to immunity, and malignant cells exploit this mechanism to suppress immune checkpoint signaling and attenuate immune responses, promoting tumor growth(33, 34). In alignment with this, malignant cells displaying high GPI exhibited increased expression of various immune checkpoint molecules, implying that the unfavorable outcomes observed in RCC patients with high GPI might be associated with heightened expression of immune checkpoint genes and immune function suppression(35). Consequently, immune checkpoint inhibitors have shown effectiveness in promoting antineoplastic immune responses, making them a promising treatment option for patients with high GPI(36).
All of the genes in the gene pair model used in this study had significant effects on cancer. PRSS23 is a serine protease that has been linked to tumor growth in a variety of malignancies is markedly up-regulated in cancer stem cells(37, 38). According to prior gene expression profiling studies,increased PRSS23 expression has been seen in a variety of cancer types, including breast cancer(39), prostate carcinoma(40), papillary thyroid carcinoma(41), and pancreatic cancers (42). However, more research has to be done on the precise function of PRSS23 and its relationship to immunity in RCC. Based on our findings, PRSS23 demonstrated a positive association with EMT and immune cell infiltration. Specifically, PRSS23 exhibited positive associations with several immune cell types, including CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells. Furthermore, our in vitro experiments provided evidence indicating that PRSS23 may have a promoting effect on renal cell metastasis. These findings illuminate the possible function of PRSS23 in immune cell regulation and its influence on renal cancer cell metastatic behavior, suggesting its potential relevance as a target for further research in relation to the development of RCC and treatment approaches.
It is important to recognize the limitations of this study. Firstly, the validation of the prognostic model was based on retrospective data, and prospective clinical trials were not incorporated to assess its effectiveness and general applicability. Secondly, the availability of scRNA-seq samples and publicly accessible data was limited, resulting in a potentially introducing biases into the study.