The tumour microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) comprises multiple cell types, which promote tumour progression and modulate drug resistance and immune cell infiltrations via ligand-receptor (LR) interactions. However, the interactions, expression patterns, and clinical relevance of LR in the TME in ccRCC are insufficiently characterised. This study characterises the complex composition of the TME in ccRCC by analysing the single-cell sequencing (scRNA-seq) data of patients with ccRCC from the Gene expression omnibus database. On analysing the scRNA-seq data combined with the cancer genome atlas kidney renal clear cell carcinoma (TCGA-KIRC) dataset, 46 LR-pairs were identified that were significantly correlated and had prognostic values. Furthermore, a new molecular subtyping model was proposed based on these 46 LR-pairs. Molecular subtyping was performed in two ccRCC cohorts, revealing significant differences in prognosis between the subtypes of the two ccRCC cohorts. Different molecular subtypes exhibited different clinicopathological features, mutational, pathway, and immune signatures. Finally, the LR.score model that was constructed using ten essential LR-pairs that were identified based on LASSO Cox regression analysis revealed that the model could accurately predict the prognosis of patients with ccRCC. In addition, the differential expression of ten LR-pairs in tumour and normal cell lines was identified. Further functional experiments showed that CX3CL1 can exert anti-tumorigenic role in ccRCC cell line. Altogether, the effects of immunotherapy were connected to LR.scores, indicating that potential medications targeting these LR-pairs could contribute to the clinical benefit of immunotherapy. Therefore, this study identifies LR-pairs that could be effective biomarkers and predictors for molecular subtyping and immunotherapy effects in ccRCC. Targeting LR-pairs provides a new direction for immunotherapy regimens and prognostic evaluations in ccRCC.

Background An increasing number of studies have demonstrated that CX3CL1 is involved in the development of tumors and may thus be considered a new potential therapeutic target for them. However, the function of CX3CL1 in clear cell renal cell carcinoma (ccRCC) remains poorly defined. Methods The pan-cancer expression pattern and prognostic value of CX3CL1 were evaluated in this study. Moreover, the relationship of CX3CL1 expression with the tumor microenvironment, especially the tumor immune microenvironment, was analyzed. Our analyses employed public repository data. Additionally, we generated stable CX3CL1-overexpressing 786-O cells to determine the role of CX3CL1 in vitro via cell viability and transwell assays. A xenograft tumor model was used to determine the role of CX3CL1 in vivo. The association between CX3CL1 and ferroptosis sensitivity of tumor cells was assessed using Ferrostatin-1. Results Our findings indicated the involvement of CX3CL1 in the occurrence and development of ccRCC by acting as a tumor suppressor. We also found that ccRCC patients with high CX3CL1 expression showed better clinical outcomes than those with low CX3CL1 expression. The findings of our epigenetic study suggested that the expression of CX3CL1 in ccRCC is correlated with its DNA methylation level. Furthermore, the CX3CL1 expression level was closely related to the infiltration level of CD8+ T cells into the tumor microenvironment (TME). CX3CL1 showed different predictive values in different immunotherapy cohorts. Finally, CX3CL1 overexpression inhibited tumor cell proliferation and metastasis and promoted tumor ferroptosis sensitivity in ccRCC. Conclusions This study revealed the role of CX3CL1 as a tumor suppressor in ccRCC. Our findings indicated that CX3CL1 plays a crucial role in regulating the ccRCC TME and is a potential predictor of immunotherapy outcomes in ccRCC. We also found that CX3CL1 can promote ferroptosis sensitivity in ccRCC cells.

IntroductionThe heterogeneity of tumor immune microenvironments is a major factor in poor prognosis among hepatocellular carcinoma (HCC) patients. Neutrophils have been identified as playing a critical role in the immune microenvironment of HCC based on recent single-cell studies. However, there is still a need to stratify HCC patients based on neutrophil heterogeneity. Therefore, developing an approach that efficiently describes "neutrophil characteristics" in HCC patients is crucial to guide clinical decision-making.MethodsWe stratified two cohorts of HCC patients into molecular subtypes associated with neutrophils using bulk-sequencing and single-cell sequencing data. Additionally, we constructed a new risk model by integrating machine learning analysis from 101 prediction models. We compared the biological and molecular features among patient subgroups to assess the model's effectiveness. Furthermore, an essential gene identified in this study was validated through molecular biology experiments.ResultsWe stratified patients with HCC into subtypes that exhibited significant differences in prognosis, clinical pathological characteristics, inflammation-related pathways, levels of immune infiltration, and expression levels of immune genes. Furthermore, A risk model called the "neutrophil-derived signature" (NDS) was constructed using machine learning, consisting of 10 essential genes. The NDS's RiskScore demonstrated superior accuracy to clinical variables and correlated with higher malignancy degrees. RiskScore was an independent prognostic factor for overall survival and showed predictive value for HCC patient prognosis. Additionally, we observed associations between RiskScore and the efficacy of immune therapy and chemotherapy drugs.DiscussionOur study highlights the critical role of neutrophils in the tumor microenvironment of HCC. The developed NDS is a powerful tool for assessing the risk and clinical treatment of HCC. Furthermore, we identified and analyzed the feasibility of the critical gene RTN3 in NDS as a molecular marker for HCC.

IntroductionCellular senescence (CS) plays a critical role in cancer development, including clear cell renal cell carcinoma (ccRCC). Traditional RNA sequencing cannot detect precise molecular composition changes within tumors. This study aimed to analyze cellular senescence’s biochemical characteristics in ccRCC using single RNA sequencing (ScRNA-seq) and traditional RNA sequencing (Bulk RNA-seq).MethodsResearchers analyzed the biochemical characteristics of cellular senescence in ccRCC using ScRNA-seq and Bulk RNA-seq. They combined these approaches to identify differences between malignant and non-malignant phenotypes in ccRCC across three senescence-related pathways. Genes from these pathways were used to identify molecular subtypes associated with senescence, and a new risk model was constructed. The function of the gene DUSP1 in ccRCC was validated through biological experiments.ResultsThe combined analysis of ScRNA-seq and Bulk RNA-seq revealed significant differences between malignant and non-malignant phenotypes in ccRCC across three senescence-related pathways. Researchers identified genes from these pathways to identify molecular subtypes associated with senescence, constructing a new risk model. Different subgroups showed significant differences in prognosis level, clinical stage and grade, immune infiltration, immunotherapy, and drug sensitivity.DiscussionSenescence signature markers are practical biomarkers and predictors of molecular typing in ccRCC. Differences in prognosis level, clinical stage and grade, immune infiltration, immunotherapy, and drug sensitivity between different subgroups indicate that this approach could provide valuable insights into senescence-related treatment options and prognostic assessment for patients with ccRCC. The function of the gene DUSP1 in ccRCC was validated through biological experiments, confirming its feasibility as a novel biomarker for ccRCC. These findings suggest that targeted therapies based on senescence-related mechanisms could be an effective treatment option for ccRCC.