Prognostic Risk Signature Research Articles

Exploration of epithelial-mesenchymal transition-related lncRNA signature and drug sensitivity in breast cancer.

Breast cancer (BRCA) has become the most diagnosed cancer worldwide for female and seriously endanger female health. The epithelial-mesenchymal transition (EMT) process is associated with metastasis and drug resistance in BRCA patients. However, the prognostic value of EMT-related lncRNA in BRCA still needs to be revealed. The aim of this study is to construct an EMT-related lncRNA (ERL) signature with accuracy predictive ability for the prognosis of BRCA patients. RNA-seq expression data and Clinical characteristics obtained from the TCGA (The Cancer Genome Atlas) were used in the study. First, we identified the EMT-related lncRNA by the Pearson correlation analysis. An EMT-related lncRNAs prognostic risk signature was constructed using univariate Cox regression and Lasso-penalized Cox regression analyses. The model's performance was validated using Kaplan-Meier (KM) survival analysis, ROC curve and C-index. Finally, a nomogram was constructed for clinical practice in evaluating the patients with BRCA and validated by calibration curve and decision curve analysis (DCA). We also evaluated the drug sensitivity of signature lncRNA and the tumor immune cell infiltration in breast cancer. We constructed a 10-lncRNA risk score signature based on the lncRNAs associated with the EMT process. We could assign BRCA patients to the high- and low-risk group according to the median risk score. The prognostic risk signature showed excellent accuracy and demonstrated sufficient independence from other clinical characteristics. The immune cell infiltration analysis showed that the prognostic risk signature was related to the infiltration of the immune cell subtype. Drug sensitivity analysis proved ERLs signature could effectively predict the sensitivity of patients to common chemotherapy drugs in BRCA and provide guidance for chemotherapy drugs for high-risk and low-risk patients. Our ERL signature and nomogram have excellent prognostic value and could become reliable tools for clinical guidance.

Better prediction of clinical outcome in clear cell renal cell carcinoma based on a 6 metabolism-related gene signature

It has been reported that metabolic disorders participate in the formation and progression of clear cell renal cell carcinoma (ccRCC). However, the predictive value of metabolism-related genes (MRGs) in clinical outcome of ccRCC is still largely unknown. Herein, a novel metabolism-related signature was generated to assess the effect of MRGs on the prognosis of ccRCC patients. Important module MRGs were selected by differentially expressed analysis and WGCNA. Subsequently, the hub MRGs were screened via univariate cox regression as well as LASSO regression. A new metabolism-related signature of 6 hub MRGs (PAFAH2, ACADSB, ACADM, HADH, PYCR1 and ITPKA) was constructed, with a good prognostic prediction ability in the TCGA cohort. The prediction accuracy of this signature was further confirmed in both GSE22541 and FAHWMU cohort. Interestingly, this MRG risk signature was highly correlated with tumor mutation burden and immune infiltration in ccRCC. Notably, lower PAFAH2, a member of 6 MRGs, was found in ccRCC. Knockdown of PAFAH2 contributed to renal cancer cell proliferation and migration. Collectively, a 6-MRG prognostic risk signature is generated to estimate the prognostic status of ccRCC patients, providing a novel insight in the prognosis prediction and treatment of ccRCC.

Construction and Validation of a Prognostic Risk Prediction Model for Lactate Metabolism-Related lncRNA in Endometrial Cancer.

Endometrial cancer (EC) is a common group of malignant epithelial tumors that mainly occur in the female endometrium. Lactate is a key regulator of signal pathways in normal and malignant tissues. However, there is still no research on lactate metabolism-related lncRNA in EC. Here, we intended to establish a prognostic risk model for EC based on lactate metabolism-related lncRNA to forecast the prognosis of EC patients. First, we found that 38 lactate metabolism-associated lncRNAs were significantly overall survival through univariate Cox regression analysis. Using minimum absolute contraction and selection operator (LASSO) regression analysis and multivariate Cox regression analysis, six lactate metabolism-related lncRNAs were established as independent predictor in EC patients and were used to establish a prognostic risk signature. We next used multifactorial COX regression analysis and receiver operating characteristic (ROC) curve analysis to confirm that risk score was an independent prognostic factor of overall patient survival. The survival time of patients with EC in different high-risk populations was obviously related to clinicopathological factors. In addition, lactate metabolism-related lncRNA in high-risk population participated in multiple aspects of EC malignant progress through Gene Set Enrichment Analysis, Genomes pathway and Kyoto Encyclopedia of Genes and Gene Ontology. And risk scores were strongly associated with tumor mutation burden, immunotherapy response and microsatellite instability. Finally, we chose a lncRNA SRP14-AS1 to validate the model we have constructed. Interestingly, we observed that the expression degree of SRP14-AS1 was lower in tumor tissues of EC patients than in normal tissues, which was consistent with our findings in the TCGA database. In conclusion, our study constructed a prognostic risk model through lactate metabolism-related lncRNA and validated the model, confirming that the model can be used to predict the prognosis of EC patients and providing a molecular analysis of potential prognostic lncRNA for EC.

Prognostic value of amino acid metabolism-related gene expression in invasive breast carcinoma.

An increasing number of studies indicated that metabolic reprogramming of amino acid metabolism may either promote or inhibit tumor progression. The purpose of this study was to investigate the ability of a gene risk signature associated with amino acid metabolism to predict the prognosis and immune characteristics of invasive breast carcinoma. LASSO Cox regression analysis was performed to construct and validate the prognostic risk signature based on the expression of 9 amino acid metabolism-related genes. The predictive value of the signature, immune characteristics, and chemotherapeutic drugs was also predicted. Finally, 9 significant genes were examined in MDA-MB-231 and MCF-7 cells, and the predicted chemotherapeutic drugs were also verified. The prognosis of the low-risk group was better than that of the high-risk group. The areas under the curve (AUCs) at 1, 2, and 3 years were 0.852, 0.790, and 0.736, respectively. In addition, the GSEA results for KEGG and GO revealed that samples with a high-risk score exhibited a variety of highly malignant manifestations. The high-risk group was characterized by an increased number of M2 macrophages, a high level of tumor purity, low levels of APC co-stimulation, cytolytic activity, HLA, para-inflammation, and type I IFN response. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) confirmed that MDA-MB-231 and MCF-7 cells express 9 amino acid metabolism-related genes differently. In addition, cell experiments were conducted to examine the effect of cephaeline-induced on cell viability, migration ability, and protein expression of the PI3K/AKT signaling pathway and HIF-1α. We established a risk signature based on 9 amino acid metabolism-related genes for invasive breast carcinoma. Further analyses revealed that this risk signature is superior to other clinical indexes in survival prediction and that the subgroups identified by the risk signature exhibit distinct immune characteristics. Cephaeline was determined to be a superior option for patients in high-risk groups.

Characteristic of molecular subtype based on lysosome-associated genes reveals clinical prognosis and immune infiltration of gastric cancer.

Lysosome are involved in nutrient sensing, cell signaling, cell death, immune responses and cell metabolism, which play an important role in the initiation and development of multiple tumors. However, the biological function of lysosome in gastric cancer (GC) has not been revealed. Here, we aim to screen lysosome-associated genes and established a corresponding prognostic risk signature for GC, then explore the role and underlying mechanisms. The lysosome-associated genes (LYAGs) were obtained from MSigDB database. Differentially expressed lysosome-associated genes (DE-LYAGs) of GC were acquired based on the TCGA database and GEO database. According to expression profiles of DE-LYAGs, we divided the GC patients into different subgroups and then explored tumor microenvironment (TME) landscape and immunotherapy response in LYAG subtypes using GSVA, ESTIMATE and ssGSEA algorithms. Univariate Cox regression analysis, LASSO algorithm and multivariate Cox regression analysis were adopted to identify the prognostic LYAGs and then establish a risk model for patients with GC. The Kaplan-Meier analysis, Cox regression analysis and ROC analysis were utilized to evaluate the performance of the prognostic risk model. Clinical GC specimens were also used to verify the bioinformatics results by qRT-PCR assay. Thirteen DE-LYAGs were obtained and utilized to distinguish three subtypes in GC samples. Expression profiles of the 13 DE-LYAGs predicted prognosis, tumor-related immunological abnormalities and pathway dysregulation in these three subtypes. Furthermore, we constructed a prognostic risk model for GC based on DEG in the three subtypes. The Kaplan-Meier analysis suggested that higher risk score related to short OS rate. The Cox regression analysis and ROC analysis indicated that risk model had independent and excellent ability in predicting prognosis of GC patients. Mechanistically, a remarkable difference was observed in immune cell infiltration, immunotherapy response, somatic mutation landscape and drug sensitivity. qRT-PCR results showed that compared with corresponding adjacent normal tissues, most screened genes showed significant abnormal expressions and the expression change trends were consistent with the bioinformatics results. We established a novel signature based on LYAGs which could be served as a prognostic biomarker for GC. Our study might provide new insights into individualized prognostication and precision treatment for GC.

Prognostic and Immunological Significance of the Molecular Subtypes and Risk Signatures Based on Cuproptosis in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) remains a challenging medical problem. Cuproptosis is a novel form of cell death that plays a crucial role in tumorigenesis, angiogenesis, and metastasis. However, it remains unclear whether cuproptosis-related genes (CRGs) influence the outcomes and immune microenvironment of HCC patients. From The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, we obtained the mRNA expression file and related clinical information of HCC patients. We selected 19 CRGs as candidate genes for this study according to previous literature. We performed a differential expression analysis of the 19 CRGs between malignant and precancerous tissue. Based on the 19 CRGs, we enrolled cluster analysis to identify cuproptosis-related subtypes of HCC patients. A prognostic risk signature was created utilizing univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. We employed independent and stratification survival analyses to investigate the predictive value of this model. The functional enrichment features, mutation signatures, immune profile, and response to immunotherapy of HCC patients were also investigated according to the two molecular subtypes and the prognostic signature. We found that 17 CRGs significantly differed in HCC versus normal samples. Cluster analysis showed two distinct molecular subtypes of cuproptosis. Cluster 1 is preferentially related to poor prognosis, high activity of immune response signaling, high mutant frequency of TP53, and distinct immune cell infiltration versus cluster 2. Through univariate and LASSO Cox regression analyses, we created a cuproptosis-related prognostic risk signature containing LIPT1, DLAT, MTF1, GLS, and CDKN2A. High-risk HCC patients were shown to have a worse prognosis. The risk signature was proved to be an independent predictor of prognosis in both the TCGA and ICGC datasets, according to multivariate analysis. The signature also performed well in different stratification of clinical features. The immune cells, which included regulatory T cells (Treg), B cells, macrophages, mast cells, NK cells, and aDCs, as well as immune functions containing cytolytic activity, MHC class I, and type II IFN response, were remarkably distinct between the high-risk and low-risk groups. The tumor immune dysfunction and exclusion (TIDE) score suggested that high-risk patients had a higher response rate to immune checkpoint inhibitors than low-risk patients. This research discovered the potential prognostic and immunological significance of cuproptosis in HCC, improved the understanding of cuproptosis, and may deliver new directions for developing more efficacious therapeutic techniques for HCC patients.

Clinical significance and immune landscape of a novel ferroptosis-related prognosis signature in osteosarcoma

BackgroundOsteosarcoma is a malignant tumor that usually occurs in adolescents aged 10–20 years and is associated with poor prognosis. Ferroptosis is an iron-dependent cell death mechanism that plays a vital role in cancer.MethodsOsteosarcoma transcriptome data were downloaded from the public database TARGET and from previous studies. A prognostic risk score signature was constructed using bioinformatics analysis, and its efficacy was determined by analyzing typical clinical features. The prognostic signature was then validated with external data. Differences in immune cell infiltration between high- and low-risk groups were analyzed. The potential of the prognostic risk signature as a predictor of immunotherapy response was evaluated using the GSE35640 (melanoma) dataset. Five key genes expression were measured by real-time PCR and western blot in human normal osteoblasts and osteosarcoma cells. Moreover, malignant biological behaviors of osteosarcoma cells were tested by modulating gene expression level.ResultsWe obtained 268 ferroptosis-related genes from the online database FerrDb and published articles. Transcriptome data and clinical information of 88 samples in the TARGET database were used to classify genes into two categories using clustering analysis, and significant differences in survival status were identified. Differential ferroptosis-related genes were screened, and functional enrichment showed that they were associated with HIF-1, T cells, IL17, and other inflammatory signaling pathways. Prognostic factors were identified by univariate Cox regression and LASSO analysis, and a 5-factor prognostic risk score signature was constructed, which was also applicable for external data validation. Experimental validation indicated that the mRNA and protein expression level of MAP3K5, LURAP1L, HMOX1 and BNIP3 decreased significantly, though meanwhile MUC1 increased in MG-63 and SAOS-2 cells compared with hFOB1.19 cells. Cell proliferation and migration ability of SAOS-2 were affected based on alterations of signature genes.ConclusionsSignificant differences in immune cell infiltration between high- and low-risk groups indicated that the five ferroptosis-related prognostic signature was constructed and could be used to predict the response to immunotherapy in osteosarcoma.

A novel DNA methylation signature to improve survival prediction of progression-free survival for testicular germ cell tumors

This study aimed to develop a nomogram for predicting the progression-free survival (PFS) of testicular germ cell tumors (TGCT) patients based on DNA methylation signature and clinicopathological characteristics. The DNA methylation profiles, transcriptome data, and clinical information of TGCT patients were obtained from the Cancer Genome Atlas (TCGA) database. Univariate Cox, lasso Cox, and stepwise multivariate Cox regression were applied to identify a prognostic CpG sites-derived risk signature. Differential expression analysis, functional enrichment analysis, immunoinfiltration analysis, chemotherapy sensitivity analysis, and clinical feature correlation analysis were performed to elucidate the differences among risk groups. A prognostic nomogram integrating CpG sites-derived risk signature and clinicopathological features was further established and evaluated likewise. A risk score model based on 7 CpG sites was developed and found to exhibit significant differences among different survival, staging, radiotherapy, and chemotherapy subgroups. There were 1452 differentially expressed genes between the high- and low-risk groups, with 666 being higher expressed and 786 being lower expressed. Genes highly expressed were significantly enriched in immune-related biological processes and related to T-cell differentiation pathways; meanwhile, down-regulated genes were significantly enriched in extracellular matrix tissue organization-related biological processes and involved in multiple signaling pathways such as PI3K-AKT. As compared with the low-risk group, patients in the high-risk group had decreased lymphocyte infiltration (including T-cell and B-cell) and increased macrophage infiltration (M2 macrophages). They also showed decreased sensitivity to etoposide and bleomycin chemotherapy. Three clusters were obtained by consensus clustering analysis based on the 7 CpG sites and showed distinct prognostic features, and the risk scores in each cluster were significantly different. Multivariate Cox regression analysis found that the risk scores, age, chemotherapy, and staging were independent prognostic factors of PFS of TGCT, and the results were used to formulate a nomogram model that was validated to have a C-index of 0.812. Decision curve analysis showed that the nomogram model was superior to other strategies in the prediction of PFS of TGCT. In this study, we successfully established CpG sites-derived risk signature, which might serve as a useful tool in the prediction of PFS, immunoinfiltration, and chemotherapy sensitivity for TGCT patients.

A cell cycle-related lncRNA signature predicts the progression-free interval in papillary thyroid carcinoma.

The cell cycle plays a vital role in tumorigenesis and progression. Long non-coding RNAs (lncRNAs) are key regulators of cell cycle processes. Therefore, understanding cell cycle-related lncRNAs (CCR-lncRNAs) is crucial for determining the prognosis of papillary thyroid carcinoma (PTC). RNA-seq and clinical data of PTC were acquired from The Cancer Genome Atlas, and CCR-lncRNAs were selected based on Pearson's correlation coefficients. According to univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses, a five-CCR-lncRNA signature (FOXD2-AS1, LOC100507156, BSG-AS1, EGOT, and TMEM105) was established to predict the progression-free interval (PFI) in PTC. Kaplan-Meier survival, time-dependent receiver operating characteristic curve, and multivariate Cox regression analyses proved that the signature had a reliable prognostic capability. A nomogram consisting of the risk signature and clinical characteristics was constructed that effectively predicted the PFI in PTC. Functional enrichment analyses indicted that the signature was involved in cell cycle- and immune-related pathways. Furthermore, we also analyzed the correlation between the signature and immune cell infiltration. Finally, we verified the differential expression of CCR-lncRNAs in vitro using quantitative real-time polymerase chain reaction. Overall, the newly developed prognostic risk signature based on five CCR-lncRNAs may become a marker for predicting the PFI in PTC.

Exploration of prognostic genes and risk signature in breast cancer patients based on RNA binding proteins associated with ferroptosis.

Background: Breast cancer (BRCA) is a life-threatening malignancy in women with an unsatisfactory prognosis. The purpose of this study was to explore the prognostic biomarkers and a risk signature based on ferroptosis-related RNA-binding proteins (FR-RBPs). Methods: FR-RBPs were identified using Spearman correlation analysis. Differentially expressed genes (DEGs) were identified by the "limma" R package. The univariate Cox and multivariate Cox analyses were executed to determine the prognostic genes. The risk signature was constructed and verified with the training set, testing set, and validation set. Mutation analysis, immune checkpoint expression analysis in high- and low-risk groups, and correlation between risk signature and chemotherapeutic agents were conducted using the "maftools" package, "ggplot2" package, and the CellMiner database respectively. The Human Protein Atlas (HPA) database was employed to confirm protein expression trends of prognostic genes in BRCA and normal tissues. The expression of prognostic genes in cell lines was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-meier (KM) plotter database analysis was applied to predict the correlation between the expression levels of signature genes and survival statuses. Results: Five prognostic genes (GSPT2, RNASE1, TIPARP, TSEN54, and SAMD4A) to construct an FR-RBPs-related risk signature were identified and the risk signature was validated by the International Cancer Genome Consortium (ICGC) cohort. Univariate and multivariate Cox regression analysis demonstrated the risk score was a robust independent prognostic factor in overall survival prediction. The Tumor Mutational Burden (TMB) analysis implied that the high- and low-risk groups responded differently to immunotherapy. Drug sensitivity analysis suggested that the risk signature may serve as a chemosensitivity predictor. The results of GSEA suggested that five prognostic genes might be related to DNA replication and the immune-related pathways. RT-qPCR results demonstrated that the expression trends of prognostic genes in cell lines were consistent with the results from public databases. KM plotter database analysis suggested that high expression levels of GSPT2, RNASE1, and SAMD4A contributed to poor prognoses. Conclusion: In conclusion, this study identified the FR-RBPs-related prognostic genes and developed an FR-RBPs-related risk signature for the prognosis of BRCA, which will be of great significance in developing new therapeutic targets and prognostic molecular biomarkers for BRCA.

A novel risk score model based on pyroptosis-related genes for predicting survival and immunogenic landscape in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer worldwide, with high incidence and mortality. Pyroptosis, a form of inflammatory-regulated cell death, is closely associated with oncogenesis. Expression profiles of HCC were downloaded from the TCGA database and validated using the ICGC and GEO databases. Consensus clustering analysis was used to determine distinct clusters. The pyroptosis-related genes (PRGs) included in the pyroptosis-related signature were selected by univariate Cox regression and LASSO regression analysis. Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to estimate the prognostic potential of the model. The characteristics of infiltration of immune cells between different groups of HCC were explored. Two independent clusters were identified according to PRG expression. Cluster 2 showed upregulated expression, poor prognosis, increased immune cell infiltration and worse immunotherapy response than cluster 1. A prognostic risk signature consisting of five genes (GSDME, NOD1, PLCG1, NLRP6 and NLRC4) was identified. In the high-risk score group, HCC patients showed decreased survival rates. In particular, multiple clinicopathological characteristics and immune cell infiltration were significantly associated with the risk score. Notably, the 5 PRGs in the risk score have been implicated in carcinogenesis, immunological pathways and drug sensitivity. A prognostic signature comprising five PRGs can be used as a potential prognostic factor for HCC. The PRG-related signature provides an in-depth understanding of the association between pyroptosis and chemotherapy or immunotherapy for HCC patients.

An immune-related prognostic signature associated with immune landscape and therapeutic responses in gastric cancer.

Immune-related genes (IRGs) have attracted attention in recent years as therapeutic targets in various tumors. However, the role of IRGs in gastric cancer (GC) has not been clearly elucidated. This study presents a comprehensive analysis exploring the clinical, molecular, immune, and drug response features characterizing the IRGs in GC. Data were acquired from the TCGA and GEO databases. The Cox regression analyses were performed to develop a prognostic risk signature. The genetic variants, immune infiltration, and drug responses associated with the risk signature were explored using bioinformatics methods. Lastly, the expression of the IRS was verified by qRT-PCR in cell lines. In this manner, an immune-related signature (IRS) was established based on 8 IRGs. According to the IRS, patients were divided into the low-risk group (LRG) and high-risk group (HRG). Compared with the HRG, the LRG was characterized by a better prognosis, high genomic instability, more CD8+ T cell infiltration, greater sensitivity to chemotherapeutic drugs, and greater likelihood of benefiting from the immunotherapy. Moreover, the expression result showed good consistency between the qRT-PCR and TCGA cohort. Our findings provide insights into the specific clinical and immune features underlying the IRS, which may be important for patient treatment.

Identification of a novel Immune-Related prognostic model for patients with colorectal cancer based on 3 subtypes

BackgroundThe mechanism of immunity in the development of colorectal cancer (CRC) has been studied in-depth, but knowledge of its role in the treatment of CRC is limited. ObjectiveThis study aimed to classify CRC based on immunology and construct an immune-related prognostic model. MethodsNine expression profile datasets of CRC, comprising 1640 samples, were downloaded from the NCBI GEO database. Immune infiltration of CRC was estimated using 5 algorithms. Based on the relative infiltration level of immune cells, immune score, and stromal score, immunosubtype analysis of tumors was conducted. Differentially expressed genes (DEGs) between the two subtypes were screened, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Hematoxylin eosin (HE) staining, immunohistochemical (IHC) staining and qPCR were used to verify the correlation between DEGs and differentiation degree of cancer and the expression of Ki67. Subsequently, a risk signature was constructed based on the least absolute shrinkage and selection operator (LASSO) model. ResultsBased on the infiltration level, immune score, and stromal score of each immune cell, CRC was divided into three immune cell subtypes. Most immune checkpoint genes showed highly significant differences among the three cell subtypes, and most of the co-stimulatory and co-inhibitory molecules were lower in cluster 1 and the highest in cluster 3. Next, 50 common DEGs were determined from the intersections of the different subtypes. Among these common DEGs, 25 were identified to be relevant to the prognosis of CRC patients. The mRNA expressions of C5orf46, CYP1B1, MIR100HG, SFRP2 and CXCL13 was related to clinical prognostic indicators. Finally, these 5 DEGs were included in a prognostic risk signature model, which effectively identified high-risk groups among CRC patients in both the training and validation sets. ConclusionIn this study, CRCs were divided into three subtypes based on immunology, and the different subtypes led to different prognosis. Additionally, a prognostic model was constructed based on five immune-related DEGs to distinguish the three subtypes.

Integrating single-cell RNA-seq and bulk RNA-seq to construct prognostic signatures to explore the role of glutamine metabolism in breast cancer.

Although breast cancer (BC) treatment has entered the era of precision therapy, the prognosis is good in the case of comprehensive multimodal treatment such as neoadjuvant, endocrine, and targeted therapy. However, due to its high heterogeneity, some patients still cannot benefit from conventional treatment and have poor survival prognoses. Amino acids and their metabolites affect tumor development, alter the tumor microenvironment, play an increasingly obvious role in immune response and regulation of immune cell function, and are involved in acquired and innate immune regulation; therefore, amino acid metabolism is receiving increasing attention. Based on public datasets, we carried out a comprehensive transcriptome and single-cell sequencing investigation. Then we used 2.5 Weighted Co-Expression Network Analysis (WGCNA) and Cox to evaluate glutamine metabolism-related genes (GRGs) in BC and constructed a prognostic model for BC patients. Finally, the expression and function of the signature key gene SNX3 were examined by in vitro experiments. In this study, we constituted a risk signature to predict overall survival (OS) in BC patients by glutamine-related genes. According to our risk signature, BC patients can obtain a Prognostic Risk Signature (PRS), and the response to immunotherapy can be further stratified according to PRS. Compared with traditional clinicopathological features, PRS demonstrated robust prognostic power and accurate survival prediction. In addition, altered pathways and mutational patterns were analyzed in PRS subgroups. Our study sheds some light on the immune status of BC. In in vitro experiments, the knockdown of SNX3, an essential gene in the signature, resulted in a dramatic reduction in proliferation, invasion, and migration of MDA-MB-231 and MCF-7 cell lines. We established a brand-new PRS consisting of genes associated with glutamine metabolism. It expands unique ideas for the diagnosis, treatment, and prognosis of BC.

A novel ferroptosis-related gene signature for predicting prognosis in multiple myeloma.

Multiple myeloma (MM) is a highly malignant hematological tumor with a poor overall survival (OS). Due to the high heterogeneity of MM, it is necessary to explore novel markers for the prognosis prediction for MM patients. Ferroptosis is a form of regulated cell death, playing a critical role in tumorigenesis and cancer progression. However, the predictive role of ferroptosis-related genes (FRGs) in MM prognosis remains unknown. This study collected 107 FRGs previously reported and utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to construct a multi-genes risk signature model upon FRGs. The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were carried out to evaluate immune infiltration level. Drug sensitivity was assessed based on the Genomics of Drug Sensitivity in Cancer database (GDSC). Then the synergy effect was determined with Cell counting kit-8 (CCK-8) assay and SynergyFinder software. A 6-gene prognostic risk signature model was constructed, and MM patients were divided into high and low risk groups. Kaplan-Meier survival curves showed that patients in the high risk group had significantly reduced OS compared with patients in the low risk group. Besides, the risk score was an independent predictor for OS. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the risk signature. Combination of risk score and ISS stage had better prediction performance. Enrichment analysis revealed immune response, MYC, mTOR, proteasome and oxidative phosphorylation were enriched in high risk MM patients. We found high risk MM patients had lower immune scores and immune infiltration levels. Moreover, further analysis found that MM patients in high risk group were sensitive to bortezomib and lenalidomide. At last, the results of the in vitro experiment showed that ferroptosis inducers (RSL3 and ML162) may synergistically enhance the cytotoxicity of bortezomib and lenalidomide against MM cell line RPMI-8226. This study provides novel insights into roles of ferroptosis in MM prognosis prediction, immune levels and drug sensitivity, which complements and improves current grading systems.

FNBP4 is a Potential Biomarker Associated with Cuproptosis and Promotes Tumor Progression in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors that lacks an efficient therapeutic approach because of its elusive molecular mechanisms. This study aimed to investigate the biological function and potential mechanism of formin-binding protein 4 (FNBP4) in HCC. FNBP4 expression in tissues and cells were detected by quantitative real-time PCR (qRT‒PCR), Western blot, and immunohistochemistry (IHC). The Kaplan-Meier method was used to explore the correlation between the FNBP4 expression and clinical survival. MTT, EdU incorporation, colony formation, and Transwell assays were performed to evaluate the function of FNBP4 in cell proliferation and migration in vitro. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to explore the potential mechanism of FNBP4. The prognostic risk signature and nomogram were constructed to demonstrate the prognostic value of FNBP4. We found that FNBP4 was upregulated in patients with HCC and associated with poor overall survival (OS). Furthermore, knockdown of FNBP4 inhibited the proliferation and migration in HCC cells. Then, we performed a KEGG pathway analysis of the coexpressed genes associated with FNBP4 and found that FNBP4 may be associated with tumor-related signaling pathways and cuproptosis. We verified that FNBP4 could cause cell cycle progression and inactivation of the hippo signaling pathway. A prognostic risk signature containing three FNBP4-related differentially expressed cuproptosis regulators (DECRs) was established and can be used as an independent risk factor to evaluate the prognosis of patients with HCC. In addition, a nomogram including a risk score and clinicopathological factors was used to predict patient survival probabilities. FNBP4, as a potential biomarker associated with cuproptosis, promotes HCC cell proliferation and metastasis. We provide a new potential strategy for HCC treatment by targeting FNBP4.

Identification and validation of a novel angiogenesis-related gene signature for predicting prognosis in gastric adenocarcinoma.

Angiogenesis is a major promotor of tumor progression and metastasis in gastric adenocarcinoma (STAD). We aimed to develop a novel lncRNA gene signature by identifying angiogenesis-related genes to better predict prognosis in STAD patients. The expression profiles of angiogenesis-related mRNA and lncRNA genes were collected from The Cancer Genome Atlas (TCGA). Then, the "limma" package was used to identify differentially expressed genes (DEGs). The expression profiles of angiogenesis-related genes were clustered by consumusclusterplus. The Pearson correlation coefficient was further used to identify lncRNAs coexpressed with angiogenesis-related clustere genes. We used Lasso Cox regression analysis to construct the angiogenesis-related lncRNAs signature. Furthermore, the diagnostic accuracy of the prognostic risk signature were validated by the TCGA training set, internal test sets and external test set. We used multifactor Cox analysis to determine that the risk score is an independent prognostic factor different from clinical characteristics. Nomogram has been used to quantitatively determine personal risk in a clinical environment. The ssGSEA method or GSE176307 data were used to evaluate the infiltration state of immune cells or predictive ability for the benefit of immunotherapy by angiogenesis-related lncRNAs signature. Finally, the expression and function of these signature genes were explored by RT-PCR and colony formation assays. Among angiogenesis-related genes clusters, the stable number of clusters was 2.A total of 289 DEGs were identified and 116 lncRNAs were screened to have a significant coexpression relationship with angiogenic DEGs (P value<0.001 and |R| >0.5). A six-gene signature comprising LINC01579, LINC01094, RP11.497E19.1, AC093850.2, RP11.613D13.8, and RP11.384P7.7 was constructed by Lasso Cox regression analysis. The multifactor Cox analysis and Nomogram results showed that our angiogenesis-related lncRNAs signature has good predictive ability for some different clinical factors. For immune, angiogenesis-related lncRNAs signature had the ability to efficiently predict infiltration state of 23 immune cells and immunotherapy. The qPCR analysis showed that the expression levels of the six lncRNA signature genes were all higher in gastric adenocarcinoma tissues than in adjacent tissues. The functional experiment results indicated that downregulation of the expression of these six lncRNA signature genes suppressed the proliferation of ASG and MKN45 cells. Six angiogenesis-related genes were identified and integrated into a novel risk signature that can effectively assess prognosis and provide potential therapeutic targets for STAD patients.

A novel exosome-derived prognostic signature and risk stratification for breast cancer based on multi-omics and systematic biological heterogeneity

Tumor heterogeneity remains a major challenge for disease subtyping, risk stratification, and accurate clinical management. Exosome-based liquid biopsy can effectively overcome the limitations of tissue biopsy, achieving minimal invasion, multi-point dynamic monitoring, and good prognosis assessment, and has broad clinical prospects. However, there is still lacking comprehensive analysis of tumor-derived exosome (TDE)-based stratification of risk patients and prognostic assessment for breast cancer with systematic dissection of biological heterogeneity. In this study, the robust corroborative analysis for biomarker discovery (RCABD) strategy was used for the identification of exosome molecules, differential expression verification, risk prediction modeling, heterogenous dissection with multi-ome (6101 molecules), our ExoBCD database (306 molecules), and 53 independent studies (481 molecules). Our results showed that a 10-molecule exosome-derived signature (exoSIG) could successfully fulfill breast cancer risk stratification, making it a novel and accurate exosome prognostic indicator (Cox P = 9.9E-04, HR = 3.3, 95% CI 1.6–6.8). Interestingly, HLA-DQB2 and COL17A1, closely related to tumor metastasis, achieved high performance in prognosis prediction (86.35% contribution) and accuracy (Log-rank P = 0.028, AUC = 85.42%). With the combined information of patient age and tumor stage, they formed a bimolecular risk signature (Clinmin-exoSIG) and a convenient nomogram as operable tools for clinical applications. In conclusion, as an extension of ExoBCD, this study conducted systematic analyses to identify prognostic multi-molecular panel and risk signature, stratify patients and dissect biological heterogeneity based on breast cancer exosomes from a multi-omics perspective. Our results provide an important reference for in-depth exploration of the "biological heterogeneity - risk stratification - prognosis prediction".

Cuproptosis-related MTF1 inhibits kidney renal clear cell carcinoma progression by suppressing proliferation and regulating immune cell infiltration

Cuproptosis is a newly identified specific form of programmed cell death. Our study aimed to identify cuproptosis-related genes (CRGs) in patients with kidney renal clear cell carcinoma (KIRC) from the The Cancer Genome Atlas database and to evaluate CRG biological functions. Using lasso regression, we identified four KIRC prognosis-associated CRGs and constructed an associated prognostic risk signature. Kaplan-Meier curves showed that patients with high-risk scores had significantly lower survival times than patients with low-risk scores. Multivariate Cox analysis identified MTF1 and FDX1 as two independent overall survival CRGs. Moreover, qRT-PCR showed that MTF1 and FDX1 expression was downregulated in KIRC and knockdown of MTF1 and FDX1 significantly promoted KIRC cell proliferation and migration ability. In addition, the MTF1 level was positively correlated with immune cell infiltration and knockdown of MTF1 promoted tumor growth in vivo. We developed a signature of prognostic risk-associated CRGs that accurately predicted the prognostic status of KIRC patients. MTF1 and FDX1 were shown to be key CRGs. MTF1 acts as a tumor suppressor, and may be involved in the progression of KIRC by inhibiting proliferation and regulating immune cell infiltration.

A novel risk signature based on autophagy-related genes to evaluate tumor immune microenvironment and predict prognosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is considered a lethal disease due to the poor prognosis and the limited therapeutic efficacy. Much more attention is being attached to the role of autophagy in HCC, and the relationship between autophagy and tumor immune microenvironment. Therefore, we developed a novel prognostic risk signature based on autophagy-related genes (ARGs) for HCC patients using gene expression profiles collected from TCGA dataset. This 2-ARG-based signature (BIRC5 and SNRPB) was validated in ICGC and GSE14520 datasets, achieving a favorable performance in both overall survival and recurrence-free survival outcomes. Additionally, the autophagy gene signature could independently predict overall survival of HCC after adjusting clinicopathological indicators in three independent datasets. A nomogram including autophagy gene signature and TNM stage was built to serve clinical practice. Significantly, differential abundance of immune cells was shown in different risk groups and the critical immune checkpoints and immunosuppressive cytokines were upregulated in the autophagy high-risk group. Furthermore, the expression levels of proteins encoded by BIRC5 and SNRPB were validated in cell lines by western blot and in tissue samples by immunohistochemistry. In summary, the 2-ARG-based prognostic risk signature could be a useful tool to identify risk subgroups, evaluate immune microenvironment and provide therapeutic targets for HCC patients.