Drug Sensitivity Analysis Research Articles

Lipid metabolism subtypes reflects the prognosis and immune profiles of bladder cancer patients by NMF clustering and building related signature

BackgroundLipid metabolism is crucial in tumor formation and progression. However, the role of lipid metabolism genes (LMGs) in bladder cancer (BLCA) are unknown. The purpose of this study was to construct a LMGs-related subtypes that predicted the treatment and prognosis of BLCA patients.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used for this study. The gene set enrichment analysis (GSEA) was utilized to distinguish functional differences between high-risk (HR) and low-risk (LR) groups. Single-sample GSEA (ssGSEA) was employed to determine potential associations between prognostic outcomes and immune status.ResultsFirst, BLCA patients were divided into two subtypes by non-negative matrix factorization (NMF) clustering, and there were substantial variations in survival status, immune cell infiltration and immune classification between the two subtypes. Next, a prognostic signature involving 8 LMGs was identified (AKR1B1, SCD, CYP27B1, UGCG, SGPL1, FASN, TNFAIP8L3, PLA2G2A). HR patients exhibited worse outcome than LR patients. Multivariate Cox regression analysis confirmed that LMGs-related signature was an independent prognostic indicator of BLCA patients’ survival. Compared with clinicopathological variables, LMGs-related signature showed higher prognostic predictive ability, with an area under curve of 0.720 at 5 years of follow-up. Through immunotherapy analysis, drug sensitivity analysis, TIDE score and immune cell infiltration characteristics, LMGs-related signature was confirmed to accurately predict the prognosis and treatment response of BLCA patients.ConclusionOur newly established prognostic signature, which involved eight LMGs, can give prognostic distinction for BLCA and may eventually lead to novel targets for treatment.

Prognostic feature based on androgen-responsive genes in bladder cancer and screening for potential targeted drugs

ObjectivesBladder cancer (BLCA) is a tumor that affects men more than women. The biological function and prognostic value of androgen-responsive genes (ARGs) in BLCA are currently unknown. To address this, we established an androgen signature to determine the prognosis of BLCA.MethodsSequencing data for BLCA from the TCGA and GEO datasets were used for research. The tumor microenvironment (TME) was measured using Cibersort and ssGSEA. Prognosis-related genes were identified and a risk score model was constructed using univariate Cox regression, LASSO regression, and multivariate Cox regression. Drug sensitivity analysis was performed using Genomics of drug sensitivity in cancer (GDSC). Real-time quantitative PCR was performed to assess the expression of representative genes in clinical samples.ResultsARGs (especially the CDK6, FADS1, PGM3, SCD, PTK2B, and TPD52) might regulate the progression of BLCA. The different expression patterns of ARGs may lead to different immune cell infiltration. The risk model indicates that patients with higher risk scores have a poorer prognosis, more stromal infiltration, and an enrichment of biological functions. Single-cell RNA analysis, bulk RNA data, and PCR analysis support the reliability of this risk model, and a nomogram was also established for clinical use. Drug prediction analysis showed that high-risk patients had a better response to fludarabine, AZD8186, and carmustine.ConclusionARGs played an important role in the progression, immune infiltration, and prognosis of BLCA. The ARGs model has high accuracy in predicting the prognosis of BLCA patients and provides more effective medication guidelines.

Palmitoylation-related gene expression and its prognostic value in ovarian cancer: insights into immune infiltration and therapeutic potential

BackgroundPalmitoylation, a key post-translational modification, plays a significant role in ovarian cancer (OV) progression. However, the impact of palmitoylation-related genes on genomic instability, immune infiltration, and therapeutic response in OV remains poorly understood. This study aimed to investigate these factors to facilitate risk stratification and therapeutic intervention, providing insights into personalized treatment strategies.MethodsData from TCGA and GEO were utilized to develop a prognostic model based on palmitoylation-related genes. Differential expression, functional enrichment, and immune infiltration analyses were performed. Immune cell composition and pathway activities in different risk groups were assessed using CIBERSORT and ssGSEA algorithms. Immunotherapy response was predicted using TIDE and SubMap, while drug sensitivity differences were evaluated using the GDSC database.ResultsUnivariate, LASSO, and multivariate Cox regression analyses identified palmitoylation-related genes with significant prognostic value. The prognostic model effectively stratified patients into high- and low-risk groups, demonstrating significant survival differences. Immune infiltration analysis revealed distinct immune cell compositions and functions between risk groups. Low-risk patients exhibited higher immune scores and increased expression of immune checkpoints (PD-1, CD274, CTLA4), suggesting greater response to immunotherapy. Drug sensitivity analysis identified compounds with differential efficacy between risk groups, highlighting potential targeted treatment options.ConclusionPalmitoylation-related genomic features significantly influence OV progression and the immune landscape, offering potential for improved risk stratification and informing immunotherapy strategies to enhance patient outcomes.

Uncovering key genes and molecular mechanisms of dendritic cell dysfunction in Esophageal Cancer: implications for Novel Diagnostic and therapeutic strategies

Dendritic cells play a crucial role in initiating and regulating immune responses, and their dysfunction is strongly associated with esophageal cancer. In this study, we aimed to develop a predictive signature and identify key genes linked to dendritic cell dysfunction in esophageal cancer. Through bioinformatics analysis of gene expression data from the TCGA database, we identified a set of 603 genes significantly associated with dendritic cell function. Further analysis using Cox regression and LASSO regression revealed six genes (GDF15, GPT, KRTAP5-5, MMP12, SLC5A1, and C5orf52) that were strongly correlated with overall survival. The prognostic signature constructed from these genes demonstrated that patients in the high-risk group had poorer survival outcomes compared to those in the low-risk group. Immune infiltration analysis indicated a higher abundance of macrophages in the high-risk group, and correlation studies showed a strong positive association between the risk score and the expression of immune checkpoint markers PD1 and PD-L1. Drug sensitivity analysis suggested that Metformin, Gefitinib, and Lapatinib may be more effective in the low-risk group, while Pyrimethamine, Axitinib, and Rapamycin may be more beneficial for high-risk patients. In summary, we identified a 6-gene signature related to dendritic cell dysfunction that can predict prognosis in esophageal cancer, offering valuable insights for personalized therapeutic strategies.

Innovative strategies to optimise colorectal cancer immunotherapy through molecular mechanism insights

BackgroundColorectal cancer (CRC) is a leading cause of cancer-related deaths globally. The heterogeneity of the tumor microenvironment significantly influences patient prognosis, while the diversity of tumor cells shapes its unique characteristics. A comprehensive analysis of the molecular profile of tumor cells is crucial for identifying novel molecular targets for drug sensitivity analysis and for uncovering the pathophysiological mechanisms underlying CRC.MethodsWe utilized single-cell RNA sequencing technology to analyze 13 tissue samples from 4 CRC patients, identifying key cell types within the tumor microenvironment. Intercellular communication was assessed using CellChat, and a risk score model was developed based on eight prognostic genes to enhance patient stratification for immunotherapeutic approaches. Additionally, in vitro experiments were performed on DLX2, a gene strongly associated with poor prognosis, to validate its potential role as a therapeutic target in CRC progression.ResultsEight major cell types were identified across the tissue samples. Within the tumor cell population, seven distinct subtypes were recognized, with the C0 FXYD5+ tumor cells subtype being significantly linked to cancer progression and poor prognosis. CellChat analysis indicated extensive communication among tumor cells, fibroblasts, and immune cells, underscoring the complexity of the tumor microenvironment. The risk score model demonstrated high accuracy in predicting 1-, 3-, and 5-year survival rates in CRC patients. Enrichment analysis revealed that the C0 FXYD5+ tumor cell subtype exhibited increased energy metabolism, protein synthesis, and oxidative phosphorylation, contributing to its aggressive behavior. In vitro experiments confirmed DLX2 as a critical gene associated with poor prognosis, suggesting its viability as a target for improving drug sensitivity.ConclusionIn summary, this study advances our understanding of CRC progression by identifying critical tumor subtypes, molecular pathways, and prognostic markers that can inform innovative strategies for predicting and enhancing drug sensitivity. These findings hold promise for optimizing immunotherapeutic approaches and developing new targeted therapies, ultimately aiming to improve patient outcomes in CRC.

Integrative multi-omics analysis reveals a novel subtype of hepatocellular carcinoma with biological and clinical relevance

BackgroundHepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and the development of accurate predictive models for prognosis and drug sensitivity remains challenging.MethodsWe integrated laboratory data and public cohorts to conduct a multi-omics analysis of HCC, which included bulk RNA sequencing, proteomic analysis, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics sequencing (ST-seq), and genome sequencing. We constructed a tumor purity (TP) and tumor microenvironment (TME) prognostic risk model. Proteomic analysis validated the TP-TME-related signatures. Joint analysis of scRNA-seq and ST-seq revealed characteristic clusters associated with TP high-risk subtypes, and immunohistochemistry confirmed the expression of key genes. We conducted functional enrichment analysis, transcription factor activity inference, cell-cell interaction, drug efficacy analysis, and mutation information analysis to identify a novel subtype of HCC.ResultsOur analyses constructed a robust HCC prognostic risk prediction model. The patients with TP-TME high-risk subtypes predominantly exhibit hypoxia and activation of the Wnt/beta-catenin, Notch, and TGF-beta signaling pathways. Furthermore, we identified a novel subtype, XPO1+Epithelial. This subtype expresses signatures of the TP risk subtype and aligns with the biological behavior of high-risk patients. Additional analyses revealed that XPO1+Epithelial is influenced primarily by fibroblasts via ligand-receptor interactions, such as FN1-(ITGAV+ITGB1), and constitute a significant component of the TP-TME subtype. Moreover, XPO1+Epithelial interact with monocytes/macrophages, T/NK cells, and endothelial cells through ligand-receptor pairs, including MIF-(CD74+CXCR4), MIF-(CD74+CD44), and VEGFA-VEGFR1R2, respectively, thereby promoting the recruitment of immune-suppressive cells and angiogenesis. The ST-seq cohort treated with Tyrosine Kinase Inhibitors (TKIs) and Programmed Cell Death Protein 1 (PD-1) presented elevated levels of TP and TME risk subtype signature genes, as well as XPO1+Epithelial, T-cell, and endothelial cell infiltration in the treatment response group. Drug sensitivity analyses indicated that TP-TME high-risk subtypes, including sorafenib and pembrolizumab, were associated with sensitivity to multiple drugs. Further exploratory analyses revealed that CTLA4, PDCD1, and the cancer antigens MSLN, MUC1, EPCAM, and PROM1 presented significantly increase expression levels in the high-risk subtype group.ConclusionsThis study constructed a robust prognostic model for HCC and identified novel subgroups at the single-cell level, potentially assisting in the assessment of prognostic risk for HCC patients and facilitating personalized drug therapy.

Coagulation factor II thrombin receptor as a promising biomarker in breast cancer management.

This study aims to comprehensively investigate the role of coagulation factor II thrombin receptor (F2R) in breast cancer (BC) and to evaluate its potential as a biomarker in this context. Data on female BC were retrieved from the TCGA database. Comparative analyses were performed, including enrichment analysis, tumor immune microenvironment analysis, drug sensitivity testing, molecular docking, and cell-based experiments, to assess the expression and function of F2R in BC. Statistical analyses and graphical representations were conducted using R software. The study confirmed a significant upregulation of F2R in BC, which was associated with a more favorable prognosis. Clinical correlation analysis revealed a strong association between F2R expression and key clinical parameters, such as estrogen receptor and progesterone receptor status. Additionally, genes co-expressed with F2R were significantly linked to various biological processes, including cell cycle regulation, oxidative phosphorylation, ribosomal function, and extracellular matrix interactions. F2R also showed associations with immune modulators, particularly CD200 and NRP1. Drug sensitivity analysis, molecular docking, and cell experiments consistently demonstrated positive correlations between F2R expression and sensitivity to dasatinib. This study underscores the potential of F2R as a valuable biomarker in BC, providing insights into the molecular mechanisms underlying tumorigenesis.

Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways.

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor, highlighting a significant need for reliable predictive models to assess clinical prognosis, disease progression, and drug sensitivity. Recent studies have highlighted the critical role of various programmed cell death pathways, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, NETotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, in tumor development. Therefore, by investigating these pathways, we aimed to develop a predictive model for HCC prognosis and drug sensitivity. We analyzed transcriptome, single-cell transcriptome, genomic, and clinical information using data from the TCGA-LIHC, GSE14520, GSE45436, and GSE166635 datasets. Machine learning algorithms were used to establish a cell death index (CDI) with seven gene signatures, which was validated across three independent datasets, showing that high CDI correlates with poorer prognosis. Unsupervised clustering revealed three molecular subtypes of HCC with distinct biological processes. Furthermore, a nomogram integrating CDI and clinical information demonstrated good predictive performance. CDI was associated with immune checkpoint genes and tumor microenvironment components using single-cell transcriptome analysis. Drug sensitivity analysis indicated that patients with high CDI may be resistant to oxaliplatin and cisplatin but sensitive to axitinib and sorafenib. In summary, our model offers a precise prediction of clinical outcomes and drug sensitivity for patients with HCC, providing valuable insights for personalized treatment strategies.

Integrating Bioinformatics and Drug Sensitivity Analyses to Identify Molecular Characteristics Associated with Targeting Necroptosis in Breast Cancer and their Clinical Prognostic Significance.

Breast cancer accounts for over 1.8 million new cases worldwide annually, and prompt diagnosis and treatment are imperative to prevent mortality. Necroptosis, a form of programmed cell death, is thought to be a critical pathway for cancer cell apoptosis, yet, its relationship with breast cancer progression and molecular mechanisms remains largely unexplored. Our study aims to investigate the molecular characteristics and clinical prognostic value of necroptosis-related genes in breast cancer using a comprehensive approach that involves integrated bioinformatics analysis along with drug sensitivity assessment. Transcriptional, clinical, and tumor mutation burden (TMB) data related to breast cancer from the TCGA and GEO databases were integrated, and the necroptosis gene set was downloaded from the GSEA website for analysis. The screening conditions were set as adjusted p < 0.05 and |log2FC(fold change)| > 0.585 to screen for differential expression genes related to breast cancer necroptosis. Survival prognosis analysis was conducted on breast cancer necroptosis genes. Further analysis was conducted on prognosis-related necroptosis genes, including immune infiltration analysis and GO/KEGG enrichment analysis, to explore the potential biological functions and signaling pathway mechanisms of breast cancer necroptosis genes. Drug sensitivity screening was conducted on the prognosis-related necroptosis to identify potential drugs that target the promotion of necroptosis gene expression, and ultimately, single-gene analysis was performed on the core target genes obtained. Through integrated bioinformatics analysis, 29 differentially expressed mRNAs related to BRCA-Necroptosis were identified, including 18 upregulated mRNAs and 11 downregulated mRNAs. In addition, single-factor analysis of differential genes showed that the expression of HSPA4, PLK1, TNFRSF1B, FLT3, and LEF1 was closely related to BRCA survival prognosis. Based on the expression of these genes, a breast cancer prognosis model was constructed, and it was found that the area under the curve (AUC) of the curve of the risk genes for necroptosis was the largest, indicating that these genes have a certain clinical predictive significance for the occurrence and prognosis of BRCA. Additionally, there were significant differences in clinical characteristics of BRCA patients with different necroptosis gene expressions. Furthermore, GSVA and immune infiltration analysis revealed that Necroptosis-DEGs mainly affect the occurrence and progression of BRCA by participating in immune functions such as APC co-inhibition, APC costimulation, CCR, checkpoint, as well as infiltrating immune cells such as B cells naive, plasma cells, and T cells CD8. Moreover, the necroptosis gene group column chart indicated a 1-year survival rate of 0.979, a 3-year survival rate of 0.883, and a 5-year survival rate of 0.774. The necroptosis gene group and column chart are important indicators for evaluating BRCA prognosis. Finally, drug sensitivity screening of BRCA-Necroptosis genes showed that compounds such as A- 770041, AC220, AP-24534, Bexarotene, and BMS-509744 have certain effects as potential targeted drugs for the treatment of BRCA necroptosis genes. Necroptosis genes are implicated in the pathogenesis and progression of breast cancer and are thought to impact the prognosis and response to drug treatments in individuals with BRCA. Consequently, understanding the role of these genes in breast cancer may aid in identifying more precise and efficacious therapeutic targets.

Deciphering the Underlying Mechanisms of Sanleng-Ezhu for the Treatment of Idiopathic Pulmonary Fibrosis Based on Network Pharmacology and Single-cell RNA Sequencing Data.

To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data. Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown. Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis. We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecular docking revealed the mode of interaction between targets and their corresponding components. Animal experiments confirmed the efficacy of SL-EZ. We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in the lowly-expressed IPF subtype, while in the highly-expressed IPF subtype, SL-EZ acted on smooth muscle cells mainly through the viral infection, apoptosis, and p53 signaling pathway.

Prognostic significance of B cell senescence-associated genes as risk markers in prostate adenocarcinoma.

Prostate adenocarcinoma (PRAD) is a common male urinary system cancer, and its targeted treatment is difficult. This study aimed to investigate the value of B cell senescence-related genes in PRAD prognosis. PRAD sample expression and clinical information were downloaded from The Cancer Genome Atlas (TCGA) Program and Gene Expression Omnibus (GEO) databases, and B cell senescence-related gene sets were obtained from the Genecards library. The prognostic model was constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses of PRAD differentially expressed genes significantly related to B cell senescence. The Kaplan-Meier (K-M) survival curve and receiver operating characteristic (ROC) curve were drawn to verify the survival rate difference between the high and low risk score groups of the model. The differences of immune characteristics between high and low risk groups were evaluated by single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT. The tumor mutation burden (TMB) score was used to assess the variation in genomic mutations across the groups. Small molecule drugs were screened through the GDSC library. Ultimately, in order to examine the risk assessment model's practicality, a nomogram was created. Three genes WNT16, INS and BMP2 related to PRAD progression and B cell senescence were selected to construct a prognostic risk assessment model. The K-M survival curve and ROC curve verified the good performance in evaluating the prognosis of patients. In terms of immune characteristics, the high-risk score group of the model showed a higher overall immune score and immune cell infiltration level, and the high-risk group showed a relatively higher TP53 and TTN mutation frequency. Drug sensitivity analysis showed that the high-risk group had higher resistance to Camptothecin, Cisplatin and WIKI4 drugs. At last, the nomogram that is created using pathological characteristics in conjunction with the risk score can reliably assess the prognosis of patients with PRAD. This study constructed and verified a B cell senescence-related gene model that can predict prognosis of PRAD. More importantly, it provides a reference standard for guiding the prognosis of PRAD patients.

Identification and validation of ion channels-related mRNA prognostic signature for glioblastomas.

Glioblastomas (GBM) is a kind of malignant brain tumor with poor prognosis. Identifying new biomarkers is promising for the treatment of GBM. The mRNA-seq and clinical data were obtained from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas databases. The differentially expressed genes were identified using limma R package. The prognosis-related genes were screened out and a risk model was constructed using univariate, least absolute shrinkage and selection operator, and multivariate Cox analysis. Receiver operating characteristic curve was used to assess the efficiency of model. Kaplan-Meier survival curve was applied for the survival analysis. Mutation analysis was conducted using maftools package. The effect of immunotherapy was analyzed according to TIDE score, and the drug sensitivity analysis was performed. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis enrichment analyses were performed for the functional analysis. The regulatory network was constructed by STRING and Cytoscape software. RT-qPCR was performed to validate the expression of 3 hub genes in vitro. A risk model was constructed based on 3 ion channels related genes (gap junction protein beta 2 [GJB2], potassium voltage-gated channel subfamily h member 6 [KCNH6], and potassium calcium-activated channel subfamily n member 4 [KCNN4]). The risk score and hub genes were positively correlated with the calcium signaling pathway. Patients were divided into 2 groups based on the risk score calculated by 3 signatures. The infiltration levels of T cell, B lineage, monocytic lineage, and neutrophils were increased in high risk group, while TIDE score was decreased. IC50 of potential drugs for GBM treatment was elevated in the high risk group. Furthermore, GJB2, KCNH6, and KCNN4 were oncogenic, and GJB2 and KCNN4 were upregulated, while KCNH6 was downregulated in high risk group and GBM cells. The regulatory network showed that KCNH6 was targeted by more miRNA and transcription factors and KCNN4 interacted with more drugs. We constructed a three-signature risk model, which could effectively predict the prognosis of GBM development. Besides, KCNH6 and KCNN4 were respectively considered as the targets of molecular targeted treatment and chemotherapy.

Single-cell multiomics reveals simvastatin inhibits pan-cancer epithelial-mesenchymal transition via the MEK/ERK pathway in XBP1+ mast cells

Distant metastasis is the leading cause of cancer-related mortality, and achieving survival benefits through advancements in systemic therapy remains challenging. Mast cells play a dual role in shaping the tumor microenvironment (TME) and influencing distant metastasis, underscoring the significant research value of targeting mast cells for systemic therapy in advanced cancer. We investigated variations in mast cell infiltration levels in primary and metastatic malignancies using immunocyte infiltration analysis. Mast cell subsets were identified from pan-cancer distant metastasis single-cell sequencing data through dimensionality reduction clustering and cell type annotation, combined with cell trajectory and communication network analyses. A prognostic model was established using WGCNA and 12 machine learning algorithms to identify potential mast cell targets. Drug sensitivity and Mendelian randomization analyses were conducted to select potential drugs targeting mast cells, and their effects on epithelial-mesenchymal transition (EMT) were validated through in vitro experiments, including wound healing, transwell, and western blot assays. Results revealed that activated mast cells show increased infiltration in metastatic tumors, correlating with poor survival duration. XBP1+ mast cells were identified as key components of the inhibitory TME, potentially involved in EMT activation. Simvastatin was identified as a potential drug, reversing EMT induced by XBP1+ mast cells in pan-cancer. Aberrant activation of MEK/ERK signaling in XBP1+ mast cells can stimulate cancer cell EMT by modulating degranulation, while Simvastatin can inhibit EMT by suppressing degranulation.

A novel necroptosis-related genes signature to predict prognosis and treatment response in bladder cancer.

Necroptosis, a form of programmed inflammatory cell death, plays a crucial role in tumor development, necrosis, metastasis, and immune response. This study aimed to explore the role of necroptosis in BLCA and construct a new prognostic model to guide clinical treatment and predict individualized treatment response. The transcriptome profiling and the corresponding clinical data of BLCA patients were obtained from the Cancer Genome Atlas database (TCGA) and GEO databases. Univariate, multivariate and LASSO Cox regression analyses were used to identify and construct prognostic features associated with necroptosis. We constructed and validated a prognostic model associated with the patient's overall survival (OS). A nomogram was established to predict the survival rates of BLCA patients. Finally, the correlation between risk scores and tumor immune microenvironment, somatic mutations, immunotherapy, and chemotherapy was comprehensively analyzed. The study found two distinct NRG clusters and three gene subtypes, with significant differences in pathway enrichment and immune cell infiltration associated with different NRG clusters in the TME. In addition, we screened out six necroptosis prognosis-related genes (including PPP2R3A; CERCAM; PIK3IP1; CNTN1; CES1 and CD96) to construct a risk score prognostic model. Significant differences in overall survival rate, immune cell infiltration status, and somatic mutations existed between the high and low-risk scores in BLCA patients. Finally, drug sensitivity analysis showed that high-risk patients benefited more from immunotherapy and chemotherapy drugs. This study explores the importance of necroptosis in the prognosis of patients with BLCA, and the prognostic features associated with necroptosis that we identified can serve as new biomarkers to help develop more precise treatment strategies.

Integration of the bulk transcriptome and single-cell transcriptome reveals efferocytosis features in lung adenocarcinoma prognosis and immunotherapy by combining deep learning

BackgroundEfferocytosis (ER) refers to the process of phagocytic clearance of programmed dead cells, and studies have shown that it is closely related to tumor immune escape.MethodsThis study was based on a comprehensive analysis of TCGA, GEO and CTRP databases. ER-related genes were collected from previous literature, univariate Cox regression was performed and consistent clustering was performed to categorize lung adenocarcinoma (LUAD) patients into two subgroups. Lasso regression and multivariate Cox regression analyses were used to construct ER-related prognostic features, and multiple immune infiltration algorithms were used to assess the correlation between the extracellular burial-related risk score (ERGRS) and tumor microenvironment (TME). And the key gene HAVCR1 was identified by deep learning, etc. Finally, pan-cancer analysis of the key genes was performed and in vitro experiments were conducted to verify the promotional effect of HAVCR1 on LUAD progression.ResultsA total of 33 ER-related genes associated with the prognosis of LUAD were identified, and the prognostic signature of ERGRS was successfully constructed to predict the overall survival (OS) and treatment response of LUAD patients. The high-risk group was highly enriched in some oncogenic pathways, while the low-ERGRS group was highly enriched in some immune-related pathways. In addition, the high ERGRS group had higher TMB, TNB and TIDE scores and lower immune scores. The low-risk group had better immunotherapeutic response and less likelihood of immune escape. Drug sensitivity analysis revealed that BRD-K92856060, monensin and hexaminolevulinate may be potential therapeutic agents for the high-risk group. And ERGRS was validated in several cohorts. In addition, HAVCR1 is one of the key genes, and knockdown of HAVCR1 in vitro significantly reduced the proliferation, migration and invasion ability of lung adenocarcinoma cells.ConclusionOur study developed a novel prognostic signature of efferocytosis-related genes. This prognostic signature accurately predicted survival prognosis as well as treatment outcome in LUAD patients and explored the role of HAVCR1 in lung adenocarcinoma progression.

Ferroptosis-related prognostic model of mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin's lymphoma. Ferroptosis, an iron-dependent programmed cell death, is closely related to cancer prognosis. In this study, we established a model of ferroptosis related genes for prognostic evaluation of patients with MCL. Using the single-cell RNA sequencing datasets GSE184031 and mRNA sequencing data GSE32018 from the Gene Expression Omnibus, we identified 139 ferroptosis-related genes in MCL. Next a prognostic model was constructed by Cox regression and Least absolute selection and shrinkage Operator regression analysis. Finally, we used CIBERSORT to analyze the immune microenvironment and the "oncoPredict" package to predict potential drugs. In our model, the prognosis of MCL patients was assessed by risk scoring using 7 genes ANXA1, IL1B, YBX1, CCND1, MS4A1, MFHAS1, and RILPL2. The patients were divided into high-risk and low-risk groups based on our model, and the high-risk patients had inferior overall survival. Finally, according to our model and computational drug sensitivity analysis, four small molecule compounds, BMS-754807, SB216763, Doramapimod, and Trametinib, were identified as potential therapeutic agents for patients with MCL. In summary, we provide a prognostic model with ferroptosis-related gene signature for MCL. This study provides a prognostic model with ferroptosis-related gene signature for MCL. The results show that the model helps predict prognosis in MCL.

Multi-omics analysis reveals the role of the autophagy-related gene AGT in chemotherapy resistance in colorectal cancer and the therapeutic potential of its inhibitors.

Autophagy is a crucial mechanism for maintaining cellular homeostasis and responding to environmental stress, and it is closely linked to tumor drug resistance. Through multi-omics analysis, this study explores the expression patterns, functions, and potential role of the autophagy-related gene Angiotensinogen (AGT) in colorectal cancer (CRC), particularly in relation to chemotherapy resistance. This study first compared AGT expression between CRC and normal tissues using the GTEx and TCGA databases. Differences in expression were assessed using Wilcoxon Rank Sum Tests, and the prognostic impact of AGT was evaluated through univariate Cox survival analysis and meta-analysis. Functional enrichment was performed using the limma and fgsea packages. Drug sensitivity analysis was conducted based on the CTRP database, while immune infiltration was assessed using the CIBERSORT and ESTIMATE methods. Spatial transcriptomic characteristics were explored through 10x Visium technology and deconvolution analysis to investigate the correlation between AGT expression levels and tumor cell content.scRNA-seq data from CRC tissues were sourced from Tumor Immune Single Cell Hub (TISCH).Functional annotation was performed with Single-sample gene set enrichment analysis (SSGSEA), and pseudotime analysis using Monocle 2 mapped their developmental trajectories. The potential of AGT inhibitors in the treatment of CRC was analyzed using drug-target Mendelian randomization.Finally, Phenome-Wide Association Study (PheWAS) was conducted to evaluate genetic associations and potential side effects of AGT inhibitors. AGT expression was significantly higher in CRC tissues compared to normal tissues and was associated with shorter recurrence-free survival (RFS). Autophagy signaling pathways were markedly enriched in the high AGT expression group. AGT expression was positively correlated with resistance to chemotherapeutic agents such as gemcitabine, cisplatin, paclitaxel, and 5-fluorouracil. Spatial transcriptomic analysis revealed that AGT was predominantly expressed in malignant tumor regions. Single-cell analysis identified 21 distinct cell subpopulations across 13 major types. AGT expression was significantly higher in tumor samples, especially in the fibroblast C6 subpopulation. Tumor-related pathways were enriched in C1, C5, C6, and C8 subpopulations. Pseudotime analysis revealed that these subpopulations, particularly C6, were in terminal developmental stages.Drug-target Mendelian randomization analysis indicated a negative causal relationship between AGT inhibitors and the risk of both heart failure(ORdrug = 0.950, 95% CI, 0.912-0.990; P = 0.014) and CRC(ORdrug = 0.874, 95% CI: 0.792-0.964; P = 0.007).PheWAS analysis showed no genetic associations between AGT inhibitors and other traits, indicating its specificity and low risk of side effects. Elevated AGT expression in CRC is associated with resistance to chemotherapy, and its inhibition may offer a therapeutic avenue for the treatment of colorectal cancer.

Construction of a novel lipid drop-mitochondria-associated genetic profile for predicting the survival and prognosis of lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is one of the most common malignant tumors. Although several treatments have been proposed, the long-term prognosis of this cancer is poor. Lipid droplets and mitochondria are important organelles that regulate energy metabolism in cells and are postulated to promote the occurrence and progression of tumors. However, few risk prediction models have been constructed based on lipid drop-mitochondria-related genes (LMRGs).MethodsIn this study, we constructed a lipid drop-mitochondrial (LD-M) risk score model based on data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Biological functions and clinical benefits associated with the various risk scores were analyzed using R software, GraphPad Prism 9, and the online database system.ResultsAn LD-M risk score model comprising ABLIM3, AK4, CAV2, CPS1, CYP24A1, DLGAP5, FGR, and SH3BP5, was developed and its predictive power was validated. The risk score was closely associated with the cell cycle. Immunophenoscore (IPS) and Tumor immune dysfunction and exclusion (TIDE) results demonstrated that the low-risk group was more sensitive to immunotherapy. Drug sensitivity analysis indicated that BMS-754807, ZM447439, SB216763, and other drugs had lower IC50 values in the low-risk group.ConclusionOur results suggest that the LD-M risk score is an effective prognostic indicator for individualized treatment of LUAD.

Multi-omics-driven discovery of invasive patterns and treatment strategies in CA19-9 positive intrahepatic cholangiocarcinoma

BackgroundIntrahepatic cholangiocarcinoma (ICC) is a malignant tumor with a poor prognosis, predominantly CA19-9 positive. High CA19-9 levels correlate with increased aggressiveness and worse outcomes. This study employs multi-omics analysis to reveal molecular features and identify therapeutic targets of CA19-9 positive ICC, aiming to support individualized treatment.MethodsData from seven clinical cohorts, two whole-exome sequencing cohorts, six RNA sequencing/microarray cohorts, one proteomic cohort, 20 single-cell RNA sequencing samples, and one spatial transcriptome sample were analyzed. Key findings were validated on tissue microarrays from 52 ICC samples.ResultsCA19-9 positive ICC exhibited poorer OS (median 24.1 v.s. 51.5 months) and RFS (median 11.7 v.s. 28.2 months) compared to negative group (all P < 0.05). Genomic analysis revealed a higher KRAS mutation frequency in the positive group and a greater prevalence of IDH1/2 mutations in the negative group (all P < 0.05). Transcriptomic analysis indicated upregulated glycolysis pathways in CA19-9 positive ICC. Single-cell analysis identified specific glycolysis-related cell subclusters associated with poor prognosis, including Epi_SLC2A1, CAF_VEGFA, and Mph_SPP1. Higher hypoxia in the CA19-9 positive group led to metabolic reprogramming and promoted these cells’ formation. These cells formed interactive communities promoting epithelial-mesenchymal transition (EMT) and angiogenesis. Drug sensitivity analysis identified six potential therapeutic drugs.ConclusionsThis study systematically elucidated the clinical, genomic, transcriptomic, and immune features of CA19-9 positive ICC. It reveals glycolysis-associated cellular communities and their cancer-promoting mechanisms, enhancing our understanding of ICC and laying the groundwork for individualized therapeutic strategies.Graphical

Identification and validation of oxidative stress-related genes in primary open-angle glaucoma by weighted gene co-expression network analysis and machine learning.

Primary open-angle glaucoma (POAG) is a common ocular disease, and there is currently no effective treatment for POAG therapy. Thus, identifying some effective diagnostic markers is beneficial to the treatment of patients. The expression profile was obtained from Gene Expression Omnibus (GEO) database. The functional enrichment was analyzed using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and gene set enrichment analysis. Co-expressed genes were identified using weighted gene co-expression network analysis (WGCNA). Hub genes were screened through Lasso regression, support vector machine-recursive feature elimination (SVM-RFE) and Random Forest, and receiver operating characteristic curve was used to assess diagnostic value. Immune cell infiltration was calculated using IOBR package. The regulatory network was constructed through STRING, miRactDB and Cytoscape. The oncoPredict package was employed to predict the candidate chemotherapy agents. According to GSE27276 database, 541 differentially expressed genes were identified. Five oxidative stress-related genes with high area under the curve value, namely HBB, MAOA, ACOX2, ALDH7A1 and TYMP, were determined using WGCNA and machine learning. Infiltration level of NK cells, CD4 T cells and dendritic cells were significantly increased in POAG group compared with normal group, while CD8 T cells and Tregs cells were significantly decreased. HBB was closely related to most immune cells. Hub genes were all targeted by 16 miRNAs. Drug sensitivity analysis exhibited that some drugs were more sensitivity for POAG, such as Acetalax_1804, Ibrutinib_1799 and OSI_027_1594. We identified 5 oxidative stress-related genes with high diagnostic value for POAG.