Lasso-penalized Cox Regression Analysis Research Articles

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.

Activated CD4 T cells/Tregs derived immune-metabolism signature provide precise prognosis assessment for gastric cancer and beneficial for treatment option

BackgroundThe prognostic significance of the ratio of activated CD4 T cells to Tregs infiltrating tumor tissues in gastric cancer (GC) remains unknown. Materials and methodsFor the quantification of infiltration of immune cells, the ssGSEA algorithm, which is a single sample gene set enrichment analysis, was utilized. Group A was defined as having activated CD4 T cells/Tregs >1, while group B was defined as having activated CD4 T cells/Tregs <1. To compare the overall survival (OS) of the two groups, the Kaplan-Meier survival analysis was employed. The R package 'limma' was used to identify the immune and metabolism related genes that were expressed differentially between the two groups, with a false discovery rate (FDR) less than 0.05. The risk score (RS) was constructed by combining univariate Cox regression analysis, LASSO penalized Cox regression analysis, and multivariate Cox regression analysis. The median RS was used to classify high-risk (HR) and low-risk (LR) groups. ResultsA predicted unfavorable outcome of GC was observed when the ratio of activated CD4 T cells to Tregs was less than 1. Our proposed RS was utilized for prognostic risk categorization in ten distinct independent cohorts (TCGA-STAD, n = 371; GSE84437, n = 433; GSE26253, n = 432; GSE13861, n = 65; GSE15459, n = 192; GSE26899, n = 93; GSE26901, n = 109; GSE28541, n = 40; GSE34942, n = 56; GSE62254, n = 300) and exhibited exceptional precision. In terms of tumor microenvironment (TME) and treatment strategies, compared to the LR group, the HR group was characterized by a higher infiltration levels of stromal cells, Tregs, macrophages, Tfh, mast cells, and NK cells, inclined to activated CD4 T cells/Tregs <1, and exhibited insensitivity to immunotherapy and multiple chemotherapy drugs. In relation to the potential molecular mechanism, the excessive activation of oncogenic pathways such as MAPK, hedgehog, WNT, calcium, and TGF-β signaling pathways may accelerate the malignant progression of GC by stimulating angiogenesis, promoting EMT, and altering ECM. Conversely, the overactivation of the P53 pathway is likely to inhibit tumor proliferation by regulating the cell cycle. ConclusionThe immune-metabolism signature associated with the ratio of activated CD4 T cells and Tregs could be used to assess prognosis, TME, and treatment strategies in GC patients.

Establishment of a prognostic signature based on fatty acid metabolism genes in HCC associated with hepatitis B

BackgroundHepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) is one of the most common and deadly cancer and often accompanied by varying degrees of liver damage, leading to the dysfunction of fatty acid metabolism (FAM). This study aimed to investigate the relationship between FAM and HBV-associated HCC and identify FAM biomarkers for predicting the prognosis of HBV-associated HCC.MethodsGene Set Enrichment Analysis (GSEA) was used to analyze the difference of FAM pathway between paired tumor and adjacent normal tissue samples in 58 HBV-associated HCC patients from the Gene Expression Omnibus (GEO) database. Next, 117 HBV-associated HCC patients from The Cancer Genome Atlas (TCGA) database were analyzed to establish a prognostic signature based on 42 FAM genes. Then, the prognostic signature was validated in an external cohort consisting of 30 HBV-associated HCC patients. Finally, immune infiltration analysis was performed to evaluate the FAM-related immune cells in HBV-associated HCC.ResultsAs a result, FAM pathway was clearly downregulated in tumor tissue of HBV-associated HCC, and survival analysis demonstrated that 12 FAM genes were associated with the prognosis of HBV-associated HCC. Lasso-penalized Cox regression analysis identified and established a five-gene signature (ACADVL, ACAT1, ACSL3, ADH4 and ECI1), which showed effective discrimination and prediction for the prognosis of HBV-associated HCC both in the TCGA cohort and the validation cohort. Immune infiltration analysis showed that the high-risk group, identified by FAM signature, of HBV-associated HCC had a higher ratio of Tregs, which was associated with the prognosis.ConclusionsCollectively, these findings suggest that there is a strong connection between FAM and HBV-associated HCC, indicating a potential therapeutic strategy targeting FAM to block the accumulation of Tregs into the tumor microenvironment of HBV-associated HCC.

A cuproptosis-related lncRNA signature for predicting prognosis and immunotherapy response of lung adenocarcinoma.

Copper-induced cell death (cuproptosis) is a new regulatory cell death mechanism. Long noncoding RNAs (lncRNAs) are related to tumor immunity and metastasis. However, the correlation of cuproptosis-related lncRNAs with the immunotherapy response and prognosis of lung adenocarcinoma (LUAD) patients is not clear. We obtained the clinical characteristics and transcriptome data from TCGA-LUAD dataset (containing 539 LUAD and 59 paracancerous tissues). By utilizing LASSO-penalized Cox regression analysis, we identified a prognostic signature composed of cuproptosis-related lncRNAs. This signature was then utilized to segregate patients into two different risk categories based on their respective risk scores. The identification of differentially expressed genes (DEGs) between high- and low-risk groups was carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We evaluated the immunotherapy response by analyzing tumor mutational burden (TMB), immunocyte infiltration and Tumor Immune Dysfunction and Exclusion (TIDE) web application. The "pRRophetic" R package was utilized to conduct further screening of potential therapeutic drugs for their sensitivity. We ultimately identified a prognostic risk signature that includes six cuproptosis-related lncRNAs (AP003778.1, AC011611.2, CRNDE, AL162632.3, LY86-AS1, and AC090948.1). Compared with clinical characteristics, the signature was significantly correlated with prognosis following the control of confounding variables (HR = 2.287, 95% CI = 1.648-3.174, p ˂ 0.001), and correctly predicted 1-, 2-, and 3-year overall survival (OS) rates (AUC value = 0.725, 0.715, and 0.662, respectively) in LUAD patients. In terms of prognosis, patients categorized as low risk exhibited more positive results in comparison to those in the high-risk group. The enrichment analysis showed that the two groups had different immune signaling pathways. Immunotherapy may offer a more appropriate treatment option for high-risk patients due to their higher TMB and lower TIDE scores. The higher risk score may demonstrate increased sensitivity to bexarotene, cisplatin, epothilone B, and vinorelbine. Based on cuproptosis-related lncRNAs, we constructed and validated a novel risk signature that may be used to predict immunotherapy efficacy and prognosis in LUAD patients.

Immunotherapy Efficacy-related Risk Classifier Differentiate Prognostic Characteristics of Gastric Cancer-A Large-scale Retrospective Study.

Prognostic signatures related to the efficacy of immunotherapy have not been determined in gastric cancer (GC). We identified the differentially expressed genes between the CR/PR and SD/PD groups with the R package "limma" (false discovery rate <0.05) in the IMvigor210 data set. The GSE13861 (n=65), GSE15459 (n=192), GSE26899 (n=93), GSE26901 (n=109), GSE28541 (n=40), GSE34942 (n=56), and GSE62254 (n=300) cohorts were merged into a training cohort (n=855). Univariate Cox regression analysis, LASSO penalized Cox regression analysis, and multivariate Cox regression analysis were jointly applied to construct the prognostic model. The Cancer Genome Atlas (TCGA)-STAD (n=371), GSE84437 (n=433), GSE26253 (n=432), and IMvigor210 (n=348) cohorts were utilized for external validation. The GC patients were divided into 16 subgroups according to clinical features for universal applicability validation. Repeated validation confirmed that the overall survival of the high-risk (HR) group was significantly reduced compared with that of the low-risk (LR) group. The HR group showed a higher infiltration abundance of regulatory T cells, macrophages, T follicular helper cells, and natural killer T cells, whereas the infiltration levels of activated CD4 T cells and monocytes were upregulated in the LR group. The calcium, TGF-β, MAPK, Hedgehog, and KRAS signaling pathways were overactivated in the HR group, while the hallmarks related to DNA damage repair and metabolism were enriched in the LR group. In addition, the LR group had high tumor mutation burden, FLG, and OBSCN mutations. A prognostic risk classifier for GC patients was identified and validated by carrying out a multicenter retrospective study.

Immune-related lncRNAs pairs prognostic score model for prediction of survival in acute myeloid leukemia patients

Acute myeloid leukemia (AML) is one of the most common malignant and aggressive hematologic tumors, and risk stratification is indispensable to ensure proper treatment. But immune-related long noncoding RNAs (ir-lncRNAs) pairs prognostic risk models used to stratify AML have yet to be reported. In this study, we established a prognostic risk model based on eight ir-lncRNAs pairs using LASSO-penalized Cox regression analysis and successfully validated the model in an independent cohort. According to risk scores, patients were divided into a high-risk group and a low-risk group. High-risk patients presented more tumor mutation frequency and higher expression of human leukocyte antigen (HLA)-related genes and immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) indicated that the transforming growth factors β (TGFβ) pathway was activated in the high-risk group; meanwhile, we found that TGFβ1 mRNA levels were significantly elevated in AML patients and correlated with poor prognosis, which is closely related to drug resistance. Consistently, in vitro studies found that exogenous TGFβ1 can protect AML cells from chemotherapy-induced apoptosis. Collectively, we developed an ir-lncRNA prognostic model that helps predict the prognosis of AML patients and provides valuable information about their response to immune checkpoint inhibitors, and we found that increased TGFβ1 levels resulting in chemoresistance may be one of the leading causes of treatment failure in high-risk AML patients.

Hypoxia-Related lncRNA Prognostic Model of Ovarian Cancer Based on Big Data Analysis.

Hypoxia is regarded as a key factor in promoting the occurrence and development of ovarian cancer. In ovarian cancer, hypoxia promotes cell proliferation, epithelial to mesenchymal transformation, invasion, and metastasis. Long non-coding RNAs (lncRNAs) are extensively involved in the regulation of many cellular mechanisms, i.e., gene expression, cell growth, and cell cycle. In our study, a hypoxia-related lncRNA prediction model was established by applying LASSO-penalized Cox regression analysis in public databases. Patients with ovarian cancer were divided into two groups based on the median risk score. The survival rate was analyzed in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets, and the mechanisms were investigated. Through the prognostic analysis of DElncRNAs (differentially expressed long non-coding RNAs), a total of 5 lncRNAs were found to be closely associated with OS (overall survival) in ovarian cancer patients. It was evaluated through Kaplan-Meier analysis that low-risk patients can live longer than high-risk patients (TCGA: p = 1.302e - 04; ICGC: 1.501e - 03). The distribution of risk scores and OS status revealed that higher risk score will lead to lower OS. It was evaluated that low-risk group had higher immune score (p = 0.0064) and lower stromal score (p = 0.00023). It was concluded that a hypoxia-related lncRNA model can be used to predict the prognosis of ovarian cancer. Our designed model is more accurate in terms of age, grade, and stage when predicting the overall survival of the patients of ovarian cancer.

Computed Tomography-Based Texture Analysis as a Predictor of Survival in Patients with T-Cell Lymphomas

Background: T-cell lymphomas constitute a heterogeneous group of hematological malignancies with global poor outcomes. Computed tomography (CT)-based texture analysis (CTTA) is a promising technique to predict the survival of these patients. Objectives: The present study aimed to investigate whether CTTA features on the pretreatment unenhanced CT scans of 18F-fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT) examination can predict the survival of patients with T-cell lymphomas. Patients and Methods: In this retrospective cohort study, patients with T-cell lymphomas, undergoing pretreatment 18F-FDG PET/CT scan during 2008 - 2019, were included, and their clinical and biological characteristics were collected. The mean gray value, entropy, kurtosis, skewness, and standard deviation were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales in up to five lesions per patient, indicating a high focal uptake on 18F-FDG PET scan. A Lasso-penalized Cox regression analysis was performed to identify independent predictors of overall survival (OS), progression-free survival at 24 months (PFS24), and PFS. Results: A total of 23 patients (7 females and 16 males; median age, 69 years; age range, 33 - 86 years) were included in this study. The CTTA was performed for 60 lesions. The median OS and PFS were 391 days (range, 10 - 3,463 days) and 268 days (range, 10 - 2,321 days), respectively. No CT texture parameter was associated with PFS or PFS24. The standard deviation at a coarse filter scale was independently associated with a poor OS (hazard ratio [HR] = 1.009, confidence interval [CI]: 1.0012 - 1.016, P = 0.02). A value above 143 was associated with a poor prognosis with a specificity of 81%. Conclusion: The pretreatment CTTA-derived tumor standard deviation at a coarse filter scale may be a predictive biomarker of OS in patients with T-cell lymphomas.

A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape.

BackgroundLung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high mortality each year. The tumor immune cell infiltration (ICI) is closely associated with clinical outcome with LUAD patients. The present study was designed to construct a gene signature based on the ICI of LUAD to predict prognosis.MethodsDownloaded the raw data of three cohorts of the TCGA-LUAD, GSE72094, and GSE68465 and treat them as training cohort, validation cohort one, and validation cohort two for this research. Unsupervised clustering detailed grouped LUAD cases of the training cohort based on the ICI profile. The univariate Cox regression and Kaplan–Meier was adopted to identify potential prognostic genes from the differentially expressed genes recognized from the ICI clusters. A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis. The Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS were constructed to assess the ability to predict the prognosis and effects of clinical variables in another two independent validation cohorts. More innovatively, we searched similar papers in the most recent year and made comprehensive comparisons with ours. GSEA was used to discover the related signaling pathway. The immune relevant signature correlation identification and immune infiltrating analysis were used to evaluate the potential role of the signature for immunotherapy and recognize the critical immune cell that can influence the signature's prognosis capability.ResultsA signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients. The thirteen-gene signature exhibited independence in evaluating the prognosis of LUAD patients in our training and validation cohorts. Compared to our predecessors, our model has an advantage in predictive power. Nine well know immunotherapy targets, including TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, and PDCD1 were recognized correlating with our signature. The mast cells were found to play vital parts in backing on the thirteen-gene signature's outcome predictive capacity.ConclusionsCollectively, the current study indicated a robust thirteen-gene signature that can accurately predict LUAD prognosis, which is superior to our predecessors in predictive ability. The immune relevant signatures, TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, PDCD1, and mast cells infiltrating were found closely correlate with the thirteen-gene signature's power.

Super-Enhancer-Associated nine-gene prognostic score model for prediction of survival in chronic lymphocytic leukemia patients.

Chronic lymphocytic leukemia (CLL) is a type of highly heterogeneous mature B-cell malignancy with various disease courses. Although a multitude of prognostic markers in CLL have been reported, insights into the role of super-enhancer (SE)–related risk indicators in the occurrence and development of CLL are still lacking. A super-enhancer (SE) is a cluster of enhancers involved in cell differentiation and tumorigenesis, and is one of the promising therapeutic targets for cancer therapy in recent years. In our study, the CLL-related super-enhancers in the training database were processed by LASSO-penalized Cox regression analysis to screen a nine-gene prognostic model including TCF7, VEGFA, MNT, GMIP, SLAMF1, TNFRSF25, GRWD1, SLC6AC, and LAG3. The SE-related risk score was further constructed and it was found that the predictive performance with overall survival and time-to-treatment (TTT) was satisfactory. Moreover, a high correlation was found between the risk score and already known prognostic markers of CLL. In the meantime, we noticed that the expressions of TCF7, GMIP, SLAMF1, TNFRSF25, and LAG3 in CLL were different from those of healthy donors (p < 0.01). Moreover, the risk score and LAG3 level of matched pairs before and after treatment samples varied significantly. Finally, an interactive nomogram consisting of the nine-gene risk group and four clinical traits was established. The inhibitors of mTOR and cyclin-dependent kinases (CDKs) were considered effective in patients in the high-risk group according to the pRRophetic algorithm. Collectively, the SE-associated nine-gene prognostic model developed here may be used to predict the prognosis and assist in the risk stratification and treatment of CLL patients in the future.

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with Wilms tumour.

Wilms tumour (WT) is the most common kidney malignancy in children. Chemoresistance is the leading cause of tumour recurrence and poses a substantial therapeutic challenge. Increasing evidence has underscored the role of the tumour immune microenvironment (TIM) in cancers and the potential for immunotherapy to improve prognosis. There remain no reliable molecular markers for reflecting the immune landscape and predicting patient survival in WT. Here, we examine differences in gene expression by high-throughput RNA sequencing, focused on differentially expressed immune-related genes (IRGs) based on the ImmPort database. Via univariate Cox regression analysis and Lasso-penalized Cox regression analysis, IRGs were screened out to establish an immune signature. Kaplan-Meier curves, time-related ROC analysis, univariate and multivariate Cox regression studies, and nomograms were used to evaluate the accuracy and prognostic significance of this signature. Furthermore, we found that the immune signature could reflect the immune status and the immune cell infiltration character played in the tumour microenvironment (TME) and showed significant association with immune checkpoint molecules, suggesting that the poor outcome may be partially explained by its immunosuppressive TME. Remarkably, TIDE, a computational method to model tumour immune evasion mechanisms, showed that this signature holds great potential for predicting immunotherapy responses in the TARGET-wt cohort. To decipher the underlying mechanism, GSEA was applied to explore enriched pathways and biological processes associated with immunophenotyping and Connectivity map (CMap) along with DeSigN analysis for drug exploration. Finally, four candidate immune genes were selected, and their expression levels in WT cell lines were monitored via qRT-PCR. Meanwhile, we validated the function of a critical gene, NRP2. Taken together, we established a novel immune signature that may serve as an effective prognostic signature and predictive biomarker for immunotherapy response in WT patients. This study may give light on therapeutic strategies for WT patients from an immunological viewpoint.

The prognostic value and immune landscapes of m1A/m5C/m6A-associated lncRNA signature in osteosarcoma.

RNA methylation modifications, mainly including N1-methyladenosine (m1A), 5-methylcytosine (m5C), and N6-methyladenosine (m6A), are widely existed in osteosarcoma and involved in the biological processes of cancers. However, there is still no study regarding the relationship between osteosarcoma and m1A/m5C/m6A-associated long non-coding RNAs (lncRNAs). Here, expression data of osteosarcoma from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were retrieved to identify ER-related lncRNAs associated with the overall survival (OS) of osteosarcoma patients. Then, Lasso penalized Cox regression analysis was applied to construct a lncRNAs risk signature. Meanwhile, patients were stratified into two clusters based on the identified m1A/m5C/m6A-associated lncRNAs. The prognostic value and immune landscape of the identified signature and clusters were further evaluated. Two m1A/m5C/m6A-associated lncRNAs were incorporated into our risk signature. The functional analyses indicated that the prognostic model was correlated with patient survival, and cancer metastasis and growth. Meanwhile, the signature model was significantly associated with the infiltration of immune cells, immune microenvironment, as well as several immune checkpoint genes. Similar results were detected for the lncRNAs clusters, which were significantly correlated with immune infiltration, cancer microenvironment, and immune-associated genes, and contributed to predicting the prognosis of patients. Moreover, our risk signature and clusters might help guide the application of immunotherapeutic drugs for osteosarcoma patients. Finally, a nomogram based on the risk score was established. Overall, a risk signature based on two m1A/m5C/m6A-associated lncRNAs was generated and presented predictive value for the prognosis and immune landscapes of osteosarcoma patients. This signature can be further used in the development of novel therapeutic strategies for osteosarcoma.

A Prognostic Model for Colon Adenocarcinoma Patients Based on Ten Amino Acid Metabolism Related Genes

BackgroundAmino acid metabolism plays a vital role in cancer biology. However, the application of amino acid metabolism in the prognosis of colon adenocarcinoma (COAD) has not yet been explored. Here, we construct an amino acid metabolism-related risk model to predict the survival outcome of COAD and improve clinical decision making.MethodsThe RNA-sequencing-based transcriptome for 524 patients with COAD from The Cancer Genome Atlas (TCGA) was selected as a training set. The integrated Gene Expression Omnibus (GEO) dataset with 1,430 colon cancer samples was used for validation. Differential expression of amino acid metabolism-related genes (AAMRGs) was identified for prognostic gene selection. Univariate cox regression analysis, LASSO-penalized Cox regression analysis, and multivariate Cox regression analysis were applied to construct a prognostic risk model. Moreover, the correlation between risk score and microsatellite instability, immunotherapy response, and drug sensitivity were analyzed.ResultsA prognostic signature was constructed based on 10 AAMRGs, including ASPG, DUOX1, GAMT, GSR, MAT1A, MTAP, PSMD12, RIMKLB, RPL3L, and RPS17. Patients with COAD were divided into high-risk and low-risk group based on the medianrisk score. Univariate and multivariate Cox regression analysis revealed that AAMRG-related signature was an independent risk factor for COAD. Moreover, COAD patients in the low-risk group were more sensitive to immunotherapy targeting PD-1 and CTLA-4.ConclusionOur study constructed a prognostic signature based on 10 AAMRGs, which could be used to build a novel prognosis model and identify potential drug candidates for the treatment of COAD.

A Novel and Robust Prognostic Model for Hepatocellular Carcinoma Based on Enhancer RNAs-Regulated Genes.

Evidence has demonstrated that enhancer RNAs (eRNAs) play a vital role in the progression and prognosis of cancers, but few studies have focused on the prognostic ability of eRNA-regulated genes (eRGs) for hepatocellular carcinoma (HCC). Using gene expression profiles of HCC patients from the TCGA-LIHC and eRNA expression profiles from the enhancer RNA in cancers (eRic) data portal, we developed a novel and robust prognostic signature composed of 10 eRGs based on Lasso-penalized Cox regression analysis. According to the signature, HCC patients were stratified into high- and low-risk groups, which have been shown to have significant differences in tumor immune microenvironment, immune checkpoints, HLA-related genes, DNA damage repair-related genes, Gene-set variation analysis (GSVA), and the lower half-maximal inhibitory concentration (IC50) of Sorafenib. The prognostic nomogram combining the signature, age, and TNM stage had good predictive ability in the training set (TCGA-LIHC) with the concordance index (C-index) of 0.73 and the AUCs for 1-, 3-, and 5-year OS of 0.82, 0.77, 0.74, respectively. In external validation set (GSE14520), the nomogram also performed well with the C-index of 0.71 and the AUCs for 1-, 3-, and 5-year OS of 0.74, 0.77, 0.74, respectively. In addition, an important eRG (AKR1C3) was validated using two HCC cell lines (Huh7 and MHCC-LM3) in vitro, and the results demonstrated the overexpression of AKR1C3 is related to cell proliferation, migration, and invasion in HCC. Altogether, our eRGs signature and nomogram can predict prognosis accurately and conveniently, facilitate individualized treatment, and improve prognosis for HCC patients.

Establishment of a Novel Combined Nomogram for Predicting the Risk of Progression Related to Castration Resistance in Patients With Prostate Cancer

Background: The emergence of castration resistance is fatal for patients with prostate cancer (PCa); however, there is still a lack of effective means to detect the early progression. In this study, a novel combined nomogram was established to predict the risk of progression related to castration resistance. Methods: The castration-resistant prostate cancer (CRPC)-related differentially expressed genes (DEGs) were identified by R packages “limma” and “WGCNA” in GSE35988-GPL6480 and GSE70768-GPL10558, respectively. Relationships between DEGs and progression-free interval (PFI) were analyzed using the Kaplan–Meier method in TCGA PCa patients. A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan–Meier curve. Finally, the univariate and multivariate Cox regression analyses were used to establish a combined nomogram. The prognostic value of the nomogram was validated by concordance index (C-index), calibration plots, ROC curve, and decision curve analysis (DCA). Results: 15 CRPC-related DEGs were identified finally, of which 13 genes were significantly associated with PFI and used as the candidate genes for modeling. A two-gene (KIFC2 and BCAS1) signature was built to predict the risk of progression. The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively. Patients with high-risk scores were significantly associated with poorer PFI (p < 0.0001). A novel combined nomogram was successfully established for individualized prediction integrating with T stage, Gleason score, and risk score. While the 1-year, 3-year, and 5-year AUC were 0.76, 0.761, and 0.762, respectively, the good prognostic value of the nomogram was also validated by the C-index (0.734), calibration plots, and DCA. Conclusion: The combined nomogram can be used to predict the individualized risk of progression related to castration resistance for PCa patients and has been preliminarily verified to have good predictive ability.

Four Types of RNA Modification Writer-Related lncRNAs Are Effective Predictors of Prognosis and Immunotherapy Response in Serous Ovarian Carcinoma

Serous ovarian carcinoma (SOC) is a gynecological malignancy with high mortality rates. Currently, there is a lack of reliable biomarkers for accurate SOC patient prognosis. Here, we analyzed SOC RNA-Seq data from The Cancer Genome Atlas (TCGA) to identify prognostic biomarkers. Through the pearson correlation analysis, univariate Cox regression analysis, and LASSO-penalized Cox regression analysis, we identified nine lncRNAs significantly associated with four types of RNA modification writers (m6A, m1A, APA, and A-I) and with the prognosis of SOC patients (P <0.05). Six writer-related lncRNAs were ultimately selected following multivariate Cox analysis. We established a risk prediction model based on these six lncRNAs and evaluated its prognostic value in multiple groups (training set, testing set, and entire set). Our risk prediction model could effectively predict the prognosis of SOC patients with different clinical characteristics and their responses to immunotherapy. Lastly, we validated the predictive reliability and sensitivity of the lncRNA-based model via a nomogram. This study explored the association between RNA modification writer-related lncRNAs and SOC prognosis, providing a potential complement for the clinical management of SOC patients.

Identification of a DNA Damage Response and Repair-Related Gene-Pair Signature for Prognosis Stratification Analysis in Hepatocellular Carcinoma.

Background: Nowadays, although the cause of hepatocellular carcinoma (HCC) mortality and recurrence remains at a high level, the 5-year survival rate is still very low. The DNA damage response and repair (DDR) pathway may affect HCC patients’ survival by influencing tumor development and therapeutic response. It is necessary to identify a prognostic DDR-related gene signature to predict the outcome of patients. Methods: Level 3 mRNA expression and clinical information were extracted from the TCGA website. The GSE14520 datasets, ICGC-LIRI datasets, and a Chinese HCC cohort were served as validation sets. Univariate Cox regression analysis and LASSO-penalized Cox regression analysis were performed to construct the DDR-related gene pair (DRGP) signature. Kaplan–Meier survival curves and time-dependent receiver operating characteristic (ROC) analysis curves were calculated to determine the predictive ability of this prognostic model. Then, a prognostic nomogram was established to help clinical management. We investigated the difference in biological processes between HRisk and LRisk by conducting several enrichment analyses. The TIDE algorithm and R package “pRRophetic” were applied to estimate the immunotherapeutic and chemotherapeutic response. Results: We constructed the prognostic signature based on 23 DDR-related gene pairs. The patients in the training datasets were divided into HRisk and LRisk groups at median cut-off. The HRisk group had significantly poorer OS than the LRisk group, and the signature was an independent prognostic indicator in HCC. Furthermore, a nomogram of the riskscore combined with TNM stage was constructed and detected by the calibration curve and decision curve. The LRisk group was associated with higher expression of HBV oncoproteins and metabolism pathways, while DDR-relevant pathways and cell cycle process were enriched in the HRisk group. Moreover, patients in the LRisk group may be more beneficial from immunotherapy. We also found that TP53 gene was more frequently mutated in the HRisk group. As for chemotherapeutic drugs commonly used in HCC, the HRisk group was highly sensitive to 5-fluorouracil, while the LRisk group presented with a significantly higher response to gefitinib and gemcitabine. Conclusion: Overall, we developed a novel DDR-related gene pair signature and nomogram to assist in predicting survival outcomes and clinical treatment of HCC patients. It also helps understand the underlying mechanisms of different DDR patterns in HCC.

Development and Validation of Nomograms to Predict Overall Survival and Cancer-Specific Survival in Patients With Pancreatic Adenosquamous Carcinoma.

BackgroundPancreatic adenosquamous carcinoma (PASC) is a heterogeneous group of primary pancreatic cancers characterized by the coexistence of both glandular and squamous differentiation. The aim of this study was to develop nomograms to predict survival outcomes in patients with PASC.MethodsIn this retrospective study, data on PASC, including clinicopathological characteristics, treatments, and survival outcomes, were collected from the SEER database between 2000 and 2018. The primary endpoints were overall survival (OS) and cancer-specific survival (CSS). The eligible patients were randomly divided into development cohort and validation cohort in a 7:3 ratio. The nomograms for prediction of OS and CSS were constructed by the development cohort using a LASSO-Cox regression model, respectively. Besides the model performance was internally and externally validated by examining the discrimination, calibration, and clinical utility.ResultsA total of 632 consecutive patients who had been diagnosed with PASC were identified and randomly divided into development (n = 444) and validation (n = 188) cohorts. In the development cohort, the estimated median OS was 7.0 months (95% CI: 6.19–7.82) and the median CSS was 7.0 months (95% CI: 6.15–7.85). In the validation cohort, the estimated median OS was 6.0 months (95% CI: 4.46–7.54) and the median CSS was 7.0 months (95% CI: 6.25–7.75). LASSO-penalized COX regression analysis identified 8 independent predictors in the OS prediction model and 9 independent risk factors in the CSS prediction model: age at diagnosis, gender, year of diagnosis, tumor location, grade, stage, size, lymph node metastasis, combined metastasis, surgery, radiation, and chemotherapy. The Harrell C index and time-dependent AUCs manifested satisfactory discriminative capabilities of the models. Calibration plots showed that both models were well calibrated. Furthermore, decision curves indicated good utility of the nomograms for decision-making.ConclusionNomogram-based models to evaluate personalized OS and CSS in patients with PASC were developed and well validated. These easy-to-use tools will be useful methods to calculate individualized estimate of survival, assist in risk stratification, and aid clinical decision-making.

Identification of a Four-Gene Signature for Determining the Prognosis of Papillary Thyroid Carcinoma by Integrated Bioinformatics Analysis.

PurposeAlthough well-differentiated papillary thyroid carcinoma (PTC) has an indolent nature and usually an excellent prognosis, some patients experience disease recurrence or death. The aim of this study was to identify prognostic markers to stratify PTC patients.Patients and MethodsEight gene-expression profiles (GSE3467, GSE3678, GSE5364, GSE27155, GSE33630, GSE53157, GSE60542, and GSE104005) were obtained from the Gene Expression Omnibus and used to analyze differentially expressed genes (DEGs) between PTC tissues and non-tumor tissues. Univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed to identify prognostic genes and establish a risk-score model based on the integrated DEGs. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were used to validate the prognostic performance of the risk score. A nomogram was constructed based on The Cancer Genome Atlas dataset and Multivariable Cox regression analysis.ResultsA total of 165 upregulated and 207 downregulated DEGs were screened. A four-gene signature including PAPSS2, PCOLCE2, PTX3, and TGFBR3 was identified. The risk-score model showed a strong diagnosis performance for identifying patients with a poor prognosis. KM analysis showed that patients with low risk scores had a significantly more favorable overall survival (OS) than those with high risk scores (p = 0.0002). ROC curves based on the four-gene signature showed better performances in predicting 1-, 3-, and 5-year survival than did the American Joint Committee on Cancer staging system (area under the curve: 0.86 vs 0.84, 0.80 vs 0.63, and 0.79 vs 0.73, respectively). Furthermore, when combined with age and tumor status from the nomogram, the four-gene signature achieved a good performance in guiding postoperative follow-up surveillance of patients with PTC.ConclusionThe four-gene signature was found to be a novel and reliable biomarker with great potential for clinical application in risk stratification and OS prediction in patients with PTC.

A Five-Gene Signature Associated With DNA Damage Repair Molecular Subtype Predict Overall Survival for Hepatocellular Carcinoma.

Background: DNA damage repair (DDR) is an important mechanism for the occurrence and development of hepatocellular carcinoma (HCC), but its impact on prognosis has not been fully understood. Materials and methods: A total of 904 HCC patients were included in our study, TCGA (n = 370) and GSE14520 (n = 239) were merged into a large-sample training cohort (n = 609). The training cohort was clustered into C1 and C2 based on prognostic DDR-related genes, the differentially expressed genes (DEGs) between C1 and C2 were identified by the Wilcoxon signed-rank test referred to criteria (|log2FC|≥1 and FDR< 0.05). The univariate Cox analysis was used to screen the prognostic-related DEGs, and Lasso penalized Cox regression analysis was used to construct the risk score. The patients were clarified into high- and low-risk groups based on the median risk score. ICGC (n = 231) and GSE116174 (n = 64) cohorts were used for external validation of the risk score’s prognostic value. Results: The Kaplan–Meier survival analysis showed that the high-risk group had a significantly reduced overall survival (OS) compared to the low-risk group in the three independent cohorts, and the time-dependent ROC curve showed that the five-gene (STMN1, PON1, PLOD2, MARCKSL1, and SPP1) risk score with a high accuracy in predicting OS. The patients with AFP >300 ng/ml, tumor poor differentiation (grade 3–4), micro and macro vascular tumor invasion, advanced stage (AJCC III-IV, BCLC stage B-C, and CLIP score >2) exhibited a higher risk score. Subgroup survival analysis found that the risk score was applicable to patients with different clinical characteristics. GO and KEGG functional enrichment analysis revealed that cell cycle, p53 signaling, TNF signaling-related pathways were upregulated in the high-risk group. The higher infiltration level of activated CD4 T cell, CD56 bright natural killer cell, plasmacytoid dendritic cell, and type 2 T helper cells were found to lead an unfavorable impact on the OS of HCC patients, and these four kinds of immune cells exhibited a higher infiltration level in the high-risk group. Conclusion: The five-gene risk score proposed in the research may provide new insights into the individualized evaluation of HCC prognosis.