ccRCC is a complex disease with diverse subtypes, making personalized treatment challenging. The response immunotherapy can vary among different subtypes of ccRCC, highlighting the need for subtype-specific approaches [4]. In this study, we integrated and classified ccRCC patients into two subgroups: a low-risk group (C2) and a high-risk group (C1), based on their immunotherapy response and immune-related gene data. We identified differential genes, related pathways, and immune infiltrating cells between these subgroups, providing a valuable foundation for targeted treatment in ccRCC patients. We also examined the association between RNA-seq data, clinical information, and ICB therapy from GSE67501 dataset, resulting in the identification of 311 DEGs associated with ICB response and non-response.
Multiple studies have highlighted the differences in various pathways across different ccRCC subtypes. In particular, pathways such as NFkB, Trail, EGFR, PI3K, and TNF-α have been found to play significant roles in the high-risk group of ccRCC [5–7]. Targeting genes and proteins within the PI3K pathway has shown promise as a therapeutic approach for ccRCC, and the EGFR pathway is known to be crucial in the development of ccRCC and represents a potential therapeutic target [7]. Our research identified significant enrichments in GO and KEGG terms, including small molecule catalytic processes, apical parts of the cell, mesenchymal development, sarcomere, growth factor activity, and MAPK signaling pathway. These findings indicate that different signaling pathways play distinct roles in the targeted therapy and immunotherapy of ccRCC.
Survival analysis demonstrated that the overall survival of the C1 group was significantly higher than that of the C2 group. Furthermore, PCA results indicated that the intersection of the two subgroup datasets obtained through cluster analysis was minimal, suggesting the effectiveness of this subtype classification in identifying ccRCC patients with poor prognoses. These findings highlight the potential of utilizing molecular subgroups and incorporating key clinical factors for accurate prognostic assessment in ccRCC. In addition, we developed a clinical prediction model by integrating immune scoring, grading, stage (T, N, M), and subgroups. Some studies have also developed immune-related prognostic models for ccRCC, however, this model does not include clinical variables [8]. While, our nomogram based on this model exhibited an area under the ROC curve of 0.732, and calibration curve analysis confirmed its good prediction efficiency, indicating that our predictive model is more accurate and reliable in predicting the progression of renal cell carcinoma. The developed nomogram holds promise for assisting clinicians in predicting the 3-year overall recurrence and aiding in personalized treatment decision-making.
The infiltration of immune cells in tumors was found to be closely associated with clinical outcomes. Notably, neutrophils, B lymphocytes, and monocytes exhibit dual promoting and inhibiting effects in tumors [9]. This study identified significant differences in the composition of infiltrating immune cells among different subtypes of ccRCC patients. Four types of immune cells showed variations across ccRCC subtypes. Immune microenvironment analysis demonstrated that the levels of immune infiltration of B cells, neutrophils, macrophages, and myeloid dendritic cells were significantly higher in group C1 compared to group C2. Previous studies [9, 10] have highlighted that the interaction between B lymphocytes and neutrophils in stimulating immunoglobulin diversity in the splenic marginal zone, which contributes to a natural antibacterial immunoglobulin defense layer. Although the impact of B lymphocyte infiltration on ccRCC progression is not yet clear, studies have indicated that high infiltration of CD20+ B lymphocytes is associated with a poor prognosis of ccRCC [11–13]. This indicates that different subsets of B lymphocytes exhibit different immune characteristics. However, further research is needed to elucidate the specific mechanism involved.
TMB is defined as the number of mutations in the somatic cell genome, which determines tumor immunogenicity. The tumor microenvironment influences T cell infiltration, distribution, and function. Hence, low immunogenicity and a highly immunosuppressive tumor microenvironment form the basis of primary drug resistance to immune checkpoint inhibitors (ICIs) and impact their efficacy [14]. The TMB score can be used to evaluate tumor immunotherapy response and prognosis. The results revealed significantly lower expression levels of SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3 and PDCD1L in group C1 compared to group C2. In addition, this study revealed that the TMB score in the C1 group was significantly higher than that in the C2 group, indicating superior immunotherapy and prognosis effects in the C1 group. However, numerous research findings have indicated that TMB in ccRCC alone cannot serve as a predictive indicator of ICI drug efficacy [15, 16]. The TIDE score can assess tumor immune evasion, where a higher TIDE score suggests a higher likelihood of immune surveillance evasion and a lower success rate of immunotherapy. The TIDE score of the C2 group was significantly higher than that of the C1 group, indicating poor ICI effects in the C2 group. These studies collectively suggest that this molecular subtype can be effectively used to evaluate immune therapy response and prognosis in ccRCC.
MAL (T Cell Differentiation Protein, MAL) was originally discovered by Alonso et al [17] in 1987 as a candidate suppressor gene expressed in the middle and late stages of T cell differentiation. MAL may inhibit tumor progression by influencing the activation and proliferation of T cells [18]. In this study, MAL was identified from the DEGs analyzed between response and non-response to treatment. Interestingly, the expression of the MAL gene was significantly lower in ccRCC. Patients with high expression of MAL exhibited significantly higher overall survival compared to those with low expression, and its expression was negatively correlated with the stage of ccRCC. These findings suggest that MAL may play a role in inhibiting tumor progression in the immune microenvironment of ccRCC, although its specific mechanism remains unclear.
Cytotoxic CD8+ T cells and CD4+ T helper cells can play a crucial role in targeting antigenic tumor cells to prevent tumor growth [19]. Higher levels of activated cytotoxic CD8+T cells have been associated with a more favorable prognosis in ccRCC [20]. In addition, other studies have shown that the infiltration status of CD4+ T cells and CD8+ T cells is related to the response to ICI drugs and patient prognosis [21]. This suggests that the intrinsic characteristics of tumors may interactively influence the infiltration status of CD4+ T cells and CD8+ T cells [22]. In our study, we observed a negative correlation between MAL gene expression and TBM score, but a positive correlation with immediate infiltration of CD8+ T cells. To examine MAL expression in different samples and immune cell populations of renal cell carcinoma, we conducted single-cell data analysis using GSE121636 data. The results showed that MAL was highly expressed in the CD8+ T cells group of peripheral blood and tumor, with significantly higher expression in peripheral blood compared to tumor tissue. In addition, Flow cytometry analysis showed that the proportion of CD8+T cells tending towards si-MAL cells was only 13.35%, while the proportion of empty control group was 38.09%, confirming that MAL can induce the recruitment of CD8+T cells into the tumor microenvironment. These findings suggest that MAL may influence the prognosis and immunotherapy response of ccRCC through the regulation of CD8+ T cells infiltration.