Prognosis-associated Genes Research Articles

Expression Profile and Prognostic Significance of Pivotal Regulators for N7-Methylguanosine Methylation in Diffuse Large B-Cell Lymphoma.

N7-methylguanosine (m7G) occurs by adding a methyl group to the N7 atom of the RNA guanine. Emerging evidence suggests that m7G modification has emerged as a crucial regulator of tumorigenesis, progression, invasion, and metastasis in multiple cancers. Nevertheless, the utility of m7G modification in diffuse large B-cell lymphoma (DLBCL) remains undefined, notably the interaction with the tumor microenvironment (TME). Here, we aimed to identify the expression profile of m7G regulators in DLBCL, construct a novel risk model, and explore their connection with TME. We initially investigated the difference and correlation in m7G regulators' expression in normal and tumor groups, classified patients by consistent clustering analysis, investigated the functional and prognostic significance of the resulting subtypes, and identified prognosis-associated genes by one-way Cox and least absolute shrinkage and selection operator (LASSO)regression calculations, and constructed a risk model. Further analysis showed that correlation among immune cell infiltration with m7G risk score and determined that it impacts the prognosis of DLBCL patients. Our research demonstrated the relevance of m7G regulators to DLBCL prognosis, providing theoretical support for precise prognostic stratification and immunotherapeutic assessment in DLBCL.

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.

Developing a prognostic signature: identifying differentially expressed genes in cardia and non-cardia gastric cancer for immunity and therapeutic sensitivity analysis.

Gastric cancer (GC) can be anatomically categorized into two subtypes; that is, cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC), which have distinct molecular mechanisms and prognoses. At present, the majority of pharmacological interventions for GC adhere to non-specific treatment regimens. The stratification of GC based on molecular disparities between CGC and NCGC has important clinical guidance value and could help in the development of precision therapies tailored to individual patient needs. Nevertheless, research in this specialized field remains notably limited. This study aims to investigate the molecular differences between CGC and NCGC and to leverage these differences to develop a prognostic risk scoring model (PRSM). We used patient data from The Cancer Genome Atlas (TCGA) and performed a differentially expressed gene (DEG) analysis between CGC and NCGC. A PRSM was developed from the prognosis-associated DEGs identified through Cox regression analyses and was well validated using Gene Expression Omnibus (GEO) data. A total of 339 DEGs were identified between CGC and NCGC, and four prognosis-associated genes were used to construct the PRSM. Using the risk coefficients and expression levels of signature genes, a median risk score (RS) was calculated to classify patients into high- and low-risk groups. The high-risk group had a significantly worse prognosis than the low-risk group. An in-depth analysis revealed that TP53 mutations were more prevalent in the high-risk group, and MUC16 mutations were more prevalent in the low-risk group. A gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to assess the differences in the significantly enriched pathways and immune microenvironment in the high- and low-risk groups, respectively. The inhibitory concentration (IC50) values of the chemotherapy drugs for GC also varied between the two groups. This study elucidated the unique molecular characteristics of GC subtypes based on the anatomical site and provided a preliminary contribution for the development of precision medicine for GC.

Identification of a Novel 4-gene Prognostic Model Related to Neutrophil Extracellular Traps for Colorectal Cancer.

Colorectal cancer (CRC) is a significant global health concern, and understanding the molecular mechanisms underlying CRC progression and prognosis is crucial. Neutrophil extracellular traps (NETs) have been implicated in various cancers, but their role in CRC and its clinical implications remain to be elucidated. Transcriptomic data from TCGA of CRC patients were analyzed to assess NETs enrichment and "NETs formation" pathway scores in NETs_high and NETs_low groups. Univariate Cox regression was used to identify prognosis-associated genes with the Log-Rank test for selection. Patients in the TCGA database were randomly split into training and testing sets to build a prognostic model with LASSO Cox regression. Model diagnostic performance was evaluated using Kaplan-Meier curves and receiver operating characteristic analysis. Single-sample gene set enrichment analysis (ssGSEA) was used to determine the abundance of 23 immune cells. ESTIMATE was used to calculate ImmuneScore and ESTIMATEScore, characterizing immune features of CRC samples. The NETs_high group in CRC showed significantly better survival than the NETs_low group. A robust prognostic model based on PRKRIP1, SERTAD2, ELFN1, and LINC00672 accurately predicted patient outcomes. NETs_high samples exhibited a more enriched immune environment with higher immune cell infiltration levels, as well as ImmuneScore and ESTIMATEScore. PRKRIP1, SERTAD2, ELFN1, and LINC00672 were significantly correlated with key immune cell types. Additionally, 18 drugs displayed differential sensitivity between NETs_high and NETs_low groups, with Daporinad and Selumetinib as potential therapeutic options. Our findings may catalyze the development of personalized treatment modalities and bestow invaluable insights into the intricate dynamics governing CRC progression.

The prognostic impact of pathogenic stromal cell-associated genes in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) stands as the most prevalent subtype among lung cancers. Interactions between stromal and cancer cells influence tumor growth, invasion, and metastasis. However, the regulatory mechanisms of stromal cells in the lung adenocarcinoma tumor microenvironment remain unclear. This study seeks to elucidate the regulatory connections among critical pathogenic genes and their associated expression variations within distinct stromal cell subtypes. MethodAnalysis and investigation were conducted on a total of 114,019 single-cell RNA data and 346 The Cancer Genome Atlas (TCGA) LUAD-related samples using bioinformatics and statistical algorithms. Differential gene expression analysis was performed for tumor samples and controls, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential genes between stromal cells and other cell clusters were identified and intersected with the differential genes from TCGA. We employed a combination of LASSO regression and multivariable Cox regression to identify the ultimate set of pathogenic gene. Survival models were trained to predict the relationship between patient survival and these pathogenic genes. Analysis of transcription factor (TF) cell specificity and pseudotime trajectories within stromal cell subpopulations revealed that vascular endothelial cells (ECs) and matrix cancer-associated fibroblasts (CAFs) are key in regulation of the prognosis-associated genes CAV2, COL1A1, TIMP1, ETS2, AKAP12, ID1 and COL1A2. ResultsSeven pathogenic genes associated with LUAD in stromal cells were identified and used to develop a survival model. High expression of these genes is linked to a greater risk of poor survival. Stromal cells were categorized into eight subtypes and one unannotated cluster. Mesothelial cells, vascular endothelial cells (ECs), and matrix cancer-associated fibroblasts (CAFs) showed cell-specific regulation of the pathogenic genes. ConclusionsThe seven disease-causing genes in vascular ECs and matrix CAFs can be used to detect the survival status of LUAD patients, providing new directions for future targeted drug design.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients’ prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients’ survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Sesamolin serves as an MYH14 inhibitor to sensitize endometrial cancer to chemotherapy and endocrine therapy via suppressing MYH9/GSK3β/β-catenin signaling

BackgroundEndometrial cancer (EC) is one of the most common gynecological cancers. Herein, we aimed to define the role of specific myosin family members in EC because this protein family is involved in the progression of various cancers.MethodsBioinformatics analyses were performed to reveal EC patients’ prognosis-associated genes in patients with EC. Furthermore, colony formation, immunofluorescence, cell counting kit 8, wound healing, and transwell assays as well as coimmunoprecipitation, cycloheximide chase, luciferase reporter, and cellular thermal shift assays were performed to functionally and mechanistically analyze human EC samples, cell lines, and a mouse model, respectively.ResultsMachine learning techniques identified MYH14, a member of the myosin family, as the prognosis-associated gene in patients with EC. Furthermore, bioinformatics analyses based on public databases showed that MYH14 was associated with EC chemoresistance. Moreover, immunohistochemistry validated MYH14 upregulation in EC cases compared with that in normal controls and confirmed that MYH14 was an independent and unfavorable prognostic indicator of EC. MYH14 impaired cell sensitivity to carboplatin, paclitaxel, and progesterone, and increased cell proliferation and metastasis in EC. The mechanistic study showed that MYH14 interacted with MYH9 and impaired GSK3β-mediated β-catenin ubiquitination and degradation, thus facilitating the Wnt/β-catenin signaling pathway and epithelial–mesenchymal transition. Sesamolin, a natural compound extracted from Sesamum indicum (L.), directly targeted MYH14 and attenuated EC progression. Additionally, the compound disrupted the interplay between MYH14 and MYH9 and repressed MYH9-regulated Wnt/β-catenin signaling. The in vivo study further verified sesamolin as a therapeutic drug without side effects.ConclusionsHerein, we identified that EC prognosis-associated MYH14 was independently responsible for poor overall survival time of patients, and it augmented EC progression by activating Wnt/β-catenin signaling. Targeting MYH14 by sesamolin, a cytotoxicity-based approach, can be applied synergistically with chemotherapy and endocrine therapy to eventually mitigate EC development. This study emphasizes MYH14 as a potential target and sesamolin as a valuable natural drug for EC therapy.Graphical

A new prognostic model for accurate assessment of hepatocellular carcinoma risk using RNA editing data and unsupervised machine learning

A-to-I RNA editing is a long-known driving factor to the progression of hepatocellular carcinoma (HCC); however, its importance is often neglected in HCC subtyping and the modeling of prognosis, partially due to the high dimensional nature of the editing data. In this paper, we first obtain a comprehensive A-to-I RNA editing profile for HCC, among which two editing sites, IGFBP7 I387M (chr4:57110120) and SUMF2 A98G (chr7:56078974), are identified as having the driving function potential. Then, we exploit the clinical-related RNA editing profile in combination with the gene expressions to develop a new prognostic model for HCC. To preserve the inner structure of this editing profile in a latent space that has a much lower dimension, we employ variational autoencoder (VAE), an unsupervised learning neural network applied for the first time to compress the RNA editing profile. Empowered by VAE, we are able to cluster the HCC patients efficiently according to their editing profile, among which a group of patients with immune dysregulation and enrichment in cell proliferation and cancer-related pathways are identified. Based on the editing-related subtyping, we develop a prognostic model that consists of an easy-computing risk score and an adaptively trained threshold. Validations using three independent cohorts (LIHC, LIRI, and GSE14520) confirm that the new model is robust, sensitive, and specific. Furthermore, it is more accurate in prediction than six state-of-the-art competitors. The model is also found applicable to 18 more cancers besides HCC, which suggests a strong ability in generalization. To facilitate clinical usage, we develop a nomogram, drug targets, prognosis-associated genes, and relevant pathways are also identified, which pave the way for upcoming studies on HCC’s molecular mechanism, therapeutic targets, and many others.

Construction and validation of a prognostic model for stemness-related genes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with poor overall prognosis. Early identification of high-risk patients and individualized treatment can help extend the survival time of patients. This study aimed to construct and validate a prognostic prediction least absolute shrinkage and selection operator (LASSO) model for stemness-related genes in LUAD. Firstly, LUAD RNA-sequencing data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. The tumor stemness index based on mRNA expression (mRNAsi) was calculated, and the relationship between mRNAsi and the survival prognosis as well as clinical features of LUAD patients was analyzed. Then, the weighted gene co-expression network analysis (WGCNA) method was used to screen for gene modules highly correlated with mRNAsi, and functional annotation [Gene Ontology (GO) analysis] and pathway enrichment analysis [Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis] were performed for the selected stemness-related gene module. Furthermore, prognosis-associated genes were determined from the stemness-related genes through univariate Cox analysis, and a prognostic model was constructed using LASSO analysis. Finally, a series of validations including survival curve analysis, receiver operating characteristic (ROC) curve analysis, and risk analysis were conducted for the prognostic model, and nomogram based on the risk model and various clinicopathological features were constructed. LUAD patients with high mRNAsi had a higher mortality rate than those with low mRNAsi. GO analysis showed that stemness-related genes were mainly involved in mRNA processing and extracellular matrix organization, while KEGG analysis revealed their involvement in cell cycle and PI3K-Akt signaling pathways. A prognostic model based on 12 stemness-related genes was constructed using LASSO regression. Validation of the prognostic model demonstrated its good accuracy in predicting the prognosis of LUAD patients. mRNAsi plays an important role in the occurrence and development of LUAD. This study successfully constructed a prognostic prediction LASSO model for stemness-related genes in LUAD, which can serve as a novel prognostic indicator for LUAD and may be an effective complement to the current Tumor Node Metastasis (TNM) clinical staging of LUAD.

Explore Key Genes and Mechanisms Involved in Colon Cancer Progression Based on Bioinformatics Analysis.

To explore underlying mechanisms related to the progression of colon cancer and identify hub genes associated with the prognosis of patients with colon cancer. GSE10950 and GSE62932 were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was utilized to screen out the differentially expressed genes (DEGs). Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on DEGs. Moreover, STRING and Cytoscape software were utilized for establishing the network of protein-protein interaction (PPI) and identifying hub genes. Afterward, data from The Cancer Genome Atlas (TCGA) was utilized for identifying prognosis-related hub genes by Kaplan-Meier survival analysis. Colon cancer cell line LOVO and human normal intestinal epithelial cell line NCM-460 were exploited to demonstrate the differential expression of selected hub genes through RT-qPCR and western blot. The LOVO cells were transfected to regulate expressions of prognosis-associated genes, followed by exploring the effects of those genes on prognosis by Cell Counting Kit-8 assay and colony-forming assay for cancer cell proliferation, cell scratch test and transwell migration assay for cancer cell migration and Annexin V-PE/7-AAD double staining as well as flow cytometry for cancer cell apoptosis. In this study, 266 common DEGs were obtained from the intersection of two datasets. The GO analysis suggested the common DEGs mainly participated in the one-carbon metabolic process, cell cycle G2/M phase transition, organelle fission, cell cycle phase transition regulation, and regulation of mitotic cell cycle phase transition. The KEGG analysis demonstrated the common DEGs were related to the p53 signaling pathway, nitrogen metabolism, mineral absorption, and cell cycle. 10 hub genes including CCNB1, KIF4A, TPX2, MT1F, PRC1, PLK4, CALD1, MMP9, CLCA1, and MMP1 were identified and CCNB1, CLCA1, and PLK4 were prognosis-related. Increased expression of CCNB1, CLCA1, and PLK4 restrained proliferation as well as migration of cancer cells and induced apoptosis of cancer cells. CCNB1, KIF4A, TPX2, MT1F, PRC1, PLK4, CALD1, MMP9, CLCA1, and MMP1 were identified as hub genes and CCNB1, CLCA1, and PLK4 could inhibit the progression of colon cancer through inhibiting proliferation as well as migration of the cancer cell and promoting apoptosis of cancer cell.

Genomic and Evolutionary Characterization of Concurrent Intraductal Carcinoma and Adenocarcinoma of the Prostate.

The genomic, transcriptomic, and epigenomic characterization of concurrent intraductal carcinoma and adenocarcinoma of the prostate deepens the biological understanding of this lethal disease and provides a genetic basis for developing targeted therapies.

2214P Identification of prognosis-associated genes in locally advanced well-differentiated thyroid cancer using TCGA cohort analysis

2214P Identification of prognosis-associated genes in locally advanced well-differentiated thyroid cancer using TCGA cohort analysis

The activity of cuproptosis pathway calculated by AUCell algorithm was employed to construct cuproptosis landscape in lung adenocarcinoma

Cuproptosis is a recently described copper-dependent cell death pathway. Consequently, there are still few studies on lung adenocarcinoma (LUAD)-related cuproptosis, and we aimed to deepen in this matter. In this study, data from 503 patients with lung cancer from the TCGA-LUAD cohort data collection and 11 LUAD single-cells from GSE131907 as well as from 10 genes associated with cuproptosis were analyzed. The AUCell R package was used to determine the copper-dependent cell death pathway activity for each cell subpopulation, calculate the CellChat score, and display cell communication for each cell subpopulation. The PROGENy score was calculated to show the scores of tumor-related pathways in different cell populations. GO and KEGG analyses were used to calculate pathway activity. Univariate COX and random forest analyses were used to screen prognosis-associated genes and construct models. The ssGSEA and xCell algorithms were used to calculate the immunocyte infiltration score. Based on data from the GDSC database, the drug sensitivity score was calculated using oncoPredict. Finally, in vitro experiments were performed to determine the role of TLE1, the most important gene in the prognostic model. The 11 LUAD single-cell samples were classified into 8 different cell populations, from which epithelial cells showed the highest copper-dependent cell death pathway activity. Epithelial cell subsets were significantly positively correlated with MAKP, hypoxia, and other pathways. In addition, cell subgroup communication showed highly active collagen and APP pathways. Using the Findmark algorithm, differentially expressed genes (DEGs) between epithelial and other cell types were identified. Combined with the bulk data in the TCGA-LUAD database, DEGs were enriched in pathways such as EGFR tyrosine kinase inhibitor resistance, Hippo signaling pathway, and tight junction. Subsequently, we selected 4 genes (out of 112) with prognostic significance, ANKRD29, RHOV, TLE1, and NPAS2, and used them to construct a prognostic model. The high- and low-risk groups, distinguished by the median risk score, showed significantly different prognoses. Finally, we chose TLE1 as a biomarker based on the relative importance score in the prognostic model. In vitro experiments showed that TLE1 promotes tumor proliferation and migration and inhibits apoptosis.

Identification of necroptosis-related gene TRAF5 as potential target of diagnosing atherosclerosis and assessing its stability

BackgroundAtherosclerosis (AS) is a leading cause of morbidity and mortality in older patients and features progressive formation of plaques in vascular tissues. With the progression of atherosclerosis, plaque rupture may occur and cause stroke, myocardial infarction, etc. Different forms of cell death promote the formation of a necrotic core of the plaque, leading to rupture. Necroptosis is a type of programmed cell death that contributes to the development of cardiovascular disease. However, the role of necroptosis in AS has not yet been investigated.MethodsThe Gene Expression Omnibus (GEO) database was used to obtain gene expression profiles. Differentially expressed genes (DEGs) and necroptosis gene sets were used to identify necroptosis-related differentially expressed genes (NRDEGs). The NRDEGs were used to construct a diagnostic model and were further screened using least absolute shrinkage selection operator (LASSO) regression and random forest (RF) analysis. The discriminatory capacity of the NRDEGs was evaluated using receiver operating characteristic (ROC) curves. Immune infiltration levels were estimated based on CIBERSORTx analysis. The GSE21545 dataset, containing survival information, was used to determine prognosis-associated genes. Univariate and multivariate Cox regression analyses combined with survival analysis determined gene prognostic values. RNA and protein levels were detected by RT-qPCR and western blotting in arteriosclerosis obliterans(ASO) and normal vascular tissues. Vascular smooth muscle cells (VSMCs) were treated with oxidized low-density lipoprotein (ox-LDL) to develop cell models of advanced AS. The effects of protein knockdown on necroptosis were assessed by western blotting and flow cytometry. EdU and Cell Counting Kit-8 assays were used to examine cell proliferation.ResultsTNF Receptor Associated Factor 5 (TRAF5) was identified as a diagnostic marker for AS based on the AUC value in both the GSE20129 and GSE43292 datasets. According to differential expression analysis, LASSO regression analysis, RF analysis, univariate analysis, multivariate analysis, and gene-level survival analysis, TRAF5 was markedly associated with necroptosis in AS. Silencing TRAF5 promotes necroptosis and attenuates the proliferation of ox-LDL-induced cell models of advanced AS.ConclusionsThis study identified a diagnostic marker of necroptosis-related atherosclerosis, TRAF5, which can also be used to diagnose and assess atherosclerotic plaque stability. This novel finding has important implications in the diagnosis and assessment of plaque stability in atherosclerosis.

Prognostic value of iron metabolism-related genes in bladder urothelial carcinoma

Abstract Objectives Iron metabolism-related genes (IMRGs) play important roles in the prognostic assessment of many tumours. However, IMRGs have not been reported as prognostic biomarkers in bladder urothelial carcinoma (BLCA). Methods Gene expression profiles and clinical data from BLCA patients were obtained from The Cancer Genome Atlas (TCGA) database. We used the DESeq2 package to screen for differentially expressed genes (DEGs). The predictive values of the differentially expressed IMRGs in BLCA patients were further evaluated using univariate Cox regression analysis. The risk-scoring model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm. The performance of this model for predicting the prognosis of BLCA patients in TCGA-BLCA cohort was assessed using Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curves. This risk-scoring model was combined with the clinicopathological characteristics of BLCA patients in a multiple regression analysis, and a nomogram was constructed using the independent predictors identified. ROC analysis and calibration curves were adapted to test the predictive ability of the nomogram. Gene set enrichment analysis (GSEA) was used to identify potential molecular pathways and processes enriched by differential expression genes between risk groups. Finally, we explored the ability of the risk-scoring model to assess immune cell infiltration levels through a correlation analysis. Results Fourteen identified IMRGs with prognostic value were incorporated into the risk-scoring model. The ROC and K–M survival curves indicated that the model could effectively predict the overall survival (OS) outcomes of BLCA patients. The multiple regression analysis revealed that the risk-scoring model could be used as an independent prognostic factor for BLCA patients, and the associated nomogram could effectively predict the OS outcomes of BLCA patients. GSEA revealed that the DEGs between the risk groups were mainly involved in biological processes such as developmental process, cell cycle, mitosis, RHO GTPase reaction, DNA repair, and extracellular matrix regulation. The immune infiltration analysis showed that the infiltration levels of immune cells such as natural killer cells, memory T cells, effector T cells, Th2 cells, and macrophages differed significantly between the risk groups. Conclusions IMRGs screening revealed prognosis-associated genes. The prognostic model constructed could effectively predict the prognosis of BLCA patients, and the identified genes represent potential targets for BLCA treatment.

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer.

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Single-Cell and Transcriptome-Based Immune Cell-Related Prognostic Model in Clear Cell Renal Cell Carcinoma.

Traditional studies mostly focus on the role of single gene in regulating clear cell renal cell carcinoma (ccRCC), while it ignores the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database to provide help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. Based on the DEGs of each cluster acquired from single-cell data, immune cell marker genes, and ligand-receptor gene data, we constructed a multilayer network. Then, the genes in the network and the genes in TCGA were used to construct the WGCNA network, which screened out prognosis-associated genes for subsequent analysis. Finally, a prognostic risk scoring model was obtained, and CPTAC data showed that the effectiveness of this model was good. A nomogram based on the predictive model for predicting the overall survival was established, and internal validation was performed well. Our findings suggest that the predictive model built and based on the immune cell scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.

Distinct genomic features and evolutionary relationships between concurrent intraductal carcinoma and adenocarcinoma of the prostate.

215 Background: Intraductal carcinoma of the prostate (IDC-P) is a lethal prostate cancer subtype that generally co-exists with invasive high-grade prostate acinar adenocarcinoma (PAC) but exhibits distinct biological features compared to concomitant adenocarcinoma. Till now, the genomic characteristics of IDC-P remain largely unknown. Methods: To explore the genomic features and evolutionary relationships between concurrent IDC-P and PAC, we isolated IDC-P, concurrent invasive high-grade PAC lesions, and adjuvant normal prostate tissues from 22 radical prostatectomy specimens. Whole-exome, RNA, and DNA-methylation sequencing were performed on the acquired tumoral and normal samples. The biological characteristics of IDC-P and concurrent PAC were compared at different molecular levels. Results: Three evolutionary patterns between concurrent IDC-P and PAC were identified. Tumors with clonally distant and early divergent evolutionary patterns showed higher genomic and pathological heterogeneities between concurrent IDC-P and PAC than those with the late-divergent pattern. Compared to co-existing PAC, increased adverse prognosis-associated genes were overexpressed in IDC-P. Besides, IDC-P was similar to Gleason pattern 5 PAC on the transcriptome profile. Survival analysis based on an independent cohort supported IDC-P resembled Gleason pattern 5 tumors. Furthermore, IDC-P exhibited robust cell-cycle progression and androgen receptor activities, characterized by an enrichment of cellular proliferation-associated master regulators and genes involved in intratumoral androgen biosynthesis. Conclusions: Our findings provide a molecular groundwork for the aggressive behavior of IDC-P and contribute to developing potential therapies for tumors with IDC-P.

A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes

BackgroundThe study of tumor metabolism is of great value to elucidate the mechanism of tumorigenesis and predict the prognosis of patients. However, the prognostic role of metabolism-related genes (MRGs) in gastric adenocarcinoma (GAD) remains poorly understood. MethodsWe downloaded the gene chip dataset GSE79973 (n = 20) of GAD from the Gene Expression Omnibus (GEO) database to compare differentially expressed genes (DEGs) between normal and tumor tissues. We then extracted MRGs from these DEGs and systematically investigated the prognostic value of these differential MRGs for predicting patients' overall survival by univariable and multivariable Cox regression analysis. Six metabolic genes (ACOX3, APOE, DIO2, HSD17B4, NUAK1, and WHSC1L1) were identified as prognosis-associated hub genes, which were used to build a prognostic model in the training dataset GSE15459 (n = 200), and then validated in the dataset GSE62254 (n = 300). ResultsPatients were divided into high-risk and low-risk subgroups based on the model's risk score, and it was found that patients in the high-risk subgroup had shorter overall survival than those in the low-risk subgroup, both in the training and testing datasets. In addition, for the training and testing cohorts, the area under the ROC curve of the prognostic model for one-year survival prediction was 0.723 and 0.667, respectively, indicating that the model has good predictive performance. Furthermore, we established a nomogram based on tumor stage and risk score to effectively predict the overall survival (OS) of GAD patients. The expression of 6 MRGs at the protein level was confirmed by immunohistochemistry (IHC). Kaplan-Meier survival analysis further confirmed that their expression influenced OS in GAD patients. ConclusionCollectively, the 6 MRGs signature might be a reliable tool for assessing OS in GAD patients, with potential application value in clinical decision-making and individualized therapy.

Construction and validation of a bladder cancer risk model based on autophagy-related genes.

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.