Drug Sensitivity Research Articles

Deciphering a hydrogen sulfide-related signature to supervise prognosis and therapeutic response in colon adenocarcinoma.

Hydrogen sulfide (H2S) is a critical molecule that participates in various molecular, physiological, and pathophysiological processes in biological systems. Emerging evidence has revealed that H2S is implicated in the progression of colon cancer and immune escape. Against this backdrop, the present study aimed to construct a prognostic risk feature for colon adenocarcinoma (COAD) by leveraging hydrogen sulfide-related genes (HSRG). Transcriptomic data and corresponding clinical-pathological information of colon cancer were obtained from The Cancer Genome Atlas and gene expression omnibus databases. Univariate Cox regression analysis was employed to assess the prognostic relevance of HSRG. Consensus clustering was utilized to perform molecular subtyping of COAD, followed by comparison of immune cell infiltration, drug sensitivity, and immune therapy response between subtypes. Differential expression gene and gene set enrichment analyses were conducted between subtypes. Univariate, lasso, and multivariate Cox regression analyses were applied to construct a prognostic model derived from HSRG. A nomogram model for predicting COAD prognosis was constructed and evaluated. In this study, we identified 12 HSRGs that were associated with COAD prognosis. Consensus clustering analysis revealed 3 COAD molecular subtypes that exhibited significant differences in terms of prognosis, tumor immune cell infiltration, drug sensitivity, and immune therapy response. Gene set enrichment analysis demonstrated that immunoregulatory processes were significantly suppressed in the poor-prognosis subtype while Wnt-related pathways and processes were significantly upregulated. Based on the differentially expressed genes between subtypes, we constructed a risk model comprising 11 genes that effectively distinguished high-risk patients from low-risk patients with significant associations with patient survival outcomes, drug treatment, pathological staging, and T staging. The HSRG-derived risk feature was an independent prognostic factor for COAD in drug treatment and pathological staging and could be integrated into a nomogram for prognosis prediction. Calibration curve, receiver operating characteristic curve, and decision curve analysis demonstrated excellent performance of the nomogram in evaluating COAD prognosis. Our study systematically assessed the prognostic significance of HSRG in COAD, identified HSRG-based molecular subtypes and risk features, and highlighted their potential utility in predicting prognosis and treatment response.

Rational Design of HER2-Targeted Combination Therapies to Reverse Drug Resistance in Fibroblast-Protected HER2+ Breast Cancer Cells

Abstract Introduction Fibroblasts, an abundant cell type in the breast tumor microenvironment, interact with cancer cells and orchestrate tumor progression and drug resistance. However, the mechanisms by which fibroblast-derived factors impact drug sensitivity remain poorly understood. Here, we develop rational combination therapies that are informed by proteomic profiling to overcome fibroblast-mediated therapeutic resistance in HER2+ breast cancer cells. Methods Drug sensitivity to the HER2 kinase inhibitor lapatinib was characterized under conditions of monoculture and exposure to breast fibroblast-conditioned medium. Protein expression was measured using reverse phase protein arrays. Candidate targets for combination therapy were identified using differential expression and multivariate regression modeling. Follow-up experiments were performed to evaluate the effects of HER2 kinase combination therapies in fibroblast-protected cancer cell lines and fibroblasts. Results Compared to monoculture, fibroblast-conditioned medium increased the expression of plasminogen activator inhibitor-1 (PAI1) and cell cycle regulator polo like kinase 1 (PLK1) in lapatinib-treated breast cancer cells. Combination therapy of lapatinib with inhibitors targeting either PAI1 or PLK1, eliminated fibroblast-protected cancer cells, under both conditions of direct coculture with fibroblasts and protection by fibroblast-conditioned medium. Analysis of publicly available, clinical transcriptomic datasets revealed that HER2-targeted therapy fails to suppress PLK1 expression in stroma-rich HER2+ breast tumors and that high PAI1 gene expression associates with high stroma density. Furthermore, we showed that an epigenetics-directed approach using a bromodomain and extraterminal inhibitor to globally target fibroblast-induced proteomic adaptions in cancer cells, also restored lapatinib sensitivity. Conclusions Our data-driven framework of proteomic profiling in breast cancer cells identified the proteolytic degradation regulator PAI1 and the cell cycle regulator PLK1 as predictors of fibroblast-mediated treatment resistance. Combination therapies targeting HER2 kinase and these fibroblast-induced signaling adaptations eliminates fibroblast-protected HER2+ breast cancer cells.

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

FOXN3-AS1: A Candidate Prognostic Marker and Epigenetic Target with Immunotherapeutic Implications in Acute Myeloid Leukemia.

We focused on the FOXN3 gene and selected its antisense transcripts (FOXN3-AS1) to investigate its potential involvement in acute myeloid leukemia (AML). Several integrated multi-omics datasets have expanded the horizons of the cancer landscape. With the emergence of new high-throughput technologies, a large number of non-coding RNAs have been confirmed to be involved in the pathogenesis of different types of hematological malignancies. We conducted experimental validation using quantitative polymerase chain reaction (qPCR) with bone marrow specimens from AML patients. Then, Kaplan-Meier (KM) and Receiver Operating Characteristic (ROC) curves were used to substantiate the prognostic association between FOXN3-AS1 and AML patients within the TCGA database. Correlation between FOXN3-AS1 expression and gene mutation, immune, and immune function using Spearman correlation analysis. To explore the physical and functional interaction between FOXN3-AS1 and the DNMT1 protein, we utilized the RPISeq web tool from Iowa State University. Subsequently, we performed qPCR experiments to test the effect of 5AzaC (DNMT1 inhibitor) on FOXN3-AS1 expression AML cell lines (THP1 and OCI-AML3). We leveraged the "OncoPredict" R package in conjunction with the Genomics of Drug Sensitivity (GDSC) database to predict drug response in AML patients expressing FOXN3-AS1. We observed a significant upregulation of FOXN3-AS1 expression in AML patients compared to healthy controls using clinical samples. The TCGA database revealed an association between high FOXN3-AS1 expression and adverse prognosis. In our subsequent analysis, genes with poor prognostic implications in AML patients were exclusively identified in the FOXN3-AS1 high-expression group, further corroborating this relationship. AML patients with higher FOXN3-AS1 expression levels may respond less optimally to immunotherapy than patients with lower levels. Besides, we computationally predicted the interaction of FOXN3- AS1 and DNMT1 protein and experimentally confirmed that DNMT1i (GSK-3484862) affects the expression level of FOXN3-AS1. We also found that the chemotherapy drugs (5-Fluorouralic, Cisplatin, Dactolisib, Sapitinib, Temozolomide, Ulixertinib, Vinorelbine, Ruxolitinib, Osimertinib and Cisplatin) showed favorable responses in AML patients with high FOXN3-AS1 expression levels. Our candidate approach identifies FOXN3-AS1 as a prognostic indicator of survival in AML with a potential immune-related role. The preliminary observations we made on FOXN3-AS1/DNMT1 crosstalk warrant more in-depth invested immunotherapeutic approaches in AML.

Identification and experimental validation of a sialylation–related long noncoding RNA signature for prognosis of bladder cancer

BackgroundThe dysregulation of sialylation plays a pivotal role in cancer progression and metastasis, impacting various aspects of tumor behavior. This study aimed to investigate the prognostic significance of long non-coding RNAs (lncRNAs) in relation to sialylation. Additionally, we aimed to develop a signature of sialylation-related lncRNAs in the context of bladder cancer.MethodsThis study used transcriptomic data and clinical information from the TCGA (the Cancer Genome Atlas) database to screen for sialylation-related lncRNAs and constructed a prognostic model. The relationships between these lncRNAs and biological pathways, immune cell infiltration, drug sensitivity, etc., were analyzed, and the expression of some lncRNAs was validated at the cellular level.ResultsThis study identified 6 prognostic lncRNAs related to sialylation and constructed a risk score model with high predictive accuracy and reliability. The survival period of patients in the high-risk group was significantly lower than that of the low-risk group, and it was related to various biological pathways and immune functions. In addition, this study found differences in the sensitivity of patients in different risk groups to chemotherapy drugs, providing a reference for personalized treatment.ConclusionIn this study, we examined the relationship between sialylation-related lncRNA and the prognosis of bladder cancer, providing new molecular markers and potential targets for diagnosis and treatment. Our research revealed correlations between sialylation-related lncRNA characteristics and clinicopathological features, potential mechanisms, somatic mutations, immune microenvironment, chemotherapy response, and predicted drug sensitivity in bladder cancer. Additionally, in vitro cellular studies were conducted to validate these findings and lay the groundwork for future clinical applications.

Second messenger c-di-AMP regulates multiple antibiotic sensitivity pathways in Mycobacterium smegmatis by discrete mechanisms.

One of the debilitating causes of high mortality in the case of tuberculosis and other bacterial infections is the resistance development against standard drugs. There are limited studies so far to describe how a bacterial second messenger molecule can directly participate in distinctive antibiotic tolerance characteristics of a cell in a mechanism-dependent manner. Here we show that intracellular cyclic di-AMP (c-di-AMP) concentration can modulate drug sensitivity of Mycobacterium smegmatis by interacting with an effector protein or interfering with the 5'-UTR regions in mRNA of the genes and thus causing transcriptional downregulation of important genes in the pathways. We studied four antibiotics with different mechanisms of action: rifampicin, ciprofloxacin, erythromycin, and tobramycin and subsequently found that the level of drug sensitivity of the bacteria is directly proportional to the c-di-AMP concentration inside the cell. Further, we unraveled the underlying molecular mechanisms to delineate the specific genes and pathways regulated by c-di-AMP and hence result in differential drug sensitivity in M. smegmatis.

Prognostic Prediction and Immune Microenvironment Characterization in Uveal Melanoma: A Novel Mitochondrial Metabolism-Related Gene Signature.

Uveal Melanoma (UM), a highly aggressive and metastatic intraocular cancer with a strong propensity for liver metastasis, presents limited therapeutic alternatives and unfavorable survival outcomes. Despite its low incidence, the underlying mechanisms of UM pathogenesis and the precise role of mitochondrial metabolism in UM remain inadequately understood. Utilizing Cox proportional hazards regression analysis was used to assess prognostic relevance, and consensus clustering was employed for molecular subtyping. A risk signature was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We further conducted comparative analyses on clinicopathological characteristics, somatic mutation profiles, drug sensitivity, gene expression patterns, and tumor microenvironment features across different molecular subtypes. Moreover, a nomogram was developed and evaluated. Among 1234 mitochondria metabolism-related genes (MMRGs), 343 were identified as significantly associated with the prognosis of UM. These prognosis-associated MMRGs facilitated the classification of UM into two distinct molecular subtypes, which displayed notable differences in prognosis and pathological staging. Furthermore, an index termed the MMRGs-derived index (MMI) was derived from eight MMRGs, serving as a quantitative measure for poor prognosis risk in UM. MMI demonstrated significant associations with clinicopathological characteristics, somatic mutations, drug responsiveness, and the tumor microenvironment, where higher MMI levels corresponded to worse prognosis, advanced pathological stages, and increased immune cell infiltration. The nomogram built upon MMI provided a potential tool for clinical prognosis assessment in UM patients. This study demonstrated the potential value of MMRGs in predicting prognosis and molecular stratification within UM; however, additional clinical and basic research is warranted to validate their applicability and elucidate the related mechanisms.

The influence of age, gender and pharmacogenetic profiles on the perspective on medicines in the German EMPAR study.

Pharmacogenetic testing in routine care could provide benefits for patients, doctors and statutory health insurances. Therefore, the aim of the retrospective, observational study Einfluss metabolischer Profile auf die Arzneimitteltherapiesicherheit in der Routineversorgung (EMPAR) was to analyze the relationship between pharmacogenetic profiles, the risk of adverse drug reactions, and patients' perceptions of drug therapy in 10748 adult (≥18 years) participants in Germany. A questionnaire was used to assess views and beliefs about medicines and participants individual perception of sensitivity to drug therapies. The questionnaire consisted of the Beliefs about Medicines Questionnaire (BMQ)-General scales (Overuse, Harm, Benefit), the Perceived Sensitivity to Medicines (PSM), Natural Remedy, and Gene Testing scales. The influence of gender, age, study collective, genotype and phenotype of relevant pharmacogenes on participant's perception were evaluated. Overuse, PSM and Benefit scores were significantly higher among patients of the collective International Classification of Diseases and Health Related Disorders (ICD)-10 Y57.9! diagnosis, which indicates complications related to drugs, compared to the anticoagulant/antiplatelet and cholesterol-lowering drug collective. Age and gender also played a significant role in patients' perceptions, with younger patients and female participants more likely to believe in medication overuse according to the Overuse scale score compared to older and male participants. Female participants compared to male participants and the old age group compared to the young and/or middle-age subgroup, scored higher in PSM and/or Harm scales, respectively. Only a tendency of increased Harm, Overuse and PSM scores was observed in the participant group with five or more relevant actionable variants compared to subgroups with 0 up to 4 variants. In conclusion, patients' beliefs about medicines and their drug sensitivity perceptions are influenced by various factors including age, gender, previous complications with medicines, and with some tendency also pharmacogenetic profiles. The higher association with more negative views related to treatment indicates that there is a need to target the underlying issues in affected patient groups in order to improve compliance to treatment and outcomes in routine care. Trial registration: EMPAR was registered in the German Clinical Trials Register (DRKS) on 06 July 2018 (DRKS00013909).

Experts consensus on standard items of the cohort construction and quality control of temporomandibular joint diseases (2024)

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients'suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Application of a risk score model based on glycosylation-related genes in the prognosis and treatment of patients with low-grade glioma.

Low-grade gliomas (LGG) represent a heterogeneous and complex group of brain tumors. Despite significant progress in understanding and managing these tumors, there are still many challenges that need to be addressed. Glycosylation, a common post-translational modification of proteins, plays a significant role in tumor transformation. Numerous studies have demonstrated a close relationship between glycosylation modifications and tumor progression. However, the biological function of glycosylation-related genes in LGG remains largely unexplored. Their potential roles within the LGG microenvironment are also not well understood. We collected RNA-seq data and scRNA-seq data from patients with LGG from TCGA and GEO databases. The glycosylation pathway activity scores of each cluster and each patient were calculated by irGSEA and GSVA algorithms, and the differential genes between the high and low glycosylation pathway activity score groups were identified. Prognostic risk profiles of glycosylation-related genes were constructed using univariate Cox and LASSO regression analyses and validated in the CGGA database. An 8 genes risk score signature including ASPM, CHI3L1, LILRA4, MSN, OCIAD2, PTGER4, SERPING1 and TNFRSF12A was constructed based on the analysis of glycosylation-related genes. Patients with LGG were divided into high risk and low risk groups according to the median risk score. Significant differences in immunological characteristics, TIDE scores, drug sensitivity, and immunotherapy response were observed between these groups. Additionally, survival analysis of clinical medication information in the TCGA cohort indicated that high risk and low risk groups have different sensitivities to drug therapy. The risk score characteristics can thus guide clinical medication decisions for LGG patients. Our study established glycosylation-related gene risk score signatures, providing new perspectives and approaches for prognostic prediction and treatment of LGG.

Identification of a senescence-related transcriptional signature to uncover molecular subtypes and key genes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a cancer caused by abnormal cell growth due to faulty signal transduction. Cells secrete tumor suppressor factors in response to potential carcinogenic signals, inducing cellular senescence (CS) as a countermeasure. However, accurately measuring CS levels in different types of tumors is challenging due to tumor heterogeneity and the lack of universal and specific CS markers. Machine learning has revealed unique molecular traits in HCC patients, leading to clinical advantages. More research is needed to understand senescence-related molecular features in these patients. In this study, the gene expression profile features of patients with HCC were analyzed by integrating single-cell RNA sequencing and bulk RNA-seq datasets from HCC samples. The analysis identified the senescence-related pathways exhibiting HCC specificity. Subsequently, genes from these pathways were used to identify senescence-related molecular subtypes in HCC, showing significant variations in biological and clinical attributes. An HCC-specific CS risk model developed in this study revealed substantial associations between the patients' CS scores and prognosis grouping, clinical staging, immune infiltration levels, immunotherapy response, and drug sensitivity levels. Within the constructed model, G6PD was identified as a key gene, potentially serving as a senescence-related target in liver cancer. Molecular biology experiments demonstrated that overexpression of G6PD effectively promotes the proliferative, invasive, and migration capacities of HepG2 and SK-HEP-1 cells. In conclusion, this analysis offers a valuable framework for understanding senescence in HCC and introduces a new biomarker. These findings improve our understanding of senescence in HCC and have potential for future research.

Development and validation of a prognostic model for colon cancer based on mitotic gene signatures and immune microenvironment analysis

BackgroundMitotic processes play a pivotal role in tumor progression and immune responses. However, the correlation between mitosis-related genes, clinical outcomes, and the tumor microenvironment (TME) in colon cancer remains unclear. This study aims to develop a prognostic and therapeutic significance model for colon cancer based on mitosis-related genes.MethodsRNA expression profiles and clinical data of 453 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Mitosis-related genes were selected from the MsigDb database. The gene model was constructed using differential analysis, univariate and multivariate Cox regression, and Lasso regression analyses. The predictive model was validated using data from the GSE17536, GSE17537, and GSE39582 datasets. Predictive accuracy was evaluated via Receiver Operating Characteristic (ROC) curves, while nomograms were developed by integrating clinical and pathological features. Gene set enrichment analysis explored biological processes and pathways linked to the model. TME was assessed using ESTIMATE, and the proportion and function of immune cells were analyzed through CIBERSORT. Drug sensitivity analysis was conducted using the CTRP database.ResultsA predictive model based on 17 mitosis-related genes (KIFC1, CCNF, EME1, CDC25C, ORC1, CCNJL, ANKRD53, MEIS2, FZD3, TPD52L1, MAPK3, CDKN2A, EDN3, NPM2, PSRC1, INHBA, BIRC5) was created. The model exhibited robust predictive performance across both training and validation cohorts. Nomograms for predicting 3-, 5-, and 7-year survival rates in colon cancer (COAD) patients were generated. The model's correlation with immune cell infiltration and function was highlighted.ConclusionThe mitosis-related gene model serves as a valuable indicator for predicting survival outcomes in colon cancer patients.

Exploring the significance and potential mechanisms of hippo pathway-associated genes in prognosis of glioma patients

PurposeDespite the efforts of countless researchers to develop glioma treatment strategies, the current therapeutic effect of glioma is still not ideal, and it is necessary to further explore the mechanism to guide treatment. Thus, this study aims to introduce a novel approach for predicting patient prognosis and guiding further treatment interventions.MethodsInitially, we conducted a differential gene expression analysis to identify Hippo pathway-associated genes overexpressed in tumors and determined genes correlated with prognosis. Subsequently, employing cluster analysis, we categorized samples into two groups and performed further analyses including prediction, immune cell infiltration abundance, and drug response rates. We utilized weighted gene co-expression analysis to reveal gene sets with high co-variation, delineate inter-sample gene correlation patterns, and conduct enrichment analysis. Prognostic models were built using ten machine learning algorithms combined in 101 different combinations, followed by evaluation and validation. Immune infiltration analysis, differential expression analysis of depleted T cell-related markers, drug sensitivity analysis, and exploration of pathway dysregulation were performed for different risk groups. Quality control and batch integration were performed, and single-cell data were analyzed using dimensionality reduction clustering algorithms and annotation tools to evaluate the activity of the prognostic model in malignant cells.ResultsWe conducted data filtering to identify genes overexpressed in tumors, intersecting these genes with Hippo pathway-related genes, identifying 62 genes correlated with prognosis, and performing cluster analysis to divide tumor tissues into two groups. Cluster 2 exhibited a poorer prognosis and demonstrated differences in immune cell infiltration. Utilizing weighted gene co-expression analysis on Cluster 2, we identified gene modules, conducted functional enrichment analysis, and delineated pathways. Employing a combined model based on ten machine learning algorithm combinations, we selected the optimal prognostic model system and validated the model’s predictive ability within the dataset. Through immune-related analysis and drug sensitivity analysis, we uncovered differences in immune infiltration and varying sensitivities to chemotherapy drugs. Additionally, the enrichment analysis of gene set revealed discrepancies in upregulation within relevant pathways between the high and low-risk groups. Finally, annotation and evaluation of malignant cells via single-cell analysis showed increased activity of the prognostic model and variations in distribution across different prognostic levels in malignant cells.ConclusionThis study introduces a novel approach utilizing the Hippo pathway and associated genes for glioma prognosis research, demonstrating the potential and significance of this method in evaluating the outcome for patients with glioma. These findings hold substantial clinical significance in guiding therapy and predicting outcomes for individuals diagnosed with glioma, offering significant clinical utility.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach.

The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

Multiple cell-death patterns predict the prognosis and drug sensitivity of melanoma patients.

Melanoma, a malignant tumor of the skin, presents challenges in its treatment process involving modalities such as surgery, chemotherapy, and targeted therapy. However, there is a need for an ideal model to assess prognosis and drug sensitivity. Programmed cell death (PCD) modes play a crucial role in tumor progression and has the potential to serve as prognostic and drug sensitivity indicators for melanoma. We analyzed 13 PCD modes including apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, disulfidptosis, and alkaliptosis. These modes were used to construct a model that incorporated genes related to these 13 PCD modes to establish a cell death index (CDI) to conduct prognosis analysis. Transcriptomic, genomic, and clinical data were collected from cohorts including TCGA-SKCM, GSE19234, and GSE65904 to validate this model. A CDI consisting of ten gene signatures was established using machine learning algorithms and divided into two groups based on CDI values. The high CDI group exhibited relatively lower numbers of immune-infiltrating cells and showed resistance to commonly used drugs such as docetaxel and axitinib. Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01). The CDI model, incorporating multiple PCD modes, accurately predicts the clinical prognosis and drug sensitivity of melanoma patients.

T cell exhaustion methylation signature drives differential immune responses in glioblastoma.

Methylation-related signatures play crucial roles in tumorigenesis and progression. However, their roles in the immune response in primary glioblastoma (GBM) remains unclear. We analyzed the differential expression of specific members of T cell exhaustion-related pathways in GBM from the perspective of T cell exhaustion. We further screened for significantly negatively correlated methylation sites as candidate methylation markers for T cell exhaustion. Using consensus clustering, we divided the samples into two categories with significant differences in overall survival (OS). We then performed univariate and multivariate Cox regression analyses to construct the T Cell Exhaustion Methylation (TEXM) signature. Finally, we confirmed that this signature served as an independent prognostic factor, and further characterized it in terms of drug resistance and immunotherapy. We identified 95 significantly differentially expressed T cell exhaustion-related genes and 51 methylation markers associated with T cell exhaustion. The cancer samples were classified according to methylation site markers, thus indicating two subtypes with significant differences in OS: subtype A and subtype B. Tumor scores, stromal scores, tumor purity, and ESTIMATE scores all showed significant differences between subtypes (P < 0.05). Univariate Cox regression analysis identified five methylation sites significantly associated with OS, and multivariate Cox regression analysis was used to construct the TEXM signature model by using these five methylation sites. Significant differences in OS were found between the groups with high and low TEXM signature scores, on the basis of calculation of the TEXM signature scores of tumor samples and using the median score to divide them into high and low score groups. Survival analysis revealed that the high score group had poorer OS and DFS than the low score group in the validation set. Notably, we observed a significant difference in drug sensitivity between the high and low TEXM signature score groups, with the high score group showing higher drug resistance and poorer prognosis. The tumor immune state, as predicted with Tracking Tumor Immunophenotype (TIP), revealed significant differences in antitumor immune scores between the high and low TEXM signature score groups. Finally, we identified 43 significantly differentially regulated metabolism-associated biological processes. The epigenetic methylation-related TEXM signature plays a key role in driving differential immune responses in GBM.

BANF1 is a novel prognostic biomarker linked to immune infiltration in head and neck squamous cell carcinoma.

Barrier-to-autointegration factor 1 (BANF1) is an abundant and ubiquitously expressed postnatal mammalian protein that is overexpressed in numerous human cancers and can promote cancer cell proliferation. However, the role of BANF1 in prognosis remains unclear in head and neck squamous cell carcinoma (HNSCC). BANF1 expression data were obtained from the GEO and TCGA databases. We used Cox regression and Kaplan-Meier curves to assess the prognostic potential of BANF1. The role of BANF1-related genes was investigated using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses. In addition, we explored the link between BANF1, drug sensitivity, and the tumor immune microenvironment. Finally, functional in vitro and in vivo assays were used to explore the effects of BANF1 on tumor growth and metastasis of HNSCC. BANF1 was markedly overexpressed in HNSCC and was correlated with clinicopathological characteristics. According to survival analysis, BANF1 can be inversely correlated with patient survival and can act as a prognostic risk indicator. IC50 values for chemotherapeutic treatments indicated that the group with high BANF1 expression was more responsive to most antitumor treatments. Furthermore, higher TIDE scores were observed in the low BANF1 expression group, indicating a decline in the efficacy of immune checkpoint inhibitor therapy. Functionally, the malignant biological behavior of HNSCC cell lines was inhibited when BANF1 expression was knocked down. BANF1 can promote tumor progression in patients with HNSCC. BANF1 shows great promise as a potential biomarker to assess the prognosis.

Identification and validation of an m7G-related lncRNAs signature for predicting prognosis, immune response and therapy landscapes in ovarian cancer.

Ovarian cancer is the most mortality malignancy in gynecology. N7-methylguanosine (m7G) is one of the most prevalent RNA modifications in the development and progression of cancer. The aim of this study is to investigate the effect of m7G-related lncRNA on ovarian cancer in terms of instruction prognosis and immunotherapy. After integrating and processing the RNA expression profiles with the clinical sample information in the TCGA database, we initially screened to the m7G-related lncRNAs by Spearman correlation analysis, and subsequently obtained a prognostic model constructed by five m7G-related lncRNAs with Univariate Cox analysis, LASSO regression analysis, and Multivariate Cox regression analysis, after which we further evaluated and validated the prognostic value of the model using Kaplan-Meier survival analysis, Principal component analysis, Nomogram, and ROC curve. In addition, based on this risk model, we explored the differentially enriched pathways and functions of the high and low risk groups, and characterized the immune cells, immune functions, gene mutations, and drug sensitivity between the two groups. After a series of rigorous filtering, we finally attained a prognostic risk model consisting of KRT7-AS, USP30-AS1, ZFHX4-AS1, ACAP2-IT1, and TWSG1-DT which is excellent in predicting the prognostic survival of ovarian cancer patients as well as existing as an independent prognostic factor. Moreover, the model has certain relevance in the immune cells and functions between high and low risk groups, and simultaneously, the signature has the role of guiding the option of immunotherapy and chemotherapeutic drugs. Altogether, our study established a tight connection between m7G-associated lncRNAs and ovarian cancer, with potential that the prognostic patterns contribute to steering the prognosis of ovarian cancer patients, measuring the efficacy of immunotherapeutic approaches, and detecting effective chemotherapeutic agents.

Multidimensional analysis of the impact of Gemmatimonas, Rhodothermus, and Sutterella on drug and treatment response in colorectal cancer.

Colorectal cancer is the third most prevalent cancer across the globe. Despite a diversity of treatment methods, the recurrence and mortality rates of the disease remain high. Recent studies have revealed a close association of the gut microbiota with the occurrence, development, treatment response, and prognosis of colorectal cancer. This study aims to integrate transcriptome and microbiome data to identify colorectal cancer subtypes associated with different gut microbiota and evaluate their roles in patient survival prognosis, tumor microenvironment (TME), and drug treatment response. An integrated analysis of microbiome data was conducted on samples of colorectal cancer from public databases. Based on this, two tumor subtypes (C1 and C2) closely associated with patient survival prognosis were identified and a risk score model was constructed. The survival status, clinical parameters, immune scores, and other features were analyzed in-depth, and the sensitivity of various potential drugs was examined. A thorough examination of microbiome information obtained from colorectal cancer patients led to the identification of two primary tumor clusters (C1 and C2), exhibiting notable variations in survival outcomes. Patients with the C1 subtype were closely associated with better prognosis, while those with the C2 subtype had higher gut microbial richness and poorer survival prognosis. A predictive model utilizing the microbiome data was developed to accurately forecast the survival outcome of patients with colorectal cancer. The TME scores provided a biological basis for risk assessment in high-risk (similar to the C2 subtype) patient cohorts. Evaluation of the sensitivity of different subtypes to various potential drugs, indicated the critical importance of personalized treatment. Further analysis showed good potential of the developed risk-scoring model in predicting immune checkpoint functions and treatment response of patients, which may be crucial in guiding the selection of immunotherapy strategies for patients with colorectal cancer. This study, through a comprehensive analysis of colorectal cancer microbiome, immune microenvironment, and drug sensitivity, enhances the current understanding of the multidimensional interactions of colorectal cancer and provides important clinical indications for improving future treatment strategies. The findings offer a new perspective on improving treatment response and long-term prognosis of patients with CRC through the regulation of microbiota or the utilization of biomarkers provided by it.

Unveiling the Power of Mitochondrial Fission and Fusion: A Five-Gene Signature for Personalized Prognosis in Gastric Cancer.

Mitochondrial fission and fusion play important roles in tumorigenesis, progression and therapy. Dysregulation of these processes may lead to tumor progression, and regulation of these processes may provide novel strategies for cancer therapy. The involvement of genes related to mitochondrial fission and fusion (MD) in gastric cancer (GC) remains poorly understood. The aim of this study was to establish an MD gene signature for GC patients and to investigate its association with prognosis, tumor microenvironment and treatment response in GC. We use the TCGA-GC database as the cohort, focusing specifically on genes associated with MD. We conducted identification and consistency clustering analysis of differentially expressed genes in MD, conducted MD gene mutation and copy number variation analysis, as well as correlation and functional enrichment analysis between MD gene cluster classification and immune infiltration. TCGA-GC and GSE15459 were used to construct training and validation cohorts for the model. We used various statistical methods, including Cox and Lasso regression, to develop the model. We validated the model using bulk transcriptome and single- cell transcriptome datasets (GSE13861, GSE26901, GSE66229, and GSE13450). We used GSEA enrichment, CIBERSORT algorithm, ESTIMATE, and TIDE to gain insight into the annotation of MD signature and the characterization of the tumor microenvironment. OncoPredict was used to analyze the relationship between the PRG signature and the drug sensitivity. We validated the expression of several key genes in MD signature on GC cell lines using quantitative real-time PCR (qRT-PCR). These MDs-related subtypes exhibited different prognosis and immune filtration patterns. A five-gene signature, comprising AGT, HCFC1, KIFC3, NOX4, and RIN1, was developed. There was a clear distinction in overall survival between low- and high-risk patients. The analyses showed further confirmation of the independent prognostic value of the gene signature. There was a notable correlation between the MD signature, immune infiltration and drug susceptibility. The expression levels of AGT, HCFC1, KIFC3, NOX4 and RIN1 mRNA were all increased in these GC cells. The MD signature has the capacity to significantly contribute to the prediction of personalized outcomes and the advancement of novel therapeutic strategies tailored for GC patients.