Five-gene Signature Research Articles

Identification of mitophagy-related gene signatures for predicting delayed graft function and renal allograft loss post-kidney transplantation.

Ischemia-reperfusion injury (IRI) is an unavoidable consequence post-kidney transplantation, which inevitably leads to kidney damage. Numerous studies have demonstrated that mitophagy is implicated in human cancers. However, the function of mitophagy in kidney transplantation remains poorly understood. This study aims to develop mitophagy-related gene (MRGs) signatures to predict delayed graft function (DGF) and renal allograft loss post-kidney transplantation. Differentially expressed genes (DEGs) were identified and intersected with the MRGs to obtain mitophagy-related DEGs (MRDEGs). Functional enrichment analyses were conducted. Subsequently, random forest and SVM-RFE machine learning were employed to identify hub genes. The DGF diagnostic prediction signature was constructed using LASSO regression analysis. The renal allograft prognostic prediction signature was developed through univariate Cox and LASSO regression analysis. In addition, ROC curves, immunological characterization, correlation analysis, and survival analysis were performed. Nineteen MRDEGs were obtained by intersecting 61 DEGs with 4897 MRGs. Seven hub genes were then identified through machine learning. Subsequently, a five-gene DGF diagnostic prediction signature was established, with ROC curves indicating its high diagnostic value for DGF. Immune infiltration analysis revealed that many immune cells were more abundant in the DGF group compared to the Immediate Graft Function (IGF) group. A two-gene prognostic signature was developed, which accurately predicted renal allografts prognosis. The mitophagy-related gene signatures demonstrated high predictive accuracy for DGF and renal allograft loss. Our study may provide new perspectives on prognosis and treatment strategies post-kidney transplantation.

In silico identification and verification of Tanshinone IIA-related prognostic genes in hepatocellular carcinoma.

Currently, adequate treatment and prognostic prediction means for Hepatocellular Carcinoma (HCC) haven't entered into medical vision. Tanshinone IIA (TanIIA) is a natural product, which can be utilized as a potential treatment of HCC due to its high anti-tumor activity. However, the effect on HCC prognosis, as well as the potential targets and molecular mechanism of TanIIA still remain ambiguous. Herein, we investigated them via network pharmacology, explored TanIIA-related prognostic genes by machine learning methods, and verified using molecular docking and cell experiments. Potential TanIIA-targeted genes and HCC-related genes were obtained from the corresponding database. The Protein-Protein Interaction (PPI) network and enrichment analyses of the intersection targets were conducted. Furthermore, a TanIIA-related prognostic model was built and verified. We attempted to explore the expression of the TanIIA-related prognostic genes and evaluate its chemotherapeutic sensitivities and the immune infiltrations. Followed by exploration of anti-tumor activity on the human HCC cells Hep3B and HepG2 cell lines in vitro (CCK-8, flow cytometry and transwell assay), the docking molecular was performed. Ultimately, the corresponding protein expressions were determined by western blotting. A total of 64 intersecting targets were collected. Similarly, GO/KEGG enrichment analysis showed that TanIIA can inhibit HCC by affecting multiple pathways, especially the MAPK signaling pathway. A five-gene signature related to TanIIA was constructed on account of Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model. Among five genes, ALB, ESR1 and SRC tend to be core genes because of probable status as potential targets for sorafenib. Molecular docking results demonstrated the potential for active interaction between the core genes relevant proteins and TanIIA. Studies in vitro had shown that TanIIA regulated the expressions of Bcl-2, Bax and MMP9 in HCC cells, inhibiting their growth, inducing apoptosis and preventing cell invasion. Additionally, we are able to detect an up-regulated trend in the expression of ALB and ESR1, while a down-regulated in the expression of SRC by TanIIA. Regulating the expression of TanIIA-related gene signatures (ALB, SRC and ESR1), and inhibiting the SRC/MAPK/ERK signaling axis might potentially contribute to the TanIIA treatment of HCC. And the three gene signatures could be identified for predicting the prognosis of HCC, which may provide novel biomarkers for HCC treatment.

Comprehensive pan-cancer analysis of FUTs family as prognostic and immunity markers based on multi-omics data

BackgroundThe dysregulation of fucosyltransferases (FUTs) contributes to alterations in fucosylated epitope expression, which serve as distinctive features of cancer cells. Nonetheless, a comprehensive elucidation of the prognostic biological marker and therapeutic target of the FUTs family in pan-cancer remains elusive.MethodsOver 10,000 individuals' profiling information was examined, including information on 750 small molecule drugs, 33 types of cancer, and 24 types of immune cells. We focused on POFUT2's function and applied GSVA (Gene Set Variation Analysis) to calculate the FUT score. Survival and cancer pathways were found to be correlated with this score. After deriving a signature via univariate Cox and LASSO regression, we generated and analyzed the ROC curve and developed a nomogram.ResultsOur comprehensive analysis revealed epigenetic, genomic, and immunogenomic changes in FUTs, particularly POFUT2, resulting in aberrant expression. Elevated frequencies of CNV (Copy number variation), SNV (Single Nucleotide Variant), and hypermethylation were observed in FUTs. Additionally, the survival of patients with various types of cancers may be predicted by FUT expression. Immune response and prognosis in numerous types of cancer were found to be strongly linked to aberrant POFUT2 expression. Pathway analysis unveiled the role of FUTs in apoptosis, epithelial-to-mesenchymal transition (EMT), cell cycle, DNA damage response, RAS/MAPK, TSC/mTOR, PI3K/AKT, AR, ER, and RTK. A prognostic index for patients diagnosed with adrenocortical carcinoma (ACC) was established by applying a risk model incorporating nine FUTs and based on the findings of the GSVA.ConclusionsFUTs, particularly POFUT2, emerge as candidate targets for improving the outcomes of immune therapy. The significance of aberrant MUC12 expression, cancer immune therapy, and patient survival in the context of diverse malignancies is enhanced by the strong correlation observed among these factors. Our five-gene risk signature provides patients with ACC with an independent prognostic indicator, emphasizing the critical function of these genes in inhibiting the immune system's response in ACC.

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.

Construction of M2 macrophage-related gene signature for predicting prognosis and revealing different immunotherapy response in bladder cancer patients.

Bladder cancer development is closely associated with the dynamic interaction and communication between M2 macrophages and tumor cells. However, specific biomarkers for targeting M2 macrophages in immunotherapy remain limited and require further investigation. In this study, we identified key co-expressed genes in M2 macrophages and developed gene signatures to predict prognosis and immunotherapy response in patients. Public database provided the bioinformatics data used in the analysis. We created and verified an M2 macrophage-related gene signature in these datasets using Lasso-Cox analysis. The predictive value and immunological functions of our risk model were examined in bladder cancer patients, and 158 genes were found to be significantly positively correlated with M2 macrophages. Moreover, we identified two molecular subgroups of bladder cancer with markedly different immunological profiles and clinical prognoses. The five key risk genes identified in this model were validated, including CALU, ECM1, LRP1, CYTL1, and CCDC102B, demonstrating the model can accurately predict prognosis and identify unique responses to immunotherapy in patients with bladder cancer. In summary, we constructed and validated a five-gene signature related to M2 macrophages, which shows strong potential for forecasting bladder cancer prognosis and immunotherapy response.

Identification and validation of a novel five-gene signature in high-risk MYCN-not-amplified neuroblastoma

ObjectiveHigh-risk neuroblastoma patients often have poor outcomes despite multi-treatment options. The risk stratification of high-risk MYCN-not-amplified (HR-MYCN-NA) patients remains difficult. This study aims to identify a gene set signature that can help further stratify HR-MYCN-NA patients for a potential personalized therapeutic strategy.MethodsThree microarrays and one single-cell RNA sequence dataset were acquired and analyzed. Firstly, the prognostic-related genes (PRGs) in HR-MYCN-NA tumor cells were identified using TARGET-NB and GSE137804 datasets. Then, the prognostic model was established by LASSO-Cox regression, and verified in external cohort (GSE49710, GSE45547). Moreover, a time-dependent receiver operating characteristic curve (ROC) and area under the ROC (AUC) was used to assess survival prediction. A nomogram was established to predict the 1-, 3- and 5-year overall survival (OS) of HR-MYCN-NA patients.ResultsIn the training set, a five-PRGs signature, which include GAL, GFRA3, MARCKS, PSMD13, and ZNHIT3 genes, was identified and successfully stratified HR-MYCN-NA patients into ultra-high risk (UHR) and high-risk (HR) subtypes (HR = 4.29, P < 0.001). ROC curve analysis confirmed its predictive power (AUC = 0.74–0.82), suggesting a good predictive efficacy. Consistently, high-risk scores also predicted worse OS (HR = 2, P = 0.033) in the external validation dataset (AUC = 0.67–0.71). Moreover, the overall C-index of the nomogram was 0.75 (P < 0.001), which indicated good agreement between the observed and predicted survival rates. Further integrating the five PRGs signature with clinical factors, these 5 gene signature (HR = 4.45, P < 0.001) and tumor grade (HR = 4.15, P = 0.02) were found to be independent prognostic factors for HR-MYCN-NA patients.ConclusionThe novel five PRGs signature could well predict the survival of HR-MYCN-NA patients, which may provide constructive information for these subsets.

Prognostic prediction signature and molecular subtype for liver cancer: A CTC/CTM‑related gene prediction model and independent external validation.

Liver cancer is the second leading cause of tumor-related death worldwide, and a serious threat to lives and health. Circulating tumor cells (CTCs) facilitate the progression of various cancers, including liver cancer. The relationship between CTC/circulating tumor microemboli-related genes (CRGs) and the prognosis of liver cancer is unclear. The aim of the present study was to identify CTC/circulating tumour microemboli-related genes (CRGs) in hepatocellular carcinoma and to investigate their clinical significance. Transcriptomic data from The Cancer Genome Atlas (International Cancer Genome Consortium (ICGC) and GSE117623 databases were combined, and differentially expressed CRGs were identified. These were subsequently analyzed via least absolute shrinkage and selection operator and multivariate Cox analyses, and a five-gene risk signature was constructed. The signature was validated in the ICGC and GSE14520 dataset with survival analysis and receiver operating characteristic curve analysis. Immunocyte infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE), and the somatic mutation rate were also compared between high- and low-risk groups, based on the median predictive index, to further evaluate the immunotherapeutic value of the model. Molecular subtypes of liver cancer were characterized by the non-negative matrix factorization method and potential therapeutic compounds were evaluated for different subtypes. Nomograms were utilized to predict the prognosis of patients, and the signature was compared with previous literature models. Additionally, the biological function of one of the CRGs, tumor protein p53 inducible protein 3 (TP53I3), in liver cancer was further explored through in vitro experiments. Analysis of the prognostic characteristics of the five CRGs led to the identification of two liver cancer subtypes. Patients in the low-risk group had a longer survival compared with those in the high-risk group, and patients in the latter group were associated with a higher TMB, immunocyte infiltration and somatic mutation rate, and lower TIDE scores. The prognostic profile was validated in the ICGC and GSE14520 datasets and exhibited a good predictive performance. In vitro analysis showed that the knockdown of TP53I3 suppressed liver cancer cell proliferation. In summary, CRGs were used to develop a new prognostic signature to predict the prognosis of patients with liver cancer. This signature may be used to assess the prognosis of patients and may provide new insights for clinical management strategies. In addition, TP53I3 is potentially a therapeutic target for liver cancer.

Systematic transcriptomic analysis of childhood medulloblastoma identifies N6- methyladenosine-dependent lncRNA signatures associated with molecular subtype, immune cell infiltration, and prognosis.

Medulloblastoma, the most common malignant pediatric brain tumor, is classified into four main molecular subgroups, but group 3 and group 4 tumors are difficult to subclassify and have a poor prognosis. Rapid point-of-care diagnostic and prognostic assays are needed to improve medulloblastoma risk stratification and management. N6-methyladenosine (m6A) is a common RNA modification and long non-coding RNAs (lncRNAs) play a central role in tumor progression, but their impact on gene expression and associated clinical outcomes in medulloblastoma are unknown. Here we analyzed 469 medulloblastoma tumor transcriptomes to identify lncRNAs co-expressed with m6A regulators. Using LASSO-Cox analysis, we identified a five-gene m6A-associated lncRNA signature (M6LSig) significantly associated with overall survival, which was combined in a prognostic clinical nomogram. Using expression of the 67 m6A-associated lncRNAs, a subgroup classification model was generated using the XGBoost machine learning algorithm, which had a classification accuracy > 90%, including for group 3 and 4 samples. All M6LSig genes were significantly correlated with at least one immune cell type abundance in the tumor microenvironment, and the risk score was positively correlated with CD4 + naïve T cell abundance and negatively correlated with follicular helper T cells and eosinophils. Knockdown of key m6A writer genes METTL3 and METTL14 in a group 3 medulloblastoma cell line (D425-Med) decreased cell proliferation and upregulated many M6LSig genes identified in our in silico analysis, suggesting that the signature genes are functional in medulloblastoma. This study highlights a crucial role for m6A-dependent lncRNAs in medulloblastoma prognosis and immune responses and provides the foundation for practical clinical tools that can be rapidly deployed in clinical settings.

Systematic transcriptomic analysis of childhood medulloblastoma identifies N6-methyladenosine-dependent lncRNA signatures associated with molecular subtype, immune cell infiltration, and prognosis

Medulloblastoma, the most common malignant pediatric brain tumor, is classified into four main molecular subgroups, but group 3 and group 4 tumors are difficult to subclassify and have a poor prognosis. Rapid point-of-care diagnostic and prognostic assays are needed to improve medulloblastoma risk stratification and management. N6-methyladenosine (m6A) is a common RNA modification and long non-coding RNAs (lncRNAs) play a central role in tumor progression, but their impact on gene expression and associated clinical outcomes in medulloblastoma are unknown. Here we analyzed 469 medulloblastoma tumor transcriptomes to identify lncRNAs co-expressed with m6A regulators. Using LASSO-Cox analysis, we identified a five-gene m6A-associated lncRNA signature (M6LSig) significantly associated with overall survival, which was combined in a prognostic clinical nomogram. Using expression of the 67 m6A-associated lncRNAs, a subgroup classification model was generated using the XGBoost machine learning algorithm, which had a classification accuracy > 90%, including for group 3 and 4 samples. All M6LSig genes were significantly correlated with at least one immune cell type abundance in the tumor microenvironment, and the risk score was positively correlated with CD4+ naïve T cell abundance and negatively correlated with follicular helper T cells and eosinophils. Knockdown of key m6A writer genes METTL3 and METTL14 in a group 3 medulloblastoma cell line (D425-Med) decreased cell proliferation and upregulated many M6LSig genes identified in our in silico analysis, suggesting that the signature genes are functional in medulloblastoma. This study highlights a crucial role for m6A-dependent lncRNAs in medulloblastoma prognosis and immune responses and provides the foundation for practical clinical tools that can be rapidly deployed in clinical settings.

Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification

BackgroundNumerous gene signatures predicting the prognosis of bladder cancer have been identified. However, a tumor-specific T cell signature related to immunotherapy response in bladder cancer remains under investigation.MethodsSingle-cell RNA and TCR sequencing from the Gene expression omnibus (GEO) database were used to identify tumor-specific T cell-related genes in bladder cancer. Subsequently, we constructed a tumor-specific T cell signature (TstcSig) and validated its clinical relevance for predicting immunotherapy response in multiple immunotherapy cohorts. Further analyses explored the immune characteristics of TstcSig in bladder cancer patients from other cohorts in the TCGA and GEO databases. Western blot (WB), multicolor immunofluorescence (MIF), qRT-PCR and flow cytometry assays were performed to validate the results of bioinformatics analysis.ResultsThe established TstcSig, based on five tumor-specific T cell-related genes, could predict outcomes in a bladder cancer immunotherapy cohort. This was verified using two additional immunotherapy cohorts and showed better predictive performance compared to 109 published T cell signatures. TstcSig was strongly correlated with immune characteristics such as immune checkpoint gene expression, tumor mutation burden, and T cell infiltration, as validated by single-cell and spatial transcriptomics datasets. Notably, the positive correlation between TstcSig and T cell infiltration was confirmed in the TCGA cohort. Furthermore, pan-cancer analysis demonstrated the heterogeneity of the prognostic value of TstcSig. Tumor-specific T cells highly expressed CD27, IFNG, GZMB and CXCL13 and secreted more effector cytokines for tumor cell killing, as validated experimentally.ConclusionWe developed a five-gene signature (including VAMP5, TIGIT, LCK, CD27 and CACYBP) based on tumor-specific T cell-related genes to predict the immunotherapy response in bladder cancer patients.

Glycolysis-related five-gene signature correlates with prognosis and immune infiltration in gastric cancer.

Gastric cancer (GC) is one of the most common malignancies worldwide. Glycolysis has been demonstrated to be pivotal for the carcinogenesis of GC. To develop a glycolysis-based gene signature for prognostic evaluation in GC patients. Differentially expressed genes correlated with glycolysis were identified in stomach adenocarcinoma data (STAD). A risk score was established through a univariate Cox and least absolute shrinkage and selection operator analysis. The model was evaluated using the area under the receiver operating characteristic curves. RNA-sequencing data from high- and low-glycolysis groups of STAD patients were analyzed using Cibersort algorithm and Spearman correlation to analyze the interaction of immune cell infiltration and glycolysis. Multiomics characteristics in different glycolysis status were also analyzed. A five-gene signature comprising syndecan 2, versican, malic enzyme 1, pyruvate carboxylase and SRY-box transcription factor 9 was constructed. Patients were separated to high- or low-glycolysis groups according to risk scores. Overall survival of patients with high glycolysis was poorer. The sensitivity and specificity of the model in prediction of survival of GC patients were also observed by receiver operating characteristic curves. A nomogram including clinicopathological characteristics and the risk score also showed good prediction for 3- and 5-year overall survival. Gene set variation analysis showed that high-glycolysis patients were related to dysregulation of pancreas beta cells and estrogen late pathways, and low-glycolysis patients were related to Myc targets, oxidative phosphorylation, mechanistic target of rapamycin complex 1 signaling and G2M checkpoint pathways. Tumor-infiltrating immune cells and multiomics analysis suggested that the different glycolysis status was significantly correlated with multiple immune cell infiltration. The patients with high glycolysis had lower tumor mutational burden and neoantigen load, higher incidence of microsatellite instability and lower chemosensitivity. High glycolysis status was often found among patients with grade 2/3 cancer or poor prognosis. The genetic characteristics revealed by glycolysis could predict the prognosis of GC. High glycolysis significantly affects GC phenotype, but the detailed mechanism needs to be further studied.

Integrated bulk and single-cell RNA sequencing identifies an aneuploidy-based gene signature to predict sensitivity of lung adenocarcinoma to traditional chemotherapy drugs and patients’ prognosis

Background Patients with lung adenocarcinoma (LUAD) often develop a poor prognosis. Currently, researches on prognostic and immunotherapeutic capacity of aneuploidy-related genes in LUAD are limited. Methods Genes related to aneuploidy were screened based on bulk RNA sequencing data from public databases using Spearman method. Next, univariate Cox and Lasso regression analyses were performed to establish an aneuploidy-related riskscore (ARS) model. Results derived from bioinformatics analysis were further validated using cellular experiments. In addition, typical LUAD cells were identified by subtype clustering, followed by SCENIC and intercellular communication analyses. Finally, ESTIMATE, ssGSEA and CIBERSORT algorithms were employed to analyze the potential relationship between ARS and tumor immune environment. Results A five-gene ARS signature was developed. These genes were abnormally high-expressed in LUAD cell lines, and in particular the high expression of CKS1B promoted the proliferative, migratory and invasive phenotypes of LUAD cell lines. Low ARS group had longer overall survival time, higher degrees of inflammatory infiltration, and could benefit more from receiving immunotherapy. Patients in low ASR group responded more actively to traditional chemotherapy drugs (Erlotinib and Roscovitine). The scRNA-seq analysis annotated 17 cell subpopulations into seven cell clusters. Core transcription factors (TFs) such as CREB3L1 and CEBPD were enriched in high ARS cell group, while TFs such as BCLAF1 and UQCRB were enriched in low ARS cell group. CellChat analysis revealed that high ARS cell groups communicated with immune cells via SPP1 (ITGA4-ITGB1) and MK (MDK-NCl) signaling pathways. Conclusion In this research, integrative analysis based on the ARS model provided a potential direction for improving the diagnosis and treatment of LUAD.

AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

Clinical Significance of a Novel Vasculogenic Mimicry-Based Prognostic Model in Hepatocellular Carcinoma.

Vasculogenic mimicry, a novel neovascularization pattern of aggressive tumors, is associated with poor clinical outcomes. The aim of this research was to establish a new model, termed VC score, to predict the prognosis, Tumor Microenvironment (TME) components, and immunotherapeutic response in Hepatocellular Carcinoma (HCC). The expression data of the public databases were used to develop the prognostic model. Consensus clustering was performed to confirm the molecular subtypes with ideal clustering efficacy. The high- and low-risk groups were stratified utilizing the VC score. Various methodologies, including survival analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Dysfunction and Exclusion scores (TIDE), Immunophenoscore (IPS), and nomogram, were utilized for verification of the model performance and to characterize the immune status of HCC tissues. GSEA was performed to mine functional pathway information. The survival and immune characteristics varied between the three molecular subtypes. A five-gene signature (TPX2, CDC20, CFHR4, SPP1, and NQO1) was verified to function as an independent predictive factor for the prognosis of patients with HCC. The high-risk group exhibited lower Overall Survival (OS) rates and higher mortality rates in comparison to the low-risk group. Patients in the low-risk group were predicted to benefit from immune checkpoint inhibitor therapy and exhibit increased sensitivity to immunotherapy. Enrichment analysis revealed that signaling pathways linked to the cell cycle and DNA replication processes exhibited enrichment in the high-risk group. The VC score holds the potential to establish individualized treatment plans and clinical management strategies for patients with HCC.

Comprehensive pan-cancer analysis of inflammatory age-clock-related genes as prognostic and immunity markers based on multi-omics data

Inflammatory age (iAge) is a vital concept for understanding the intricate interplay between chronic inflammation and aging in the context of cancer. However, the importance of iAge-clock-related genes (iAge-CRGs) across cancers remains unexplored. This study aimed to explore the mechanisms and applications of these genes across diverse cancer types. We analyzed profiling data from over 10,000 individuals, covering 33 cancer types, 750 small molecule drugs, and 24 immune cell types. We focused on DCBLD2’s function at the single-cell level and computed an iAge-CRG score using GSVA. This score was correlated with cancer pathways, immune infiltration, and survival. A signature was then derived using univariate Cox and LASSO regression, followed by ROC curve analysis, nomogram construction, decision curve analysis, and immunocytochemistry. Our comprehensive analysis revealed epigenetic, genomic, and immunogenomic alterations in iAge-CRGs, especially DCBLD2, leading to abnormal expression. Aberrant DCBLD2 expression strongly correlated with cancer-associated fibroblast infiltration and prognosis in multiple cancers. Based on GSVA results, we developed a risk model using five iAge-CRGs, which proved to be an independent prognostic index for uveal melanoma (UVM) patients. We also systematically evaluated the correlation between the iAge-related signature risk score and immune cell infiltration. iAge-CRGs, particularly DCBLD2, emerge as potential targets for enhancing immunotherapy outcomes. The strong correlation between abnormal DCBLD2 expression, cancer-associated fibroblast infiltration, and patient survival across various cancers underscores their significance. Our five-gene risk signature offers an independent prognostic tool for UVM patients, highlighting the crucial role of these genes in suppressing the immune response in UVM.Kindly check and confirm whether the corresponding affiliation is correctly identified.I identified the affiliation is correctly.thank you.Per style, a structured abstract is not allowed so we have changed the structured abstract to an unstructured abstract. Please check and confirm.I confirm the abstract is correctly ,thank you.

Abstract PO3-15-06: Identification of a Novel Five Ferroptosis-Related Gene Signature as a Promising Prognostic Model for Breast Cancer

Abstract Background: Breast cancer (BCa) is a major challenge for women's health worldwide. Ferroptosis is closely related to tumorigenesis and cancer progression. However, the prognostic value of ferroptosis-related genes in BCa remains unclear, and more accurate prognostic models are urgently needed. Methods: Gene expression profiles and clinical information of BCa patients were collected from public databases. LASSO and multivariate Cox regression analysis were utilized to construct the prognostic gene signature. Kaplan-Meier plotter, Receiver Operating Characteristic (ROC) curves, and nomogram were used to validate the prognostic value of the gene signature. Gene set enrichment analysis was performed to explore the molecular functions and signaling pathways. Results: Differentially expressed ferroptosis-related genes between BCa samples and normal tissues were obtained. A novel five-gene signature including BCL2, SLC40A1, TFF1, APOOL, and PRAME was established for prognosis prediction. Patients stratified into high-risk or low-risk group displayed significantly different survival. Kaplan-Meier and ROC curves showed a good performance for survival prediction in different cohorts. Biological function analysis revealed that the five-gene signature was associated with cancer progression, immune infiltration, immune response, and drug resistance. Nomogram including the five-gene signature was established. Conclusions: A novel five ferroptosis-related gene signature and nomogram could be used for prognostic prediction in BCa. Key words: breast cancer, ferroptosis, gene signature, overall survival, prognosis Citation Format: Tian-cheng Cheng, Jia-hao Wu, Bei Zhu, Lin Zheng, Wei-xian Chen. Identification of a Novel Five Ferroptosis-Related Gene Signature as a Promising Prognostic Model for Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-15-06.

A Five-gene Signature based on MicroRNA for Predicting Prognosis and Immunotherapy in Stomach Adenocarcinoma.

We aimed to classify molecular subtypes and establish a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in Stomach adenocarcinoma (STAD). STAD is a common diagnosed gastrointestinal malignancy and its heterogeneity is a big challenge that influences prognosis and precision therapies. Present study was designed to classify molecular subtypes and construct a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in STAD. The objective of this study is to investigate the molecular subtypes and prognostic model for STAD. A STAD specific miRNA-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was generated using the RNA-Seq and miRNA expression profiles from The Cancer Genome Atlas (TCGA) database, in which miRNA-related mRNAs were screened. Molecular subtypes were then determined using miRNA-related genes. Through univariate Cox analysis and multivariate regression analysis, a prognostic model was established in GSE84437 Train dataset and validated in GSE84437 Test, TCGA, GSE84437 and GSE66229 datasets. Immunotherapy datasets were employed for assessing the performance of the risk model. Finally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was applied to validate the expression of hub genes used for the risk score signature. We constructed a ceRNA network containing 84 miRNAs and 907 mRNAs and determined two molecular subtypes based on 26 genes from the intersection of TCGASTAD and GSE84437 datasets. Subtype S2 had poor prognosis, lower tumor mutational burden, higher immune score and lower response to immunotherapy. Subtype S1 was more sensitive to Sorafenib, Pyrimethamine, Salubrinal, Gemcitabine, Vinorelbine and AKT inhibitor VIII. Next, a five-gene signature was generated and its robustness was validated in Test and external datasets. This risk model also had a good prediction performance in immunotherapy datasets. This study promotes the underlying mechanisms of miRNA-based genes in STAD and offers directions for classification. A five-gene signature accurately predicts the prognosis and helps therapeutic options.

Construction and analysis of a reliable five-gene prognostic signature for colon adenocarcinoma associated with the wild-type allelic state of the COL6A6 gene.

Tumors emerge by acquiring a number of mutations over time. The first mutation provides a selective growth advantage compared to adjacent epithelial cells, allowing the cell to create a clone that can outgrow the cells that surround it. Subsequent mutations determine the risk of the tumor progressing to metastatic cancer. Some secondary mutations may inhibit the aggressiveness of the tumor while still increasing the survival of the clone. Meaningful mutations in genes may provide a strong molecular foundation for developing novel therapeutic strategies for cancer. The somatic mutation and prognosis in colon adenocarcinoma (COAD) were analyzed. The copy number variation (CNV) and differentially expressed genes (DEGs) between the collagen type VI alpha 6 chain (COL6A6) mutation (COL6A6-MUT) and the COL6A6 wild-type (COL6A6-WT) subgroups were evaluated. The independent prognostic signatures based on COL6A6-allelic state were determined to construct a Cox model. The biological characteristics and the immune microenvironment between the two risk groups were compared. COL6A6 was found to be highly mutated in COAD at a frequency of 9%. Patients with COL6A6-MUT had a good overall survival (OS) compared to those with COL6A6-WT, who had a different CNV pattern. Significant differences in gene expression were established for 593 genes between the COL6A6-MUT and COL6A6-WT samples. Among them, MUC16, ASNSP1, PRR18, PEG10, and RPL26P8 were determined to be independent prognostic factors. The internally validated prognostic risk model, constructed using these five genes, demonstrated its value by revealing a significant difference in patient prognosis between the high-risk and low-risk groups. Specifically, patients in the high-risk group exhibited a considerably worse prognosis than did those in the low-risk group. The high-risk group had a significantly higher proportion of patients over 60 years of age and patients in stage III. Moreover, the tumor immune dysfunction and exclusion (TIDE) score and the expression of human leukocyte antigen (HLA) family genes were all higher in the high-risk group than that in the low-risk group. The allelic state of COL6A6 and the five associated DEGs were identified as novel biomarkers for the diagnosis and prognosis of COAD and may be therapeutic targets in COAD.

Identification and validation of a novel signature based on macrophage marker genes for predicting prognosis and drug response in kidney renal clear cell carcinoma by integrated analysis of single cell and bulk RNA sequencing.

Macrophages are found in a variety of tumors and play a critical role in shaping the tumor microenvironment, affecting tumor progression, metastasis, and drug resistance. However, the clinical relevance of marker genes associated with macrophage in kidney renal clear cell carcinoma (KIRC) has yet to be documented. In this study, we initiated a thorough examination of single-cell RNA sequencing (scRNA-seq) data for KIRC retrieved from the Gene Expression Omnibus (GEO) database and determined 244 macrophage marker genes (MMGs). Univariate analysis, LASSO regression, and multivariate regression analysis were performed to develop a five-gene prognostic signature in The Cancer Genome Atlas (TCGA) database, which could divide KIRC patients into low-risk (L-R) and high-risk (H-R) groups. Then, a nomogram was constructed to predict the survival rate of KIRC patients at 1, 3, and 5 years, which was well assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analyses (DCA). Functional enrichment analysis showed that immune-related pathways (such as immunoglobulin complex, immunoglobulin receptor binding, and cytokine-cytokine receptor interaction) were mainly enriched in the H-R group. Additionally, in comparison to the L-R cohort, patients belonging to the H-R cohort exhibited increased immune cell infiltration, elevated expression of immune checkpoint genes (ICGs), and a higher tumor immune dysfunction and exclusion (TIDE) score. This means that patients in the H-R group may be less sensitive to immunotherapy than those in the L-R group. Finally, IFI30 was validated to increase the ability of KIRC cells to proliferate, invade and migrate in vitro. In summary, our team has for the first time developed and validated a predictive model based on macrophage marker genes to accurately predict overall survival (OS), immune characteristics, and treatment benefit in KIRC patients.

Immunogenic cell death related mRNAs associated signature to predict immunotherapeutic response in osteosarcoma

BackgroundImmunogenic cell death (ICD) is related to cancer prognosis, which has a synergic effect in combination with chemotherapy or immunotherapy. Yet, the relationship between ICD and osteosarcoma remained unclear. Materials and methodsThree osteosarcoma datasets including therapeutically applicable research to generate effective treatments (TARGET), GSE126209 and GSE21257 datasets were included. A protein-protein interaction network was constructed based on ICD-related genes. We performed unsupervised consensus clustering to classify molecular subtypes (clusters). Survival analysis, Estimation of stromal and immune cells in malignant tumour tissues using expression data (ESTIMATE), Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and differential analysis were employed to characterize the molecular differences between different clusters. Univariate Cox regression analysis was conducted to confirm prognostic genes. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to demonstrate the aberrant expression of ICD-correlated signature genes in osteosarcoma. A series of cellular experiments, including cell counting kit-8 (CCK-8), transwell, and flow cytometry, were used to demonstrate the regulatory role of key genes in the ICD model on the malignant phenotype of osteosarcoma. ResultsThree clusters (cluster1, 2, 3) were constructed and they showed distinct overall survival and immune infiltration. ICD-related genes were highly expressed in cluster1. Moreover, Cluster1 had the best prognosis, high immune score and high expression of human leukocyte antigen (HLA)-related genes. TLR4, LY96, IFNGR1, CD4, and CASP1 were identified as prognostic genes for establishing an ICD-related risk signature. According to the risk signature, two risk groups (high and low risks) showing differential prognosis and response to immunotherapy. The low risks group had a better prognosis but was not sensitive to immunotherapy. Molecular assays verified that prognostic genes were abnormally under-expressed in osteosarcoma. Cellular assays demonstrated that LY96, the most significantly down-regulated gene in osteosarcoma, inhibited the migration, invasion, and proliferation phenotypes of osteosarcoma cells and prolonged the cell cycle. Analysis of oxidative stress related pathway enrichment in tumor microenvironment was conducted by single-sample gene set enrichment analysis (ssGSEA). ConclusionsThis study demonstrated the prognostic significance of ICD-correlated genes in osteosarcoma patients. The five-gene risk signature facilitate prognostic evaluation and prediction of osteosarcoma patients’ response to immunotherapy. The risk signature also offered a possibility for the exploit of novel ICD-related treatment.