Signature For Overall Survival Prediction Research Articles

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer.

Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups. To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations. Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed. We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy. In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

A novel ferroptosis-related gene signature for overall survival prediction in patients with gastric cancer.

The global diagnosis rate and mortality of gastric cancer (GC) are among the highest. Ferroptosis and iron-metabolism have a profound impact on tumor development and are closely linked to cancer treatment and patient's prognosis. In this study, we identified six PRDEGs (prognostic ferroptosis- and iron metabolism-related differentially expressed genes) using LASSO-penalized Cox regression analysis. The TCGA cohort was used to establish a prognostic risk model, which allowed us to categorize GC patients into the high- and the low-risk groups based on the median value of the risk scores. Our study demonstrated that patients in the low-risk group had a higher probability of survival compared to those in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden (TMB) and a longer 5-year survival period when compared to the high-risk group. In summary, the prognostic risk model, based on the six genes associated with ferroptosis and iron-metabolism, performs well in predicting the prognosis of GC patients.

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

[This retracts the article DOI: 10.1155/2022/1063130.].

Clinical significance and immune infiltration analyses of a novel coagulation-related signature in ovarian cancer

Ovarian cancer (OV) is the most lethal gynecological malignancies worldwide. The coagulation cascade could induce tumor cell infiltration and contribute to OV progression. However, coagulation-related gene (CRG) signature for OV prognosis hasn’t been determined yet. In this study, we evaluated the prognostic value of coagulation scores through receiver operating characteristics (ROC) analysis and K-M curves, among OV patients at our institution. Based on the transcriptome data of TCGA-OV cohort, we stratified two coagulation-related subtypes with distinct differences in prognosis and tumor immune microenvironment (p < 0.05). Moreover, from the 6406 differentially-expressed genes (DEGs) between the GTEx (n = 180) and TCGA-OV cohorts (n = 376), we identified 138 potential CRGs. Through LASSO-Cox algorithm, we finally distinguished a 3-gene signature (SERPINA10, CD38, and ZBTB16), with promising prognostic ability in both TCGA (p < 0.001) and ICGC cohorts (p = 0.040). Stepwise, we constructed a nomogram based on the clinical features and coagulation-related signature for overall survival prediction, with the C-index of 0.6761, which was evaluated by calibration curves. Especially, based on tissue microarrays analysis, Quantitative real-time fluorescence PCR (qRT-PCR), and Western Blot, we found that aberrant upregulation of CRGs was related to poor prognosis in OV at both mRNA and protein level (p < 0.05). Collectively, the coagulation-related signature was a robust prognostic biomarker, which could provide therapeutic benefits for chemotherapy/immunotherapy and assist clinical decision in OV patients.

An integrated mRNA-lncRNA signature for overall survival prediction in cholangiocarcinoma.

The combination of mRNA and lncRNA profiles for establishing an integrated mRNA-lncRNA prognostic signature has remained unexplored in cholangiocarcinoma (CCA) patients. We utilized a training dataset of 36 samples from The Cancer Genome Atlas dataset and a validation cohort (GSE107943) of 30 samples from Gene Expression Omnibus. Two mRNAs (CFHR3 and PIWIL4) and 2 lncRNAs (AC007285.1 and AC134682.1) were identified to construct the integrated signature through a univariate Cox regression (P-value = 1.35E-02) and a multivariable Cox analysis (P-value = 3.07E-02). Kaplan-Meier curve showed that patients with low risk scores had notably prolonged overall survival than those with high risk scores (P-value = 4.61E-03). Subsequently, the signature was validated in GSE107943 cohort with an area under the curve of 0.750 at 1-year and 0.729 at 3-year. The signature was not only independent from diverse clinical features (P-value = 3.07E-02), but also surpassed other clinical characteristics as prognostic biomarkers with area under the curve of 0.781 at 3-year. Moreover, the weighted gene co-expression network analysis and gene enrichment analyses found that the integrated signature were associated with metabolic-related biological process and lipid metabolism pathway, which has been implicated in the pathogenesis of CCA. Taken together, we developed an integrated mRNA-lncRNA signature that had an independent prognostic value in the risk stratification of patients with CCA.

A Novel Neutrophil Extracellular Traps Signature for Overall Survival Prediction and Tumor Microenvironment Identification in Gastric Cancer.

Neutrophil extracellular traps (NETs) released by neutrophils are crucial for cancer development, metastasis, and can indicate gastric cancer (GC) patients' prognosis. This study reveals the relevance of NETs-related genes to GC through transcriptome analysis. We obtained transcriptome sequencing data of GC from UCSC Xena and screened prognostic NETs-related genes by GEPIA2 database. The signature for NETs was subsequently created using the LASSO-Cox regression. The clinical value of model was further explored using the nomogram and was externally validated by the GEO database. After that, we employed GO, KEGG, and GSEA enrichment analyses to evaluate the bio-functional enrichment and related pathways. Additionally, ESTIMATE, MCP counter, and ssGSEA scores were used to investigate the immunological microenvironment of GC patients. Finally, in the external cohort, neutrophil elastase (NE)-DNA complexes were measured by ELISA, and the prognostic value of NE-DNA in GC was investigated using Cox analysis. Seven NETs-associated genes (PDE4B, CD93, CTSG, IL6, ELANE, KCNJ15, and CRISPLD2) were filtered to establish the signature and participated in building the nomogram. In comparison to the high-risk group, the overall survival (OS) was much longer in the low-risk group (P=0.005). The validation cohort demonstrated the acceptable predictive ability of the nomogram. The signature was enriched in biological features such as extracellular matrix organization, epithelial-mesenchymal transition and inflammatory response. Moreover, there were substantial differences in immune cell infiltration across the different risk groups (p<0.001), especially the high-risk group having more immune cells that are engaged in the antigen presentation process and associated functions. Finally, in the external cohort, NE-DNA levels were shown to be an independent factor affecting OS prognosis (p=0.006). Overall, this research identified a novel signature based on seven NETs-associated genes to predict prognosis and identify tumor microenvironment of GC. And high NE-DNA level may be a critical factor in the poor OS associated with NETs.

A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas.

Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. In the context of the transfer learning approach, we aimed to train and test an MRI-based radiomics model for predicting survival in GBM patients and validate it in LGG patients. From each patient's 704 MRI-based radiomics features, we chose seventeen optimal radiomics signatures in the GBM training set (n = 71) and used these features in both the GBM testing set (n = 31) and LGG validation set (n = 107) for further analysis. Each patient's risk score, calculated based on those optimal radiomics signatures, was chosen to represent the radiomics model. We compared the radiomics model with clinical, gene status models, and combined model integrating radiomics, clinical, and gene status in predicting survival. The average iAUCs of combined models in training, testing, and validation sets were respectively 0.804, 0.878, and 0.802, and those of radiomics models were 0.798, 0.867, and 0.717. The average iAUCs of gene status and clinical models ranged from 0.522 to 0.735 in all three sets. The radiomics model trained in GBM patients can effectively predict the overall survival of GBM and LGG patients, and the combined model improved this ability.

A novel extrachromosomal circular DNA related genes signature for overall survival prediction in patients with ovarian cancer

ObjectiveOvarian cancer (OV) has a high mortality rate all over the world, and extrachromosomal circular DNA (eccDNA) plays a key role in carcinogenesis. We wish to study more about the molecular structure of eccDNA in the UACC-1598–4 cell line and how its genes are associated with ovarian cancer prognosis.MethodsWe sequenced and annotated the eccDNA by Circle_seq of the OV cell line UACC-1598–4. To acquire the amplified genes of OV on eccDNA, the annotated eccDNA genes were intersected with the overexpression genes of OV in TCGA. Univariate Cox regression was used to find the genes on eccDNA that were linked to OV prognosis. The least absolute shrinkage and selection operator (LASSO) and cox regression models were used to create the OV prognostic model, as well as the receiver operating characteristic curve (ROC) curve and nomogram of the prediction model. By applying the median value of the risk score, the samples were separated into high-risk and low-risk groups, and the differences in immune infiltration between the two groups were examined using ssGSEA.ResultsEccDNA in UACC-1598–4 has a length of 0-2000 bp, and some of them include the whole genes or gene fragments. These eccDNA originated from various parts of chromosomes, especially enriched in repeatmasker, introns, and coding regions. They were annotated with 2188 genes by Circle_seq. Notably, the TCGA database revealed that a total of 198 of these eccDNA genes were overexpressed in OV (p < 0.05). They were mostly enriched in pathways associated with cell adhesion, ECM receptors, and actin cytoskeleton. Univariate Cox analysis showed 13 genes associated with OV prognosis. LASSO and Cox regression analysis were used to create a risk model based on remained 9 genes. In both the training (TCGA database) and validation (International Cancer Genome Consortium, ICGC) cohorts, a 9-gene signature could successfully discriminate high-risk individuals (all p < 0.01). Immune infiltration differed significantly between the high-risk and low-risk groups. The model’s area under the ROC curve was 0.67, and a nomograph was created to assist clinician.ConclusionEccDNA is found in UACC-1598–4, and part of its genes linked to OV prognosis. Patients with OV may be efficiently evaluated using a prognostic model based on eccDNA genes, including SLC7A1, NTN1, ADORA1, PADI2, SULT2B1, LINC00665, CILP2, EFNA5, TOMM.

A Novel Platinum Resistance-Related Immune Gene Signature for Overall Survival Prediction in Patients with Ovarian Cancer.

Ovarian cancer (OV) is a highly heterogeneous gynecological tumor that makes the prognostic prediction challenging. Resistance to platinum-based chemotherapy is associated with a poor prognosis in OV. There seems to be an overlap between molecular mechanisms responsible for platinum resistance and immunogenicity in OV. However, the predictive role of platinum resistance-related immune genes for OV prognosis needs to be further explored. In our study, the mRNA expression data of OV patients with corresponding clinical information were collected from The Cancer Genome Atlas (TCGA) cohort and International Cancer Genome Consortium (ICGC) cohort. A multigene signature was constructed for OV patients in the TCGA cohort using the least absolute shrinkage and selection operator (LASSO) Cox regression model according to the optimal value of λ and was validated in the ICGC cohort. Furthermore, we performed functional analysis to explore the immune status between low- and high-risk groups based on the median value of the risk score for the multigene signature. Our data showed that there were 41.1% of the platinum resistance-related genes which differentially expressed between immune score low- and high-OV patients in the TCGA cohort. Univariate Cox regression analysis identified 30 differentially expressed genes (DEGs) associated with overall survival (OS) (P < 0.05). 14 genes were identified to construct a novel platinum resistance-related immune model for classifying OV patients into the low- and high- risk groups. Patients in the low-risk group showed significantly higher OS than those in the high-risk group (P < 0.0001 in the both TCGA and ICGC cohort), which was associated with different immune status for the two risk groups. A novel platinum resistance-related immune model can be used for prognostic prediction in OV. Targeting tumor immunity may be a therapeutic alternative for OV with platinum resistance.

A Novel 7-Methylguanosine (m7G)-Related Gene Signature for Overall Survival Prediction in Patient with Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common pathology type of renal cancer that has an abysmal prognosis. Although a crucial role for 7-methylguanosine modification in cancer cell development has been reported, its role in ccRCC remains uncertain. This study was conducted to determine the efficacy of predictive biomarkers based on m7G-related genes in ccRCC. Firstly, we extracted clinical data and gene expression profiles of ccRCC patients from publicly accessible databases. It identified that 22 of the m7G-related 34 genes were related to overall survival, and 5 of the 22 genes were significantly expressed differently in tumor tissues. Based on Lasso regression analysis, five optimal genes (CYFIP2, EIF4A1, NUDT1, NUDT10, and NUDT4) were chosen to build a new predictive risk model in the TCGA cohort. Validation was carried out with the E-MTAB-1980 cohort. Then, a prognostic nomogram was erected, including the m7G-related gene risk score, age, histological grade, and stage status. Further studies and analysis showed that immune cell infiltration might be associated with the m7G-related risk genes. In addition, the relationship between gene expression and drug response was evaluated by the Pearson correlation test. Therefore, the risk signature with five selected m7G-related genes may be a promising prognostic biomarker and contribute to standardized prognostic assessment for ccRCC.

A novel cuproptosis-related gene signature for overall survival prediction in uterine corpus endometrial carcinoma (UCEC)

Cuproptosis is a copper-dependent model of cell death involved in tumor genesis and progression. Its roles in uterine corpus endometrial carcinoma (UCEC) remains elusive. Here, we aimed to explore the expression and prognostic values of cuprotosis-related genes (CRGs) in UCEC. Expression profiles and clinical data of UCEC were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into testing or training cohort (1:1 ratio). The CRG signature was identified by LASSO regression analysis. The differentially expressed genes and their functional enrichment analysis were performed by the “limma” R package and Metascape, respectively. The immunocytes infiltration was measured by TIMER, and “GSVA” R package. In total, seven differentially expressed prognostic genes of CRGs in UCEC were identified, and four genes (GLS, CDKN2A, PC, and SUCLG1) were selected to construct a predictive model in training cohort. UCEC patients from training and testing cohorts were further divided into high- or low-risk groups according to the median risk score. High-risk group favored poor prognosis compared to low-risk group. Functional enrichment analysis revealed this CRG signature were got involved in the process of cell-cell adhesion and immune activities (e.g., IL-1 signaling pathway, cellular response to cytokine stimulus). Further analyses revealed there were significant differences between high- and low-risk patients regarding immunocytes infiltration, chemokines, and chemokine receptors. Finally, the expression and biological functions of identified CRGs were confirmed by UCEC samples and experimental methods in vitro. In summary, the CRG signature was significantly correlated with patients' overall survival, which could provide insights into the diagnosis and prognosis prediction for UCEC.

Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer.

The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan-Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan-Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist.

Somatic Super-Enhancer Epigenetic Signature for Overall Survival Prediction in Patients with Breast Invasive Carcinoma.

To analyze genome-wide super-enhancers (SEs) methylation signature of breast invasive carcinoma (BRCA) and its clinical value. Differential methylation sites (DMS) between BRCA and adjacent tissues from The Cancer Genome Atlas (TCGA) database were identified by using ChAMP package in R software. Super-enhancers were identified sing ROSE software. Overlap analysis was used to assess the potential DMS in SEs region. Feature selection was performed by Cox regression and least absolute shrinkage and selection operator (LASSO) algorithm based on TCGA training cohort. Prognosis model validation was performed in TCGA training cohort, TCGA validation cohort, and gene expression omnibus (GEO) test cohort. The gene ontology and KEGG analysis revealed that SEs target genes were significantly enriched in cell-migration-associated processes and pathways. A total of 83 654 DMS were identified between BRCA and adjacent tissues. Around 2397 DMS in SEs region were identified by overlap study and used to feature selection. By using Cox regression and LASSO algorithm, 42 features were selected to develop a clinical prediction model (CPM). Both training (TCGA) and validation cohorts (TCGA and GEO) show that the CPM has ideal discrimination and calibration. The CPM based on DMS at SE regions has ideal discrimination and calibration, which combined with tumor node metastasis (TNM) stage could improve prognostication, and thus contribute to individualized medicine.

Integrated analysis of ferroptosis-related gene signature for overall survival prediction in Asian patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent types of cancers in Asia. Accumulating evidence suggests that ferroptosis is a non-apoptotic form of cell death, and has played an important role in cancer biology. Based on the manually curated ferroptosis-related gene set and TCGA-LIHC dataset of Asian patients, we used DESeq2, Kaplan-Meier analysis, and univariate Cox regression to identify differentially expressed ferroptosis-related genes with significantly prognostic capacity. A risk signature was constructed based on the selected genes for predicting the survival of HCC patients in Asia. The survival prediction accuracy was confirmed by the time-dependent receiver operating characteristic (ROC) curve analysis. Gene set variation analysis (GSVA) was used to explore the functional associations of the signature. Ferroptosis potential index (FPI) and xCell algorithm was applied to quantify ferroptosis and immune cell infiltration, respectively. Two independent datasets from the GEO and the ICGC database were used for external validation. The ferroptosis-related signature could accurately predict the survival outcomes of HCC patients in Asian (p value < 0.0001). We showed that the signature was an independent factor and was beneficial in elevating risk stratification of current clinicopathologic features, such as the amount of alpha-fetoprotein (AFP) and residual tumor classification. Functional characterization showed that critical processes in tumorigenesis belonged to the high-risk groups, for example inflammatory response, which may be the main driver of HCC. The high-risk group had higher FPIs and infiltrations of macrophages and T-helper cells than the low-risk group. Furthermore, two independent cohorts confirmed the prognostic value of our signature. Overall, our results demonstrated potential application of ferroptosis-related genes as independent biomarkers in Asian HCC patients. Targeting ferroptosis may be clinically useful beyond known clinicopathological factors and provide benefit in immunotherapy.

A novel cuproptosis-related gene signature for overall survival prediction in patients with hepatocellular carcinoma

Prognosis prediction is difficult in hepatocellular carcinoma (HCC) due to high heterogeneity and complex etiology. It has recently been discovered that cuproptosis is a type of programmed cell death. However, its significance for HCC is still unclear. We analyzed mRNA expression profiles and clinical information from public databases to determine whether cuproptosis-related genes are associated with improved prognoses for HCC patients. The training cohort consisted of HCC patients from The Cancer Genome Atlas (TCGA), and the validation cohort relied on the International Cancer Genome Consortium (ICGC) database. We constructed a signature containing four genes using the least absolute shrinkage and selection operator (LASSO) COX regression model for calculating risk scores. Two risk groups were formed based on the median score. A significant improvement in survival was observed in the low-risk group compared to the high-risk. The multivariate Cox regression analysis showed that the risk score was an independent predictor of overall survival (OS). Further confirmation of the predictive accuracy of this signature is provided by receiver operating characteristic (ROC) analysis. Functional analysis revealed differences in immune status between the two risk groups. All the results described above were confirmed in the validation cohort. Therefore, a novel cuproptosis-related signature has the potential as a prognostic biomarker for HCC patients. Drugs developed to target cuproptosis-related genes may open up new pathways for treating HCC.

A novel ferroptosis‑related gene signature for overall survival prediction and immune infiltration in patients with breast cancer.

Breast cancer is the most prevalent type of cancer among women worldwide. The heterogeneous nature of breast cancer poses a serious challenge for prognostic prediction and individualized therapies. Recently, ferroptosis, an iron-dependent form of programmed cell death, has been reported to serve a significant role in the regulation of the biological behavior of tumors. Several studies have revealed the prognostic significance of the ferroptosis-related gene (FRG) model; however, additional efforts are required to elucidate the details. Moreover, genes that modulate ferroptosis may be promising candidate bioindicators in cancer therapy. The present study systematically assessed the expression profiles of FRGs to reveal the relationship between FRGs and the prognostic features of patients with breast cancer based on data obtained from the Gene Expression Omnibus and Molecular Taxonomy of Breast Cancer International Consortium. Using a non-negative matrix factorization clustering method, patients with breast cancer were classified into two sub-groups (cluster 1 and cluster 2) based on the expression of FRGs. Furthermore, Cox regression, and least absolute shrinkage and selection operator methods were used to construct a risk score formula comprised of nine genes, which stratified patients with breast cancer into two risk groups. Patients belonging to the high-risk group exhibited significantly shorter overall survival (OS) time compared with patients in the low-risk group. The prognostic value of this signature was further verified in the training and validation cohorts. The results for univariate and multivariate Cox regression analyses indicated that risk score acted as an independent predictor for OS. Subsequently, a nomogram was constructed. Receiver operating characteristic analysis further confirmed that the resulting nomogram exhibited powerful discriminatory ability. Functional analysis revealed that the immune environment differed notably between the two groups and indicated an association between ferroptosis and breast cancer proliferation, migration and drug resistance. Taken together, the present study demonstrated that FRGs were significantly associated with breast cancer progression, and thus could be used as novel biomarkers for prognostic prediction and individualized treatment of patients with breast cancer.

Identification and validation of a hypoxia-immune signature for overall survival prediction in lung adenocarcinoma.

Objective: The interaction between immunity and hypoxia in tumor microenvironment (TME) has clinical significance, and this study aims to explore immune-hypoxia related biomarkers in LUAD to guide accurate prognosis of patients. Methods: The LUAD gene expression dataset was downloaded from GEO and TCGA databases. The immune-related genes and hypoxia-related genes were acquired from ImmPort and MSigDB databases, respectively. Genes related to immune and hypoxia in LUAD were obtained by intersection. The significantly prognostic genes in LUAD were obtained by LASSO and Cox regression analyses and a prognostic model was constructed. Kaplan-Meier and receiver operating characteristic curves were generated to evaluate and validate model reliability. Single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) were employed to analyze immune cell infiltration and pathway differences between high- and low-risk groups. Nomogram and calibration curves for survival curve and clinical features were drawn to measure prognostic value of the model. Results: The prognosis model of LUAD was constructed based on seven immune-hypoxia related genes: S100P, S100A16, PGK1, TNFSF11, ARRB1, NCR3, and TSLP. Survival analysis revealed a poor prognosis in high-risk group. ssGSEA result suggested that activities of immune cells in high-risk group was remarkably lower than in low-risk group, and GSVA result showed that immune-related pathway was notably activated in low-risk group. Conclusion: Immune-hypoxia related genes were found to be prognostic biomarkers for LUAD patients, based on which a 7-immune-hypoxia related gene-signature was constructed. This model can assess immune status of LUAD patients, and provide clinical reference for individualized prognosis, treatment and follow-up of LUAD patients.

Development and validation of a TRP-related gene signature for overall survival prediction in lung adenocarcinoma.

The transient receptor potential (TRP) channel is a type of channel protein widely distributed in peripheral and central nervous systems. Genes encoding TRP can be regulated by natural aromatic substances and serve as a therapeutic target for many diseases. However, the role of TRP-related genes in lung adenocarcinoma (LUAD) remains unclear. In this study, we used data from TCGA to screen and identify 17 TRP-related genes that are differentially expressed between LUAD and normal lung tissues. Based on these differentially expressed genes (DEGs), we classified all patients with LUAD into two subtypes. Significant differences in prognosis, clinical features, and immune cell infiltration characteristics were observed between the two subtypes. Subsequently, a prognostic signature with 12 genes was established by applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, and all patients with LUAD were classified into low- and high-risk groups. Patients with LUAD in the low-risk group had a significantly longer survival time than those in the high-risk group (p < 0.001), which was confirmed by LUAD data from the GSE72094 and GSE68571 validation datasets. Combined with clinical characteristics, the risk score was found to be an independent predictor of overall survival (OS) in patients with LUAD. Additionally, patients with high TRP scores exhibited poorer clinical characteristics and immune status while showing a sensitive response to chemotherapeutic agents. In conclusion, the TRP score is a promising biomarker for determining the prognosis, molecular subtype, tumor microenvironment, and guiding personalized treatment in patients with LUAD.

A Novel Cuprotosis-Related Gene FDX1 Signature for Overall Survival Prediction in Clear Cell Renal Cell Carcinoma Patients.

Background Ferredoxin 1 (FDX1) is a newly discovered gene regulating cuprotosis. However, the effect of FDX1 expression on clear renal cell carcinoma (ccRCC) is unknown. Methods Gene expression profiles and clinical data of ccRCC patients were downloaded from the Cancer Genome Atlas (TCGA) database. The differences in FDX1 expression between ccRCC and nonneoplastic tissues adjacent to cancer were analyzed by R software. The results were validated by GEO data, quantitative real-time polymerase chain reaction (qRT-PCR), western blotting (WB), and immunohistochemistry (IHC). Chi-square test was used to analyze the clinical pathological parameters. Kaplan-Meier survival analysis and Cox proportional hazard regression model selection were used to evaluate the effect of FDX1 expression on overall survival. Protein interaction networks were used to analyze other proteins that interact with FDX1. Signal pathway analysis was performed for possible FDX1 enrichment using GSEA and ssGSEA algorithms. Pan-cancer analysis of FDX1 was carried out through TCGA database. Results The FDX1 expression in nontumor tissues was significantly higher than that in ccRCC, and the expression difference was verified by GEO data, qRT-PCR, WB, and IHC. The high expression of FDX1 was significantly related to the well overall survival rate (P < 0.05). The chi-square test showed that the high expression of FDX1 was related to gender, TNM stage, T stage, lymph node metastasis, and pathological grade. Additionally, the FDX1 expression level was different in groups classified based on pathological grade, gender, TNM stage, T stage, lymph node metastasis, and distant metastasis (P < 0.05). The multivariate analysis revealed the high expression of FDX1 as an important independent predictor for overall survival. STRING database results showed that LIAS and LIPT1 may interact with FDX1 in the PPI network, which are also involved in the regulation of cuprotosis. The GSEA and ssGSEA results showed that the FDX1 was enriched in the anticancer pathway. The FDX1 high expression is associated with better prognosis in many cancers, as revealed by pan-cancer analysis. Conclusion FDX1 may play a role in the progression of ccRCC as a tumor suppressor gene. It can be used as a potential prognostic indicator and therapeutic target of ccRCC. However, the cuprotosis regulatory role in the development of ccRCC needs to be further verified.

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma.

Background: Ferroptosis is associated with cancer initiation and progression. However, the molecular mechanism and prognostic value of ferroptosis-related genes in lung squamous cell carcinoma (LUSC) are poorly understood.Methods: The mRNA expression profiles, methylation data, and clinical information of patients with LUSC were downloaded from TCGA and GEO database. Ferroptosis-related differentially expressed genes (DEGs) were identified between cancerous and non-cancerous tissues, and their prognostic value was systemically investigated by bioinformatic analyses.Results: A ferroptosis-related gene signature (ALOX5, TFRC, PHKG2, FADS2, NOX1) was constructed using multivariate Cox regression analysis and represented as a risk score. Overall survival (OS) probability was significantly lower in the high-risk group than in the low-risk group (P<0.001), and receiver operating characteristic curve showed a good predictive capacity (AUC = 0.739). The risk score was an independent prognostic factor for LUSC. A nomogram was constructed to predict the OS probabilities at 1, 3, and 5 years. High-risk score was associated with increased immune infiltration, lower methylation levels, higher immune checkpoint genes expression levels, and better chemotherapy response. Cell adhesion molecules, focal adhesion, and extracellular matrix receptor interaction were the main pathways in the high-risk group. The signature was validated using the TCGA test cohort, entire TCGA cohort, GSE30219, GSE157010, GSE73403, and GSE4573 datasets. The gene disorders in patients with LUSC were validated using real-time PCR and single-cell RNA sequencing analysis.Conclusions: A ferroptosis-related gene signature was constructed to predict OS probability in LUSC. This could facilitate novel therapeutic methods and guide individualized therapy.