Signature For Survival Prediction Research Articles

Comprehensive analysis of the cancer driver genes constructs a seven-gene signature for prediction of survival and tumor immunity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a highly malignant and heterogeneous tumor with poor prognosis. Cancer driver genes (CDGs) play an important role in the carcinogenesis and progression of HCC. In this study, we comprehensively investigated the expression, mutation, and prognostic significance of 568 CDGs in HCC. A prognostic risk model was constructed based on seven CDGs (CDKN2C, HRAS, IRAK1, LOX, MYCN, NRAS, and PABPC1) and verified to be an independent prognostic factor in both TCGA and ICGC cohorts. The low-score group, which showed better prognosis, had a high proportion of CD8+ T cells and elevated expression of interferon-related signaling pathways. Additionally, we constructed a nomogram to extend the clinical applicability of the prognostic model, which exhibits excellent predictive accuracy for survival. Our study showed the important role of CDGs in HCC and provides a novel prognostic indicator for HCC.

Autophagy-related prognostic signature for survival prediction of triple negative breast cancer.

BackgroundTriple-negative breast cancer (TNBC) is a highly aggressive type of cancer with few available treatment methods. The aim of the current study was to provide a prognostic autophagy-related gene (ARG) model to predict the outcomes for TNBC patients using bioinformatic analysis.MethodsmRNA expression data and its clinical information for TNBC samples obtained from The Cancer Genome Atlas (TCGA) and Metabric databases were extracted for bioinformatic analysis. Differentially expressed autophagy genes were identified using the Wilcoxon rank sum test in R software. ARGs were downloaded from the Human Autophagy Database. The Kaplan–Meier plotter was employed to determine the prognostic significance of the ARGs. The sample splitting method and Cox regression analysis were employed to establish the risk model and to demonstrate the association between the ARGs and the survival duration. The corresponding ARG-transcription factor interaction network was visualized using the Cytoscape software.ResultsA signature-based risk score model was established for eight genes (ITGA3, HSPA8, CTSD, ATG12, CLN3, ATG7, MAP1LC3C, and WIPI1) using the TCGA data and the model was validated with the GSE38959 and Metabric datasets, respectively. Patients with high risk scores had worse survival outcomes than those with low risk scores. Of note, amplification of ATG12 and reduction of WIPI were confirmed to be significantly correlated with the clinical stage of TNBC.ConclusionAn eight-gene autophagic signature model was developed in this study to predict the survival risk for TNBC. The genes identified in the study may favor the design of target agents for autophagy control in advanced TNBC.

A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma.

Simple SummarySoft tissue sarcomas (STS) still lack effective clinical stratification and prognostic models. The aim of this study is to establish a reliable prognostic gene signature in STS. Using 189 STS samples from the TCGA database, a four-gene signature (including DHRS3, JRK, TARDBP and TTC3) and nomograms that can be used to predict the overall survival and relapse free survival of STS patients was developed. The predictive ability for metastasis free survival was externally verified in the GEO cohort. We demonstrated that the novel gene signature provides an attractive platform for risk stratification and prognosis prediction of STS patients, which is of great importance for individualized clinical treatment and long-term management of patients with this rare and severe disease.Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

Development of an Interferon Gamma Response-Related Signature for Prediction of Survival in Clear Cell Renal Cell Carcinoma.

BackgroundInterferon plays a crucial role in the pathogenesis and progression of tumors. Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant urinary system tumor. An effective predictive model is required to evaluate the prognosis of patients to optimize treatment.Materials and MethodsRNA-sequencing data and clinicopathological data from TCGA were involved in this retrospective study. The IFN-γ response genes with significantly different gene expression were screened out. Univariate Cox regression, LASSO regression and multivariate Cox regression were used to establish a new prognostic scoring model for the training group. Survival curves and ROC curves were drawn, and nomogram was constructed. At the same time, we conducted subgroup analysis and experimental verification using our own samples. Finally, we evaluated the relatedness between the prognostic signature and immune infiltration landscapes. In addition, the sensitivity of different risk groups to six drugs and immune checkpoint inhibitors was calculated.ResultsThe IFN-γ response-related signature included 7 genes: C1S, IFI44, ST3GAL5, NUP93, TDRD7, DDX60, and ST8SIA4. The survival curves of the training and testing groups showed the model’s effectiveness (P = 4.372e-11 and P = 1.08e-08, respectively), the ROC curves showed that the signature was stable, and subgroup analyses showed the wide applicability of the model (P<0.001). Multivariate Cox regression analysis showed that the risk model was an independent prognostic factor of ccRCC. A high-risk score may represent an immunosuppressive microenvironment, while the high-risk group exhibited poor sensitivity to drugs.ConclusionOur findings strongly indicate that the IFN-γ response-related signature can be used as an effective prognostic indicator of ccRCC.

Identification and validation of a two-gene metabolic signature for survival prediction in patients with kidney renal clear cell carcinoma.

Metabolic reprogramming contributes to the high mortality of advanced stage kidney renal clear cell carcinoma (KIRC), the most common renal cancer subtype. This study aimed to identify a metabolism-related gene (MRG) signature to improve survival prediction in KIRC patients. We downloaded RNA sequencing data and corresponding clinical information for KIRC and control samples from The Cancer Genome Atlas database and identified, based on an MRG dataset in the Molecular Signatures Database, 123 MRGs with differential expression in KIRC. Following Cox regression analysis and least absolute shrinkage and selection operator selection, RRM2 and ALDH6A1 were identified as prognosis-related genes and used to construct a prognostic signature with independent prognostic significance. After risk score-based patient separation, stratified survival analysis indicated that high-risk patients showed poorer overall survival than low-risk patients. We then constructed a clinical nomogram that showed a concordance index of 0.774 and good performance based upon calibration curves. Gene set enrichment analysis revealed several metabolic pathways significantly enriched in the target genes. The two-gene metabolic signature identified herein may represent a highly valuable tool for KIRC prognosis prediction, and might also help identify new metabolism-related biomarkers and therapeutic targets for KIRC.

Survival prediction and response to immune checkpoint inhibitors: A prognostic immune signature for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers all over the world. Several studies have explored if immune-related genes and tumor immune microenvironment could play roles in HCC prognoses. This study is aimed at developing a prognostic signature of HCC based on immune-related genes or tumor immune microenvironment to predict survival and response to immune checkpoint inhibitors (ICIs). We constructed a prognostic signature using bioinformatics method and validated its predictive capability. The mechanisms of the signature prediction were explored with The Cancer Immunome Atlas (TCIA) and mutation analysis. We also explored the association between the signature and immunophenoscore (IPS), which is the marker of ICIs response. A 6 immune-related-gene (6-IRG) signature was developed. It was revealed in a multivariate analysis that the 6-IRG signature was an independent prognostic factor of overall survival and progression-free interval among HCC patients. In the high-risk group of 6-IRG signature score, macrophage M0 cells and regulatory T cells, which are observed associated with poor overall survival in our study, were higher. The low-risk group had a higher IPS, which meant a better response to ICIs. Taken together, we constructed a reliable 6-IRG signature for prediction of survival and response to ICIs. The signature needs further testing for clinical application.

Identification of prognostic signature of non\u2013small cell lung cancer based on TCGA methylation data

Non–small lung cancer (NSCLC) is a common malignant disease with very poor outcome. Accurate prediction of prognosis can better guide patient risk stratification and treatment decision making, and could optimize the outcome. Utilizing clinical and methylation/expression data in The Cancer Genome Atlas (TCGA), we conducted comprehensive evaluation of early-stage NSCLC to identify a methylation signature for survival prediction. 349 qualified cases of NSCLC with curative surgery were included and further grouped into the training and validation cohorts. We identified 4000 methylation loci with prognostic influence on univariate and multivariate regression analysis in the training cohort. KEGG pathway analysis was conducted to identify the key pathway. Hierarchical clustering and WGCNA co-expression analysis was performed to classify the sample phenotype and molecular subtypes. Hub 5′-C-phosphate-G-3′ (CpG) loci were identified by network analysis and then further applied for the construction of the prognostic signature. The predictive power of the prognostic model was further validated in the validation cohort. Based on clustering analysis, we identified 6 clinical molecular subtypes, which were associated with different clinical characteristics and overall survival; clusters 4 and 6 demonstrated the best and worst outcomes. We identified 17 hub CpG loci, and their weighted combination was used for the establishment of a prognostic model (RiskScore). The RiskScore significantly correlated with post-surgical outcome; patients with a higher RiskScore have worse overall survival in both the training and validation cohorts (P < 0.01). We developed a novel methylation signature that can reliably predict prognosis for patients with NSCLC.

S1PR1-Associated Molecular Signature Predicts Survival in Patients with Sepsis.

Sepsis is a potentially life-threatening complication of an underlying infection that quickly triggers tissue damage in multiple organ systems. To date, there are no established useful prognostic biomarkers for sepsis survival prediction. Sphingosine-1-phosphate (S1P) and its receptor S1P receptor 1 (S1PR1) are potential therapeutic targets and biomarkers for sepsis, as both are active regulators of sepsis-relevant signaling events. However, the identification of an S1PR1-related gene signature for prediction of survival in sepsis patients has yet to be identified. This study aims to find S1PR1-associated biomarkers which could predict the survival of patients with sepsis using gene expression profiles of peripheral blood to be used as potential prognostic and diagnostic tools. Gene expression analysis from sepsis patients enrolled in published datasets from Gene Expression Omnibus was utilized to identify both S1PR1-related genes (co-expression genes or functional-related genes) and sepsis survival-related genes. We identified 62-gene and 16-gene S1PR1-related molecular signatures (SMS) associated with survival of patients with sepsis in discovery cohort. Both SMS genes are significantly enriched in multiple key immunity-related pathways that are known to play critical roles in sepsis development. Meanwhile, the SMS performs well in a validation cohort containing sepsis patients. We further confirmed our SMSs, as newly developed gene signatures, perform significantly better than random gene signatures with the same gene size, in sepsis survival prognosis. Our results have confirmed the significant involvement of S1PR1-dependent genes in the development of sepsis and provided new gene signatures for predicting survival of sepsis patients.

Developing a Combined Radiomic/Genomic Signature for Prediction of Survival in Glioblastoma

Extraction of multiscale radiomic features from preoperative MRI scans provides an opportunity for quantitative, non-invasive, image-based phenotyping of glioblastoma (GBM). Upon obtaining tumor tissue, genomic sequencing can further enhance predictive value. This study aims to predict overall survival (OS) following gross total resection of primary glioblastoma using a combination of MRI radiomics, tumor genomics, and patient clinical factors. In this retrospective study, preoperative image data from 61 patients from the 2018 Multimodal Brain Tumor Image Segmentation (BraTS) Competition publicly-available dataset were used to generate a radiomic signature. A total of 968 radiomic features were extracted from three manually delineated structures (Enhancing Tumor, Tumor Core, and Tumor Edema) on the T1, T2, T1-contrast, and FLAIR sequences. The features associated with overall survival were selected using the univariate Cox Proportional Hazards (CPH) model (p-value<0.05) and 10 cross-fold SVM recursive feature elimination (SVM-RFE). These features were then tested on a validation set of patients with GBM treated at our institution over five years (2013-2018). A total of 152 patients were identified, of which 20 had radiologically confirmed gross total resection on postoperative MRI as well as genomic sequencing information available. Institutional preoperative MRI images—T1, T2, T1-contrast, and FLAIR sequences—were manually contoured according to established BraTS benchmarks. Genomic features included expression levels of nine genes involved in neuroactive ligand-receptor interaction, cysteine metabolism, and ephrin A reverse signaling. Clinical variables included in the analysis were age, sex, tumor location, and Karnofsky Performance Score (KPS). A multivariate Cox proportional hazards analysis was performed to assess the association between OS and the radiomic, genomic, and clinical features. Ten radiomic features were found to be statistically relevant with OS from a training set of 61 patients. Features associated with OS on the univariate analysis included age, tumor location, KPS, LDHA gene expression and EPHA5 gene expression. In the multivariate analysis, the features significantly associated with OS included tumor location (p=0.03, Hazard ratio (HR) = 1.09), LDHA (p<0.005, HR=0.14), and two radiomics features from gray-level size-zone matrix: minimum value of large-zone-low-gray-level emphasis from T2 (p=0.04, HR=0.49) and kurtosis of small-zone-low-grey-level-emphasis from FLAIR (p=0.01, HR=2.02). A more robust validation set of patients is needed to draw more meaningful conclusions, but there exist potential imaging features that can provide clinical prognostic information. One aspect that necessitates further investigation is the failure of well-established clinical and genomics factors to reach significance in the present study.