High Risk Score Group Research Articles

Cell senescence-associated genes predict the malignant characteristics of glioblastoma

BackgroundGlioblastoma (GBM) is the most malignant, aggressive and recurrent primary brain tumor. Cell senescence can cause irreversible cessation of cell division in normally proliferating cells. According to studies, senescence is a primary anti-tumor mechanism that may be seen in a variety of tumor types. It halts the growth and spread of tumors. Tumor suppressive functions held by cellular senescence provide new directions and pathways to promote cancer therapy.MethodsWe comprehensively analyzed the cell senescence-associated genes expression patterns. The potential molecular subtypes were acquired based on unsupervised cluster analysis. The tumor immune microenvironment (TME) variations, immune cell infiltration, and stemness index between 3 subtypes were analyzed. To identify genes linked with GBM prognosis and build a risk score model, we used weighted gene co-expression network analysis (WGCNA), univariate Cox regression, Least absolute shrinkage and selection operator regression (LASSO), and multivariate Cox regression analysis. And the correlation between risk scores and clinical traits, TME, GBM subtypes, as well as immunotherapy responses were estimated. Immunohistochemistry (IHC) and cellular experiments were performed to evaluate the expression and function of representative genes. Then the 2 risk scoring models were constructed based on the same method of calculation whose samples were acquired from the CGGA dataset and TCGA datasets to verify the rationality and the reliability of the risk scoring model. Finally, we conducted a pan-cancer analysis of the risk score, assessed drug sensitivity based on risk scores, and analyzed the pathways of sensitive drug action.ResultsThe 3 potential molecular subtypes were acquired based on cell senescence-associated genes expression. The Log-rank test showed the difference in GBM patient survival between 3 potential molecular subtypes (P = 0.0027). Then, 11 cell senescence-associated genes were obtained to construct a risk-scoring model, which was systematically randomized to distinguish the train set (n = 293) and the test set (n = 292). The Kaplan-Meier (K-M) analyses indicated that the high-risk score in the train set (P < 0.0001), as well as the test set (P = 0.0053), corresponded with poorer survival. In addition, the high-risk score group showed a poor response to immunotherapy. The reliability and credibility of the risk scoring model were confirmed according to the CGGA dataset, TCGA datasets, and Pan-cancer analysis. According to drug sensitivity analysis, it was discovered that LJI308, a potent selective inhibitor of RSK pathways, has the highest drug sensitivity. Moreover, the GBM patients with higher risk scores may potentially be more beneficial from drugs that target cell cycle, mitosis, microtubule, DNA replication and apoptosis regulation signaling.ConclusionWe identified potential associations between clinical characteristics, TME, stemness, subtypes, and immunotherapy, and we clarified the therapeutic usefulness of cell senescence-associated genes.

Identification of prognosis-related gene features in low-grade glioma based on ssGSEA.

Low-grade gliomas (LGG) are commonly seen in clinical practice, and the prognosis is often poor. Therefore, the determination of immune-related risk scores and immune-related targets for predicting prognoses in patients with LGG is crucial. A single-sample gene set enrichment analysis (ssGSEA) was performed on 22 immune gene sets to calculate immune-based prognostic scores. The prognostic value of the 22 immune cells for predicting overall survival (OS) was assessed using the least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox analyses. Subsequently, we constructed a validated effector T-cell risk score (TCRS) to identify the immune subtypes and inflammatory immune features of LGG patients. We divided an LGG patient into a high-risk-score group and a low-risk-score group based on the optimal cutoff value. Kaplan-Meier survival curve showed that patients in the low-risk-score group had higher OS. We then identified the differentially expressed genes (DEGs) between the high-risk-score group and low-risk-score group and obtained 799 upregulated genes and 348 downregulated genes. The analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) show that DEGs were mainly concentrated in immune-related processes. In order to further explore the immune-related genes related to prognosis, we constructed a protein-protein interaction (PPI) network using Cytoscape and then identified the 50 most crucial genes. Subsequently, nine DEGs were found to be significantly associated with OS based on univariate and multivariate Cox analyses. It was further confirmed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were independent prognostic factors for LGG through batch survival analysis and a nomogram prediction model. In addition, we used an RT-qPCR assay to validate the bioinformatics results. The results showed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were highly expressed in LGG. Our study can provide a reference value for the prediction of prognosis in LGG patients and may help in the clinical development of effective therapeutic agents.

Added Value of Extragenital Sexually Transmitted Infection Testing in "IWantTheKit" Program Users.

The 2021 Centers for Disease Control and Prevention sexually transmitted infection treatment guidelines recommend extragenital testing for gonorrhea and chlamydia in men who have sex with men and for women based on reported behaviors and exposures. The "IWantTheKit (IWTK)" program is a free online platform for specimen self-collection and mail-in for combined chlamydia/gonorrhea testing. We sought to assess the additional diagnostic value of extragenital testing compared with genital testing only for chlamydia/gonorrhea and determine factors associated with a positive extragenital test result among IWTK users. From August 2013 to January 2022, 7612 unique IWTK users returned swabs for testing; 3407 (45%) users requested both genital and extragenital tests and were included in this analysis. Descriptive statistics were summarized for demographic characteristics, reported behaviors, and genital and extragenital test results, and data were stratified by gender and age group. A logistic regression model was used to estimate associations between factors and extragenital sexually transmitted infection positivity. Chlamydia positivity rates were 4.7%, 2.4%, and 1.5% at genital, extragenital, and both sites, respectively; for gonorrhea, 0.4%, 1.1%, and 0.4% were positive at those sites, respectively. Among women, age 25 years and younger was significantly associated with extragenital chlamydia (odds ratio [OR], 4.0; P = 0.010). Being in high-risk quiz score group was associated with extragenital chlamydia (OR, 2.6; P = 0.005) and extragenital gonorrhea in men and women (OR, 8.5; P = 0.005). Extragenital testing detected additional chlamydia and gonorrhea cases in the IWTK user population that would have been missed by genital-only testing, especially for women younger than 25 years and people reported to be at high risk.

Development and validation of the prognostic model based on autophagy-associated genes in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease. Many studies suggest that autophagy may be related to disease progression and prognosis in IPF. However, the mechanisms involved have not been fully elucidated. We incorporated 232 autophagy-associated genes (AAGs) and two datasets, GSE28042 and GSE27957, from the GEO database. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression were used to construct the autophagy-associated prognostic model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the functions of these autophagy-associated genes. CIBERSORT algorithm was used to calculate the immune cell infiltration between patients in the high-risk score and low-risk score groups. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was performed to explore the mRNA expression of five genes in the autophagy-associated risk model. We constructed a 5-autophagy-associated genes signature based on Univariate Cox analysis and LASSO regression. In our autophagy-associated risk model, IPF patients in the high-risk group demonstrated a poor overall survival rate compared to patients in the low-risk group. For 1-, 2-, and 3-year survival rates, the AUC predictive value of the AAG signature was 0.670, 0.787, and 0.864, respectively. These results were validated in the GSE27957 cohort, confirming the good prognostic effect of our model. GO and KEGG pathway analyses enriched immune-related pathways between the high-risk and low-risk groups. And there was also a significant difference in immune cell infiltration between two groups. And the results of qRT-PCR showed that the expression levels of FOXO1, IRGM, MYC, and PRKCQ were significantly decreased in the Peripheral Blood Mononuclear Cell (PBMC) of IPF patient samples. Our study constructed and validated an autophagy-associated risk model based on MYC, MAPK1, IRGM, PRKCQ, and FOXO1. And those five genes may influence the progression of IPF by regulating immune responses and immune cells.

HPB P46 Does the CholeS pre-operative risk score for laparoscopic cholecystectomy correlate to operative time? A single centre experience

Abstract Background NHS waiting lists for elective surgeries including non-urgent laparoscopic cholecystectomies (LC) have severely escalated during the Covid-19 pandemic, with some patients waiting over 2 years for their operations. LCs are highly variable in terms of operative time, difficulty, and risk making theatre utilisation a challenge to effectively clear waiting lists. Nassar et al.[1] developed and validated a pre-operative risk prediction score for predicting the difficult LC using an objective operative difficulty grading system. We aimed to assess if application of CholeS could be used to predict which LC may have longer operative times, and so aid theatre utilisation planning. Methods Consecutive elective LCs performed between May and October 2021 at our institution's day surgery unit were included and analysed. Each patient was scored retrospectively using the CholeS pre-operative risk score from electronic patient records. Operative time was obtained from theatre electronic record systems. Data was collected on conversion to open surgery, post-operative day of discharge, and intra or post-operative complications. Outliers with operating time recorded as &amp;lt;20 minutes were considered data entry errors and excluded from analysis. A ROC analysis was used, which determined a threshold value of 3. This value was used to divide patients into a low-risk (≤3 points) and high-risk (four and above) group. Two-sample independent t-test was used to compare mean operative time between the high-risk and low-risk CholeS score groups. Levene's test was used to determine if variance was equal between groups. SPSS version 27[2] was used for data analysis and statistical tests and p&amp;lt;0.05 was deemed significant. Results 81 LC were included for analysis. 53 patients were low-risk and 26 patients were high-risk. There was a significantly lower operative time in the low-risk group: low-risk = 57.6 minutes (95% CI 52.4–63.0) vs high-risk = 75.8 minutes (95% CI 58.7–92.9), p=0.046. Nine patients had surgeries lasting &amp;gt;90 minutes; 66% of these were in the high-risk LC group. 95% of patients were discharged on day 0, two patients on day 1, and two on day 2 or later. Three patients had conversion to open cholecystectomy and five patients had post-operative complications. Two out of three patients who required conversion to open cholecystectomies were in high-risk patients with high CholeS scores (7 and 10). In one patient, a cholecystoduodenal fistula was found. Cystic duct avulsion occurred in the other. Three out of five patients with post-operative complications were in the high-risk group, with corresponding higher Clavien-Dindo scores (3b, 2 and 2) when compared to the low-risk group (1 for both patients). Conclusions The CholeS pre-operative scoring system could be used to optimise LC theatre allocation. A score of ≤3 has a shorter operative time than a patient with a score of four or more (mean difference = 18.1 mins, 95% CI 4.4–31.9). Prediction of which LCs will be shorter operations could improve theatre utilisation and allow extra cases booking on the operating list. This, in turn, could help reduce the number of patients on the waiting list. Additionally, CholeS could be used to predict patients with more challenging and prolonged operations as well as those at higher risk of open conversion and complications. This could allow such patients to be managed by allocation to inpatient specialist upper GI specialist lists. [1] Nassar, A.H.M., Hodson, J., Ng, H.J. et al. Predicting the difficult laparoscopic cholecystectomy: development and validation of a pre-operative risk score using an objective operative difficulty grading system. Surg Endosc 34, 4549–4561 (2020). [2] IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.

Role of CHA2DS2-VASc score in predicting atrial fibrillation recurrence in patients undergoing pulmonary vein isolation with cryoballoon ablation.

Pulmonary vein isolation by cryoablation (PVI-C) is a standard therapy for the treatment of atrial fibrillation (AF). The CHA2DS2-VASc score is a well-established predictor of AF-related stroke. Whether the CHA2DS2-VASc score can also be useful in predicting the long-term clinical outcomes following PVI-C is still unsettled. The aim of this analysis was to evaluate the role of the CHA2DS2-VASc score in predicting AF recurrence after PVI-C. Patients with symptomatic AF underwent an index PVI-C. Data were collected prospectively in the framework of the 1STOP ClinicalService project. Patients were categorized into two groups: low risk (LR) and high risk (HR) based on CHA2DS2-VASc score (0-1 and ≥ 2, respectively). Out of 3313 patients, 1910 (57.6%) had a CHA2DS2-VASc score between 0 and 1, while 1403 (42.3%) had CHA2DS2-VASc > = 2. Patient characteristics were significantly different between the two cohorts, including age, sex, BMI, paroxysmal AF, history of stroke, diabetes, and ischemic cardiomyopathy. On the contrary, procedural times and acute complications were comparable. The 36-month freedom from AF after a single procedure was 72.5% (95% CI: 69.8-75.0) in the LR group and 65.9% (95% CI: 62.3-69.2) in the HR score group (HR: 1.26, 95% CI: 1.08-1.47, p = 0.001). After multivariate analysis, higher CHA2DS2-VASc score was still a significant predictor of the risk of AF recurrence (HR: 1.33; 1.10-1.60, p = 0.003). PVI-C is highly effective in the treatment of AF over the long term. A CHA2DS2-VASc score ≥ 2 is an independent predictor of AF recurrence during the follow-up and should be considered during the clinical management after the index procedure.

A Prognostic Risk Model of a Novel Oxidative Stress-Related Signature Predicts Clinical Prognosis and Demonstrates Immune Relevancy in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is among the most prevalent malignant lung cancers with a poor prognosis due to high invasiveness and lethality despite multiple treatments. Since the lung is an important organ associated with oxidative stress, and it has been confirmed that oxidative stress represents a potential cancer-specific depletion, it is of important significance to investigate and evaluate the clinical value of oxidative stress mechanisms regulating tumor cell apoptosis. Furthermore, there are few studies on the impact of the microenvironment on reaction to immune-checkpoint inhibitors (ICIs) in patients with LUAD. Based on the TCGA-LUAD dataset, which is stratified into a training set as well as a validation set in a ratio of 2 : 1, this investigation constructs and validates a prognostic predictive power of a gene signature model of oxidative stress-related prognostic signatures. To ascertain the differences between the high-risk score group and the low-risk score group in tumor-infiltrating lymphocytes and patients' response to ICI therapy. This oxidative stress-related prognostic gene signature is composed of MAP3K19 and NTSR1 and is an independent prognosis-related factor in the LUAD group. The outcome of patients having a low risk score is better, and the difference was statistically significant, and individuals with a low risk score had a larger number of infiltrating immune cell distribution in the tumor microenvironment, which was closely related to clinical outcome. Our study suggests that the synergistic effect of oxidative stress-related prognostic gene markers-MAP3K19 and NTSR1 has clinical significance in the prognosis identification and immunotherapy of LUAD patients. Thus, the results may help to better intersect the oxidative stress-related mechanisms in clinical value in LUAD but requires prospective validation.

Identification of a novel oxidative stress-related prognostic model in lung adenocarcinoma.

Background: Oxidative stress (OxS) participates in a variety of biological processes, and is considered to be related to the occurrence and progression of many tumors; however, the potential diagnostic value of OxS in lung cancer remains unclear. Methods: The clinicopathological and transcriptome data for lung adenocarcinoma (LUAD) were collected from TCGA and GEO database. LASSO regression was used to construct a prognostic risk model. The prognostic significance of the OxS-related genes was explored using a Kaplan-Meier plotter database. The prediction performance of the risk model was shown in both the TCGA and GSE68465 cohorts. The qRT-PCR was performed to explore the expression of genes. CCK-8, Edu and transwell assays were conducted to analyze the role of CAT on cell proliferation migration and invasion in lung cancer. Immune infiltration was evaluated by CIBERSORT and mutational landscape was displayed in the TCGA database. Moreover, the relationship between risk score with drug sensitivity was investigated by pRRophetic. Results: We identified a prognosis related risk model based on a four OxS gene signature in LUAD, including CYP2D6, FM O 3, CAT, and GAPDH. The survival analysis and ROC curve indicated good predictive power of the model in both the TCGA and GEO cohorts. LUAD patients in the high-risk group had a shorter OS compared to the low-risk group. QRT-PCR result showed that the expression of four genes was consistent with previous analysis in cell lines. Moreover, overexpression of CAT could decrease the proliferation, invasion and migration of lung cancer cells. The Cox regression analysis showed that the risk score could be used as an independent prognostic factor for OS. LUAD patients in the high-risk score group exhibited a higher tumor mutation burden and risk score were closely related to tumor associated immune cell infiltration, as well as the expression of immune checkpoint molecules. Both the high- and low-risk groups have significant differences in sensitivity to some common chemotherapy drugs, such as Paclitaxel, Docetaxel, and Vinblastine, which may contribute to clinical treatment decisions. Conclusion: We established a robust OxS-related prognostic model, which may contribute to individualized immunotherapeutic strategies in LUAD.

Identification and validation of ubiquitin-proteasome system related genes as a prognostic signature for papillary renal cell carcinoma.

The Cancer Genome Atlas (TCGA) and GeneCards databases were utilized to find the clinical data and gene expression patterns of patients with PRCC. Univariate Cox regression analysis and absolute shrinkage and selection operator (LASSO) analyses identified a risk signature formed by ten optimal UPS genes. The predictive value of the risk signature in TCGA-PRCC cohorts was evaluated using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. By utilizing GO enrichment and the KEGG pathway, the interactions of differentially expressed genes connected to ubiquitin-mediated proteolysis were functionally examined. The protein expression of the hub genes was affirmed using the Human Protein Atlas (HPA) database. The effectiveness of particular CDC20 and UBE2C in vitro was confirmed by experimental research. Ten of the best ubiquitin-mediated proteolysis genes (UBE2C, DDB2, CBLC, BIRC3, PRKN, UBE2O, SIAH1, SKP2, UBC, and CDC20) were detected to create a risk signature. The high-risk score group stratified was associated with advanced tumor status and poor survival of PRCC patients. 10 genes were also found to be associated with the cell cycle pathway and ubiquitin-mediated proteolysis to GO and KEGG analysis. Of these 10 genes, CDC20 and UBE2C are highly expressed in tumor tissue and correlated with cancer immunity founded on the analyses of the expression of human protein atlas and TISIDB. The downregulation of UBE2C facilitated tumor inhibition and the anti-immune effect was confirmed by in vitro experiments. Our results indicate that the risk model created from the ubiquitin-mediated proteolysis genes can be reliably and accurately predict the prognosis of PRCC patients, highlighting its targeted value for PRCC treatment. Particularly, the expression of UBE2C may be crucial for the prognosis and immunological treatment of renal cancer.

Potential value of the homologous recombination deficiency signature we developed in the prognosis and drug sensitivity of gastric cancer.

Background: Homologous recombination is an important DNA repair mechanism, which deficiency is a common feature of many cancers. Defining homologous recombination deficiency (HRD) status can provide information for treatment decisions of cancer patients. HRD score is a widely accepted method to evaluate HRD status. This study aimed to explored HRD in gastric cancer (GC) patients' clinical outcomes with genes related to HRD score and HRD components score [HRD-loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (NtAI)]. Methods: Based on LOH, NtAI scores, LST, and integrated HRD scores-related genes, a risk model for stratifying 346 TCGA GC cases were developed by Cox regression analysis and LASSO Cox regression. The risk scores of 33 cancers in TCGA were calculated to analyze the relationship between risk scores of each cancer and HRD scores and 3 HRD component scores. Relationship between the risk model and patient survival, BRCA1, BRCA2 mutation, response to Cisplatin and Talazoparib treatment was analyzed by generating Kaplan-Meier curve, mutations waterfall map and conducting Pearson correlation analysis. Results: An gene signature was constructed based on 11 HRD scores-related gene (BEX2, C1QL2, DKK1, DRC1, GLUD2, HCAR1, IGFBP1, NXPH1, PROC, SERPINA5, and SLCA1A2). Risk groups were stratified by risk score. Prognosis of the high-risk score group was worse than the low-risk ones. Risk score was associated with BRCA2 mutation, and patients grouped according to BRCA2 mutation status had distinguishable risk score, NtAI score, HRD-LOH, LST, and HRD scores. The low-score group showed higher sensitivity to Cisplatin and Talazoparib. The risk score of adrenocortical carcinoma (ACC), stomach adenocarcinoma (STAD), uterine corpus endometrial carcinoma (UCEC), kidney renal clear cell carcinoma (KIRC), sarcoma (SARC), prostate adenocarcinoma (PRAD), breast invasive carcinoma (BRCA) was significantly positively correlated with HRD score. Conclusion: We developed an 11 HRD scores-related genes risk model and revealed the potential association between HRD status and GC prognosis, gene mutations, patients' sensitivity to therapeutic drugs.

Characterization of the basement membrane in kidney renal clear cell carcinoma to guide clinical therapy.

Renal cell carcinoma (RCC) is the most common kidney cancer in adults. According to the histological features, it could be divided into several subtypes, of which the most common one is kidney renal clear cell carcinoma (KIRC), which contributed to more than 90% of cases for RCC and usually ends with a dismal outcome. Previous studies suggested that basement membrane genes (BMGs) play a pivotal role in tumor development. However, the significance and prognostic value of BMGs in KIRC still wrap in the mist. KIRC data were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A prognostic risk score (PRS) model based on BMGs was established using univariate and least absolute shrinkage and selection operator (LASSO) and the Cox regression analysis was performed for prognostic prediction. The Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curves, nomogram, and calibration curves were utilized to evaluate and validate the PRS model. All KIRC cases were divided into the high-risk score (HRS) group and the low-risk score (LRS) group according to the median risk scores. In addition, single-sample gene set enrichment analysis (ssGSEA), immune analysis, tumor microenvironment (TME) analysis, principal component analysis (PCA), and half-maximal inhibitory concentration (IC50) were also applied. Expression levels of BMGs were confirmed by qRT-PCR in both human renal cancer cell lines and tissues. We established the BMGs-based prognostic model according to the following steps. Within the TCGA cohort, patients' prognosis of the HRS group was significantly worse than that of the LRS group, which was consistent with the analysis results of the GEO cohort. PCA patterns were significantly distinct for LRS and HRS groups and pathological features of the HRS group were more malignant compared with the LRS group. Correlation analysis of the PRS model and TME features, such as immune cell scores, stromal cell scores, and ESTIMATE values, revealed a higher immune infiltration in the HRS group compared with the LRS group. The chemotherapeutic response was also evaluated in KIRC treatment. It showed that the HRS group exhibited stronger chemoresistance to chemotherapeutics like FR-180204, GSK1904529A, KIN001-102, and YM201636. The therapeutic reactivity of the other 27 chemotherapeutic agents was summarized as well. Furthermore, the FREM2 level was measured in both human kidney tissues and associated cell lines, which suggested that lower FREM2 expression prompts a severer pathology and clinical ending. Our study showed that KIRC is associated with a unique BMG expression pattern. The risk scores related to the expression levels of 10 BMGs were assessed by survival status, TME, pathological features, and chemotherapeutic resistance. All results suggested that FREM2 could be a potential candidate for KIRC prognosis prediction. In this study, we established a valid model and presented new therapeutic targets for the KIRC prognosis prediction as well as the clinical treatment recommendation, and finally, facilitated precision tumor therapy for every single individual.

Abstract 14926: VT LVAD Score and Survival Free From Ventricular Arrhythmias: The University of Michigan Experience

Introduction: Ventricular arrhythmias (VA) are a common complication after left ventricular assist device (LVAD) implantation and contribute to increased mortality. The VT LVAD Score was created to help stratify patients into four risk categories: Low Risk, Moderate Risk, High Risk, and Very High Risk. Hypothesis: We sought to assess the performance of the VT LVAD score at our institution. Methods: Consecutive patients who underwent LVAD placement from 2015-2019 were reviewed. VT LVAD scores were correlated with VA free survival (VA-FS). Only 1 patient was in the low-risk group and was excluded from analysis. Results: A total of 237 patients who underwent LVAD implant (age 57.9 ± 14.6 years, 180 male, 89 ischemic cardiomyopathy, 102 with prior VAs, 102 Heartmate 3) were included. Of the 5 variables included in the VT LVAD score, only prior VA was associated VA-FS (HR 3.09[1.89-5.03], p&lt;0.001 (Figure 1A), log rank p=0.003, Figure 1D). There was a significant difference in VA-FS between moderate versus both high and very high-risk groups (p=0.033 and p=0.029 respectively) as well as moderate vs high/very high-risk combined (p=0.041, Figure 1B). The significant difference in VA-FS between moderate vs high/very high-risk score groups persisted among all patients with prior VA (p=0.04, Fig 1C). No other between-group differences in VA-FS were seen (Figure 1B and 1C, p&gt;0.05 for all). Conclusions: Patients with a VT LVAD score of high or very high-risk had worse survival free from VA than patients with moderate-risk score, no other between group differences were noted. History of VA is a simple and powerful risk factor for late VA post LVAD.

THBS1, a fatty acid-related metabolic gene, can promote the development of laryngeal cancer

Laryngeal cancer is the second most prevalent head and neck tumor and it is one of the most common malignancies of the upper respiratory tract. Fatty acid metabolism affects cancer cell biology in several ways, and alterations in fatty acid metabolism are characteristic of both tumorigenesis and metastasis. Despite advances in laryngeal cancer diagnosis and treatment over the years, there has been no significant improvement in survival or mortality. Studying the role of fatty acid metabolism-related genes in laryngeal cancer will facilitate our search for valuable biomarkers to guide prognostic management and treatment selection. We constructed a prognostic risk score model for fatty acid metabolism-related genes by downloading and analyzing laryngeal cancers from the TCGA and GEO databases. We predicted survival outcomes of laryngeal cancer patients using a prognostic risk score model of fatty acid metabolism-related genes and analyzed the resistance of laryngeal cancer in different individuals to multiple drugs. In addition, the relationship between the prognostic risk score model and cellular infiltration characteristics of the tumor microenvironment were investigated. Through the prognostic risk scoring model, the genes with risk-prompting effect and related to prognosis were screened out for further research. Through the study of gene expression levels in the TCGA database, we screened out 120 differentially expressed fatty acid metabolism genes. LASSO-Cox and Cox regression analyses identified nine genes associated with prognosis to construct a prognostic risk score model for genes related to fatty acid metabolism. Both TCGA and GEO confirmed that samples in the high-risk score group had a worse prognosis than those in the low-risk score group. We found significant differences between the high-risk and low-risk groups for 22 drugs (P < 0.05). In addition, we found differences in immune cell infiltration between the different risk score groups. Finally, through the risk assessment model, combined with multiple databases, THBS1, a high-risk and prognosis-related gene, was screened. We also found that THBS1 could promote the migration, invasion and proliferation of laryngeal cancer cells by constructing THBS1 knockout cell lines. In our study, we identified key fatty acid-related genes differentially expressed in laryngeal carcinoma that can be used to adequately predict prognosis using a comprehensive bioinformatic experimental approach. It was also found that THBS1, a high-risk and prognosis-related gene, may regulate the occurrence and development of laryngeal cancer through fatty acid metabolism, which has further helped us to explore the role of fatty acid metabolism genes in laryngeal cancer.

Identification of tryptophan metabolic gene-related subtypes, development of prognostic models, and characterization of tumor microenvironment infiltration in gliomas.

Epigenetic regulation and immunotherapy of tumor microenvironment (TME) is a hot topic in recent years. However, the potential value of tryptophan metabolism genes in regulating TME and immunotherapy is still unclear. A comprehensive study of glioma patients was carried out based on 40 tryptophan metabolic genes. Subsequently, these prognostic tryptophan metabolic genes are systematically associated with immunological characteristics and immunotherapy. A risk score model was constructed and verified in the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts to provide guidance for prognosis prediction and immunotherapy of glioma patients. We described the changes of tryptophan metabolism genes in 966 glioma samples from genetic and transcriptional fields and evaluated their expression patterns from two independent data sets. We identified two different molecular subtypes and found that two subtypes were associated with clinicopathological features, prognosis, TME cell infiltration, and immune checkpoint blockers (ICBs). Then, four genes (IL4I1, CYP1A1, OGDHL, and ASMT) were screened out by univariate and multivariate cox regression analysis of tryptophan metabolism genes, and a risk score model for predicting the overall survival (OS) of glioma patients was constructed. And its predictive ability is verified using the CGGA database. At the same time, we verified the expression of IL4I1, CYP1A1, OGDHL, and ASMT four genes in glioma specimens and cell lines in GES4260 and GES15824. Therefore, we constructed a nomogram to improve the clinical applicability of the risk assessment model. The high risk score group, characterized by increased TMB and immune cell infiltration, was also sensitive to temozolomide immunotherapy. Our comprehensive analysis of tryptophan metabolic genes in gliomas shows that they play a potential role in tumor immune stromal microenvironment, clinicopathological features, and prognosis. Tryptophan metabolism genes play an indispensable role in the complexity, diversity, and prognosis of TME. This risk score model based on tryptophan metabolism gene is a new predictor of clinical prognosis and immunotherapy response of glioma, and guides a more appropriate immunotherapy strategy for glioma patients.

A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population.

This study aimed to distinguish the risk factors for type 2 diabetes mellitus (T2DM) and construct a predictive model of T2DM in Japanese adults with abdominal obesity. This study was a post hoc analysis. A total of 2012 individuals with abdominal obesity were included and randomly divided into training and validation groups at 70% (n = 1518) and 30% (n = 494), respectively. The LASSO method was used to screen for risk variables for T2DM, and to construct a nomogram incorporating the selected risk factors in the training group. We used the C-index, calibration plot, decision curve analysis, and cumulative hazard analysis to test the discrimination, calibration and clinical significance of the nomogram. In the training cohort, the C-index and receiver operating characteristic were 0.819 and the 95% CI was 0.776-0.858, with a specificity and sensitivity of 77% and 74.68%, respectively. In the validation cohort, the C-index was 0.853; sensitivity and specificity were 77.6% and 88.1%, respectively. The decision curve analysis showed that the model's prediction was effective and cumulative hazard analysis demonstrated that the high-risk score group was more likely to develop T2DM than the low-risk score group. This nomogram may help clinicians screen abdominal obesity at a high risk for T2DM.

Comprehensive analysis of chemokines family and related regulatory ceRNA network in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the world's commonest malignancies with a high fatality rate. Chemokines not only regulate immune response but also participate in tumor development and metastasis and yet the mechanism of chemokines in LUAD remains unclear. In this study, transcriptional expression profiles, mutation data, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Risk gene protein expression was assessed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (HPA). Gene Expression Omnibus (GEO) data was used to validate the prognostic model. We summarized the genetic mutation variation landscape of chemokines. The risk prognosis model was developed based on differentially expressed chemokines, and patients in the high-risk score (RS) group had lower survival rates. Gene Set Enrichment Analysis (GSEA) revealed that high-RS patients were associated with metabolic transformation pathways, while low-RS patients were associated with immune-related pathways. Compared with the high-RS group, the low-RS group had higher immune/stromal/estimate scores calculated by the ESTIMATE package. The proportion of immune cells obtained using the CIBERSORT package was significantly different between the two groups. Most of the immune checkpoints were highly expressed in low-RS samples. Finally, we discovered that the lncRNA MIR17HG/AC009299.3/miR-21–5p/CCL20 regulatory network might be crucial in the pathogenesis of LUAD. In conclusion, we developed a risk signature and chemokine-related competing endogenous RNA (ceRNA) network.

Differences of macrophages in the tumor microenvironment as an underlying key factor in glioma patients.

Macrophages, the major immune cells in glioma microenvironment, are closely related to tumor prognosis. Further studies are needed to investigate macrophages, which will be helpful to fully understand the role of it and early achieve clinical translation. A total of 1334 glioma cases were enrolled in this study from 3 databases. In our works, the single cell cohorts from GSE89567, GSE84465, and the Chinese Glioma Genome Atlas (CGGA) datasets were used to analyze the key genes of macrophage. The bulk sequencing data from the Cancer Genome Atlas (TCGA) and CGGA datasets were respectively divided into the training set and validation set to test prognostic value of the key genes from single cell analysis. Quantitative and functional differences significantly emerge in macrophage clusters between LGG and GBM. Firstly, we used the Seurat R package to identify 281 genes differentially expressed genes in macrophage clusters between LGG and GBM. Furthermore, based on these genes, we developed a predictive risk model to predict prognosis and reflect the immune microenvironment in glioma. The risk score calculation formula was yielded as follows: Risk score = (0.11 × EXPMACC1) + (-0.31 × EXPOTUD1) + (-0.09 × EXPTCHH) + (0.26 × EXPADPRH) + (-0.40× EXPABCG2) + (0.21 × EXPPLBD1) + (0.12 × EXPANG) + (0.29 × EXPQPCT). The risk score was independently related to prognosis. Further, significant differences existed in immunological characteristics between the low- and high-risk score groups. What is more, mutation analysis found different genomic patterns associated with the risk score. This study further confirms that the proportion of macrophage infiltration is not only significantly different, but the function of them is also different. The signature, identified from the differentially expressed macrophage-related genes impacts poor prognosis and short overall survival and may act as therapeutic targets in the future.

Identification of tumor antigens and immunogenic cell death-related subtypes for the improvement of immunotherapy of breast cancer.

The current immunotherapy strategy for breast cancer is limited. Tumor neoantigens have been proven to be a promising biomarker and potential target of immunotherapy in a variety of tumors. However, their effectiveness for breast cancer remains unclear. Immunogenic cell death (ICD) is a regulated form of cell death that can reshape the tumor immune microenvironment and activate adaptive immune responses. To this end, we screened potential antigens that could be used both for the development of immunotherapy and differentiating the patient-specific immune responses based on ICD-related risk signatures, in order to formulate an accurate scheme for breast cancer immunotherapy. We retrieved the gene expression profiles of the breast invasive cancer cohort and their corresponding clinical control data from The Cancer Genome Atlas. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to evaluate tumor antigen expression, the cBioPortal program was used to identify genetic variations, and the TIMER website was used to estimate the immune infiltration signatures. The risk score predictive model based on the ICD-related genes was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm, and the cohort was divided into low- and high-risk score groups. Two tumor antigens, namely, CCNE1 and PLK1, were associated with poor prognosis and infiltration of antigen-presenting cells. Furthermore, the ICD-related risk signature could significantly predict survival outcomes. The risk groups based on the ICD-related signature predictive model showed diverse immune infiltration and molecular and clinical features. The high-risk group was associated with low immune cell infiltration, immune score, expression of immune checkpoints, and human leukocyte antigen genes but high levels of CCNE1 and PLK1 and poor survival outcome. In conclusion, CCNE1 and PLK1 were identified as potential antigens in breast cancer. The ICD-related prognostic model distinguished immune response heterogeneity and predicted prognosis. Patients with high ICD-related risk scores were suitable to receive combination treatments based on CCNE1 or PLK1 and immune checkpoint inhibitors. In the future, these results will help us develop more accurate treatment schemes for patients with breast cancer.

Identification of cuproptosis-related subtypes and development of a prognostic signature in colorectal cancer

Cuproptosis, a novel form of copper-mediated regulated cell death, participates in tumor progression. However, the role of cuproptosis-related genes (CRGs) in colorectal cancer (CRC) remains unclear. We aimed to investigate the cuproptosis subtypes and build a predictive model to improve the prognosis of patients with CRC. Gene expression data were downloaded from the TCGA database to identify distinct molecular subtypes using a non-negative matrix factorization algorithm. A robust and efficient prognostic signature was constructed by performing multivariate Cox regression analysis and further validated using the Gene Expression Omnibus cohort. Based on the gene expression matrix of CRC, the abundance of infiltrating immune cells and tumour microenvironment scores were calculated using the CIBERSORT and ESTIMATE algorithms, respectively. The pRRophetic algorithm was used to predict the sensitivity of the patients to different chemotherapy drugs. Two distinct molecular subtypes were identified based on 41 CRGs, with subtype C1 being characterized by an advanced clinical stage and worse overall survival. A prognostic signature was constructed based on the DEGs between the two cuproptosis subtypes, and its predictive ability was validated in an external database. Patients with CRC who belonged to the low-risk group had significantly higher survival rates than those who belonged to the high-risk group. Additionally, it remained a valid prognostic indicator in strata of age, sex, tumor location, and TNM stage, and its significance persisted after the multivariate Cox regression analysis. By further analyzing the prognostic signature, a higher immune score was observed in the low-risk group, which presented a better prognosis. AKT.inhibitor.VIII, doxorubicin, lenalidomide, and tipiparnib were more sensitive in the high-risk score group. A highly accurate nomogram was constructed to improve clinical application of the risk score. Compared with an ideal nomogram, our model, consisting of clinicopathological features, performed well in predicting patient survival. In conclusion, our study provides new ways and perspectives for the prediction of the prognosis of patients with CRC and guide more effective treatment regimens.

Development and validation of a gene signature for pancreatic cancer: based on inflammatory response-related genes.

Pancreatic cancer (PC) is one of the most common malignant tumors in the world with a poor prognosis. There were limited studies investigating the genetic signatures associated with inflammatory responses, tumor microenvironment (TME), and tumor drug sensitivity prediction. In the Cancer Genome Atlas (TCGA) dataset, we constructed an inflammatory response-related genes prognostic signature for PC, and predictive ability of the model was assessed via the International Cancer Genome Consortium (ICGC) database. Then, we explored the differences of TME, immune checkpoint genes and drug resistance genes, and the cancer cell sensitivity to chemotherapy drugs between different risk score group. Based on the TCGA and ICGC databases, we constructed and validated a prognostic model, which consisted of 5 genes (including AHR, F3, GNA15, IL18, and INHBA). Moreover, the prognostic model was independent prognostic factors affecting overall survival (OS). The low-risk score group had better OS, and lower stromal score, compared with patients in the high-risk score group. The difference of antigen-presenting cells, T cell regulation, and drug resistance genes between different risk score groups was found. In addition, the immune checkpoint genes were positively correlation to risk score. The expression levels of AHR, GNA15, IL18, and INHBA were related to the sensitivity of anti-tumor chemotherapy drugs. Gene set enrichment analysis (GSEA) showed significant pathway such as calcium signaling pathway and p53 signaling pathway. We successfully constructed a 5-inflammatory response-related gene signature to predict survival, TME, and cancer cell sensitivity to chemotherapy drugs in PC patients. Furthermore, substantiation was warranted to verify the role of these genes in tumorigenesis.