Prognosis-related Genes Research Articles

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.

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

BackgroundNecroptosis, 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.MethodsThe 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.ResultsThe 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.ConclusionThis 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.

TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells

BackgroundFerroptosis and inflammation are involved in cancer progression. The aim of this study was to identify inflammation-associated ferroptosis regulators in hepatocellular carcinoma (HCC).MethodsFerrDb database was searched for ferroptosis-related genes. RNA sequencing data and clinicopathologic information of HCC patients were downloaded from the Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis was applied to obtain the genes probably involved in inflammation-associated ferroptosis. Univariate Cox regression analysis was conducted to screen prognostic genes, and 10 machine learning algorithms were combined to find the optimal strategy to evaluate the prognosis of the patients based on the prognosis-related genes. The patients were divided into high risk group and low risk group, and the differentially expressed genes were obtained. Thymosin beta 4 X-linked (TMSB4X) was overexpressed or knocked down in HCC cell lines, and then qPCR, CCK-8, Transwell, flow cytometery assays were performed to detect the change of HCC cells’ phenotypes, and Western blot was used to detect the change of ferroptosis markers.Results157 genes related to inflammation and ferroptosis in HCC were obtained by WGCNA. rLasso algorithm, with the highest C-index, screened out 29 hub genes, and this model showed good efficacy to predict the prognosis of HCC patients. The patients in high risk group and low risk groups showed distinct molecular characteristics. TMSB4X was the most important gene which dominated the classification, and it was highly expressed in HCC samples. TMSB4X promoted the viability, migration and invasion, and repressed ferroptosis of HCC cells.ConclusionThe risk model constructed based on the inflammation-associated ferroptosis regulators is effective to predict the clinical outcome of HCC patients. TMSB4X, involved in inflammation-associated ferroptosis, is a potential biomarker and therapeutic target for HCC.

Comprehensive analysis of single-cell and bulk RNA sequencing reveals postoperative progression markers for non-muscle invasive bladder cancer and predicts responses to immunotherapy

BackgroundNon-muscle-invasive bladder cancer (NMIBC) is renowned for its high recurrence, invasiveness, and poor prognosis. Consequently, developing new biomarkers for risk assessment and investigating innovative therapeutic targets postoperative in NMIBC patients are crucial to aid in treatment planning.ApproachesDifferential gene expression analysis was performed using multiple Gene Expression Omnibus (GEO) datasets to identify differentially expressed genes (DEGs) between NMIBC and normal tissue, as well as between NMIBC and muscle-invasive bladder cancer (MIBC). Functional enrichment analysis was conducted based on the DEGs identified. Subsequently, prognosis-related genes were selected using Kaplan–Meier (KM) analysis and Cox regression analysis. The Boruta algorithm was utilized to further screen for core DEGs related to postoperative progression in NMIBC based on the aforementioned prognosis-related genes. Single-cell and clinical correlation studies were performed to verify their expression across various stages of bladder cancer. To investigate the link between core genes and the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA) was utilized, and Receiver Operating Characteristic (ROC) and KM analyses were performed to confirm predictive power for immune therapy outcomes. Machine learning (ML) models were constructed using the DepMap dataset to predict the efficacy of core gene inhibitors in treating bladder cancers. The prognostic performance of the core genes was evaluated using ROC curve analysis. An online prediction tool was developed based on the core genes to provide prognostic predictions. Finally, RT-qPCR, CCK-8, and Transwell assays were used to verify the pro-tumor effects of the GINS2 in bladder cancer.ResultsA total of 70 DEGs were identified, among which 11 prognostic genes were obtained through KM analysis, and an additional 8 prognostic genes were obtained through COX analysis. The Boruta algorithm selected AURKB, GINS2, and UHRF1 as the three core DEGs. Single-cell and clinical variable correlation analyses indicated that the core genes promoted the progression of bladder cancer. The analysis of immune infiltration revealed a strong positive association between the core genes and both activated CD4 T cells and Type 2 helper T cells. Two random forest (RF) models were constructed to effectively predict the treatment effect of bladder cancer after targeted inhibition of AURKB and GINS2. In addition, an online nomogram tool was developed to effectively predict the risk of postoperative progression in NMIBC patients undergoing TURBT. Finally, RT-qPCR, CCK8, and Transwell assays showed that GINS2 promoted the growth and progression of bladder cancer.ConclusionAURKB, GINS2, and UHRF1 have the potential to enhance postoperative management of NMIBC patients undergoing transurethral resection of bladder tumor (TURBT) and can predict immunotherapy response, establishing them as promising therapeutic targets.

Liquid-Liquid Phase Separation in the Prognosis of Lung Adenocarcinoma: An Integrated Analysis.

Lung adenocarcinoma (LUAD) is a highly lethal malignancy. Liquid-Liquid Phase Separation (LLPS) plays a crucial role in targeted therapies for lung cancer and in the progression of lung squamous cell carcinoma. However, the role of LLPS in the progression and prognosis of LUAD remains insufficiently explored. This study employed a multi-step approach to identify LLPS prognosis-related genes in LUAD. First, differential analysis, univariate Cox regression analysis, Random Survival For-est (RSF) method, and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regres-sion analysis were utilized to identify five LLPS prognosis-related genes. Subsequently, LASSO Cox regression was performed to establish a prognostic score termed the LLPS-related progno-sis score (LPRS). Comprehensive analyses were then conducted based on the LPRS, including survival analysis, clinical feature analysis, functional enrichment analysis, and tumor microenvi-ronment assessment. The LPRS was integrated with additional clinicopathological factors to de-velop a prognostic nomogram for LUAD patients. Immunohistochemical validation was per-formed on clinical tissue samples to further validate the findings. Finally, the relationship be-tween KRT6A, one of the identified genes, and epidermal growth factor receptor (EGFR) muta-tions was investigated. The LPRS was established using five LLPS-related genes: IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. Higher LPRS was closely associated with poor survival outcomes, gender, progression-free survival (PFS), and advanced TNM stage. Furthermore, LPRS emerged as an independent prognostic factor for LUAD. A nomogram integrating LPRS, TNM stage, and age demonstrated remarkable predictive accuracy for prognosis among patients with LUAD. LLPS likely influences LUAD prognosis through the activity of IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. KRT6A exhibits significant upregulation in LUAD, particularly in patients with EGFR mutations. This study introduces a novel LPRS model that demonstrates high accuracy in pre-dicting the clinical prognosis of LUAD. Moreover, the findings suggest that KRT6A may play a critical role in the LLPS-mediated malignant progression of LUAD.

High level of aneuploidy and recurrent loss of chromosome 11 as relevant features of somatotroph pituitary tumors

BackgroundSomatotroph neuroendocrine pituitary tumors (sPitNET) are a subtype of pituitary tumors that commonly cause acromegaly. Our study aimed to determine the spectrum of DNA copy number abnormalities (CNAs) in sPitNETs and their relevance.MethodsA landscape of CNAs in sPitNETs was determined using combined whole-genome approaches involving low-pass whole genome sequencing and SNP microarrays. Fluorescent in situ hybridization (FISH) was used for microscopic validation of CNAs. The tumors were also subjected to transcriptome and DNA methylation analyses with RNAseq and microarrays, respectively.ResultsWe observed a wide spectrum of cytogenetic changes ranging from multiple deletions, recurrent chromosome 11 loss, stable genomes, to duplication of the majority of the chromosomes. The identified CNAs were confirmed with FISH. sPitNETs with multiple duplications were characterized by intratumoral heterogeneity in chromosome number variation in individual tumor cells, as determined with FISH. These tumors were separate CNA-related sPitNET subtype in clustering analyses with CNA signature specific for whole genome doubling-related etiology. This subtype encompassed GNAS-wild type, mostly densely granulated tumors with favorable expression level of known prognosis-related genes, notably enriched with POUF1/NR5A1-double positive PitNETs. Chromosomal deletions in sPitNETs are functionally relevant. They occurred in gene-dense DNA regions and were related to genes downregulation and increased DNA methylation in the CpG island and promoter regions in the affected regions. Recurrent loss of chromosome 11 was reflected by lowered MEN1 and AIP. No such unequivocal relevance was found for chromosomal gains. Comparisons of transcriptomes of selected most cytogenetically stable sPitNETs with tumors with recurrent loss of chromosome 11 showed upregulation of processes related to gene dosage compensation mechanism in tumors with deletion. Comparison of stable tumors with those with multiple duplications showed upregulation of processes related to mitotic spindle, DNA repair, and chromatin organization. Both comparisons showed upregulation of the processes related to immune infiltration in cytogenetically stable tumors and deconvolution of DNA methylation data indicated a higher content of specified immune cells and lower tumor purity in these tumors.ConclusionssPitNETs fall into three relevant cytogenetic groups: highly aneuploid tumors characterized by known prognostically favorable features and low aneuploidy tumors including specific subtype with chromosome 11 loss.

Computational dissection of the regulatory mechanisms of aberrant metabolism in remodeling the microenvironment of breast cancer.

The composition of T cell subsets and tumor-specific T cell interactions within the tumor microenvironment (TME) contribute to the heterogeneity observed in breast cancer. Moreover, aberrant tumor metabolism is often intimately linked to dysregulated anti-tumor immune function of T cells. Identifying key metabolic genes that affect immune cell interactions thus holds promise for uncovering potential therapeutic targets in the treatment of breast cancer. This study leverages single-cell transcriptomic data from breast cancer to investigate tumor-specific T-cell subsets and their interacting subnetworks in the TME during cancer progression. We further assess the metabolic pathway activities of tumor-specifically activated T-cell subsets. The results reveal that metabolic pathways involved in insulin synthesis, secretion, degradation, as well as fructose catabolism, significantly influence multiple T cell interactions. By integrating the metabolic pathways that significantly up-regulate T cells in tumors and influence their interactions, we identify key abnormal metabolic genes associated with T-cell collaboration and further develop a breast cancer risk assessment model. Additionally, using gene expression profiles of prognosis-related genes significantly associated with aberrant metabolism and drug IC50 values, we predict targeted drugs, yielding potential candidates like GSK-J4 and PX-12. This study integrate the analysis of abnormal T-cell interactions and metabolic pathway abnormalities in the breast cancer TME, elucidating their roles in cancer progression and providing leads for novel breast cancer therapeutic strategies.

Deciphering the role of sphingolipid metabolism in the immune microenvironment and prognosis of esophageal cancer via single-cell sequencing and bulk data analysis

BackgroundEsophageal squamous cell carcinoma (ESCC) stands as a significant global health challenge, distinguished by its aggressive progression from the esophageal epithelium. Central to this malignancy is sphingolipid metabolism, a critical pathway that governs key cellular processes, including apoptosis and immune regulation, thereby influencing tumor behavior. The advent of single-cell and transcriptome sequencing technologies has catalyzed significant advancements in oncology research, offering unprecedented insights into the molecular underpinnings of cancer.MethodsWe explored sphingolipid metabolism-related genes in ESCC using scRNA-seq data from GEO and transcriptome data from TCGA. We assessed 97 genes in epithelial cells with AUCell, UCell, and singscore algorithms, followed by bulk RNA-seq and differential analysis to identify prognosis-related genes. Immune infiltration and potential immunotherapeutic strategies were also investigated, and tumor gene mutations and drug treatment strategies were analyzed.ResultOur study identified distinct gene expression patterns, highlighting ARSD, CTSA, DEGS1, and PPTQ's roles in later cellular stages. We identified seven independent prognostic genes and created a precise nomogram for prognosis.ConclusionThis study integrates single-cell and transcriptomic data to provide a reliable prognostic model associated with sphingolipid metabolism and to inform immunotherapy and pharmacotherapy for ESCC at the genetic level. The findings have significant implications for precision therapy in esophageal cancer.

Pyroptosis-related genes features on prediction of the prognosis in liver cancer: An integrated analysis of bulk and single-cell RNA sequencing

ObjectiveThis study explores the impact of pyroptosis-related genes (PRG) on the prognosis of liver cancer (LC). Methods421 samples (371 tumor samples and 50 normal samples) from the Cancer Genome Atlas (TCGA) were included in this study. GSE14520 dataset (data of RNA expression and relevant clinicopathological features), GSE125449 dataset (single-cell data in LC) and HCCDB18 dataset (validation on the reliability of the model) were downloaded as appropriate. Download the PRG and its corresponding pathway information from the gene set enrichment analysis (GSEA) website. The consensus clustering was performed by ConsensusClusterPlus package. Differentially expressed genes (DEGs) were identified using limma package, and prognostic features were constructed using un/multivariate and Lasso Cox regression. Pathway enrichment analysis was conducted by ssGSEA method. Receiver Operating Characteristic and the survival analysis were conducted by timeROC and Survminer packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, following the quantification on the key genes via reverse-transcription quantitative PCR, the Transwell and scratch assays were applied to evaluate the in-vitro invasion and migration of LC cells Huh-7. Results12 prognosis-related genes were identified to be related to the progression of LC. Three subtypes including C1, C2 and C3 were categorized using the 12 prognosis-related genes and PRGs significantly related to the prognosis of LC patients. The worst and best prognosis was seen in C3 subtype and C2 subtype, respectively. Hallmark pathway enrichment analysis has shown the concurrent immunoactivation and immune escape in C3 subtype. A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes. The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set. A nomogram model combining the risk score had the extreme higher benefit. Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score was seen in Monocytic-Macrophages. The quantification on the key genes has suggested the high expressions of KPNA2, UCK2, CBX2, RAB32, HMMR and S100A9 and the low expressions of FTCD and ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded and migrated LC cells in vitro. ConclusionThe risk model associated with pyproptosis is crucial for the tumor immunity of LC and may serve as a prognostic indicator for patients suffering from LC. Our findings will offer new perspectives for immunotherapies targeting LC.

Establish a novel immune-related gene prognostic risk index (IRGPRI) associated with CD8+ cytotoxic T lymphocytes in non-small-cell lung cancer (NSCLC)

BackgroundThe aim of this study is to create an index called IRGPRI (immune-related gene prognostic risk index) that can be utilized for predicting the prognosis and assessing the efficacy of immune checkpoint inhibitors (ICIs) therapy in patients with non-small-cell lung cancer (NSCLC). MethodsDistinguishing gene expression patterns (DEGs) were detected in CD8+ cytotoxic T lymphocytes (CTLs) compared to other cellular types such as CD4 T cells, B cells, plasma cells, and CD8 Tex using the advanced technology of Single-cell RNA Sequencing (scRNA-seq). The construction of IRGPRI was accomplished by employing LASSO Cox regression analysis. We conducted a comparative analysis on clinical characteristics and molecular features, such as pathway enrichment and gene mutation, among the distinct subgroups of IRGPRI. Furthermore, we explored the correlation between immunological characteristics and IRGPRI subgroups to comprehensively assess the effectiveness of ICIs in NSCLC patients. ResultsA total of 109 genes were identified by intersecting immune-related genes with DEGs obtained from single-cell RNA sequencing data (GSE131907), specifically comparing CTLs to other cell types. From these, we selected 7 prognosis-related genes, namely TRBC1, HLA-DMA, CTSH, RAC1, CTSL, ANXA2, and CEBPB. These genes were used to construct the IRGPRI. The prognosis of patients diagnosed with NSCLC was found to be significantly better in the low-risk group compared to the high-risk group, as demonstrated by Kaplan-Meier (K-M) survival analysis. This observation was further confirmed through the utilization of data from the GEO cohort. The low-risk group demonstrated an increase in pathways linked with immune response, whereas the high-risk group exhibited a higher prevalence of pathways related to cancer. Furthermore, it was noted in the TCGA cohort that there existed a significant rise in the mutation frequency of every gene within the high-risk group as opposed to the low-risk group. Missense variation emerged as the most prevalent form of mutation. According to the analysis of immune cell infiltration and function, the comprehensive findings suggest that the group with a low risk is characterized by an increased presence of plasma cells, CTLs, T cells follicular helper, Tregs, and Dendritic cell resting. Additionally, they exhibit a higher score in terms of immune function for B cells, CD8+ T cells, checkpoint activity, T cell inhibition and stimulation. Moreover, this low-risk group demonstrates greater efficacy when treated with ICIs therapy compared to the high-risk group. ConclusionsOur research effectively developed and verified a unique IRGPRI, showcasing its association with immune-related characteristics. As a result, the potential of IRGPRI as a valuable biomarker for predicting prognosis and evaluating the effectiveness of ICIs treatment in cancer is evident.

Multi-omics profiling and experimental verification of tertiary lymphoid structure-related genes: molecular subgroups, immune infiltration, and prognostic implications in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), characterized by a low 5-year survival rate, is the most common and aggressive type of lung cancer. Recent studies have shown that tertiary lymphoid structures (TLS), which resemble lymphoid structures, are closely linked to the immune response and tumor prognosis. The functions of the tertiary lymphoid structure-related genes (TLS-RGs) in the tumor microenvironment (TME) are poorly understood. Based on publicly available data, we conducted a comprehensive study of the function of TLS-RGs in LUAD. Initially, we categorized LUAD patients into two TLS and two gene subtypes. Subsequently, risk scores were calculated, and prognostic models were constructed using seven genes (CIITA, FCRL2, GBP1, BIRC3, SCGB1A1, CLDN18, and S100P). To enhance the clinical application of TLS scores, we have developed a precise nomogram. Furthermore, drug sensitivity, tumor mutational burden (TMB), and the cancer stem cell (CSC) index were found to be substantially correlated with the TLS scores. Single-cell sequencing results reflected the distribution of TLS-RGs in cells. Finally, we took the intersection of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) prognosis-related genes and then further validated the expression of these genes by qRT-PCR. Our in-depth investigation of TLS-RGs in LUAD revealed their possible contributions to the clinicopathological features, prognosis, and characteristics of TME. These findings underscore the potential of TLS-RGs as prognostic biomarkers and therapeutic targets for LUAD, thereby paving the way for personalized treatment strategies.

F13B regulates angiogenesis and tumor progression in hepatocellular carcinoma via the HIF-1α/VEGF pathway.

Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor with a poor prognosis. This research aimed to investigate the role of F13B in HCC and its underlying mechanisms. Through comprehensive bioinformatics analysis of the GSE120123 and The Cancer Genome Atlas (TCGA)-Liver hepatocellular carcinoma (LIHC) datasets, we identified 220 overlapping prognosis-related genes. Eight key genes, including the previously unreported CCDC170 and F13B in HCC, were identified through Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis. F13B emerged as a significant prognostic factor in HCC, warranting further investigation in subsequent analyses. In vitro experiments showed that F13B expression was notably reduced in HCC cell lines and tissues, particularly in Huh-7 and SMMC-7721 cells. Overexpression of F13B inhibited cell invasion, migration, and proliferation, while its knockdown produced the opposite effect. A lactate dehydrogenase (LDH) activity assay in human umbilical vein endothelial cells (HUVECs) demonstrated that F13B overexpression reduced vascular endothelial growth factor (VEGF)-induced cytotoxicity, whereas knockdown increased it. Further analysis revealed that F13B negatively regulates VEGFA expression, affecting HUVEC proliferation. In HUVECs, F13B overexpression reversed VEGF-induced upregulation of key angiogenesis markers, including phospho-VEGF receptor 2 (p-VEGFR2), matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), as well as AKT/mTOR signaling proteins, phospho-Akt (p-AKT), and phospho-mTOR (p-mTOR). Additionally, F13B negatively regulated VEGFA and hypoxia-inducible factor 1 A (HIF1A) under hypoxic conditions, counteracting the hypoxia-induced increase in cell viability. These findings suggest that F13B regulates angiogenesis through the HIF-1α/VEGF pathway and plays a crucial role in HCC progression. Our results highlight the potential of F13B as a therapeutic target in HCC, providing novel insights into the molecular mechanisms of HCC and its prognostic significance.

Identification of Biomarkers for Endometrial Cancer Based on Natural Killer Cell-Related Genes

Endometrial cancer (EC) is a malignant tumor. Natural killer (NK) cells play a crucial role in various cancers, but their role in EC is unclear. To this purpose, in this paper, differential expression analysis was performed on transcriptome data from the TCGA database, and the obtained DEGs and the collected NRGs were intersected, and single-factor Cox regression analysis and Lasso-Cox regression analysis were performed on the intersected genes to obtain prognosis-related genes and risk model, respectively. These genes and models were validated by Kaplan-Meier (KM) survival curve analysis and ROC analysis on the internal and external test sets. In addition, nomogram models were constructed based on prognosis-associated genes, sample risk scores, and clinical factors. Finally, we explored the immune landscape of high- and low-risk groups of EC. The results showed that the risk models constructed in this paper exhibited excellent predictive effects, which will facilitate research on the precision treatment of EC. There were significant differences in prognosis, immune cell infiltration abundance, immune checkpoint-associated genes, and HLA gene expression between high- and low-risk groups of EC. The risk model in this paper can provide a reference for the personalized treatment of EC. In addition, we performed RT-qPCR to validate the levels of genes significantly associated with prognosis.

Prognostic model of lung adenocarcinoma based on immunoprognosis-related genes and related drug prediction.

Lung cancer (LC) is the most common malignant tumor in the world, and lung adenocarcinoma (LUAD) is the most common type of LC. Immune microenvironment plays a critical role in cancer from onset to relapse. We aimed to identify an effective immune-related prediction model for assessing prognosis and predicting the relevant tumor therapeutic drugs. According to the RNA sequencing data of LUAD transcriptome in The Cancer Genome Atlas (TCGA) database and the immune-related genes obtained from IMMPORT (The Immunology Database and Analysis Portal) database, immune prognosis-related genes were screened. Weighted gene co-expression network analysis (WGCNA) identified hub genes in differentially expressed immune-related genes (DEIRGs). Least absolute shrinkage and selection operator (LASSO) Cox and ten rounds of cross-validation were used to screen core genes to establish a prognostic model, and in situ hybridization was used to verify the expression of core genes in LUAD. Then the patients from the TCGA database were divided into high-risk group and low-risk group. The survival, tumor microenvironment (TME) and immune cell infiltration of different groups were further analyzed, and the differential genes between the two groups were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) enrichment analyses. Finally, the small molecular drugs that can inhibit the prognosis of LUAD were screened by Connectivity Map (CMAP), and the therapeutic mechanism of small molecular drug oxibendazole was verified by Cell Counting Kit-8 (CCK-8) experiment. A four-immunoprognosis-related gene model was established to forecast the overall survival (OS) of LUAD through LASSO Cox regression and ten rounds of cross-validation analysis. This prognostic model stratified LUAD patients into low-risk and high-risk groups. According to the findings of the survival analysis, the low-risk group had a greater OS than the high-risk group and the content of immune cells in LUAD was corrected with the survival prognosis of patients. Univariate and multivariate Cox regression also revealed that the prognostic model was an independent prognosis factor in LUAD. Five kinds of small molecular drugs which can inhibit the prognosis of LUAD were screened by CMAP. As shown by CCK-8 test, the small molecular drug "oxibendazole" can effectively inhibit the proliferation of LUAD cells. Based on immune-related prognostic genes, a new prognostic model for LUAD was constructed. Oxibendazole can inhibit the proliferation of LUAD cells, which provides a new idea for the treatment of LUAD.

PANoptosis-based molecular subtype and prognostic model predict survival and immune landscape in esophageal cancer

PurposeTo establish a prognostic model to predict the survival of patients with esophageal cancer (EC).MethodsWe extracted the expression profiles of prognostic-related genes and clinicopathological data from TCGA and GEO databases. Subsequently, a comprehensive bioinformatics analysis was conducted to construct a prognostic model utilizing LASSO and multivariate Cox regression. The stability of the risk signature was validated through Kaplan-Meier and ROC curve analyses on the training, internal testing, and external testing sets. Furthermore, we developed a nomogram that incorporates the risk score and clinical features to predict the suvival. Additionally, a nomogram incorporating the risk score and relevant clinical parameters was developed to enhance survivorship prediction. Furthermore, we delved into exploring the correlation between the risk score and immune cell abundance, expression of cancer checkpoints, as well as responses to immunotherapy and chemotherapeutic agents.ResultsIn this study, we successfully identified 19 prognosis-related genes out of a pool of 65 PANoptosis-related genes (PRGs) sourced from existing literature. Through consensus clustering analysis, we classified patients into two distinct groups as PANcluster A and B. Furthermore, the risk score derived from the five PANoptosis-related signatures emerged as an independent prognostic factor among patients with EC. To enhance the prognostic accuracy, we devised a nomogram integrating the risk score with clinical risk characteristics, enabling the prediction of 1-year, 2-year, and 3-year overall survival (OS) rates. Notably, individuals classified in the high-risk group demonstrated poorer prognoses compared to their low-risk counterparts. Furthermore, the risk score displayed substantial correlations with immune cell abundance, expression levels of cancer checkpoints, and responses to immunotherapy and chemotherapeutic agents. These pivotal findings underscore the significance of considering PANoptosis-related patterns in improving prognostic assessment and predicting treatment responses in patients diagnosed with esophageal cancer.ConclusionWe constructed a reliable prognostic risk model for EC utilizing five PRGs. The developed nomogram serves as a valuable tool in predicting patient outcomes, offering crucial insights that can inform and guide treatment decisions for individuals diagnosed with EC.

Identification and Prognostic Value of m6A-Related Genes in Glioblastoma.

N6-methyladenosine (m6A) is one of the most common forms of mRNA modification, which is dynamically regulated by the m6A-related genes; however, its effect in glioblastoma (GBM) is still unknown. We sought to investigate the association between m6A-related genes (m6A-RGs) and GBM. Transcriptome data and the relevant clinical data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The m6A-RGs were identified from differently expressed genes, and COX and lasso regression models were applied to locate the prognosis-related genes. We identified 15 out of 19 m6A-RGs differentially expressed between GBM and nontumor tissues. We identified two subgroups of GBM (clusters 1 and 2) by applying consensus clustering. Compared with the cluster 1 subgroup, the cluster 1 subgroup correlates with a poorer prognosis, and most of the 19 m6A-RGs are higher expressed in cluster 1. Through univariate Cox and lasso regression model, we identified three m6A-RGs, namely HNRNPC, ALKBH5, and FTO, which were used to construct a Cox regression risk model to predict the prognosis of GBM patients. We identified a valuable m6A model for predicting the prognosis of GBM patients, which can provide useful epigenetic biomarkers.

Development and validation of a glioma prognostic model based on telomere-related genes and immune infiltration analysis.

Gliomas are the most prevalent primary brain tumors, and patients typically exhibit poor prognoses. Increasing evidence suggests that telomere maintenance mechanisms play a crucial role in glioma development. However, the prognostic value of telomere-related genes in glioma remains uncertain. This study aimed to construct a prognostic model of telomere-related genes and further elucidate the potential association between the two. We acquired RNA-seq data for low-grade glioma (LGG) and glioblastoma (GBM), along with corresponding clinical information from The Cancer Genome Atlas (TCGA) database, and normal brain tissue data from the Genotype-Tissue Expression (GTEX) database for differential analysis. Telomere-related genes were obtained from TelNet. Initially, we conducted a differential analysis on TCGA and GTEX data to identify differentially expressed telomere-related genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on these genes. Subsequently, univariate Cox analysis and log-rank tests were employed to obtain prognosis-related genes. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were sequentially utilized to construct prognostic models. The model's robustness was demonstrated using receiver operating characteristic (ROC) curve analysis, and multivariate Cox regression of risk scores for clinical characteristics and prognostic models were calculated to assess independent prognostic factors. The aforementioned results were validated using the Chinese Glioma Genome Atlas (CGGA) dataset. Finally, the CIBERSORT algorithm analyzed differences in immune cell infiltration levels between high- and low-risk groups, and candidate genes were validated in the Human Protein Atlas (HPA) database. Differential analysis yielded 496 differentially expressed telomere-related genes. GO and KEGG pathway analyses indicated that these genes were primarily involved in telomere-related biological processes and pathways. Subsequently, a prognostic model comprising ten telomere-related genes was constructed through univariate Cox regression analysis, log-rank test, LASSO regression analysis, and multivariate Cox regression analysis. Patients were stratified into high-risk and low-risk groups based on risk scores. Kaplan-Meier (K-M) survival analysis revealed worse outcomes in the high-risk group compared to the low-risk group, and establishing that this prognostic model was a significant independent prognostic factor for glioma patients. Lastly, immune infiltration analysis was conducted, uncovering notable differences in the proportion of multiple immune cell infiltrations between high- and low-risk groups, and eight candidate genes were verified in the HPA database. This study successfully constructed a prognostic model of telomere-related genes, which can more accurately predict glioma patient prognosis, offer potential targets and a theoretical basis for glioma treatment, and serve as a reference for immunotherapy through immune infiltration analysis.

A novel amino acid metabolism-related gene signature to predict the overall survival of esophageal squamous cell carcinoma patients.

Esophageal squamous cell carcinoma (ESCC) has a poor early detection rate, prognosis, and survival rate. Effective prognostic markers are urgently needed to assist in the prediction of ESCC treatment outcomes. There is accumulating evidence of a strong relationship between cancer cell growth and amino acid metabolism. This study aims to determine the relationship between amino acid metabolism and ESCC prognosis. This study comprehensively evaluates the association between amino acid metabolism-related gene (AAMRG) expression profiles and the prognosis of ESCC patients based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the expression of prognosis-related genes. A univariate Cox regression analysis of TCGA data identified 18 prognosis-related AAMRGs. The gene expression profiles of 90 ESCC tumor and normal tissues were obtained from the GSE20347 and GSE67269 datasets. Two differently expressed genes (DEGs) were considered as ESCC prognosis-related genes; and they were branched-chain amino acid transaminase 1 (BCAT1) and methylmalonic aciduria and homocystinuria type C protein (MMACHC). These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients. Both BCAT1 and MMACHC expression were verified by RT-qPCR. A prognostic nomogram that incorporated clinical factors and BCAT1 and MMACHC gene expression was constructed, and the calibration plots showed that it had good prognostic performance. The AAMRG signature established in our study is efficient and could be used in clinical settings to predict the early prognosis of ESCC patients.

Identification and validation of Cystatin A as a novel promising therapeutic target for gastric cancer.

The effect of pharmacological treatment of gastric cancer (GC) is limited, thus, it holds significant scientific importance to thoroughly investigate the molecular mechanisms underlying GC development and identify novel molecules capable of substantially extending patients' survival. This study utilized bioinformatics techniques to identify 11 genes associated with recurrence-free survival (RFS) in GC patients and investigated the potential biological functions of these genes through single-cell transcriptomic analysis. Subsequently, a single gene Cystatin A (CSTA) was selected for further analysis to explore its impact on signaling pathways and treatment. Differentially expressed genes (DEGs) were identified and overlapped in the analysis of RFS to identify potential prognostic genes for GC patients, based on data from the Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) and GSE54129. Subsequently, a prognostic model based on RFS in GC patients was established. Single-cell sequencing data were employed to explore the potential functions of these model genes. CSTA, one of the RFS-related genes, was further investigated using immunohistochemistry (IHC), Cell Counting Kit 8 (CCK-8), transwell, scratch, colony formation assays, flow cytometry, and Western blotting methods. Through bioinformatics analysis, we identified 23 RFS-related genes in GC. Using the least absolute shrinkage and selection operator (LASSO)-Cox method, an RFS prognostic model was developed which pinpointed 11 GC prognosis-related (GPR) genes as significant factors influencing RFS in GC patients. The single-cell analysis revealed their potential role in affecting differentiation and maturation of pre-fibroblasts thereby impacting RFS in GC patients. CSTA exhibited low expression levels in GC tissues. Overexpression of CSTA promoted apoptosis in GC cells through the caspase-dependent apoptotic pathway and enhanced their response to cisplatin via this same pathway. The 11 GPR genes are primarily enriched within a specific type of stromal cell exhibiting heightened communication, metabolism, and differentiation levels. The gene signature of these stromal cells has implications for patient prognosis. Additionally, CSTA, a gene related to prognosis, has been shown to influence apoptosis levels in GC cells.

Tumor microenvironment characteristics of lipid metabolism reprogramming related to ferroptosis and EndMT influencing prognosis in gastric cancer

BackgroundGastric cancer (GC) is a refractory malignant tumor with high tumor heterogeneity, a low rate of early diagnosis, and poor patient prognosis. Lipid metabolism reprogramming plays a critical role in tumorigenesis and progression, but its prognostic role and regulatory mechanism in GC are rarely studied. Thus, the identification of signatures related to lipid metabolism is necessary and may present a new avenue for improving the overall prognosis of GC. MethodsLipid metabolism-associated genes (LMAGs) with differential expression in tumor and tumor-adjacent tissue were acquired to identify lipid metabolism-associated subtypes. The differentially expressed genes (DEGs) between the two clusters were then utilized for prognostic analysis and signature construction. Additionally, pathway enrichment analysis and immune cell infiltration analysis were employed to identify the characteristics of the prognostic model. Further analyses were conducted at the single-cell level to better understand the model’s prognostic mechanism. Finally, the prediction of immunotherapy response was used to suggest potential treatments. ResultsTwo lipid metabolism-associated subtypes were identified and 9 prognosis-related genes from the DEGs between the two clusters were collected for the construction of the prognostic model named lipid metabolism-associated signature (LMAS). Then we found the low LMAS patients with favorable prognoses were more sensitive to ferroptosis in the Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD). Meanwhile, the tumor cells exhibiting high levels of lipid peroxidation and accumulation of reactive oxygen species (ROS) in single-cell levels were primarily enriched in the low LMAS group, which was more likely to induce ferroptosis. In addition, endothelial cells and cancer-associated fibroblasts (CAFs) facilitated tumor angiogenesis, proliferation, invasion, and metastasis through endothelial-mesenchymal transition (EndMT), affecting the prognosis of the patients with high LMAS scores. Moreover, CD1C− CD141− dendritic cells (DCs) also secreted pro-tumorigenic cytokines to regulate the function of endothelial cells and CAFs. Finally, the patients with low LMAS scores might have better efficacy in immunotherapy. ConclusionsA LMAS was constructed to guide GC prognosis and therapy. Meanwhile, a novel anti-tumor effect was found in lipid metabolism reprogramming of GC which improved patients’ prognosis by regulating the sensitivity of tumor cells to ferroptosis. Moreover, EndMT may have a negative impact on GC prognosis.