Telomerase related molecular subtype and risk model reveal immune activity and evaluate prognosis and immunotherapy response in prostate cancer.

BACKGROUND: Prostate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear. METHODS: Somatic mutations and copy number alterations of TEASEs were comprehensively analyzed. Subsequently, the transcripts of prostate cancer patients in TCGA and GEO databases were integrated to delineate new molecular subtypes. Followed by constructing a risk model containing nine characteristic genes through Lasso regression and Cox prognostic analysis among different subtypes. Various aspects including prognosis, tumor microenvironment (TME), landscape of immunity, tumor mutational burden (TMB), stem cell correlation, and median inhibitory concentration amongst different risk groups were compared. Finally, the expression, prognosis, and malignant biological behavior of ZW10 interactor (ZWINT) in vitro was explored. RESULTS: TEASEs exhibited a notably high mutation frequency. Three distinct molecular subtypes and two gene subclusters based on TEASEs were delineated, displaying significant associations with prognosis, immune function regulation, and clinical characteristics. Low-risk patients demonstrated superior prognosis and better response to immunotherapy. Conversely, high-risk patients exhibited higher TMB and stronger stem cell correlations. It was also found that the patients' sensitivity to chemotherapy agents was impacted by the risk score. Finally, ZWINT's potential as a novel diagnostic and prognostic biomarker for prostate cancer was validated. CONCLUSIONS: TEASEs play a pivotal role in modulating immune regulation and immunotherapeutic responses, thereby significantly impacting the diagnosis, prognosis, and treatment strategies for affected patients.

Prognostic risk model under the immune-associated long chain non-coding ribonucleic acid and its application in survival prognosis assessment of patients with breast cancer.

This study aimed to develop a prognostic risk model based on immune-related long non-coding RNAs (lncRNAs). By analyzing the expression profiles of specific long non-coding RNAs, the objective was to construct a predictive model to accurately assess the survival prognosis of breast cancer (BC) patients. This effort seeks to provide personalized treatment strategies for patients and improve clinical outcomes. Based on the median risk value, 300 samples of triple-negative BC (TNBC) patients were rolled into a high-risk group (HR group, n = 140) and a low-risk group (LR group, n = 160). Multivariate Cox (MVC) analysis was performed by combining the patient risk score and clinical information to evaluate the prognostic value of the prognostic risk (PR) model. A total of 371 immune-related lncRNAs associated with the prognosis of TNBC were obtained from 300 TNBC samples. Nine associated with prognosis were obtained by univariate Cox (UVC) analysis, and 3 (AC090181.2, LINC01235, and LINC01943) were selected by MVC analysis for the construction of TNBC PR model. Survival analysis showed a great difference in TNBC patients in different groups (P < 0.001). The receiver operator characteristic (ROC) curve showed the model possessed a good area under ROC curve (AUC), which was 0.928. The patient RS jointing with clinical information as well as the MVC analysis revealed that RS was an independent risk factor (IRF) for prognosis of TNBC (P < 0.05, HR = 1.033286). Therefore, the lncRNAs associated with TNBC immunity can be screened by bioinformatics analysis, and the established PR model of TNBC could better predict the prognosis of patients with TNBC, exhibiting a high application value in clinic.

Validation of Variables for Use in Pediatric Obesity Risk Score Development in Demographically and Racially Diverse US Cohorts.

OBJECTIVE: To evaluate the performance of childhood obesity prediction models in four independent cohorts in the United States, using previously validated variables obtained easily from medical records as measured in different clinical settings. STUDY DESIGN: Data from four prospective cohorts, Latinx, Eating, and Diabetes (LEAD); Stress in Pregnancy Study (SIPS); Project Viva; and Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) were used to test childhood obesity risk models and predict childhood obesity by ages 4 through 6, using five clinical variables (maternal age, maternal pre-pregnancy body mass index, birth weight Z-score, weight-for-age Z-score change, and breastfeeding), derived from a previously validated risk model and as measured in each cohort's clinical setting. Multivariable logistic regression was performed within each cohort, and performance of each model was assessed based on discrimination and predictive accuracy. RESULTS: The risk models performed well across all four cohorts, achieving excellent discrimination. The area under the receiver operator curve (AUROC) was 0.79 for CHAMACOS and Project Viva, 0.83 for SIPS, and 0.86 for LEAD. At a 50th percentile threshold, the sensitivity of the models ranged from 12 to 53%, and specificity was greater than or equal to 90%. The negative predictive values (NPV) were ≥ 80% for all cohorts, and the positive predictive values (PPV) ranged from 62-86%. CONCLUSION: All four risk models performed well in each independent and demographically diverse cohort, demonstrating the utility of these five variables for identifying children at high risk for developing early childhood obesity in the United States.

Construction and validation of a prognostic model for overall survival time of patients with ovarian cancer by metabolism-related genes.

BACKGROUND: Ovarian cancer is a female-specific malignancy with high morbidity and mortality. The metabolic reprogramming of tumor cells is closely related to the biological behavior of tumors. METHODS: The prognostic signature of the metabolism-related gene (MRGs) was established by LASSO-Cox regression analysis. The prognostic signature of MRGs was also prognosticated in each clinical subgroup. These genes were subjected to functional enrichment analysis and tissue expression exploration. Analysis of the MRG prognostic signature in terms of immune cell infiltration and antitumor drug susceptibility was also performed. RESULTS: A MRG prognostic signature including 21 genes was established and validated. Most of the 21 MRGs were expressed at different levels in ovarian cancer than in normal ovarian tissue. The enrichment analysis suggested that MRGs were involved in lipid metabolism, membrane organization, and molecular binding. The MRG prognostic signature demonstrated the predictive value of overall survival time in various clinical subgroups. The monocyte, NKT, Tgd and Tex cell scores showed differences between the groups with high- and low-risk score. The antineoplastic drug analysis we performed provided information on ovarian cancer drug therapy and drug resistance. In vitro experiments verified that PLCH1 in 21 MRGs can regulate the apoptosis and proliferation of ovarian cancer cells. CONCLUSION: This metabolism-related prognostic signature was a potential prognostic factor in patients with ovarian cancer, demonstrating high stability and accuracy.

A Comprehensive Analysis Exploring the Impact of an Immunogenic Cell Death-Related Panel for Ovarian Cancer.

Ovarian cancer (OV) is a malignant tumor that ranks first among gynecological cancers, thus posing a significant threat to women's health. Immunogenic cell death (ICD) can regulate cell death by activating the adaptive immune system. Here, we aimed to comprehensively characterize the features of ICD-associated genes in ovarian cancer, and to investigate their prognostic value and role in the response to immunotherapy. After analyzing datasets from The Cancer Genome Atlas, we utilized weighted gene coexpression network analysis to screen for hub genes strongly correlated with ICD genes in OV, which was subsequently validated with OV samples from the Gene Expression Omnibus (GEO) database. A prognostic risk model was then constructed after combining univariate, multivariate Cox regression and LASSO regression analysis to recognize nine ICD-associated molecules. Next, we stratified all OV patients into two subgroups according to the median value. The multivariate Cox regression analysis showed that the risk model could predict OV patient survival with good accuracy. The same results were also found in the validation set from GEO. We then compared the degree of immune cell infiltration in the tumor microenvironment between the two subgroups of OV patients, and revealed that the high-risk subtype had a higher degree of immune infiltration than the low-risk subtype. Additionally, in contrast to patients in the high-risk subgroup, those in the low-risk subgroup were more susceptible to chemotherapy. In conclusion, our research offers an independent and validated model concerning ICD-related molecules to estimate the prognosis, degree of immune infiltration, and chemotherapy susceptibility in patients with OV.

STAC3 as a poor prognostic biomarker in renal clear cell carcinoma: relationship with immune infiltration.

Calcium ions (Ca2+) are crucial in tumorigenesis and progression, with their elevated levels indicating a negative prognosis in Kidney Renal Clear Cell Carcinoma (KIRC). The influence of genes regulating calcium ions on the survival outcomes of KIRC patients and their interaction with the tumor's immune microenvironment is yet to be fully understood. This study analyzed gene expression data from KIRC tumor and adjacent non-tumor tissues using the TCGA-KIRC dataset to pinpoint genes that are differentially expressed in KIRC. Intersection of these genes with those regulating calcium ions highlighted specific calcium ion-regulating genes that exhibit differential expression in KIRC. Subsequently, prognostic risk models were developed using univariate Cox and LASSO-Cox regression analyses to verify their diagnostic precision. Additionally, the study investigated the correlation between tumor immunity and KIRC patient outcomes, assessing the contribution of STAC3 genes to tumor immunity. Further exploration entailed SSGASE, single-cell analysis, pseudotime analysis and both in vivo and in vitro experiments to evaluate STAC3's role in tumor immunity and progression. Notably, STAC3 was significantly overexpressed in tumor specimens and positively correlated with the degree of malignancy of KIRC, affecting patients' prognosis. Elevated STAC3 expression correlated with enhanced immune infiltration in KIRC tumors. Furthermore, silencing STAC3 curtailed KIRC cell proliferation, migration, invasion, and stemness properties. Experimental models in mice confirmed that STAC3 knockdown led to a reduction in tumor growth. Elevated STAC3 expression is intricately linked with immune infiltration in KIRC tumors, as well as with the aggressive biological behaviors of tumor cells, including their proliferation, migration, and invasion. Targeting STAC3 presents a promising strategy to augment the efficacy of current therapeutic approaches and to better the survival outcomes of patients with KIRC.

Unveiling an anoikis-related risk model and the role of RAD9A in colon cancer.

OBJECTIVE: Colorectal cancer (CRC), specifically colon adenocarcinoma, is the third most prevalent and the second most lethal form of cancer. Anoikis is found to be specialized form of programmed cell death (PCD), which plays a pivotal role in tumor progression. This study aimed to investigate the role of the anoikis related genes (ARGs) in colon cancer. METHODS: Consensus unsupervised clustering, differential expression analysis, tumor mutational burden analysis, and analysis of immune cell infiltration were utilized in the study. For the analysis of RNA sequences and clinical data of COAD patients, data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were obtained. A prognostic scoring system for overall survival (OS) prediction was developed using Cox regression and LASSO regression analysis. Furthermore, loss-of-function assay was utilized to explore the role of RAD9A played in the progression of colon cancer. RESULTS: The prognostic value of a risk score composed of NTRK2, EPHA2, RAD9A, CDC25C, and SNAI1 genes was significant. Furthermore, these findings suggested potential mechanisms that may influence prognosis, supporting the development of individualized treatment plans and management of patient outcomes. Further experiments confirmed that RAD9A could promote proliferation and metastasis of colon cancer cells. These effects may be achieved by affecting the phosphorylation of AKT. CONCLUSION: Differences in survival time and the tumor immune microenvironment (TIME) were observed between two gene clusters associated with ARGs. In addition, a prognostic risk model was established and confirmed as an independent risk factor. Furthermore, our data indicated that RAD9A promoted tumorigenicityby activating AKT in colon cancer.

High cardiovascular mortality risk among older merkel cell carcinoma patients.

OBJECTIVE: Previous research has primarily focused on the incidence and mortality rates of Merkel cell carcinoma (MCC), neglecting the examination of cardiovascular mortality (CVM) risk among survivors, particularly older patients. This study aims to assess the risk of CVM in older individuals diagnosed with MCC. METHODS: Data pertaining to older MCC patients were obtained from the Surveillance, Epidemiology, and End Results database (SEER). CVM risk was measured using standardized mortality ratio (SMR) and cumulative mortality. Multivariate Fine-Gray's competing risk model was utilized to evaluate the risk factors contributing to CVM. RESULTS: Among the study population of 2,899 MCC patients, 465 (16.0%) experienced CVM during the follow-up period. With the prolongation of the follow-up duration, the cumulative mortality rate for CVM reached 27.36%, indicating that cardiovascular disease (CVD) became the second most common cause of death. MCC patients exhibited a higher CVM risk compared to the general population (SMR: 1.69; 95% CI: 1.54-1.86, p < 0.05). Notably, the SMR for other diseases of arteries, arterioles, and capillaries displayed the most significant elevation (SMR: 2.69; 95% CI: 1.16-5.29, p < 0.05). Furthermore, age at diagnosis and disease stage were identified as primary risk factors for CVM, whereas undergoing chemotherapy or radiation demonstrated a protective effect. CONCLUSION: This study emphasizes the significance of CVM as a competing cause of death in older individuals with MCC. MCC patients face a heightened risk of CVM compared to the general population. It is crucial to prioritize cardiovascular health starting from the time of diagnosis and implement personalized CVD monitoring and supportive interventions for MCC patients at high risk. These measures are essential for enhancing survival outcomes.

Construction of an autophagy-related genes risk model as predicting prognosis: BAG1 suppresses growth of clear cell renal cell carcinoma.

BACKGROUND: The incidence of clear cell renal cell carcinoma (ccRCC) is increasing annually. While the cure rate and prognosis of early ccRCC are promising, the 5-year survival rate of patients with metastatic ccRCC is below 12%. Autophagy disfunction is closely related to infection, cancer, neurodegeneration and aging. Nevertheless, there has been limited exploration of the association between autophagy and ccRCC through bioinformatics analysis. METHODS: A novel risk model of autophagy-related genes (ARGs) was constructed to predict the prognosis of patients with ccRCC and guide the individualized treatment to some extent. Relevant data samples were obtained from the TCGA database, and ccRCC-related ARGs were identified by Pearson correlation analysis, leading to the establishment of a risk model covering 10 ccRCC-related ARGs. Many indicators were used to assess the accuracy of the risk model. RESULTS: Receiver operating characteristic (ROC) curve analysis showed that the risk model had high accuracy, indicating that the risk model could predict the prognosis of ccRCC patients. Moreover, the findings revealed significant differences about immune and metabolic features in low- and high-risk groups. The study also found that BAG1 within the risk model was closely related to the prognosis of ccRCC and an independent risk factor. In vitro and in vivo experiments validated for the first time that BAG1 could suppress the proliferation, migration, and invasion of ccRCC. CONCLUSION: The construction of ARGs risk model, can well predict the prognosis of ccRCC patients, and provide guidance for individual therapy to patients. It was also found that BAG1 has significant prognostic value for ccRCC patients and acts as a tumor suppressor gene in ccRCC. These findings have crucial implications for the prognosis and treatment of ccRCC patients.

Modeling creatine-kinase MB concentrations following coronary artery bypass grafting.

OBJECTIVES: An increase in cardiac biomarkers is a prerequisite to diagnose periprocedural myocardial infarction (PMI) after coronary artery bypass grafting (CABG). Early-phase risk detection may be aided by modeling time-dependent serum creatine kinase-MB (CK-MB) concentrations. This study aimed to model the kinetics of CK-MB, while identifying its influencing factors. METHODS: Patients who underwent elective CABG and had CK-MB measurements within 72 hours postoperatively were included. The primary outcome was the modeled post-hoc kinetics of CK-MB in patients without potential PMI. These patients were defined as having no potential PMI in case of absence of: ischemic electrocardiographic abnormalities, imaging abnormalities, in-hospital cardiac arrest, mortality, or postoperative unplanned catheterization. A web-based application was created using mixed-effect modeling to provide an interactive and individualized result. RESULTS: 635 patients underwent elective isolated CABG, resulting in 1589 CK-MB measurements. Of these, 609 patients (96%) had no potential PMI, while 26 (4%) had potential PMI. Male sex, aortic cross-clamp time, and cardioplegia type significantly impacted CK-MB concentrations. The diagnostic accuracy of the model had an area under the ROC curve of 82.8% (72.6-90.2%). A threshold of 7 µg/L yielded a sensitivity of 94% and a specificity of 80% (positive predictive value, 17%; negative predictive value, 99%) for excluding potential PMI in our own study population. CONCLUSION: CK-MB release after CABG depends on the timing of measurement, sex, aortic cross-clamp time, and cardioplegia type. The model at https://www.cardiomarker.com/ckmb can be validated, reproduced, refined, and applied to other biomarkers.

Establishment of precise prevention strategies for the occurrence and progression of coronary atherosclerotic heart disease using machine learning.

Background: Coronary atherosclerotic heart disease (CHD) is highly prevalent in Northwest China; however, effective preventive measures are limited. This study aimed to develop metabolic risk models tailored for the primary and secondary prevention of CHD in Northwest China. Methods: This hospital-based cross-sectional study included 744 patients who underwent coronary angiography. Data on demographic characteristics, comorbidities, and serum biochemical indices of the participants were collected. Three machine learning algorithms-recursive feature elimination, random forest, and least absolute shrinkage and selection operator-were employed to construct risk models. Model validation was performed using receiver operating characteristic and calibration curves, and the optimal cutoff values for significant risk factors were determined. Results: The predictive model for CHD onset included sex, overweight/obesity, and hemoglobin A1c (HbA1c) levels. For CHD progression to multiple coronary artery disease, the model included age, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and HbA1c levels. The model predicting an increased coronary Gensini score included sex, overweight/obesity, TC, LDL-C, high-density lipoprotein cholesterol, lipoprotein(a), and HbA1c levels. Notably, the optimal cutoff values for HbA1c and lipoprotein(a) for determining CHD progression were 6 % and 298 mg/L, respectively. Conclusions: Robust metabolic risk models were established, offering significant value for both the primary and secondary prevention of CHD in Northwest China. Weight loss, strict hyperglycemic control, and improvement in dyslipidemia may help prevent or delay the occurrence and progression of CHD in this region.

Endoplasmic reticulum stress-related features predict the prognosis of osteosarcoma and reveal STC2 as a novel risk indicator for disease progression.

Endoplasmic reticulum (ER) stress exerts significant effects on cell growth, proliferation, migration, invasion, chemoresistance, and angiogenesis in various cancers. However, the impact of ER stress on the outcomes of osteosarcoma patients remains unclear. In this study, we established an ER stress risk model based on The Cancer Genome Atlas (TARGET) osteosarcoma dataset to reflect immune features and predict the prognosis of osteosarcoma patients. Survival analysis revealed significant differences in overall survival among osteosarcoma patients with different ER stress-related risk scores. Furthermore, ER stress-related risk features were significantly associated with the clinical pathological characteristics of osteosarcoma patients and could serve as independent prognostic indicators. Functional enrichment analysis indicated associations of the risk model with cell chemotaxis, leukocyte migration, and regulation of leukocyte migration. Additionally, the ER stress-related risk model suggested the presence of an immunosuppressive microenvironment and immune checkpoint responses. We validated the significance of 7 ER stress-related genes obtained from LASSO regression analysis through RT-qPCR testing on osteosarcoma samples from a local hospital, and inferred the importance of STC2 based on the literature. Subsequently, IHC experiments using samples from 70 osteosarcoma cases and 21 adjacent tissue samples confirmed differential expression of STC2 between cancer and normal tissues, and explored the gene's expression in pan-cancer and its association with clinical pathological parameters of osteosarcoma. In conclusion, we have proposed an ER stress risk model as an independent prognostic factor and identified STC2 as a novel risk indicator for disease progression, providing a promising direction for further research and treatment of osteosarcoma.

Construction and validation of a risk model of proteinuria in patients with omicron COVID-19: retrospective cohort study.

BACKGROUND: To explore the risk factors of proteinuria in Omicron variant patients and to construct and verify the risk predictive model. METHODS: 1091 Omicron patients who were hospitalized from August 2022 to November 2022 at Tianjin First Central Hospital were defined as the derivation cohort. 306 Omicron patients who were hospitalized from January 2022 to March 2022 at the same hospital were defined as the validation cohort. The risk factors of proteinuria in derivation cohort were screened by univariate and multivariate logistic regression analysis, and proteinuria predicting scoring system was constructed and the receiver operating characteristic(ROC)curve was drawn to test the prediction ability. The proteinuria risk model was externally validated in validation cohort. RESULTS: 7 factors including comorbidities, blood urea nitrogen (BUN), serum sodium (Na), uric acid (UA), C reactive protein (CRP) and vaccine dosages were included to construct a risk predictive model. The score ranged from -5 to 16. The area under the ROC curve(AUC) of the model was 0.8326(95% CI 0.7816 to 0.8835, p < 0.0001). Similarly to that observed in derivation cohort, the AUC is 0.833(95% CI 0.7808 to 0.9002, p < 0.0001), which verified good prediction ability and diagnostic accuracy in validation cohort. CONCLUSIONS: The risk model of proteinuria after Omicron infection had better assessing efficiency which could provide reference for clinical prediction of the risk of proteinuria in Omicron patients.

Establishment of a prognostic model for pancreatic cancer based on mitochondrial metabolism related genes.

AIM: Pancreatic ductal adenocarcinoma (PAAD) is recognized as an exceptionally aggressive cancer that both highly lethal and unfavorable prognosis. The mitochondrial metabolism pathway is intimately involved in oncogenesis and tumor progression, however, much remains unknown in this area. In this study, the bioinformatic tools have been used to construct a prognostic model with mitochondrial metabolism-related genes (MMRGs) to evaluate the survival, immune status, mutation profile, and drug sensitivity of PAAD patients. METHOD: Univariate Cox regression and LASSO regression were used to screen the differentially expressed genes (DEGs), and multivariate Cox regression was used to develop the risk model. Kaplan-Meier estimator was employed to identify MMRGs signatures associated with overall survival (OS). ROC curves were utilized to evaluate the model's performance. Maftools, immunedeconv and CIBERSORT R packages were applied to analyze the gene mutation profiles and immune status. The corresponding sensitivity to pharmaceutical agents was assessed using oncoPredict R packages. RESULTS: A prognostic model with five MMRGs was developed, which defined the patients as high-risk showed lower survival rates. There was good consistency among individuals categorized as high-risk, showing elevated rates of genetic alterations, particularly in the TP53 and KRAS genes. Furthermore, these patients exhibited increased levels of immunosuppression, characterized by an increased presence of macrophages, neutrophils, Th2 cells, and regulatory T cells. Additionally, high-risk patients showed increased sensitivity to Sabutoclax and Venetoclax. CONCLUSION: By utilizing a gene signature associated with mitochondrial metabolism, a prognostic model has been established which could be a highly efficient method for predicting the outcomes of PAAD patients.

The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models.

PURPOSE: The HUNT Lung Cancer Model (HUNT LCM) predicts individualized 6-year lung cancer (LC) risk among individuals who ever smoked cigarettes with high precision based on eight clinical variables. Can the performance be improved by adding genetic information? METHODS: A polygenic model was developed in the prospective Norwegian HUNT2 study with clinical and genotype data of individuals who ever smoked cigarettes (n = 30749, median follow up 15.26 years) where 160 LC were diagnosed within six years. It included the variables of the original HUNT LCM plus 22 single nucleotide polymorphisms (SNPs) highly associated with LC. External validation was performed in the prospective Norwegian Tromsø Study (n = 2663). RESULTS: The novel HUNT Lung-SNP model significantly improved risk ranking of individuals over the HUNT LCM in both HUNT2 (p < 0.001) and Tromsø (p < 0.05) cohorts. Furthermore, detection rate (number of participants selected to detect one LC case) was significantly better for the HUNT Lung-SNP vs. HUNT LCM in both cohorts (42 vs. 48, p = 0.003 and 11 vs. 14, p = 0.025, respectively) as well as versus the NLST, NELSON and 2021 USPSTF criteria. The area under the receiver operating characteristic curve (AUC) was higher for the HUNT Lung-SNP in both cohorts, but significant only in HUNT2 (AUC 0.875 vs. 0.844, p < 0.001). However, the integrated discrimination improvement index (IDI) indicates a significant improvement of LC risk stratification by the HUNT Lung-SNP in both cohorts (IDI 0.019, p < 0.001 (HUNT2) and 0.013, p < 0.001 (Tromsø)). CONCLUSION: The HUNT Lung-SNP model could have a clinical impact on LC screening and has the potential to replace the HUNT LCM as well as the NLST, NELSON and 2021 USPSTF criteria in a screening setting. However, the model should be further validated in other populations and evaluated in a prospective trial setting.

A depression-related lncRNA signature predicts the clinical outcome and immune characteristics of gastric cancer.

Background: Depression and long non-coding RNA (lncRNA) have been reported to be associated with tumor progression and prognosis in gastric cancer (GC). This study aims to build a GC risk classification and prognosis model based on depression-related lncRNAs (DRLs). Methods: To develop a risk model, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses using RNA sequencing data of GC from The Cancer Genome Atlas (TCGA) and depression-related genes (DRGs) from previous studies. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, nomogram construction, pathway enrichment analysis, assessment of immunological features, and drug sensitivity testing were conducted using a series of bioinformatics methods. Results: Seven DRLs were identified to build a prognostic model, whose robustness was verified in an internal cohort. Subsequent prognostic analyses found that high risk scores were associated with worse overall survival (OS). Univariate and multivariate analyses revealed that the risk score could be used as an independent prognostic factor. Furthermore, the ROC curve indicated that the risk score had higher diagnostic efficiency than traditional clinicopathological features. The calibration curve confirmed the accuracy and reliability of the nomogram. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that there were differences in digestive system and nervous system-related pathways between the high- and low-risk groups. Results of tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) analyses indicated that patients in the low-risk group had a better response rate to immunotherapy. Finally, the results of drug sensitivity analysis showed that risk score could influence sensitivity to EHT 1864 in GC. Conclusion: We have successfully developed and verified a 7-DRL risk model which can assess the prognosis and immunological features and guide individualized therapy of GC patients.

A risk model of mortality rate in dialysis patients following transcatheter aortic valve replacement.

BACKGROUND: Dialysis patients undergoing transcatheter aortic valve replacement (TAVR) generally have poor prognosis compared with non-dialysis patients. Furthermore, there are few reliable risk models in this clinical setting. Therefore, we aimed to establish a risk model in dialysis patients undergoing TAVR that would be informative for their prognosis and the decision-making process of TAVR. METHODS: A total 118 dialysis patients (full cohort) with severe aortic stenosis underwent TAVR in our institute between 2012 and 2022. The patients of the full cohort were randomly assigned to two groups in a 2:1 ratio to form derivation and validation cohorts. Risk factors contributing to deaths were analyzed from the preoperative variables and a risk model was established from Cox proportional hazard model. RESULTS: There were 69 deaths following TAVR derived from infectious disease (43.5 %), cardiovascular-related disease (11.6 %), cerebral stroke or hemorrhage (2.9 %), cancer (1.4 %), unknown origin (18.8 %), and others (21.7 %) during the observational period (811 ±â€¯719 days). The cumulative overall survival rates using the Kaplan-Meier method at 1 year, 3 years, and 5 years in the full cohort were 82.8 %, 41.9 %, and 24.2 %, respectively. An optimal risk model composed of five contributors: peripheral vascular disease, serum albumin, left ventricular ejection fraction < 40 %, operative age, and hemoglobin level, was established. The estimated C index for the developed models were 0.748 (95 % CI: 0.672-0.824) in derivation cohort and 0.705 (95 % CI: 0.578-0.832) in validation cohort. The prediction model showed good calibration [intraclass correlation coefficient = 0.937 (95%CI: 0.806-0.981)] between actual and predicted survival. CONCLUSIONS: The risk model was a good indicator to estimate the prognosis in dialysis patients undergoing TAVR.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

A Nomogram for Predicting Cancer-Specific Survival in Young Patients With Advanced Lung Cancer Based on Competing Risk Model.

BACKGROUND: Young lung cancer is a rare subgroup accounting for 5% of lung cancer. The aim of this study was to compare the causes of death (COD) among lung cancer patients of different age groups and construct a nomogram to predict cancer-specific survival (CSS) in young patients with advanced stage. METHODS: Lung cancer patients diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and stratified into the young (18-45 years) and old (> 45 years) groups to compare their COD. Young patients diagnosed with advanced stage (IVa and IVb) from 2010 to 2015 were reselected and divided into training and validation cohorts (7:3). Independent prognostic factors were identified through the Fine-Gray's test and further integrated to the competing risk model. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), and calibration curve were applied for validation. RESULTS: The proportion of cancer-specific death (CSD) in young patients was higher than that in old patients with early-stage lung cancer (p < 0.001), while there was no difference in the advanced stage (p = 0.999). Through univariate and multivariate analysis, 10 variables were identified as independent prognostic factors for CSS. The AUC of the 1-, 3-, and 5-year prediction of CSS was 0.688, 0.706, and 0.791 in the training cohort and 0.747, 0.752, and 0.719 in the validation cohort. The calibration curves demonstrated great accuracy. The C-index of the competing risk model was 0.692 (95% CI: 0.636-0.747) in the young patient cohort. CONCLUSION: Young lung cancer is a distinct entity with a different spectrum of competing risk events. The construction of our nomogram can provide new insights into the management of young patients with lung cancer.

Prognostic role of chemokine-related genes in acute myeloid leukemia.

Background: Chemotactic cytokines play a crucial role in the development of acute myeloid leukemia (AML). Thus, investigating the mechanisms of chemotactic cytokine-related genes (CCRGs) in AML is of paramount importance. Methods: Using the TCGA-AML, GSE114868, and GSE12417 datasets, differential expression analysis identified differentially expressed CCRGs (DE-CCRGs). These genes were screened by overlapping differentially expressed genes (DEGs) between AML and control groups with CCRGs. Subsequently, functional enrichment analysis and the construction of a protein-protein interaction (PPI) network were conducted to explore the functions of the DE-CCRGs. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses identified relevant prognostic genes and developed a prognostic model. Survival analysis of the prognostic gene was performed, followed by functional similarity analysis, immune analysis, enrichment analysis, and drug prediction analysis. Results: Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study. Functional enrichment analysis indicated that DE-CCRGs were primarily involved in chemotactic cytokine-related functions and pathways. Six prognostic genes (CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2) were identified and incorporated into the risk model. The model's performance was validated using the GSE12417 dataset. Survival analysis showed significant differences in AML overall survival (OS) between prognostic gene high and low expression groups, indicating that prognostic gene might be significantly associated with patient survival. Additionally, nine different immune cells were identified between the two risk groups. Correlation analysis revealed that CCR2 had the most significant positive correlation with monocytes and the most significant negative correlation with resting mast cells. The tumor immune dysfunction and exclusion score was lower in the high-risk group. Conclusion: CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2 were identified as prognostic genes correlated to AML and the tumor immune microenvironment. These findings offerred novel insights into the prevention and treatment of AML.