Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA. METHODS: We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS). RESULTS: The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups. CONCLUSIONS: In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Coordinating single-cell and bulk RNA-seq in deciphering the intratumoral immune landscape and prognostic stratification of prostate cancer patients.

INTRODUCTION: Prostate cancer is a common cancer among male population. The aberrant expression of histone modifiers has been identified as a potential driving force in numerous cancer types. However, the mechanism of histone modifiers in the development of prostate cancer remains unknown. METHODS: Expression profiles and clinical data were obtained from GSE70769, GSE46602, and GSE67980. Seruat R package was utilized to calculate the gene set enrichment of the histone modification pathway and obtain the Histone score. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify marker genes with prognostic value. Kaplan-Meier survival analysis was conducted to assess the efficacy of the prognostic model. In addition, microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms were employed for immune infiltration analysis. Drug sensitivity prediction was performed using oncoPredict R package. RESULTS: We screened differentially expressed genes (DEGs) between Histone-high score (Histone-H) and Histone-low score (Histone-L) groups, which were enriched in RNA splicing and DNA-binding transcription factor binding pathways. We retained four prognostic marker genes, including TACC3, YWHAH, TAF1C and TTLL5. The risk model showed significant efficacy in stratification of the prognosis of prostate cancer patients in both internal and external cohorts (p < .0001 and p = .032, respectively). In addition, prognostic gene YWHAH was infiltrated in abundance of fibroblasts and highly correlated with Entinostat_1593 drug sensitivity score and the value of risk score. CONCLUSION: We innovatively developed a histone modification-related prognostic model with high prognostic potency and identified YWHAH as possible diagnostic and therapeutic biomarkers for prostate cancer. It provides novel insights to address prostate cancer and enhance clinical outcomes, thereby opening up a new avenue for customized treatment alternatives.

The single-cell evolution trajectory presented different hypoxia heterogeneity to reveal the carcinogenesis of genes in clear cell renal cell carcinoma: Based on multiple omics and real experimental verification.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is the most prevalent and aggressive subtype of renal cell carcinoma, originating from renal tubular epithelial cells in the kidney. Hypoxia proves to be a feature commonly observed in solid tumors, leading to increased resistance to treatment and tumor progression. METHODS: scRNA-seq data were procured from GSE159115 data set. We utilized UMAP and NMF algorithm for clustering and dimensionality reduction. The FindAllMarkers function was used to compare various groups and identify potential hypoxia marker genes. A series of in vitro experiments, including CFA, flow cytometry targeting cell cycle, CCK-8, and EDU, was applied to investigate how ANGPTL4 regulated the ccRCC progression. Two cell lines of ccRCC cells, 786-O and Caki, were used for si-ANGPTL4 transfection. RESULTS: We annotated a total of a total of 6 cell clusters, namely ccRCC malignant cells, T cells, endothelial cells, myeloid cells, smooth muscle cells, and B cells. We observed higher levels of hypoxia-score in the ccRCC malignant cells, while lowest hypoxia-score in T and B cells. We detected multiple hypoxia-related subclusters of TME cells in ccRCC, among which S100A4 CD8+ T cells and nonhypoxia CD8+ T cells were found with a marked elevation of T cell inhibitory gene score. We identified that ANGPTL4+ endothelial cells might function as an integrative role in tumor angiogenesis. Multiple TME subclusters showed high potency in stratification of the prognosis of ccRCC patients. Moreover, by a series of in vitro experiment, we found ANGPTL4 regulated the ccRCC cell proliferation, probably through ERK/P38 pathway. CONCLUSION: We discerned multiple hypoxia-related subclusters of TME cells in ccRCC, which displayed distinct functional features and great potency in predicting prognosis of ccRCC patients. We identified the role of ANGPTL4 in regulating ccRCC proliferation via ERK/p38 pathway.

Investigating the prognostic role of lncRNAs associated with disulfidptosis-related genes in clear cell renal cell carcinoma.

INTRODUCTION: Renal cell carcinoma (RCC) is a grave malignancy that poses a significant global health burden with over 400,000 new cases annually. Disulfidptosis, a newly discovered programmed cell death process, is linked to the actin cytoskeleton, which plays a vital role in maintaining cell shape and survival. The role of disulfidptosis is poorly depicted in the clear cell histologic variant of RCC (ccRCC). METHODS: Three sets of ccRCC cohorts, ICGC_RECA-EU (n = 91), GSE76207 (n = 32) and TCGA-KIRC (n = 607), were included in our study, the batch effect of which was removed using the "combat" function. Correlation was calculated using the "rcorr" function of the "Hmisc" package for Pearson analysis, which was visualized using the "pheatmap" package. Principal component analysis was performed by the "vegan" package, visualized using the "scatterplot3d" package. Long non-coding RNAs (lncRNAs) associated with disulfidptosis were screened out using least absolute shrinkage and selection operator (LASSO) and COX analysis. Tumor mutation, immune landscaping and immunotherapy prediction were performed for further characterization of two risk groups. RESULTS: A total of 1822 disulfidptosis-related lncRNAs was selected, among which 308 lncRNAs were found to be significantly associated with the clinical outcome of ccRCC patients. We retained 11 disulfidptosis-related lncRNAs, namely, AP000439.3, RP11-417E7.1, RP11-119D9.1, LINC01510, SNHG3, AC156455.1, RP11-291B21.2, EMX2OS, AC093850.2, HAGLR and RP11-389C8.2, through LASSO and COX analysis for prognosis model construction, which displayed satisfactory accuracy (area under the curve, AUC, values all above 0.6 in multiple cohorts) in stratification of ccRCC prognosis. A nomogram model was constructed by integrating clinical factors with risk score, which further enhanced the prediction efficacy (AUC values all above 0.7 in multiple cohorts). We found that patients of male gender, higher clinical stages and advanced pathological T stage were inclined to have higher risk score values. Dactinomycin_1911, Vinblastine_1004, Daporinad_1248 and Vinorelbine_2048 were identified as promising candidate drugs for treating ccRCC patients of higher risk score value. Moreover, patients of higher risk value were prone to be resistant to immunotherapy. CONCLUSION: We developed a prognosis predicting model based on 11 selected disulfidptosis-related lncRNAs, the efficacy of which was verified in different cohorts. Furthermore, we delineated an intricate portrait of tumor mutation, immune topography and pharmacosensitivity evaluations within disparate risk stratifications.

Collaborating single-cell and bulk RNA sequencing for comprehensive characterization of the intratumor heterogeneity and prognostic model development for bladder cancer.

INTRODUCTION: Gaining a deeper insight into the single-cell RNA sequencing (scRNA-seq) results of bladder cancer (BLCA) provides a transcriptomic profiling of individual cancer cells, which may disclose the molecular mechanisms involved in BLCA carcinogenesis. METHODS: scRNA data were obtained from GSE169379 dataset. We used the InferCNV software to determine the copy number variant (CNV) with normal epithelial cells serving as the reference, and performed the pseudo-timing analysis on subsets of epithelial cell using Monocle3 software. Transcription factor analysis was conducted using the Dorothea software. Intercellular communication analysis was performed using the Liana software. Cox analysis and LASSO regression were applied to establish a prognostic model. RESULTS: We investigated the heterogeneity of tumors in four distinct cell types of BLCA cancer, namely immune cells, endothelial cells, epithelial cells, and fibroblasts. We evaluated the transcription factor activity of different immune cells in BLCA and identified significant enrichment of TCF7 and TBX21 in CD8+ T cells. Additionally, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs), namely iCAFs and myoCAFs, which exhibited distinct communication patterns. Using sub-cluster and cell trajectory analyses, we identified different states of normal-to-malignant cell transformation in epithelial cells. TF analysis further revealed high activation of MYC and SOX2 in tumor cells. Finally, we identified five model genes (SLCO3A1, ANXA1, TENM3, EHBP1, LSAMP) for the development of a prognostic model, which demonstrated high effectiveness in stratifying patients across seven different cohorts. CONCLUSIONS: We have developed a prognostic model that has demonstrated significant efficacy in stratifying patients with BLCA.

Exploring oncogenes for renal clear cell carcinoma based on G protein-coupled receptor-associated genes.

G protein-coupled receptors (GPCRs) are a class of receptors on cell membranes that regulate various biological processes in cells, such as cell proliferation, differentiation, migration, apoptosis, and metabolism, by interacting with G proteins. However, the role of G protein-coupled receptors in predicting the prognosis of renal clear cell carcinoma is still unknown. The transcriptome data and clinical profiles of renal clear cell carcinoma patients, were downloaded from TCGA databases, and the validation group data were downloaded from number GSE167573, including 63 tumor samples and 14 normal samples. Single-cell RNA sequencing data were downloaded from the GEO database, No. GSE152938 and selected samples were used for GSEA enrichment analysis, WGCNA subgroup analysis, single-cell data analysis, and mutation analysis to explore the role of G protein-coupled receptor-related genes in the diagnosis and prognosis of renal clear cell carcinoma and to verify their reliability with cellular experiments. Finally, this study establishes a disease model based on G protein-coupled receptor-related genes, which may help to propose targeted therapeutic regimens in different strata of renal cell carcinoma patients.Author names: Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author: Given name [Lisa Jia] Last name [Tran].It's ok!

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay.

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Single-cell transcriptome analysis revealing the intratumoral heterogeneity of ccRCC and validation of MT2A in pathogenesis.

Clear-cell renal cell carcinoma (ccRCC) appears as the most common type of kidney cancer, the carcinogenesis of which has not been fully elucidated. Tumor heterogeneity plays a crucial role in cancer progression, which could be largely deciphered by the implement of scRNA-seq. The bulk and single-cell RNA expression profile is obtained from TCGA and study conducted by Young et al. We utilized UMAP, TSNE, and clustering algorithm Louvain for dimensionality reduction and FindAllMarkers function for determining the DEGs. Monocle2 was utilized to perform pseudo-time series analysis. SCENIC was implemented for transcription factor analysis of each cell subgroup. A series of WB, CFA, CCK-8, and EDU analysis was utilized for the validation of the role of MT2A in ccRCC carcinogenesis. We observed higher infiltration of T/NK and B cells in tumorous tissues, indicating the role of immune cells in ccRCC carcinogenesis. Transcription factor analysis revealed the activation of EOMES and ETS1 in CD8 + T cells, while CAFs were divided into myo-CAFs and i-CAFs, with i-CAFs showing distinct enrichment of ATF3, JUND, JUNB, EGR1, and XBP1. Through cell trajectory analysis, we discerned three distinct stages of cellular evolution, where State2 symbolizes normal renal tubular cells that underwent transitions into State1 and State3 as the CNV score ascended. Functional enrichment examination revealed an amplification of interferon gamma and inflammatory response pathways within tumor cells. The consensus clustering algorithm yielded two molecular subtypes, with cluster 2 being associated with advanced tumor stages and an abundance of infiltrated immune cells. We identified 17 prognostic genes through Cox and LASSO regression models and used them to construct a prognostic model, the efficacy of which was verified in multiple cohorts. Furthermore, we investigated the role of MT2A, one of our hub genes, in ccRCC carcinogenesis, and found it to regulate proliferation and migration of malignant cells. We depicted a detailed single-cell landscape of ccRCC, with special focus on CAFs, endothelial cells, and renal tubular cells. A prognostic model of high stability and accuracy was constructed based on the DEGs. MT2A was found to be actively implicated in ccRCC carcinogenesis, regulating proliferation and migration of the malignant cells.

Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells.

Background: Melanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity. Materials and methods: Cells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell-cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines. Results: Following batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells' proliferation, invasion, and migration. Conclusion: The differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells.

CD8 + T-cell marker genes reveal different immune subtypes of oral lichen planus by integrating single-cell RNA-seq and bulk RNA-sequencing.

BACKGROUND: Oral lichen planus (OLP) is a local autoimmune disease induced by T-cell dysfunction that frequently affects middle-aged or elderly people, with a higher prevalence in women. CD8 + T cells, also known as killer T cells, play an important role in the progression and persistence of OLP. In order to identify different OLP subtypes associated with CD8 + T cell pathogenesis, consensus clustering was used. METHODS: In this study, we preprocessed and downscaled the OLP single-cell dataset GSE211630 cohort downloaded from Gene Expression Omnibus (GEO) to finally obtain the marker genes of CD8 + T cells. Based on the expression of marker genes, we classified OLP patients into CMGs subtypes using unsupervised clustering analysis. The gene expression profiles were analyzed by WGCNA using the "WGCNA" R package based on the clinical disease traits and typing results, and 108 CD8 + T-cell related OLP pathogenicity-related genes were obtained from the intersection. Patients were once again classified into gene subtypes based on intersection gene expression using unsupervised clustering analysis. RESULTS: After obtaining the intersecting genes of CD8 + T cells related to pathogenesis, OLP patients can be precisely classified into two different subtypes based on unsupervised clustering analysis, and subtype B has better immune infiltration results, providing clinicians with a reference for personalized treatment. CONCLUSIONS: Classification of OLP into different subtypes improve our current understanding of the underlying pathogenesis of OLP and provides new insights for future studies.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC.

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Revolutionizing anti-tumor therapy: unleashing the potential of B cell-derived exosomes.

B cells occupy a vital role in the functioning of the immune system, working in tandem with T cells to either suppress or promote tumor growth within the tumor microenvironment(TME). In addition to direct cell-to-cell communication, B cells and other cells release exosomes, small membrane vesicles ranging in size from 30-150 nm, that facilitate intercellular signaling. Exosome research is an important development in cancer research, as they have been shown to carry various molecules such as major histocompatibility complex(MHC) molecules and integrins, which regulate the TME. Given the close association between TME and cancer development, targeting substances within the TME has emerged as a promising strategy for cancer therapy. This review aims to present a comprehensive overview of the contributions made by B cells and exosomes to the tumor microenvironment (TME). Additionally, we delve into the potential role of B cell-derived exosomes in the progression of cancer.