Background: Kidney renal clear cell carcinoma (KIRC) is the most frequently diagnosed subtype of renal cell carcinoma (RCC); however, the pathogenesis and diagnostic approaches for KIRC remain elusive. Using single-cell transcriptomic information of KIRC, we constructed a diagnostic model depicting the landscape of programmed cell death (PCD)-associated genes, namely cell death-related genes (CDRGs).Methods: In this study, six CDRG categories, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA sequencing (RNA-seq) data of blood-derived exosomes from the exoRBase database, RNA-seq data of tissues from The Cancer Genome Atlas (TCGA) combined with control samples from the GTEx databases, and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database were downloaded. Next, we intersected the differentially expressed genes (DEGs) of the KIRC cohort from exoRBase and the TCGA databases with CDRGs and DEGs obtained from single-cell datasets, further screening out the candidate biomarker genes using clinical indicators and machine learning methods and thus constructing a diagnostic model for KIRC. Finally, we investigated the underlying mechanisms of key genes and their roles in the tumor microenvironment using scRNA-seq, single-cell assays for transposase-accessible chromatin sequencing (scATAC-seq), and the spatial transcriptomics sequencing (stRNA-seq) data of KIRC provided by the GEO database.Result: We obtained 1,428 samples and 216,155 single cells. After the rational screening, we constructed a 13-gene diagnostic model for KIRC, which had high diagnostic efficacy in the exoRBase KIRC cohort (training set: AUC = 1; testing set: AUC = 0.965) and TCGA KIRC cohort (training set: AUC = 1; testing set: AUC = 0.982), with an additional validation cohort from GEO databases presenting an AUC value of 0.914. The results of a subsequent analysis revealed a specific tumor epithelial cell of TRIB3^high subset. Moreover, the results of a mechanical analysis showed the relatively elevated chromatin accessibility of TRIB3 in tumor epithelial cells in the scATAC data, while stRNA-seq verified that TRIB3 was predominantly expressed in cancer tissues.Conclusions: The 13-gene diagnostic model yielded high accuracy in KIRC screening, and TRIB3^high tumor epithelial cells could be a promising therapeutic target for KIRC.

The cathepsin S (CTSS) gene encodes a lysine cysteine protease and serves an important role in the development of autoimmune diseases, inflammation and nervous system diseases. Furthermore, CTSS is implicated in tumor invasion and metastasis by the induction of tumor angiogenesis and the degradation of the tumor extracellular matrix. Nevertheless, the precise impact of CTSS on predicting pan‑cancer prognosis and its influence on the tumor microenvironment and immune infiltration in human cancers remains unknown. This present study employed a comprehensive array of bioinformatic methods to evaluate the expression of CTSS and its associations with prognosis, clinicopathological characteristics, tumor microenvironment, tumor immune infiltration, tumor mutational burden and microsatellite instability across numerous cancer types. The current study demonstrated abnormal expression and distinct genomic alteration profiles of CTSS in many of the cancers tested. Furthermore, CTSS expression exhibited close associations with the prognosis of numerous cancers. High CTSS expression was significantly associated with better overall survival and disease‑specific survival in bladder urothelial carcinoma (BLCA) and skin cutaneous melanoma (SKCM) but worse outcomes in brain lower grade glioma (LGG) and uveal melanoma (UVM). Moreover, CTSS demonstrated significant correlations with tumor mutational burden and microsatellite instability in 8 and 12 cancer types respectively, as well as different responses in immunotherapy sub‑cohorts, especially in melanoma and bladder cancers. CTSS expression showed a positive correlation with stromal and immune cell scores in the four aforementioned cancers. Moreover, CTSS expression was correlated with the number of infiltrating CD8⁺ T cells, CD4⁺ T cells and macrophages. Conversely, CTSS was negatively associated with resting Mast cells, resting NK cells and resting memory CD4⁺ T cell infiltration in BLCA, SKCM and kidney renal clear cell carcinoma (KIRC). Furthermore, CTSS expression was correlated with immune‑related gene expression, notably PDCD1, LAG3, PDCD1 and TIGIT in BLCA, KIRC, SKCM, LGG and UVM. Functional enrichment analysis suggested that CTSS could drive a dynamic adjustment of biological functions and pathways in BLCA, SKCM, LGG and UVM, including immune response regulating signaling pathways, regulation of lymphocyte activation and T cell receptor singling pathways. The current study suggested that CTSS could be an essential biomarker for prognosis and immune infiltration features in multiple cancers.

Background: Apolipoprotein E (APOE) is a polymorphic protein that plays a role in lipoprotein transformation and metabolism. It is involved in numerous physiological processes within the body and is closely associated with tumor growth and metastasis. However, the role of APOE in pan-cancer has yet to be evaluated. Therefore, studying the association between APOE and various cancer types is crucial for providing a basis for individualized treatment strategies and clinical prognosis assessment. Methods: We investigated the diagnostic and prognostic significance of APOE across 33 tumor types, as well as its correlation with tumor mutation burden (TMB) and microsatellite instability (MSI). Additionally, we employed the ESTIMATE and CIBERSORT algorithms to analyze the potential impact of APOE on the immune system. Furthermore, gene set enrichment analysis (GSEA) was conducted to explore its underlying physiological function. Results: Based on observations from a pan-cancer dataset, APOE expression was significantly different between cancer and normal tissues, and was simultaneously associated with survival outcomes in terms of cancer type, clinical annotation, TMB, MSI, and TICs abundance. In addition, the results also showed that expression of APOE may respond to a variety of cancer chemotherapy. Conclusion: The findings from this study strongly indicate a close association between APOE and tumor development. Moreover, APOE shows promise as a potential biomarker for predicting prognosis and response to immunotherapy in patients with pan-cancer.

To investigate the correlation between cluster of differentiation-44 (CD44) expression and immunotherapy response and identify its possible predictive value in pan-cancer. Datasets of 33 cancer types from The Cancer Genome Atlas (TCGA) database were applied to investigate the relationship of CD44 expression with prognosis, tumor mutational burden (TMB), and microsatellite instability (MSI), and determine its potential prognostic value in pan-cancer. Patients were split into high-risk and low-risk cancer groups based on the survival outcomes of various cancer types. Additionally, the underlying mechanisms of CD44 in the tumor microenvironment (TME) were analyzed using ESTIMATE and CIBERSORT algorithms and Gene Set Enrichment Analysis (GSEA). Subsequently, the biological role of CD44 at single-cell level was investigated using CancerSEA database. Variable expression levels of CD44 between tumor and adjacent normal tissues were identified in pan-cancer datasets, further survival analysis revealed that CD44 expression was associated with multiple clinical annotations and survival indicators. Besides, the expression of CD44 was significantly associated with TMB and MSI in 10 types and 6 types of cancer, respectively, indicating it could be exploited as a potential biomarker predicting immunotherapy outcomes. Meanwhile, CD44 could influence several crucial immune cell-related pathways. and the results revealed by CancerSEA database denoted the correlation of CD44 with malignant phenotype and functional states, further indicating it can serve as a potential therapeutic target in cancer management. Our study demonstrated that CD44 shows great promise as a prognostic biomarker in numerous cancers, which will assist in developing new strategies in cancer management.

Introduction: Hepatocellular carcinoma (HCC), representing more than 80% of primary liver cancer cases, lacks satisfactory etiology and diagnostic methods. This study aimed to elucidate the role of programmed cell death-associated genes (CDRGs) in HCC by constructing a diagnostic model using single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data.Methods: Six categories of CDRGs, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA-seq data from blood-derived exosomes were sourced from the exoRBase database, RNA-seq data from cancer tissues from the TCGA database, and scRNA-seq data from the GEO database. Subsequently, we intersected the differentially expressed genes (DEGs) of the HCC cohort from exoRBase and TCGA databases with CDRGs, as well as DEGs obtained from single-cell datasets. Candidate biomarker genes were then screened using clinical indicators and a machine learning approach, resulting in the construction of a seven-gene diagnostic model for HCC. Additionally, scRNA-seq and spatial transcriptome sequencing (stRNA-seq) data of HCC from the Mendeley data portal were used to investigate the underlying mechanisms of these seven key genes and their association with immune checkpoint blockade (ICB) therapy. Finally, we validated the expression of key molecules in tissues and blood-derived exosomes through quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry experiments.Results: Collectively, we obtained a total of 50 samples and 104,288 single cells. Following the meticulous screening, we established a seven-gene diagnostic model for HCC, demonstrating high diagnostic efficacy in both the exoRBase HCC cohort (training set: AUC = 1; testing set: AUC = 0.847) and TCGA HCC cohort (training set: AUC = 1; testing set: AUC = 0.976). Subsequent analysis revealed that HCC cluster 3 exhibited a higher stemness index and could serve as the starting point for the differentiation trajectory of HCC cells, also displaying more abundant interactions with other cell types in the microenvironment. Notably, key genes TRIB3 and NQO1 displayed elevated expression levels in HCC cells. Experimental validation further confirmed their elevated expression in both tumor tissues and blood-derived exosomes of cancer patients. Additionally, stRNA analysis not only substantiated these findings but also suggested that patients with high TRIB3 and NQO1 expression might respond more favorably to ICB therapy.Conclusions: The seven-gene diagnostic model demonstrated remarkable accuracy in HCC screening, with TRIB3 emerging as a promising diagnostic tool and therapeutic target for HCC.

Background: Ovarian cancer (OV) is the deadliest gynecologic malignancy owing to its late diagnosis and high metastatic propensity. Current biomarkers lack sufficient sensitivity and specificity for the detection of early-stage cancer. To address this gap, we integrated single‐cell transcriptomic profiling of tumor tissues with analysis of circulating exosomal RNA, aiming to uncover candidate markers that reflect tumor heterogeneity and metastatic potential and that may serve as sensitive, non‐invasive diagnostics.Methods: We integrated single-cell RNA sequencing (scRNA-seq) data from primary tumors and metastatic lesions with bulk tissue transcriptomes and plasma-derived exosomal RNA sequencing (RNA-seq). Differentially expressed genes (DEGs) shared across tumor cells, metastatic subpopulations, and exosomes were identified through intersection analysis. Candidate genes were validated in clinical specimens using qPCR and immunohistochemistry. We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. Tumor cell differentiation states were evaluated using CytoTRACE, and intercellular communication was analyzed with CellChat.Results: Intersection analysis highlighted 52 overlapping DEGs, of which SCNN1A and EFNA1 emerged as the top prognostic indicators. Both genes were significantly upregulated in tumor tissues, metastatic foci, and plasma exosomes (P < 0.01). The exosome-based Adaboost model had an area under the curve of 0.955 in an independent test cohort. Single-cell subcluster analyses revealed high SCNN1A/EFNA1 expression correlated with stem-like differentiation states and enriched pathways associated with immune evasion and adhesion. CellChat analysis demonstrated that highly differentiated tumor cells extensively engaged with fibroblasts and endothelial cells, implying their role in niche formation.Conclusions: By coupling single-cell, bulk tissue, and exosomal transcriptomics, we elucidated the key molecular drivers of OV metastasis and established SCNN1A and EFNA1 as promising non-invasive biomarkers. This multi-omics framework provides an effective strategy for early detection and a better understanding of metastatic progression in OV.