Least Absolute Shrinkage And Selection Operator Regression Analysis Research Articles

Establishment and Validation of a Dynamic Nomogram for Persistent Organ Failure in Acute Biliary Pancreatitis: A Retrospective Study.

The objective of this study was to create a predictive model for the onset of persistent organ failure (POF) in individuals suffering from acute biliary pancreatitis (ABP) by utilizing indicators observed within 24hours of hospital admission. Early detection of high-risk POF patients is crucial for clinical decision-making. Clinical data and laboratory indicators within 24hours of admission from ABP patients diagnosed at The First Affiliated Hospital of Wenzhou Medical University between January 1, 2016, and January 1, 2024 were collected and retrospectively analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression (stepwise regression) methods were employed to identify variables for constructing the prediction model. The prediction model's performance was evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). It was compared with other scoring systems such as SIRS, BISAP, APACHE II, CTSI, and MCTSI. Additionally, a web-based calculator was created to simplify the calculation process. Out of 324 ABP patients, 25 developed POF. Initial screening identified 18 variables; through LASSO regression and multivariable logistic regression analysis, five variables including BMI, Hb, ALB, Ca, and LIP were determined as independent predictors of POF. According to these factors to build prediction model, draw the nomogram. The AUC's receiver operating characteristic curve analysis demonstrated a significantly higher value in comparison to other scoring systems. Calibration curve and DCA show that the established model to predict the accuracy of POF is higher, clinical decision of net benefit is also higher. A network calculator utilizing this predictive model was developed. A predictive model incorporating five risk indicators has been established exhibiting high discriminatory power and accuracy which aids in early identification of ABP patients at risk for developing POF. This holds significant value in guiding clinical decision-making.

Construction and validation of prognostic model for colorectal mucinous adenocarcinoma patients and identification of a new prognosis related gene FAM174B.

Mucinous adenocarcinoma (MAC) is a peculiar histological subtype of colorectal cancer (CRC) with distinct medical, disease-related, and genetic characteristics. The prognosis of MAC is generally poorer less favorable compared to non-specific adenocarcinoma (AC), but the prognostic indicator of MAC is rare. Therefore, this study aims to identify potential biomarkers and construct a prognostic model to better predict patient outcomes in MAC. We conducted differential genes expression investigation, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) to pinpoint hub genes. Then, the hub genes were used to construct a prognostic model for MAC. Kaplan-Meier survival, receiver operating characteristic (ROC), and Cox regression analysis were used to assess the prognostic utility of the model. The potential biological function of the hub gene was examined using gene set enrichment analysis (GSEA). Four hub genes, FAM174B, CREB3L1, SPDEF, and RAP1GAP, were identified between MAC and AC by differential genes expression analysis, WGCNA, and LASSO regression analysis. The prognostic signature model was constructed based on these four hub genes, which could divide MAC into low- and high-risk groups. The overall survival (OS) was notably lower in the high-risk group compared to the low-risk group (P=0.007). The area under the curves (AUCs) for 1-, 3-, and 5-year OS were 0.61 [95% confidence interval (CI): 0.73-0.49], 0.69 (95% CI: 0.76-0.63), and 0.77 (95% CI: 0.83-0.71), respectively. We also found that FAM174B expression was closely related to the OS of MAC (P=0.02). Further, the expression of FAM174B was positively correlated with MAC's Mucin type O-glycan biosynthesis. Finally, it was indicated that FAM174B was positively correlated with the critical molecules of mucus formation, MUC5AC (P=0.004, r=0.33), MUC5B (P<0.001, r=0.43), and MUC2 (P<0.001, r=0.39). We have developed and validated a four-gene prognostic model to predict the survival of MAC. Additionally, we found that FAM174B might correlate with mucin production in MAC.

Prediction model for poor short-term prognosis in patients with chronic subdural hematoma after burr hole drainage: a retrospective cohort study.

Chronic subdural hematoma (CSDH) is a common condition in neurosurgery. With an aging population, there is increasing attention on the prognosis of patients following surgical intervention. We developed a postoperative short-term prognostic prediction model using preoperative clinical indicators, aiming to assist in perioperative medical decision-making and management. The dataset was randomly divided into training and validation cohorts. An mRS score greater than 2 one month after discharge was considered indicative of a poor prognosis. In the training cohort, the least absolute shrinkage and selection operator (LASSO) regression analysis was used for multivariate analysis to identify independent risk factors and construct a prediction nomogram for poor prognosis one month after discharge. The performance of the nomogram was assessed using the Receiver Operating Characteristic (ROC) curve and calibration curve. A Decision Curve Analysis (DCA) was also conducted to determine the net benefit threshold of the prediction model. Among the 505 participants, 18.8% (95/505) had a poor prognosis one month after discharge. The baseline characteristics did not significantly differ between the training cohort and the validation cohort. LASSO regression analysis in the training cohort reduced the predictors to four potential factors. Further multivariate logistic analyses in the training cohort identified four independent predictors: age, admission Glasgow Coma Scale (GCS) score, hemiparesis, and hemoglobin count. These predictors were incorporated into the nomogram prediction model. Internal validation using ROC analysis, calibration curves, and other methods demonstrated a strong correlation between the observed and predicted likelihood of poor prognosis one month after discharge. The visualized nomogram prediction model we developed for short-term postoperative prognosis of chronic subdural hematoma after burr hole drainage aids in predicting short-term outcomes and guiding clinical treatment decisions. Further external validation is needed in the future to confirm its effectiveness.

Development and validation of a prognostic nomogram for esophageal cancer patients based on SEER Asian population

This study aims to develop and validate a nomogram for predicting overall survival (OS) in Asian patients with Esophageal Cancer (EC). Data from Asian EC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for initial variable selection, followed by multivariate Cox regression analysis to identify independent prognostic factors. A nomogram was subsequently constructed based on these factors. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves and calibration curves, while the clinical utility of the nomogram was assessed through decision curve analysis (DCA). The LASSO regression and multivariate Cox regression analysis identified age, sex, marital status, tumor size, M stage, surgery, and chemotherapy as independent prognostic factors. The ROC curve results demonstrated that the area under the curve (AUC) values for predicting 1-year, 3-year, and 5-year OS in the training cohort were 0.770, 0.756, and 0.783, respectively. In the validation cohort, the AUC values were 0.814, 0.763, and 0.771, respectively. Calibration curves indicated a high concordance between predicted and actual OS. The DCA demonstrated that the nomogram has significant clinical applicability. This nomogram provides reliable predictions and valuable guidance for personalized survival estimates and high-risk patient identification.

Identification of Molecular Correlations of GSDMD with Pyroptosis inAlzheimer's Disease.

An analysis of bioinformatics and cell experiments was performed to verify the relationship between gasdermin D (GSDMD), an executive protein of pyroptosis, and Alzheimer's disease (AD). The training set GSE33000 was utilized to identify differentially expressed genes (DEGs) in both the AD group and control group, as well as in the GSDMD protein high/low expression group. Subsequently, the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) regression analysis were conducted, followed by the selection of the key genes for the subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The association between GSDMD and AD was assessed and confirmed in the training set GSE33000, as well as in the validation sets GSE5281 and GSE48350. Immunofluorescence (IF) was employed to detect the myelin basic protein (MBP), a distinctive protein found in the rat oligodendrocytes (OLN-93 cells). A range of concentrations (1-15 μmol/L) of β-amyloid 1-42 (Aβ1-42) were exposed to the cells, and the subsequent observations were made regarding cell morphology. Additionally, the assessments were conducted to evaluate the cell viability, the lactate dehydrogenase (LDH) release, the cell membrane permeability, and the GSDMD protein expression. A total of 7,492 DEGs were screened using GSE33000. Subsequently, WGCNA analysis identified 19 genes that exhibited the strongest correlation with clinical traits in AD. Additionally, LASSO regression analysis identified 13 key genes, including GSDMD, AFF1, and ATOH8. Furthermore, the investigation revealed that the key genes were associated with cellular inflammation based on GO and KEGG analyses. Moreover, the area under the curve (AUC) values for the key genes in the training and validation sets were determined to be 0.95 and 0.70, respectively. Significantly, GSDMD demonstrated elevated levels of expression in AD across both datasets. The positivity of MBP expression in cells exceeded 95%. As the concentration of Aβ1-42 action gradually escalated, the detrimental effects on cells progressively intensified, resulting in a gradual decline in cell survival rate, accompanied by an increase in lactate dehydrogenase release, cell membrane permeability, and GSDMD protein expression. The association between GSDMD and AD has been observed, and it has been found that Aβ1-42 can induce a significant upregulation of GSDMD in OLN-93 cells. This suggests that Aβ1-42 has the potential to induce cellular pyroptosis and can serve as a valuable cellular pyroptosis model for the study of AD.

Predictive model for prolonged hospital stay risk after gastric cancer surgery.

Prolonged postoperative hospital stay following gastric cancer (GC) surgery is an important risk factor affecting patients' mood and increasing complications. We aimed to develop a nomogram to predict risk factors associated with prolonged postoperative length of stay (PLOS) in patients undergoing gastric cancer resection. Data were collected from 404 patients. The least absolute shrinkage and selection operator (LASSO) was used for variable screening, and a nomogram was designed. The nomogram performance was evaluated by the area under the receiver operating characteristic curve (AUC). The consistency between the predicted and actual values was evaluated via a calibration map, and the clinical application value was evaluated via decision curve analysis (DCA) and clinical impact curve analysis (CICA). A total of 404 patients were included in this study. Among these patients, 287 were assigned to the training cohort, and 117 were assigned to the validation cohort. According to the PLOS quartile distance, 103 patients were defined as having prolonged PLOS. LASSO regression and logistic multivariate analysis revealed that 4 clinical characteristics, the neutrophil-lymphocyte ratio (NLR) on postoperative day one, the NLR on postoperative day three, the preoperative prognostic nutrition index and the first time anal exhaust was performed, were associated with the PLOS and were included in the construction of the nomogram. The AUC of the nomogram prediction model was 0.990 for the training set and 0.983 for the validation set. The calibration curve indicated good correlation between the predicted results and the actual results. The Hosmer-Lemeshow test revealed that the P values for the training and validation sets were 0.444 and 0.607, respectively, indicating that the model had good goodness of fit. The decision curve analysis and clinical impact curve of this model showed good clinical practicability for both cohorts. We explored the risk factors for prolonged PLOS in GC patients via the enhanced recovery after surgery (ERAS) program and developed a predictive model. The designed nomogram is expected to be an accurate and personalized tool for predicting the risk and prognosis of PLOS in GC patients via ERAS measures.

Association of R-loop binding proteins with prognosis and anti-tumor drug sensitivity in lung adenocarcinoma: a bioinfor-matic study.

To investigate the association of R-loop binding proteins with prognosis and chemotherapy efficacy in lung adenocarcinoma. The data related to R-loop regulatory genes were obtained from literature of R-loop proteomics and relevant databases. We used 403 cases of lung adenocarcinoma in the Cancer Genome Atlas as training set, and two datasets GSE14814 and GSE31210 in Gene Expression Omnibus as validation sets. The weighted gene co-expression network analysis (WGCNA) was employed to identify R-loop genes with a significant impact on the clinical phenotype of lung adenocarcinoma. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to eliminate genes exhibiting multicollinearity. A multivariate Cox regression analysis was employed to scrutinize clinical variables and R-loop characteristic genes that exert independent prognostic effects on patient survival. Subsequently, a risk score model was constructed. The predictive capacity of this model for the prognosis of patients was analyzed and validated. Additionally, the performance of risk model on the anti-tumor drug sensitivity was assessed. The mutations of R-loop genes were analyzed by maftools. The effect of PLEC expression on anti-tumor drug sensitivity was tested on non-small cell lung adenocarcinoma H1299 and A549 cells in vitro. A collection of 1551 R-loop genes were obtained, and 78 genes exhibited significant effects on the clinical phenotype shown on WGCNA. The LASSO regression analysis retained fourteen R-loop genes. A multivariate Cox regression analysis further identified three R-loop genes (HEXIM1, GLI2, PLEC) and a clinical variable (tumor grading) that were associated with patient prognosis. Risk prediction model was established according to the regression coefficients of each parameter. Kaplan-Meier survival analysis showed that the prognosis of high-risk group was significantly worse than that of low-risk group (P<0.01). The time-dependent ROC curve showed that the risk model had good predictive ability in both training and validation sets. Predictive analyses of anti-neoplastic drug sensitivity indicated a diminished responsiveness to both chemotherapy and targeted treatment drugs among high-risk patients. The expression of PLEC was strongly correlated with sensitivity to gefitinib, a classical EGFR inhibitor. R-loop binding proteins have been identified as significant determinants in the prognosis and therapeutic strategies for lung adenocarcinoma, which indicates that therapeutic interventions targeting these specific R-loop binding proteins might contribute to a better survival of the patients.

Identification and validation of ferroptosis related markers in erythrocyte differentiation of umbilical cord blood-derived CD34+ cell by bioinformatic analysis.

Ferroptosis has been observed to play an important role during erythrocyte differentiation (ED). However, the biological gene markers and ferroptosis mechanisms in ED remain unknown. We downloaded the datasets of ED in human umbilical cord blood-derived CD34+ cells from the Gene Expression Omnibus database. Using median differentiation time, the sample was categorized into long and short groups. The differentially expressed ferroptosis-related genes (DE-FRGs) were screened using differential expression analysis. The enrichment analyses and a protein-protein interaction (PPI) network were conducted. To predict the ED stage, a logistic regression model was constructed using the least absolute shrinkage and selection operator (LASSO). Overall, 22 DE-FRGs were identified. Ferroptosis-related pathways were enriched using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Gene Set Enrichment Analysis and Gene Set Variation Analysis revealed the primary involvement of DE-FRGs in JAK-STAT, MAPK, PI3K-AKT-mTORC1, WNT, and NOTCH signaling pathways. Ten-hub DE-FRGs were obtained using PPI analysis. Furthermore, we constructed mRNA-microRNA (miRNA) and mRNA-transcription factor networks. Immune cell infiltration levels differed significantly during ED. LASSO regression analysis established a signature using six DE-FRGs (ATF3, CDH2, CHAC1, DDR2, DPP4, and GDF15) related to the ED stage. Bioinformatic analyses identified ferroptosis-associated genes during ED, which were further validated. Overall, we identified ferroptosis-related genes to predict their correlations in ED. Exploring the underlying mechanisms of ferroptosis may help us better understand pathophysiological changes in ED and provide new evidence for clinical transformation.

The relationship between the collagen score at the anastomotic site of esophageal squamous cell carcinoma and anastomotic leakage.

Anastomotic leakage (AL) has always been one of the most serious complications of esophagectomy with gastric conduit reconstruction. There are many strong risk factors for AL in clinical practice. Notably, the tension at the esophagogastric anastomosis and the blood supply to the gastric conduit directly affect the integrity of the anastomosis. However, there has been a lack of quantitative research on the tension and blood supply of the gastric conduit. Changes in extracellular matrix collagen reflect tension and blood supply, which affect the quality of the anastomosis. This study aimed to establish a quantitative collagen score to describe changes in the collagen structure in the extracellular matrix and to identify patients at high risk of postoperative AL. A retrospective study of 213 patients was conducted. Clinical and pathological data were collected at baseline. Optical imaging of the "donut" specimen at the anastomotic gastric end and collagen feature extraction were performed. Least absolute shrinkage and selection operator (LASSO) regression models were used to select the significant collagen features, compute collagen scores, and validate the predictive efficacy of the collagen scores for ALs. LASSO regression analysis revealed three collagen-related parameters in the gastric donuts: histogram mean, histogram variance, and histogram energy. Based on this analysis, we established a formula to calculate the collagen score. The results of the univariate analysis revealed significant differences in the preoperative low albumin values (P=0.002) and collagen scores between the AL and non-AL groups (P=0.001), while the results of the multivariate analysis revealed significant differences in the collagen scores between the AL and non-AL groups (P=0.002). The areas under the curve (AUCs) of the experimental and validation cohorts were 0.978 [95% confidence interval (CI): 0.931-0.996] and 0.900 (95% CI: 0.824-0.951), respectively. The collagen score established herein was shown to be related to AL and can be used to predict AL in patients who underwent esophagectomy.

Development and validation of a predictive model for PACU hypotension in elderly patients undergoing sedated gastrointestinal endoscopy

BackgroundHypotension, characterized by abnormally low blood pressure, is a frequently observed adverse event in sedated gastrointestinal endoscopy procedures. Although the examination time is typically short, hypotension during and after gastroscopy procedures is frequently overlooked or remains undetected. This study aimed to construct a risk nomogram for post-anesthesia care unit (PACU) hypotension in elderly patients undergoing sedated gastrointestinal endoscopy.MethodsThis study involved 2919 elderly patients who underwent sedated gastrointestinal endoscopy. A preoperative questionnaire was used to collect data on patient characteristics; intraoperative medication use and adverse events were also recorded. The primary objective of the study was to evaluate the risk of PACU hypotension in these patients. To achieve this, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection, involving cyclic coordinate descent with tenfold cross-validation. Subsequently, multivariable logistic regression analysis was applied to build a predictive model using the selected predictors from the LASSO regression. A nomogram was visually developed based on these variables. To validate the model, a calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used. Additionally, external validation was conducted to further assess the model’s performance.ResultsThe LASSO regression analysis identified predictors associated with an increased risk of adverse events during surgery: age, duration of preoperative water abstinence, intraoperative mean arterial pressure (MAP) <65 mmHg, decreased systolic blood pressure (SBP), and use of norepinephrine (NE). The constructed model based on these predictors demonstrated moderate predictive ability, with an area under the ROC curve of 0.710 in the training set and 0.778 in the validation set. The DCA indicated that the nomogram had clinical applicability when the risk threshold ranged between 20 and 82%, which was subsequently confirmed in the external validation with a range of 18–92%.ConclusionIncorporating factors such as age, duration of preoperative water abstinence, intraoperative MAP <65 mmHg, decreased SBP, and use of NE in the risk nomogram increased its usefulness for predicting PACU hypotension risk in elderly patient undergoing sedated gastrointestinal endoscopy.

Development and validation of a nomogram for premature coronary artery disease patients in Guangzhou

BackgroundData regarding risk factors for premature coronary artery disease (PCAD) is scarce given that few research focus on it. This study aimed to develop and validate a clinical nomogram for PCAD patients in Guangzhou. MethodsWe recruited 108 PCAD patients (female ≤65 years old and male ≤55 years old) and 96 healthy controls from Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 01/01/2021 and 31/12/2022. Twenty potentially relevant indicators of PCAD were extracted. Next, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. The nomogram was developed based on the selected variables visually. ResultsIndependent risk factors, including body mass index (BMI), history of PCAD, glucose, Apolipoprotein A1(ApoA1), high density lipoprotein 2-cholesterol (HDL2-C), total cholesterol and triglyceride, were identified by LASSO and logistic regression analysis. The nomogram showed accurate discrimination (area under the receiver operator characteristic curve, ROC, 87.45 %, 95 % CI: 82.58 %-92.32 %). Decision curve analysis (DCA) suggested that the nomogram was clinical beneficial. HDL2, one risk factor, was isolated by a two-step discontinuous density-gradient ultracentrifugation method. And HDL2 from PCAD patients exhibited less 3H-cholesterol efflux (22.17 % vs 26.64 %, P < 0.05) and less delivery of NBD-cholesterol detecting by confocal microscope compared with healthy controls. ConclusionsIn conclusion, the seven-factor nomogram can achieve a reasonable relationship with PCAD, and a large cohort were needed to enhance the credibility and effectiveness of our model in future.

Development and validation of a glioma prognostic model based on telomere-related genes and immune infiltration analysis.

Gliomas are the most prevalent primary brain tumors, and patients typically exhibit poor prognoses. Increasing evidence suggests that telomere maintenance mechanisms play a crucial role in glioma development. However, the prognostic value of telomere-related genes in glioma remains uncertain. This study aimed to construct a prognostic model of telomere-related genes and further elucidate the potential association between the two. We acquired RNA-seq data for low-grade glioma (LGG) and glioblastoma (GBM), along with corresponding clinical information from The Cancer Genome Atlas (TCGA) database, and normal brain tissue data from the Genotype-Tissue Expression (GTEX) database for differential analysis. Telomere-related genes were obtained from TelNet. Initially, we conducted a differential analysis on TCGA and GTEX data to identify differentially expressed telomere-related genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on these genes. Subsequently, univariate Cox analysis and log-rank tests were employed to obtain prognosis-related genes. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were sequentially utilized to construct prognostic models. The model's robustness was demonstrated using receiver operating characteristic (ROC) curve analysis, and multivariate Cox regression of risk scores for clinical characteristics and prognostic models were calculated to assess independent prognostic factors. The aforementioned results were validated using the Chinese Glioma Genome Atlas (CGGA) dataset. Finally, the CIBERSORT algorithm analyzed differences in immune cell infiltration levels between high- and low-risk groups, and candidate genes were validated in the Human Protein Atlas (HPA) database. Differential analysis yielded 496 differentially expressed telomere-related genes. GO and KEGG pathway analyses indicated that these genes were primarily involved in telomere-related biological processes and pathways. Subsequently, a prognostic model comprising ten telomere-related genes was constructed through univariate Cox regression analysis, log-rank test, LASSO regression analysis, and multivariate Cox regression analysis. Patients were stratified into high-risk and low-risk groups based on risk scores. Kaplan-Meier (K-M) survival analysis revealed worse outcomes in the high-risk group compared to the low-risk group, and establishing that this prognostic model was a significant independent prognostic factor for glioma patients. Lastly, immune infiltration analysis was conducted, uncovering notable differences in the proportion of multiple immune cell infiltrations between high- and low-risk groups, and eight candidate genes were verified in the HPA database. This study successfully constructed a prognostic model of telomere-related genes, which can more accurately predict glioma patient prognosis, offer potential targets and a theoretical basis for glioma treatment, and serve as a reference for immunotherapy through immune infiltration analysis.

Eleven inflammation-related genes risk signature model predicts prognosis of patients with breast cancer.

Changes in gene expression are associated with malignancy. Analysis of gene expression data could be used to reveal cancer subtypes, key molecular drivers, and prognostic characteristics and to predict cancer susceptibility, treatment response, and mortality. It has been reported that inflammation plays an important role in the occurrence and development of tumors. Our aim was to establish a risk signature model of breast cancer with inflammation-related genes (IRGs) to evaluate their survival prognosis. We downloaded 200 IRGs from the Molecular Signatures Database (MSigDB). The data of breast cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential gene expression analysis, the least absolute shrinkage and selection operator (LASSO), Cox regression analysis, and overall survival (OS) analysis were used to construct a multiple-IRG risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to annotate functions of the differentially expressed IRGs (DEIRGs) The predictive accuracy of the prognostic model was evaluated by time-dependent receiver operating characteristic (ROC) curves. Subsequently, nomograms were constructed to guide clinical application according to the univariate and multivariate Cox proportional hazards regression analyses. Eventually, we applied gene set variation analysis (GSVA), mutation analysis, immune infiltration analysis, and drug response analysis to compare the differences between high- and low-risk patients. Totally, 65 DEIRGs were obtained after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA. Among them, 11 IRGs (IL18, IL12B, RASGRP1, HPN, CLEC5A, SCARF1, TACR3, VIP, CCL2, CALCRL, ABCA1) were screened with nonzero coefficient by LASSO regression analysis to construct the prognostic model, which was validated in GSE96058.The 11-gene IRGs risk signature model stratified patients into high- or low-risk groups, with those in the low-risk group having longer survival time and less deaths. Multivariate Cox analysis manifested that risk score, age, and stage were the three independent prognostic factors for breast cancer patients. There were 12 pathways with higher activities and 24 pathways with lower activities in the high-risk group compared with the low-risk group, yet no difference of gene mutation load was observed between the two groups. In immune infiltration analysis, we noted that the proportion of T cells showed a decreased trend according to the increase of risk score and most of the immune cells were enriched in the low-risk group. Inversely, macrophages M2 were more highly distributed in the high-risk group. We identified 67 approved drugs that showed a different effect between the high- and low-risk patients and the top 2 gene-drug pairs were IL12B-sunitinib and SCARF1-ruxolitinib. The 11-IRG risk signature model is a promising tool to predict the survival of breast cancer patients and the expressions of IL12B and SCARF1 may serve as potential targets for therapy of breast cancer.

The prognostic value of immune escape-related genes in lung adenocarcinoma.

Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted. RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219. Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue. We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.

This study aimed to develop several new machine learning models based on hibernating myocardium to predict the major adverse cardiac events(MACE) of ischemic left ventricular systolic dysfunction(LVSD) patients receiving either percutaneous coronary intervention(PCI) or optimal medical therapy(OMT). This study included 329 LVSD patients, who were randomly assigned to the training or validation cohort. Least absolute shrinkage and selection operator(LASSO) regression was used to identify variables associated with MACE. Subsequently, various machine learning models were established. Model performance was compared using receiver operating characteristic(ROC) curves, the Brier score(BS), and the concordance index(C-index). A total of 329 LVSD patients were retrospectively enrolled between January 2016 and December 2021. Utilizing LASSO regression analysis, five factors were selected. Based on these factors, RSF, GBM, XGBoost, Cox, and DeepSurv models were constructed. In the development and validation cohorts, the C-indices were 0.888 vs. 0.955 (RSF). The RSF model (0.991 vs. 0.982 vs. 0.980) had the highest area under the ROC curve (AUC) compared with the other models. The BS (0.077 vs. 0.095vs. 0.077) of RSF model were less than 0.25 at 12, 18, and 24months. This study developed a novel predictive model based on RSF to predict MACE in LVSD patients who underwent either PCI or OMT.

Risk score constructed with neutrophil extracellular traps-related genes predicts prognosis and immune microenvironment in multiple myeloma.

Multiple myeloma (MM) exhibits considerable heterogeneity in treatment responses and survival rates, even when standardized care is administered. Ongoing efforts are focused on developing prognostic models to predict these outcomes more accurately. Recently, neutrophil extracellular traps (NETs) have emerged as a potential factor in MM progression, sparking investigation into their role in prognostication. In this study, a multi-gene risk scoring model was constructed using the intersection of NTEs and differentially expressed genes (DEGs), applying the least absolute shrinkage and selection operator (LASSO) Cox regression model. A nomogram was established, and the prognostic model's effectiveness was determined via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were employed to evaluate the level of immune infiltration. The sensitivity of chemotherapy drugs was assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Ultimately, the presence of the detected genes was confirmed through quantitative real-time polymerase chain reaction (qRT-PCR) analysis in MM cell specimens. 64 NETs-DEGs were yielded, and through univariate Cox regression and LASSO regression analysis, we constructed a risk score composed of six genes: CTSG, HSPE1, LDHA, MPO, PINK1, and VCAM1. MM patients in three independent datasets were classified into high- and low-risk groups according to the risk score. The overall survival (OS) of patients in the high-risk group was significantly reduced compared to the low-risk group. Furthermore, the risk score was an independent predictive factor for OS. In addition, interactions between the risk score, immune score, and immune cell infiltration were investigated. Further analysis indicated that patients in the high-risk group were more sensitive to a variety of chemotherapy and targeted drugs, including bortezomib. Moreover, the six genes provided insights into the progression of plasma cell disorders. This study offers novel insights into the roles of NETs in prognostic prediction, immune status, and drug sensitivity in MM, serving as a valuable supplement and enhancement to existing grading systems.

Comprehensive Analysis of angiogenesis associated genes and tumor microenvironment infiltration characterization in cervical cancer

BackgroundCervical cancer is among the most prevalent malignancies worldwide. This study explores the relationships between angiogenesis-related genes (ARGs) and immune infiltration, and assesses their implications for the prognosis and treatment of cervical cancer. Additionally, it develops a diagnostic model based on angiogenesis-related differentially expressed genes (ARDEGs). MethodsWe systematically evaluated 15 ARDEGs using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA). Immune cell infiltration was assessed using a single-sample gene-set enrichment analysis (ssGSEA) algorithm. We then constructed a diagnostic model for ARDEGs using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and evaluated the diagnostic value of this model and the hub genes in predicting clinical outcomes and immunotherapy responses in cervical cancer. ResultsA set of ARDEGs was identified from the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UCSC Xena database. We performed KEGG, GO, and GSEA analyses on these genes, revealing significant involvement in cell proliferation, differentiation, and apoptosis. The ARDEGs diagnostic model, constructed using LASSO regression analysis, showed high predictive accuracy in cervical cancer patients. We developed a reliable nomogram and decision curve analysis to evaluate the clinical utility of the ARDEG diagnostic model. The 15 ARDEGs in the model were associated with clinicopathological features, prognosis, and immune cell infiltration. Notably, ITGA5 expression and the abundance of immune cell infiltration (specifically mast cell activation) were highly correlated. ConclusionThis study identifies the prognostic characteristics of ARGs in cervical cancer patients, elucidating aspects of the tumor microenvironment. It enhances the predictive accuracy of immunotherapy outcomes and establishes new strategies for immunotherapeutic interventions.

Machine learning identification of hypoxia-related genes and prognostic risk-scoring model for effective survival stratification in hepatocellular carcinoma.

e16261 Background: Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related mortality globally, where treatment and prognostic assessment have important implications in clinical practice. Hypoxia, as a common feature within solid tumors, can directly change the tumor microenvironment, which affects the efficacy of cancer treatment and prognosis. In this study, we constructed and validated a hypoxia-based prognostic model using bioinformatics and machine learning. Methods: Two public datasets, GSE14520 and GSE41666, were collected from the Gene Expression Omnibus: (1) HCC tumor tissues compared to adjacent normal tissues (N = 214) and (2) HepG2 cells under normoxic and hypoxic conditions (N = 6). Differential expression analysis was performed to identify HCC characteristic genes and hypoxia-related genes, including their common genes (HCC-Hypoxia Overlap genes, HHOs). Using RNA-seq data of HCC patients (N = 367) from the TCGA Liver Cancer (LIHC) database, univariate Cox regression models were identified, and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm selected hypoxia-characteristic genes for the multivariate survival model. A hypoxia-related risk score was calculated based on the model of these characteristic genes and dichotomized cases into high-risk (HR) and low-risk (LR) groups. The model was validated using liver cancer cases (N = 232) from the International Cancer Genome Consortium database (ICGC-LIRI-JP). Results: Through differential expression analysis of the two datasets, we identified 52 HHOs. Univariate Cox analysis of these HHOs indicated that 21 genes were significantly associated with HCC patient survival. Through LASSO regression analysis, a total of 9 characteristic genes, including CENPA, KIF20A, DLGAP5, HMMR, UPB1, AFM, CABYR, PHLDA2, and N4BP2L1 were ultimately retained in the survival model. Based on these 9 genes, TCGA-LIHC samples were classified into HR and LR groups, and Kaplan-Meier (KM) analysis revealed significant differences in survival outcomes (p &lt; 0.032). Risk scoring of the ICGC-LIRI-JP validation set classified samples into HR and LR. KM analysis showed that the survival times of patients in the HR group were significantly shorter than those in the LR group (p &lt; 0.0001). Receiver Operating Characteristic Analysis analysis of the survival model showed area under the curve values of 0.815, 0.774, and 0.771 at 1, 2, and 3 years, respectively, demonstrating high performance in risk stratification. Conclusions: This study established a prognostic risk-scoring model based on 9 characteristic genes associated with hypoxia. This model can effectively stratify risks among HCC patients and demonstrate excellent performance in predicting survival. These findings may offer new biomarkers and therapeutic targets for the personalized treatment of HCC.

A disulfidptosis-related classification and risk signature identifies immunotherapy biomarkers and predicts prognosis in gastric cancer: An observational study.

Gastric cancer (GC) is one of the most prevalent types of cancer globally, often detected at advanced stages. However, its prognosis remains poor, necessitating the exploration of new biomarkers. Disulfidptosis, a recently identified form of programmed cell death, has not yet been investigated in relation to GC and its associated mechanisms. We analyzed and identified potential associations between disulfidptosis genes and GC clinical risk using TCGA (The Cancer Genome Atlas)-STAD (stomach adenocarcinoma) as the training set and GSE84433 as the validation set. In addition, we explored the prognostic value and potential biological mechanisms of disulfide genes in GC by consensus clustering, enrichment analysis, mutation histology analysis and immune infiltration analysis. Finally, we constructed a disulfidptosis-related risk signature (DRRS) to assess the association between risk class, survival prognosis, and immune infiltration. By utilizing data from 19 disulfidptosis-related genes, we successfully identified subgroups of C1 and C2 patients through consensus clustering. Notably, the 2 groups exhibited significant variations in terms of survival rates, immune scores, and immune cell infiltration. Subsequently, we developed a DRRS via LASSO (least absolute shrinkage and selection operator) regression analysis, incorporating PRICKLE1, NRP1, APOD, MISP3, and SERPINE1. This scoring system effectively distinguished individuals with high and low risks, as verified with a validation set. These findings strongly indicate a close association between disulfidptosis and the immune microenvironment of GC tumors. Moreover, the DRRS demonstrated commendable predictive capabilities for the survival outcomes of GC patients. In this study, we have identified the association between different subtypes of disulfidptosis and alterations in the GC immunotumour microenvironment. Furthermore, we have developed and verified the accuracy of the DRRS, a valuable tool for predicting survival, biological function, and immune infiltration in patients with GC. These findings contribute to a better comprehension of disulfidptosis and offer potential opportunities for innovative approaches in GC treatment.

A Nomogram Prediction Model for Persistent Pulmonary Hypertension of the Newborn in Neonates Hospitalized for the First Time After Birth.

Persistent pulmonary hypertension of the newborn (PPHN) is one of the critical neonatal diseases associated with high morbidity and mortality. This study attempted to conduct a nomogram prediction model for performing early identification of PPHN and providing effective information for clinical practice. A total of 456 newborns who first admitted to the hospital after birth were included in the analysis, including 138 newborns with PPHN and 318 newborns without PPHN (as controls). The optimal predictive variables selection was performed based on LASSO (least absolute shrinkage and selection operator) regression and multivariate logistic regression. Using the selected variables, a nomogram prediction model was developed. To validate the model, the model was assessed using the receiver operating characteristic curve, calibration plot, and clinical impact curve. Six predictors, namely, gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pa o2 , were identified by LASSO and multivariate logistic regression analysis from the original 30 variables studied. The constructed model, using these predictors, exhibited favorable predictive ability for PPHN, with an area under the receiver operating characteristic of 0.897 (sensitivity = 0.876, specificity = 0.785) in the training set and 0.871 (sensitivity = 0.902, specificity = 0.695) in the validation set, and was well calibrated, as indicated by the PHosmer-Lemeshow test values of 0.233 and 0.876 for the training and validation sets, respectively. The model included gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pa o2 had good prediction performance for predicting PPHN among newborns first admitted to the hospital after birth.