Signature For Overall Survival Research Articles

Construction of a Cancer Stem Cell Marker Genes-Related 22-Gene Signature for Overall Survival Prediction in High-Risk Wilms’ Tumor

Background: This study aimed to investigate the comprehensive expression profile of cancer stem cell (CSC)-related genes and construct a prognostic signature for overall survival (OS) prediction in high-risk Wilms’ tumor (WT). Materials and methods: Gene expression and survival data from 120 high-risk WT cases in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-WT were used. Results: In total, 229 CSC-related genes were found to be significantly dysregulated in WT compared to tumor-adjacent normal tissues, among which 34 were associated with OS. Using LASSO regression, a 22-gene signature was developed, which exhibited excellent performance in 3-, 5-, and 10-year OS predictions (AUC > 0.86). The high-risk score group showed markedly poorer OS compared to the low-risk score group (median separation, HR = 6.41, 95% CI: 3.18–12.92, p = 3.2e − 9). The 22-gene signature was an independent prognostic factor for OS (HR = 5.086, 95% CI: 3.019–8.568, p < 0.001). Conclusion: This study identified a robust prognostic signature that can effectively support OS prediction.

Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study

Background and purposeTo develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification. Materials and methodsThis study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature. ResultsWithin the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk. ConclusionsThe CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.

A novel peroxisome-related gene signature predicts clinical prognosis and is associated with immune microenvironment in low-grade glioma.

Low-grade glioma (LGG), a common primary tumor, mainly originates from astrocytes and oligodendrocytes. Increasing evidence has shown that peroxisomes function in the regulation of tumorigenesis and development of cancer. However, the prognostic value of peroxisome-related genes (PRGs) in LGG has not been reported. Therefore, it is necessary to construct a prognostic risk model for LGG patients based on the expression profiles of peroxisome-related genes. Our study mainly concentrated on developing a peroxisome-related gene signature for overall survival (OS) prediction in LGG patients. First, according to these peroxisome-related genes, all LGG patients from The Cancer Genome Atlas (TCGA) database could be divided into two subtypes. Univariate Cox regression analysis was used to find prognostic peroxisome-related genes in TCGA_LGG dataset, and least absolute shrinkage and selection operator Cox regression analysis was employed to establish a 14-gene signature. The risk score based on the signature was positively associated with unfavorable prognosis. Then, multivariate Cox regression incorporating additional clinical characteristics showed that the 14-gene signature was an independent predictor of LGG. Time-dependent ROC curves revealed good performance of this prognostic signature in LGG patients. The performance about predicting OS of LGG was validated using the GSE107850 dataset derived from the Gene Expression Omnibus (GEO) database. Furethermore, we constructed a nomogram model based on the gene signature and age, which showed a better prognostic power. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that neuroactive ligand-receptor interaction and phagosome were enriched and that the immune status was decreased in the high-risk group. Finally, cell counting kit-8 (CCK8) were used to detect cell proliferation of U251 and A172 cells. Inhibition of ATAD1 (ATPase family AAA domain-containing 1) and ACBD5 (Acyl-CoA binding-domain-containing-5) expression led to significant inhibition of U251 and A172 cell proliferation. Flow cytometry detection showed that ATAD1 and ACBD5 could induce apoptosis of U251 and A172 cells. Therefore, through bioinformatics methods and cell experiments, our study developed a new peroxisome-related gene signature that migh t help improve personalized OS prediction in LGG patients.

Osteosarcoma-Associated Immune Genes as Potential Immunotherapy and Prognosis Biomarkers.

Immune-modulating therapies exhibit abundant promise compared to traditional treatment for osteosarcoma. We aim to establish an immune-related prognostic signatures in osteosarcoma. We identified the differentially expressed genes in osteosarcoma compared with normal controls using the GEO dataset. The intersection with immune-related genes was considered as differentially expressed immune genes. Potential prognosis-related differentially expressed genes were first analyzed with the multifactor Cox regression and then the step function performed the iteration. The best model was finally chosen as the immunological risk score signature model. And finally, we evaluated the correlation of genes in the prognostic model with immune cells, common immune checkpoints, and immune checkpoint blockade responses. We identified 1527 significantly upregulated and 2407 significantly downregulated genes in osteosarcoma compared to normal samples. In the 258 differentially expressed immune genes, 20 genes with independent prognostic significance were included in the step function. Finally, we constructed a prognostic signature for overall survival based on five immune genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) in osteosarcoma. These five genes are closely related to immune infiltration. Osteosarcoma with high JAG2 expression or low CMKLR1 expression may be associated with better immune checkpoint blockade response. JAG2 overexpression or CMKLR1 inhibition induced sensitivity to PD-1 antibody in osteosarcoma cells. We constructed a prognosis prediction signature containing five immune-related genes (JAG2, PLXNB1, CMKLR1, NUDT6, and PSMC4) with a significant prognostic value in osteosarcoma. Significant differences in immune infiltration and immunotherapy responses were identified between groups with different levels of these genes.

Establishment and validation of lncRNA-related prognostic signatures in cholangiocarcinoma

BackgroundThe prognosis of CCA is extremely poor, making it one of the most lethal cancers. Therefore, there is a need to elucidate the pathogenic mechanisms of CCA. In this study, we aimed at identifying lncRNA-related prognostic signatures for CCA through bioinformatics analysis and further validated their functions in CCA tumorigenesis and progression. MethodsThe RNA-seq data of CCA were downloaded from public databases. Differentially expressed lncRNAs (DElncRNAs) were screened. Then, candidate OS- and DFS-related DElncRNAs were selected through Kaplan–Meier survival analysis. Furthermore, LASSO regression was performed to establish the OS and DFS signatures, respectively. Multivariate COX models and nomograms for overall survival (OS) and disease-free survival (DFS) were established based on OS/DFS signature and clinical data. Hub lncRNAs were identified and enrichment analyses were performed to explore their potential functions. Finally, in vitro and in vivo models were used to validate the effects of the hub lncRNAs in CCA tumorigenesis and progression. ResultsA total of 925 DElncRNAs were selected, of which six candidate OS-related lncRNAs and 15 candidate DFS-related lncRNAs were identified. The OS and DFS signatures were then established using four lncRNAs, respectively. We found that the OS signature and vascular invasion were independent risk factors for the OS of CCA, while the DFS signature, vascular invasion, and CA19–9 were independent risk factors for the DFS of CCA. Then, nomograms were established to achieve personalized CCA recurrence and death prediction. Furthermore, our study uncovered that MIR4435-2HG and GAPLINC might play crucial roles in CCA progression and be selected as hub lncRNAs. GO and KEGG enrichment analyses revealed that the two hub lncRNAs were closely related to CCA tumorigenesis. Finally, we demonstrated that MIR4435-2HG and GAPLINC can stimulate CCA proliferation and migration in vitro and in vivo. ConclusionsThe established OS and DFS signatures are independent risk factors for OS and DFS of CCA patients, respectively. MIR4435-2HG and GAPLINC were identified as hub lncRNAs. In vitro and in vivo models revealed that MIR4435-2HG and GAPLINC can prompt CCA progression, which might be novel prognostic biomarkers and therapeutic targets for CCA.

Identification of Immune-Related Seven-Long Non-Coding RNA Signature for Overall Survival and Validation of the Effect of LINC01270 in Malignant Phenotypes of Clear Cell Renal Carcinoma.

The overall survival of patients with high-stage clear cell renal carcinoma (ccRCC) is poor. However, promising molecular-level prognostic marker is still lacking to date. We systematically evaluated the prognostic potential of immune-related long non-coding RNAs (lncRNAs) in ccRCC. The expressions of lncRNAs were validated in clinical tissues of ccRCC. Functional experiments were performed to investigate the role of lncRNAs in ccRCC. Eight hundred and ninety-three lncRNAs were differentially expressed in ccRCC and compared with normal controls and were screened out using three independent cohorts. Among them, 290 immune-related lncRNAs were identified. We identified a seven-lncRNA signature (LINC01270, FIRRE, RP11-37B2.1, RP11-253I19.3, RP11-438L19.1, RP11-504P24.9, and CTB-41I6.1) associated with the overall survival of late-stage ccRCC patients. Further multivariate Cox analysis using clinical factors as covariates showed that our lncRNA signature was an independent biomarker in training (P < 0.001, Log rank test) and validation cohorts (P = 0.003). The seven lncRNAs were closely related to the major targets (PD-1, PD-L1, and CTLA4) of immune checkpoint blockade drugs, implying that they may have potential value in predicting immunotherapy response. The seven lncRNAs may play an important role in tumor-infiltrating immune cells (eg, T/B cells) and tumor progression through regulating the binding of protein receptors/complexes, as revealed by functional analysis. qRT-PCR showed that LINC01270 was upregulated in ccRCC tissues (n=20) compared with paired normal samples. Functional experiments showed that LINC01270 silencing inhibited the proliferation, invasion, and migration of ccRCC cells. In summary, the seven-lncRNA signature has great potential in prognosis for patients with late-stage ccRCC, which could be a novel clinical biomarker. LINC01270 could be a novel therapeutic target of ccRCC.

Identification of a Novel Ferroptosis-Related Gene Signature for Predicting Prognosis and Responsiveness to Immunotherapy in Hepatocellular Carcinoma.

Ferroptosis has been reported to regulate multiple biological behaviors. However, the prognostic and oncologic values of ferroptosis-related genes (FRGs) have not been comprehensively elucidated in hepatocellular carcinoma (HCC). Here, we aimed to construct FRGs-associated signature for stratification of the prognosis of HCC patients. A list of FRGs was generated from FerrDb. Public databases were used to extract expression matrices and clinical information. TCGA cohort was randomly divided into a training set and a validation set. Prognostic signature for Overall Survival (OS) was established in training set and validated in internal cohorts (TCGA validation set and entire set) and external cohort (ICGC cohort). Additionally, the role of signature in HCC was well investigated by analysis of mutations, gene set enrichment analysis (GSEA), analysis of immune infiltrates, and analysis of response to immune checkpoint blockade (ICB) treatment. The oncogenic effects of ZFP69B on HCC were also investigated in vitro. We identified 12 FRGs-based signature for OS with LASSO regression. Patients were partitioned into different risk groups based on the signature. Overall, patients in different groups had different prognosis. The signature independently predicted OS in multivariate Cox regression analyses. Anti-tumor immune cells including activated CD8 T cells, cytolytic activity, and Th1 cells were negatively correlated with risk score in both TCGC and ICGC cohorts. Furthermore, low-risk patients responded better to ICB than high-risk patients. In addition, knockdown of ZFP69B reduced proliferation, migration, and invasion, and promoted erastin-induced ferroptosis of HCC cells. We developed a prognostic signature based on FRGs to predict OS of HCC patients. And the signature may facilitate clinicians in identifying those who are likely to benefit from ICIs. The results also indicated that ZFP69B might regulate the process of ferroptosis and could be used as a novel potential target for HCC.

A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature.

Multiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. However, the ubiquitin-proteasome system (UPS) genes have not yet been established as a prognostic predictor for MM, despite their potential applications in other cancers. RNA sequencing data and corresponding clinical information were acquired from Multiple Myeloma Research Foundation (MMRF)-COMMPASS and served as a training set (n=787). Validation of the prediction signature were conducted by the Gene Expression Omnibus (GEO) databases (n=1040). To develop a prognostic signature for overall survival (OS), least absolute shrinkage and selection operator regressions, along with Cox regressions, were used. A six-gene signature, including KCTD12, SIAH1, TRIM58, TRIM47, UBE2S, and UBE2T, was established. Kaplan-Meier survival analysis of the training and validation cohorts revealed that patients with high-risk conditions had a significantly worse prognosis than those with low-risk conditions. Furthermore, UPS-related signature is associated with a positive immune response. For predicting survival, a simple to use nomogram and the corresponding web-based calculator (https://jiangyanxiamm.shinyapps.io/MMprognosis/) were built based on the UPS signature and its clinical features. Analyses of calibration plots and decision curves showed clinical utility for both training and validation datasets. As a result of these results, we established a genetic signature for MM based on UPS. This genetic signature could contribute to improving individualized survival prediction, thereby facilitating clinical decisions in patients with MM.

A cellular senescence-related classifier based on a tumorigenesis- and immune infiltration-guided strategy can predict prognosis, immunotherapy response, and candidate drugs in hepatocellular carcinoma.

Cellular senescence plays an irreplaceable role in tumorigenesis, progression, and tumor microenvironment (TME) remodeling. However, to date, there is limited research delineating the landscape of cellular senescence in hepatocellular carcinoma (HCC), and an improved understanding on the interaction of tumor-associated cellular senescence with HCC prognosis, TME, and response to immunotherapy is warrant. Tumorigenic and immune infiltration-associated senescence genes were determined by weighted gene co-expression network analysis (WGCNA) and the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, and subsequently, a prognostic scoring model (named TIS) was constructed using multiple survival analysis algorithms to classify the senescence-related subtypes of HCC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to identify the distinct hallmark pathways between high- and low-risk subtypes. Additionally, we carried out correlation analyses for TIS and clinical traits, senescence-associated secretory phenotype (SASP), immune infiltration and evasion, immune checkpoint factors, drug response, and immunotherapeutic efficacy. External experimental validation was conducted to delineate the association of CPEP3 (a TIS gene) with HCC phenotypes through assays of proliferation, colony formation, and invasion. A five-gene TIS, composed of NET1, ATP6V0B, MMP1, GTDC1, and CPEB3, was constructed and validated using TCGA and ICGC datasets, respectively, and showed a highly robust and plausible signature for overall survival (OS) prediction of HCC in both training and validation cohorts. Patients in the TIS-high group were accompanied by worse OS, activation of carcinogenetic pathways, infiltration of immunosuppressive cells, exclusion of effector killing cells, overexpression of immunomodulatory genes and SASP, and unsatisfied response to immunotherapy. In response to anticancer drugs, patients in the TIS-high group exhibited enhanced susceptibility to several conventional chemotherapeutic agents (5-fluorouracil, docetaxel, doxorubicin, gemcitabine, and etoposide), as well as several inhibitors of pathways involved in cellular senescence (cell-cycle inhibitors, bromodomain and extraterminal domain family (BET) inhibitors, PI3K-AKT pathway inhibitors, and multikinase inhibitors). Additionally, four putative drugs (palbociclib, JAK3 inhibitor VI, floxuridine, and lestaurtinib) were identified as potential compounds for patients in the TIS-high group. Notably, in vitro functional validation showed that CPEB3 knockdown boosted the phenotypes of proliferation, clonogenicity, and invasion in HCC cells, whereas CPEB3 overexpression attenuated these phenotypes. Our study provides comprehensive clues demonstrating the role of novel TIS in predicting HCC prognosis, immunotherapeutic response, and candidate drugs. This work highlights the significance of tumorigenesis- and immune infiltration-related cellular senescence in cancer therapy.

An M0 macrophage-related prognostic model for hepatocellular carcinoma

BackgroundThe role of M0 macrophages and their related genes in the prognosis of hepatocellular carcinoma (HCC) remains poorly characterized.MethodsMultidimensional bioinformatic methods were used to construct a risk score model using M0 macrophage-related genes (M0RGs).ResultsInfiltration of M0 macrophages was significantly higher in HCC tissues than in normal liver tissues (P = 2.299e-07). Further analysis revealed 35 M0RGs that were associated with HCC prognosis; two M0RGs (OLA1 and ATIC) were constructed and validated as a prognostic signature for overall survival of patients with HCC. Survival analysis revealed the positive relationship between the M0RG signature and unfavorable prognosis. Correlation analysis showed that this risk model had positive associations with clinicopathological characteristics, somatic gene mutations, immune cell infiltration, immune checkpoint inhibitor targets, and efficacy of common drugs.ConclusionsThe constructed M0RG-based risk model may be promising for the clinical prediction of prognoses and therapeutic responses in patients with HCC.

Circulating Protein Biomarkers for Prognostic Use in Patients with Advanced Pancreatic Ductal Adenocarcinoma Undergoing Chemotherapy.

Simple SummaryWe investigated the connection between the levels of 92 circulating proteins and survival in patients with advanced pancreatic ductal adenocarcinoma. Serum samples from 363 patients with advanced pancreatic ductal adenocarcinoma were examined using the Olink Immuno-Oncology panel. Two protein signatures were found containing seven and four proteins, respectively. These two protein indices discriminated patients with very short overall survival (<90 days) from patients with long overall survival (>2 years), with AUC values of 0.97–0.99 in the discovery cohort, and 0.89–0.82 in the replication cohorts. Further analyses were conducted exploring early changes in protein levels, and protein expression in different treatment groups.Patients with advanced pancreatic ductal adenocarcinoma (PDAC) have a dismal prognosis. We aimed to find a prognostic protein signature for overall survival (OS) in patients with advanced PDAC, and to explore whether early changes in circulating-protein levels could predict survival. We investigated 92 proteins using the Olink Immuno-Oncology panel in serum samples from 363 patients with advanced PDAC. Protein panels for several survival cut-offs were developed independently by two bioinformaticians using LASSO and Ridge regression models. Two panels of proteins discriminated patients with OS < 90 days from those with OS > 2 years. Index I (CSF-1, IL-6, PDCD1, TNFRSF12A, TRAIL, TWEAK, and CA19-9) had AUCs of 0.99 (95% CI: 0.98–1) (discovery cohort) and 0.89 (0.74–1) (replication cohort). For Index II (CXCL13, IL-6, PDCD1, and TNFRSF12A), the corresponding AUCs were 0.97 (0.93–1) and 0.82 (0.68–0.96). Four proteins (ANGPT2, IL-6, IL-10, and TNFRSF12A) were associated with survival across all treatment groups. Longitudinal samples revealed several changes, including four proteins that were also part of the prognostic signatures (CSF-1, CXCL13, IL-6, TNFRSF12A). This study identified two circulating-protein indices with the potential to identify patients with advanced PDAC with very short OS and with long OS.

Survival-related indicators ALOX12B and SPRR1A are associated with DNA damage repair and tumor microenvironment status in HPV 16-negative head and neck squamous cell carcinoma patients

ObjectivesTo investigate prognostic-related gene signature based on DNA damage repair and tumor microenvironment statue in human papillomavirus 16 negative (HPV16-) head and neck squamous cell carcinoma (HNSCC).MethodsFor the RNA-sequence matrix in HPV16- HNSCC in the Cancer Genome Atlas (TCGA) cohort, the DNA damage response (DDR) and tumor microenvironment (TM) status of each patient sample was estimated by using the ssGSEA algorithm. Through bioinformatics analysis in DDR_high/TM_high (n = 311) and DDR_high/TM_low (n = 53) groups, a survival-related gene signature was selected in the TCGA cohort. Two independent external validation cohorts (GSE65858 (n = 210) and GSE41613 (n = 97)) with HPV16- HNSCC patients validated the gene signature. Correlations among the clinical-related hub differentially expressed genes (DEGs) and infiltrated immunocytes were explored with the TIMER2.0 server. Drug screening based on hub DEGs was performed using the CellMiner and GSCALite databases. The loss-of-function studies were used to evaluate the effect of screened survival-related gene on the motility of HPV- HNSCC cells in vitro.ResultsA high DDR level (P = 0.025) and low TM score (P = 0.012) were independent risk factors for HPV16- HNSCC. Downregulated expression of ALOX12B or SPRR1A was associated with poor survival rate and advanced cancer stages. The pathway enrichment analysis showed the DDR_high/TM_low samples were enriched in glycosphingolipid biosynthesis-lacto and neolacto series, glutathione metabolism, platinum drug resistance, and ferroptosis pathways, while the DDR_high/TM_low samples were enriched in Th17 cell differentiation, Neutrophil extracellular trap formation, PD − L1 expression and PD − 1 checkpoint pathway in cancer. Notably, the expression of ALOX12B and SPRR1A were negatively correlated with cancer-associated fibroblasts (CAFs) infiltration and CAFs downstream effectors. Sensitivity to specific chemotherapy regimens can be derived from gene expressions. In addition, ALOX12B and SPRR1A expression was associated with the mRNA expression of insulin like growth factor 1 receptor (IGF1R), AKT serine/threonine kinase 1 (AKT1), mammalian target of rapamycin (MTOR), and eukaryotic translation initiation factor 4E binding protein 1 (EIF4EBP1) in HPV negative HNSCC. Down-regulation of ALOX12B promoted HPV- HNSCC cells migration and invasion in vitro.ConclusionsALOX12B and SPRR1A served as a gene signature for overall survival in HPV16- HNSCC patients, and correlated with the amount of infiltrated CAFs. The specific drug pattern was determined by the gene signature.

Development and validation of novel prognostic models based on RNA-binding proteins in breast cancer.

ObjectivesWe aimed to construct novel prognostic models based on RNA-binding proteins (RBPs) in breast cancer (BRCA) and explore their roles in this disease and their effects on tumor-infiltrating immune cells (TIICs).MethodsDatasets were downloaded from the Gene Expression Omnibus (GEO) database. Functions and prognostic values of RBPs were systematically investigated using a series of bioinformatics analysis methods. TIICs were assessed using CIBERSORT.ResultsOverall, 138 differentially expressed RBPs were identified, of which 86 were upregulated and 52 were downregulated. Of these, 13 RBPs were identified as prognosis-related and adopted to construct an overall survival (OS) model, while 12 RBPs were used for the relapse-free survival (RFS) model. High-risk patients had poorer OS and RFS rates than low-risk patients. The results indicate that the OS and RFS models are good prognostic models with reliable predictive abilities. In addition, the proportions of CD8, CD4 naïve, and CD4 memory resting T cells, as well as resting dendritic cells, were significantly different between the low-risk and high-risk groups in the OS model.ConclusionsOS and RFS signatures can be used as reliable BRCA prognostic biomarkers. This work will help understand the prognostic roles and functions of RBPs in BRCA.

Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.

Background: Multiple myeloma (MM) is characterized by abnormal proliferation of bone marrow clonal plasma cells. Tumor immunotherapy, a new therapy that has emerged in recent years, offers hope to patients, and studying the expression characteristics of immune-related genes (IRGs) based on whole bone marrow gene expression profiling (GEP) in MM patients can help guide personalized immunotherapy. Methods: In this study, we explored the potential prognostic value of IRGs in MM by combining GEP and clinical data from the GEO database. We identified hub IRGs and transcription factors (TFs) associated with disease progression by Weighted Gene Co-expression Network Analysis (WGCNA), and modeled immune-related prognostic signature by univariate and multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, the prognostic ability of signature was verified by multiple statistical methods. Moreover, ssGSEA and GSEA algorithm reveled different immunological characteristics and biological function variation in different risk groups. We mapped the hub IRGs by protein-protein interaction network (PPI) and extracted the top 10 ranked genes. Finally, we conducted vitro assays on two alternative IRGs. Results: Our study identified a total of 14 TFs and 88 IRGs associated with International Staging System (ISS). Ten IRGs were identified by Cox -LASSO regression analysis, and used to develop optimal prognostic signature for overall survival (OS) in MM patients. The 10-IRGs were BDNF, CETP, CD70, LMBR, LTBP1, NENF, NR1D1, NR1H2, PTK2B and SEMA4. In different groups, risk signatures showed excellent survival prediction ability, and MM patients also could be stratified at survival risk. In addition, IRF7 and SHC1 were hub IRGs in PPI network, and the vitro assays proved that they could promote tumor progression. Notably, ssGSEA and GSEA results confirmed that different risk groups could accurately indicate the status of tumor microenvironment (TME) and activation of biological pathways. Conclusion: Our study suggested that immune-related signature could be used as prognostic markers in MM patients.

Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer.

BackgroundMost prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.MethodsPrognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan–Meier (K–M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.ResultsA total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.ConclusionA credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.

A Glycolysis-Related Gene Signature Correlates With the Characteristics of the Tumor Immune Microenvironment and Predicts Prognosis in Patients With Hepatocellular Carcinoma.

Aim: To develop a glycolysis-related gene signature that correlated with the characteristics of the tumor immune microenvironment and had good predictive power for overall survival (OS) in hepatocellular carcinoma (HCC). Methods: Gene expression profiles, RNA sequencing data, clinical characteristics and survival information for 407 patients with HCC and 58 healthy controls were downloaded from the TCGA database. GSEA 4.1.0 software was used to evaluate the glycolysis-related pathways enriched in HCC compared to normal liver tissue. Univariate Cox, Least Absolute Shrinkage, Selection Operator, and two-step multivariate Cox analyses were used to construct a glycolysis-related gene signature for prognostic prediction. The glycolysis-related gene signature was combined with clinical characteristics to generate a nomogram. Tumor-infiltrating immune cell profiles and PD-L1 protein expression in HCC tissues were investigated. Results: The gene expression profiles of HCC tissues were enriched in glycolysis-related pathways. A glycolysis-related gene signature was used to categorize patients as high-risk or low-risk, where high-risk patients had significantly worse OS. Receiver operating characteristic curves confirmed the predictive capability of the glycolysis-related gene signature for OS (AUC >0.80). There was a significant difference in M0 macrophage (p = 0.017), dendritic cell (p = 0.043), B cell (p = 0.0018), CD4 T cell (p = 0.003), Treg (p = 0.01) and mast cell (p = 0.02) content and PD-L1 protein expression (p = 0.019) between HCC tissues in patients in the high-risk and low-risk groups. Conclusion: We established a glycolysis-related gene signature for OS in HCC that was predictive in training and test TCGA cohorts and correlated with the characteristics of the HCC tumor immune microenvironment. The glycolysis-related gene signature may guide clinical decision-making concerning patient selection for immunotherapy in HCC.

Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population.

ObjectiveGastric cancer (GC) is one of the most common tumour diseases worldwide and has poor survival, especially in the Asian population. Exploration based on biomarkers would be efficient for better diagnosis, prediction, and targeted therapy.MethodsExpression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Survival-related genes were identified by gene set enrichment analysis (GSEA) and univariate Cox. Then, we applied a Bayesian hierarchical lasso Cox model for prognostic signature screening. Protein-protein interaction and Spearman analysis were performed. Kaplan–Meier and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prediction performance. Multivariate Cox regression was used to identify prognostic factors, and a prognostic nomogram was constructed for clinical application.ResultsWith the Bayesian lasso Cox model, a 9-gene signature included TNFRSF11A, NMNAT1, EIF5A, NOTCH3, TOR2A, E2F8, PSMA5, TPMT, and KIF11 was established to predict overall survival in GC. Protein-protein interaction analysis indicated that E2F8 was likely related to KIF11. Kaplan-Meier analysis showed a significant difference between the high-risk and low-risk groups (P<0.001). Multivariate analysis demonstrated that the 9-gene signature was an independent predictor (HR = 2.609, 95% CI 2.017–3.370), and the C-index of the integrative model reached 0.75. Function enrichment analysis for different risk groups revealed the most significant enrichment pathway/term, including pyrimidine metabolism and respiratory electron transport chain.ConclusionOur findings suggested that a novel prognostic model based on a 9-gene signature was developed to predict GC patients in high-risk and improve prediction performance. We hope our model could provide a reference for risk classification and clinical decision-making.

A Novel Immune-Associated Gene Signature for Overall Survival Prediction in Patients with Oral Squamous Cell Carcinoma.

Objective To explore a novel Immune-associated gene signature for overall survival (OS) in patients with oral squamous cell carcinoma (OSCC). Methods Expression profiles of genes and corresponding clinical materials of OSCC patients were obtained through the TCGA database. With a LASSO Cox regression model, a multigene signature was established to predict the OS of OSCC patients. Some molecular experiments including RNA interference, MTT, and Transwell assay were applied to verify the role of the risk gene FGF9 in OSCC. Results 43 immune-related prognostic DEGs were identified in OSCC. A 17-gene signature was established to assign the patients to either a high-risk group (HG) or a low-risk group (LG). The HG presented a shorter OS than the LG (P < 0.05). According to multivariate Cox regression analyses, the risk score was considered an independent factor for OS prediction (training set: HR = 3.485, 95% CI = 2.037–5.961, P < 0.001; test set: HR = 4.531, 95% CI = 2.120–9.682, P < 0.001). ROC curve-based analysis revealed the signature's ability for prediction. According to functional analysis, the immune cell expression and immune function of the HG were significantly inhibited. After knocking down the high-risk gene FGF9, the migration, proliferation, and invasion capabilities of OSCC cells HSC6 were significantly suppressed (P < 0.05). Conclusion A novel immune-associated gene signature was identified for predicting the prognosis of OSCC. These risk genes show great potential as targets for OSCC treatment.

LMO1 Plays an Oncogenic Role in Human Glioma Associated With NF-kB Pathway.

BackgroundLIM domain only protein1(LMO1), a nuclear transcription coregulator, is implicated in the pathogenesis of T-cell acute lymphoblastic leukemia and neuroblastoma. However, the clinical significance and potential mechanism of LMO1 in human gliomas remain to be determined.MethodsIn this study, expression level data and clinical information were obtained via three databases. The Cox proportional hazards regression model was used to predict outcomes for glioma patients. In vitro and in vivo assays were used to explore the function of LMO1 in human glioma. Gene set enrichment analysis (GSEA), RNA-seq and western blot were used to explore the potential molecular mechanisms. A prognostic model was built for predicting the overall survival(OS) of human glioma patients.ResultsHigh LMO1 expression was associated with a high tumor grade and a poor prognosis in patients. High levels of LMO1 mRNA were correlated with poor prognosis in patients with isocitrate dehydrogenase (IDH)-wild-type (wt) and 1p/19q non-codeletion gliomas. Gene silencing of LMO1 significantly inhibited tumor growth, invasion and migration in vitro. In contrast, LMO1 over-expression promoted tumor growth, invasion and migration. Mechanically, LMO1 may positively regulate the level of NGFR mRNA and protein. NGFR mediated the regulation between LMO1 and NF-kB activation. Consistently, the nude mice study further confirmed that knockdown of LMO1 blocked tumor growth via NGFR-NF-kB axis. Finally, The nomogram based on the LMO1 signature for overall survival (OS) prediction in human glioma patients exhibited good performance in the individual mortality risk.ConclusionThis study provides new insights and evidences that high level expression of LMO1 is significantly correlated with progression and prognosis in human gliomas. LMO1 played a critical role in tumorigenesis and progression. The present study first investigated the LMO1–NGFR–NF-kB axis regulate cell growth and invasion in human glioma cells, whereby targeting this pathway may be a therapeutic target for glioma.

Construction and Validation of a Prognostic Risk Model for Triple-Negative Breast Cancer Based on Autophagy-Related Genes.

BackgroundAutophagy plays an important role in triple-negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.MethodsA total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed autophagy-related genes (DE-ARGs) between normal samples and TNBC samples were determined by the DESeq2 package. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed. According to the LASSO regression results based on univariate Cox, we identified a prognostic signature for overall survival (OS), which was further validated by using the Gene Expression Omnibus (GEO) cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of an ARG in our model for TNBC cell survival and metastasis.ResultsThere are 43 DE-ARGs identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3 was established based on univariate Cox regression and LASSO regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan–Meier curves and receiver operating characteristic (ROC) curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as an independent predictor. Subsequently, a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of the ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells.ConclusionBased on six ARGs (CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3), we developed a risk prediction model that can help clinical doctors effectively predict the survival status of TNBC patients. Our data suggested that EIF4EBP1 might promote the proliferation and migration in TNBC cell lines. These findings provided a novel insight into the vital role of the autophagy-related genes in TNBC and may provide new therapeutic targets for TNBC.