Univariate Cox Analysis Research Articles

Prognostic significance of the novel immunonutritional marker of cholesterol-to-lymphocyte ratio in patients with non-metastatic breast cancer

BackgroundAlthough there is a strong correlation between the novel cholesterol-to-lymphocyte ratio (CLR) and tumor survival, its prognostic significance in breast cancer (BC) is unknown. After analyzing the relationship between CLR and the overall survival (OS) of patients with BC, we created a predictive model.MethodsFollowing retrospective enrollment, 1316 patients with BC were randomized into two cohorts: validation (n = 392) and training (n = 924). Distinct factors within the training dataset were identified for OS by univariate and multivariate Cox analyses; two-tailed P-value < 0.05 were considered to indicate statistical significance. On this premise, we developed novel signals for survival prediction and utilized the calibration curve, receiver operating characteristic curves, and concordance index (C-index) to validate their efficacy across both datasets.ResultsPatients with BC were categorized into two categories with differing prognoses based on the CLR score [hazard ratio = 0.492; 95% confidence interval (CI): 0.286–0.846, P = 0.009]. A prediction nomogram was created based on multivariate analysis, which showed that N stage, postoperative pathological categorization, and CLR score were all independently correlated with OS. In the training [C-index = 0.831 (95% CI: 0.788–0.874)] and validation [C-index = 0.775 (95% CI: 0.694–0.856)] cohorts, the nomogram demonstrated favorable performance in predicting OS. In both the training and validation cohorts, it outperformed the traditional staging system [C-index = 0.702 (95% CI: 0.623–0.782)] and [C-index = 0.709 (95% CI: 0.570–0.847)]. The accurate prediction by the signature was further demonstrated by the time-dependent receiver operating characteristic curves.ConclusionsThe novel immunonutritional marker CLR could function as a simplified, cost-effective, easily accessible, non-invasive, and readily promotive prognostic indicator for patients with early-stage BC and demonstrates superior predictive power than the traditional staging system.

Knockdown of Anoikis-Associated Gene OCIAD2 Reduces Proliferation and Migration of Glioblastoma Cell Lines.

Glioblastoma (GBM) severely disrupts the quality of life of patients. Anoikis represents a significant mechanism in cancer invasion and metastasis. Our study focused on the prognostic relationship between the anoikis-associated gene and GBM and its effect on GBM cell progression. We downloaded 656 and 979 GBM sample data from TCGA and CGGA cohort datasets, respectively. Fifteen anoikis-associated genes were obtained from the GeneCards database and were subsequently clustered to identify differential genes associated with them. After LAASO screening, the expression values of the 5 differential genes were the sum of LASSO regression coefficients. Survival analysis and ROC curve analysis of anoikis scores were performed using the TCGA training and CGGA validation sets. The prognostic factors were analyzed using Cox regression analysis in GBM. Moreover, CCK-8, colony formation, wound healing, and transwell assay were used to evaluate GBM cell proliferation and migration. Significant differences were observed in the 5-year survival of GBM patients between the two subgroups. Then, our analysis demonstrated that high OCIAD2, FTLP3, IGFBP2, and H19 levels were associated with lower 5-year GBM survival rates, whereas high SFRP2 levels were associated with higher survival rates. Univariate Cox analysis indicated that GBM risk was linked to both anoikis score and grade, while multivariate Cox analysis indicated that GBM risk was associated with both anoikis score and age. Additionally, OCIAD2 was highly expressed in U251MG and T98G cells. Moreover, OCIAD2 silencing inhibited GBM cell proliferation and migration. This study demonstrated the potential of the anoikis-associated gene OCIAD2 as a prognostic biomarker for GBM. Furthermore, we validated in vitro that OCIAD2 promoted GBM cell progression.

LncRNA signature mediates mitochondrial permeability transition-driven necrosis in regulating the tumor immune microenvironment of cervical cancer

Mitochondrial permeability transition (MPT)-driven necrosis (MPTDN) was a regulated variant of cell death triggered by specific stimuli. It played a crucial role in the development of organisms and the pathogenesis of diseases, and may provide new strategies for treating various diseases. However, there was limited research on the mechanisms of MPTDN in cervical cancer (CESC) at present. In this study, Weighted Gene Co-expression Network Analysis (WGCNA) was performed on differentially expressed genes in CESC. The module MEyellow, which showed the highest correlation with the phenotype, was selected for in-depth analysis. It was found that the genes in the MEyellow module may be associated with the tumor immune microenvironment (TIME). Through COX univariate regression and LASSO regression analysis, 6 key genes were identified. These genes were further investigated from multiple perspectives, including their independent diagnostic value, prognostic value, specific regulatory mechanisms in the tumor immune microenvironment, drug sensitivity analysis, and somatic mutation analysis. This study provided a comprehensive exploration of the mechanisms of action of these 6 key genes in CESC patients. And qRT-PCR validation was also conducted. Through COX univariate regression and LASSO coefficient screening of the MEyellow module, 6 key genes were identified: CHRM3-AS2, AC096734.1, BISPR, LINC02446, LINC00944, and DGUOK-AS1. Evaluation of the independent diagnostic value of these 6 key genes revealed that they can serve as independent diagnostic biomarkers. Through correlation analysis among these 6 genes, a potential regulatory mechanism among them was identified. Therefore, a risk prognostic model was established based on the collective action of these 6 genes, and the model showed good performance in predicting the survival period of CESC patients. By studying the relationship between these 6 key genes and the tumor microenvironment of CESC patients from multiple angles, it was found that these 6 genes are key regulatory factors in the tumor immune microenvironment of CESC patients. Additionally, 16 drugs that are associated with these 6 key genes were identified, and 8 small molecule drugs were predicted based on the lncRNA-mRNA network. The 6 key genes can serve as independent biomarkers for diagnosis, and the Risk score of these genes when acting together can be used as an indicator for predicting the clinical survival period of CESC patients. Additionally, these 6 key genes were closely related to the tumor immune microenvironment of CESC patients and were the important regulatory factors in the tumor immune microenvironment of CESC patients.

Construction of prognostic markers for gastric cancer and comprehensive analysis of pyroptosis-related long non-coding RNAs.

China's most frequent malignancy is gastric cancer (GC), which has a very poor survival rate, and the survival rate for patients with advanced GC is dismal. Pyroptosis has been connected to the genesis and development of cancer. The function of pyroptosis-related long non-coding RNAs (PRLs) in GC, on the other hand, remains uncertain. To explore the construction and comprehensive analysis of the prognostic characteristics of long non-coding RNA (lncRNA) related to pyroptosis in GC patients. The TCGA database provided us with 352 stomach adenocarcinoma samples, and we obtained 28 pyroptotic genes from the Reactome database. We examined the correlation between lncRNAs and pyroptosis using the Pearson correlation coefficient. Prognosis-related PRLs were identified through univariate Cox analysis. A predictive signature was constructed using stepwise Cox regression analysis, and its reliability and independence were assessed. To facilitate clinical application, a nomogram was created based on this signature. we analyzed differences in immune cell infiltration, immune function, and checkpoints between the high-risk group (HRG) and low-risk group (LRG). Five hundred and twenty-three PRLs were screened from all lncRNAs (absolute correlation coefficient > 0.4, P < 0.05). Nine PRLs were included in the risk prediction signature that was created through stepwise Cox regression analysis. We determined the risk score for GC patients and employed the median value as the dividing line between HRG and LRG. The ability of the risk signature to predict the overall survival (OS) of GC is demonstrated by the Kaplan-Meier analysis, risk curve, receiver operating characteristic curve, and decision curve analysis curve. The risk signature was shown to be an independent prognostic factor for OS in both univariate and multivariate Cox regression analyses. HRG showed a more efficient local immune response or modulation compared to LRG, as indicated by the predicted signal pathway analysis and examination of immune cell infiltration, function, and checkpoints (P < 0.05). In general, we have created a brand-new prognostic signature using PRLs, which may provide ideas for immunotherapy in patients with GC.

Prognostic value of lactate dehydrogenase to absolute lymphocyte count ratio and albumin to fibrinogen ratio in diffuse large B-cell lymphoma.

With the continuous improvement of treatment strategy, the prognostic value of international prognostic index (IPI) alone is limited for diffuse large B-cell lymphoma (DLBCL). Our study aims to explore the effect of lactate dehydrogenase (LDH)to absolute lymphocyte count (ALC) ratio (LAR) and albumin to fibrinogen ratio (AFR) on the prognosis of patients with DLBCL. The venous blood LDH, ALC, albumin and fibrinogen within 1 week before the first chemotherapy in 74 DLBCL patients were collected to calculate the LAR and AFR values. The impact of LAR and AFR on the progression-free survival (PFS) of patients with DLBCL was studied by the survival analysis. The area under the receiver operating characteristic curve (AUC) and concordance index (C-index) were used to analyze the predictive efficiency of each model for the PFS of DLBCL patients. Cox univariate analysis suggested that elevated LAR (P < .001) and decreased AFR (P < .001) were risk factors for PFS in DLBCL patients. Multivariate analysis revealed that LAR (P < .001) and AFR (P = .004) were 2 independent prognostic parameters. The AUC values of IPI, AFR + IPI, LAR + IPI and AFR + LAR + IPI to predict the PFS of DLBCL patients were 0.806 (95%CI 0.707-0.905, P < .001), 0.839 (95%CI 0.747-0.932, P < .001), 0.851 (95%CI 0.764-0.938, P < .001), and 0.869 (95%CI 0.787-0.952, P < .001), respectively. The C-index values of above 4 models were 0.802 (95%CI 0.629-0.975, P < .001), 0.842 (95% CI 0.735-0.949, P < .001), 0.846 (95%CI 0.716-0.976, P < .001), and 0.864 (95%CI 0.781-0.941, P < .001), respectively. The results suggest that both LAR and AFR are independent prognostic factors for PFS in DLBCL patients. Furthermore, their combination with IPI has better predictive efficiency for the prognosis of DLBCL patients.

Construction of a prognostic model for ovarian cancer based on a comprehensive bioinformatics analysis of cuproptosis-associated long non-coding RNA signatures

Ovarian cancer (OCa) is a common malignancy in women, and the role of cuproptosis and its related genes in OCa is unclear. Using the GSE14407 dataset, we analyzed the expression and correlation of cuproptosis-related genes (CRGs) between tumor and normal groups. From the TCGA-OV dataset, we identified 20 cuproptosis-related long non-coding RNAs (CuLncs) associated with patient survival through univariate Cox analysis. OCa patients were divided into early-stage and late-stage groups to analyze CuLncs expression. Cluster analysis classified patients into two clusters, with Cluster1 having a poorer prognosis. Significant differences in “Lymphatic Invasion” and “Cancer status” were observed between clusters. Seven CRGs showed significant expression differences, validated using the human protein atlas (HPA) databases. Immune analysis revealed a higher ImmuneScore in Cluster1. GSEA identified associated signaling pathways. LASSO regression included 11 CuLncs to construct and validate a survival prediction model, classifying patients into high-risk and low-risk groups. Correlations between riskScore, Cluster phenotype, ImmuneScore, and immune cell infiltration were explored. Cell experiments showed that knocking down AC023644.1 decreases OCa cell viability. In conclusion, we constructed an accurate prognostic model for OCa based on 11 CuLncs, providing a basis for prognosis assessment and potential immunotherapy targets.

Preoperative blood-based nutritional biomarkers as significant prognostic factors after intravesical BCG therapy in patients with non-muscle-invasive bladder cancer.

The aim of this study was to investigate the prognostic role of blood-based nutritional biomarkers, including red blood cell (RBC count), hemoglobin (Hb), total protein (TP), albumin, the serum albumin to globulin ratio (AGR) and the prognostic nutritional index (PNI) in patients who underwent intravesical treatment for non-muscle invasive bladder cancer (NMIBC). A total of 501 NMIBC patients who received intravesical Bacillus Calmette-Guerin (BCG) treatment following transurethral resection of bladder tumor (TURBT) were included. The optimal cutoff values for these nutrition-based indicators were determined using receiver operating characteristic curve analysis. We observed a significantly higher recurrence-free survival (RFS) rate in patients with elevated levels of RBC count, Hb, TP, and albumin. Cox univariate and multivariate Cox regression analyses demonstrated that serum albumin (P = 0.002, HR = 0.51, 95%CI: 0.33-0.78), RBC count (P = 0.002, HR = 0.50, 95%CI: 0.32-0.77), TP (P = 0.028, HR = 0.62, 95%CI: 0.41-0.95), Hb (P = 0.004, HR = 0.53, 95%CI: 0.33-0.84), AGR (P = 0.003, HR = 0.46, 95%CI: 0.27-0.76) and PNI (P = 0.019, HR = 0.56, 95%CI: 0.35-0.91) were significant independent factors predicting RFS. These cost-effective and convenient blood-based nutritional biomarkers have the potential to serve as valuable prognostic indicators for predicting recurrence in NMIBC patients undergoing BCG-immunotherapy.

NLR, MLR, and PLR are adverse prognostic variables for sleeve lobectomy within non-small cell lung cancer.

The goal of the research was to examine the value of peripheral blood indicators in forecasting survival and recurrence among people suffering central-type non-small cell lung cancer (NSCLC) undergoing sleeve lobectomy (SL). Clinical information was gathered from 146 individuals suffering from NSCLC who had SL at our facility between January 2014 and May 2023. Peripheral blood neutrophil lymphocyte ratio (NLR), monocyte lymphocyte ratio (MLR), and platelet lymphocyte ratio (PLR) levels were determined by receiver operating characteristic (ROC) curve to establish the threshold points. Kaplan-Meier survival analysis was employed to evaluate the prognostic value of different groupings, and both univariate and multivariate Cox proportional hazards model (referred to as COX) were performed. The disease-free survival (DFS) and overall survival (OS) cutoff values were carried out via ROC analysis. Kaplan-Meier survival analysis revealed notable differences in OS for NLR (≥2.196 vs. <2.196, p = 0.0009), MLR (≥0.2763 vs. <0.2763, p = 0.0018), and PLR (≥126.11 vs. <126.11, p = 0.0354). Similarly, significant differences in DFS were observed for NLR (≥3.010 vs. <3.010, p = 0.0005), MLR (≥0.2708 vs. <0.2708, p = 0.0046), and PLR (≥126.11 vs. <126.11, p = 0.0028). Univariate Cox analysis showed that NLR (hazard ratio [HR]: 2.469; 95% confidence interval [CI]: 1.416-4.306, p < 0.001), MLR (HR: 2.192, 95% CI: 1.319-3.643, p = 0.002) and PLR (HR: 1.696, 95% CI: 1.029-2.795, p = 0.038) were correlated alongside OS. Multivariate Cox analysis showed that NLR (HR: 2.036, 95% CI: 1.072-3.864, p = 0.030) was a separate OS risk variable. Additionally, the pN stage (HR: 3.163, 95% CI: 1.660-6.027, p < 0.001), NLR (HR: 2.530, 95% CI: 1.468-4.360, p < 0.001), MLR (HR: 2.229, 95% CI: 1.260-3.944, p = 0.006) and PLR (HR: 2.249, 95% CI: 1.300-3.889, p = 0.004) were connected to DFS. Multivariate Cox analysis showed that pN stage (HR: 3.098, 95% CI: 1.619-5.928, p < 0.001) was a separate DFS risk variable. The study demonstrates that NLR, MLR, and PLR play a convenient and cost-effective role in predicting survival and recurrence among individuals alongside central-type NSCLC having SL.

Prognostic analysis of anoikis-related genes in bladder cancer: An observational study.

Anoikis is proved to play a crucial role in the development of cancers. However, the impact of anoikis on the prognosis of bladder cancer (BLCA) is currently unknown. Thus, this study aimed to find potential effect of anoikis in BLCA. The Cancer Genome Atlas (TCGA)-BLCA and GSE13507 cohorts were downloaded from TCGA and the Gene Expression Omnibus (GEO) databases, respectively. Differentially expressed genes (DEGs) were screened between BLCA and normal groups, which intersected with anoikis-related genes to yield anoikis-related DEGs (AR DEGs). Univariate COX, rbsurv, and multivariate COX analyses were adopted in order to build a prognostic risk model. The differences of risk score in the different clinical subgroups and the relevance between survival rate and clinical characteristics were explored as well. Finally, chemotherapy drug sensitivity in different risk groups was analyzed. In total, 78 AR DEGs were acquired and a prognostic signature was build based on the 6 characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1), where the patients of low-risk group had longer survival time. The survival rate of BLCA patients was significantly differential in different groups of age, stage, smoking history, pathologic-T, and pathologic-N. The IC50 of 56 drugs showed significant differences between 2 risk groups, such as imatinib, docetaxel, and dasatinib. At last, the results of real time quantitative-polymerase chain reaction (RT-qPCR) demonstrated that the expression trend of CALR, HGF, and INHBB was consistent with the result obtained previously based on public databases. Taken together, this study identified 6 anoikis-related characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1) for the prognosis of BLCA patients, providing a scientific reference for further research on BLCA.

A novel cancer-associated fibroblasts risk score model predict survival and immunotherapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. Cancer-associated fibroblasts (CAFs) are a special type of fibroblasts, which play an important role in the development and immune escape of tumors. Weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression module. In combination with univariate Cox regression and analysis of least absolute shrinkage operator (LASSO), characteristics associated with CAFs were developed for a prognostic model. The migration and proliferation of lung cancer cells were evaluated in vitro. Finally, the expression levels of proteins were analyzed by Western blot. LASSO Cox regression algorithm was then performed to select hub genes. Finally, a total of 2 Genes (COL5A2, COL6A2) were obtained. We then divided LUAD patients into high- and low-risk groups based on CAFs risk scores. Survival analysis, CAFs score correlation analysis and tumor mutation load analysis showed that COL5A2 and COL6A2 were high-risk genes for LUAD. Human Protein Atlas (HPA), western blot and PCR results showed that COL5A2 and COL6A2 were up-regulated in LUAD tissues. When COL5A2 and COL6A2 were knocked down, the proliferation, invasion and migration of lung cancer cells were significantly decreased. Finally, COL5A2 can affect LUAD progression through the Wnt/β-Catenin and TGF-β signaling pathways. Our CAFs risk score model offers a new approach for predicting the prognosis of LUAD patients. Furthermore, the identification of high-risk genes COL5A2 and COL6A2 and drug sensitivity analysis can provide valuable candidate clues for clinical treatment of LUAD.

Development of a Cancer-associated Fibroblast Signature for Evaluating Immunotherapy Response and Prognosis of Hepatocellular Carcinoma.

Alcohol abuse, non-alcoholic fatty liver disease (NAFLD), and hepatitis B and C are the main pathogenic factors of hepatocellular carcinoma (HCC). Though the current understanding of risk factors for HCC has been improved, patients with this type of cancer are normally diagnosed at advanced stages, posing significant challenges to effective treatment. This study analyzed the HCC datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database (GSE14520 and GSE116174). Stromal and immune cell infiltration in the tumor microenvironment (TME) was quantified by the ESTIMATE algorithm. To identify gene modules associated with cancer-associated fibroblasts (CAFs), weighted gene co-expression network analysis (WGCNA) was performed to develop gene co-expression networks. A CAF prognosis score (CAFPS) model was established based on the prognostic CAF genes screened by univariate and multivariate Cox regression analyses. To determine the role of the genes in the vital module in HCC, we conducted Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Finally, the relationship between CAFPS and drug sensitivity was analyzed using Genomic Data for Cancer Drug Sensitivity (GDSC). In this study, we found significant differences in immune scores, stromal scores, CAFs scores, and CD4/8 T-cell scores between normal samples and samples with different TNM staging. In particular, the proportion of CAFs was higher than all other cells in normal samples. Gene modules related to CAFs were identified by developing a gene co-expression network using WGCNA analysis. The lightyellow and greenyellow modules showed the highest correlation with CAF scores. Univariate COX analysis identified 12 genes related to HCC prognosis from a total of 191 genes in the two modules. The Kaplan-Meier (KM) survival analysis revealed that a high expression of these genes was associated with a lower survival chance. Based on the 12 genes obtained by univariate COX analysis, multivariate COX analysis was performed to construct a risk score model for the characteristics of CAFs (CAFPS). The KM survival curves of patients in the high CAFPS and low CAFPS groups showed that patients in the low CAFPS group had better survival. CAFs played a crucial role in the pathogenesis and treatment response of HCC. Targeting the CAFs milieu may provide therapeutic benefits, highlighting the importance of CAFS in developing a personalized treatment for HCC patients. Further studies are required to verify the current findings and explore their implications in clinical settings.

NOP16 promotes hepatocellular carcinoma progression and triggers EMT through the Keap1-Nrf2 signaling pathway.

Nucleolar protein 16 (NOP16) is present in the protein complex of the nucleolus. The NOP16 promoter contains a c-Myc binding site, and the transcriptional regulation by c-Myc directly regulates NOP16 expression levels. Dysregulation of NOP 16 is currently reported in only a small number of cancers. In this study, the expression profile of NOP 16 in hepatocellular carcinoma (LIHC) and its clinical significance were analyzed. NOP16 expression in hepatocellular carcinoma (LIHC) and its relationship with the clinical characters of LIHC were examined using the Cancer Genome Atlas (TCGA), the Gene Expression comprehensive database (GEO), Kaplan-Meier survival analysis, univariate Cox analysis, multivariate Cox analysis, ROC curve analysis of KEGG enrichment, GSEA enrichment, in vitro experiments (e.g., siRNA interference of NOP16 expression in hepatoma cells, Keap1-Nrf2 pathway, cell cycle, cell apoptosis and Transwell assays), and LIHC single-cell sequencing (scRNA). Pan-cancer analysis revealed that NOP16 was highly expressed in 20 cancer types, including LIHC, and high NOP16 expression was an independent adverse prognostic factor in LIHC patients. The expression levels of NOP16 mRNA and protein were significantly increased in tumour tissues of LIHC patients compared to normal tissues. The functions of co-expressed genes were primarily enriched in the cell cycle and reactive oxygen species metabolism. The experimental results showed that knockdown of NOP16 activated the Keap/Nrf2 signalling pathway and inhibited the invasion, migration, and EMT progression of LIHC cells. LIHC scRNA-seq data showed that NOP16 was primarily expressed in T lymphocytes. NOP16 promoted cancer development in LIHC and caused an imbalance in Keap/Nrf2 signalling, which subsequently caused the aberrant expression of genes typical for EMT, cell cycle progression and apoptosis. NOP16 is a potential prognostic marker and therapeutic target for hepatocellular carcinoma progression.

Long-term mortality after isolated coronary artery bypass grafting and risk factors for mortality

BackgroundPatients requiring coronary artery bypass grafting (CABG) have multiple co-morbidities which need to be considered in totality when determining surgical risks. The objective of this study is to evaluate short-term and long-term mortality rates of CABG surgery, as well as to identify the most significant risk factors for mortality after isolated CABG.MethodsAll patients with complete dataset who underwent isolated CABG between January 2008 and December 2017 were included. Univariate and multivariate Cox regression was performed to determine the risk factors for all-cause mortality. Classification and regression tree analysis was performed to identify the relative importance of these risk factors.Results3,573 patients were included in the study. Overall mortality rate was 25.7%. In-hospital mortality rate was 1.62% overall. 30-day, 1-year, 5-year, 10-year and 14.5-year mortality rates were 1.46%, 2.94%, 9.89%, 22.79% and 36.30% respectively. Factors associated with death after adjustment for other risk factors were older age, lower body mass index (BMI), hypertension, diabetes mellitus, chronic obstructive pulmonary disease, pre-operative renal failure on dialysis, higher last pre-operative creatinine level, lower estimated glomerular filtration rate (eGFR), heart failure, lower left ventricular ejection fraction and New York Heart Association class II, III and IV. Additionally, female gender and logistic EuroSCORE were associated with death on univariate Cox analysis, but not associated with death after adjustment with multivariate Cox analysis. Using CART analysis, the strongest predictor of mortality was pre-operative eGFR < 46.9, followed by logistic EuroSCORE ≥ 2.4.ConclusionPoorer renal function, quantified by a lower eGFR, is the best predictor of post-CABG mortality. Amongst other risk factors, logistic EuroSCORE, age, diabetes and BMI had a relatively greater impact on mortality. Patients with chronic kidney disease stage 3B and above are at highest risk for mortality. We hope these findings heighten awareness to optimise current medical therapy in preserving renal function upon diagnosis of any atherosclerotic disease and risk factors contributing to coronary artery disease.

Efficacy of PD-1 inhibitors combined with nab-paclitaxel and cisplatin in the neoadjuvant treatment of locally advanced hypopharyngeal squamous cell carcinoma

Objective: To assess the efficacy of neoadjuvant treatment with PD-1 (programmed cell death protein 1) inhibitors combined with paclitaxel (albumin-conjugated) and cisplatin (TP regimen) for locally advanced hypopharyngeal squamous cell carcinoma and laryngeal organ function preservation. Methods: Data of 53 patients, including 51 males and 2 females, aged 38-70 years old, who were diagnosed with locally advanced hypopharyngeal squamous carcinoma confirmed by histology and enhanced CT at the Cancer Prevention and Control Center of Sun Yat-sen University during the initial treatment from January 1, 2019 to January 15, 2023, were retrospectively analyzed. All patients received neoadjuvant therapy with PD-1 inhibitors combined with albumin-bound paclitaxel (260 mg/m2) and cisplatin (60 mg/m2) for 3 to 4 cycles. The main outcome measures were larynx dysfunction-free survival (LDFS), overall survival (OS), and progression-free survival (PFS). Survival curves were plotted using the Kaplan-Meier method, and Cox multifactorial analysis was further performed if Cox univariate analysis was statistically significant. Results: The overall efficiency was 90.6% (48/53). The 1-year and 2-year LDFS rates were 83.8% (95%CI: 74.0% to 94.8%) and 50.3% (95%CI: 22.1% to 91.6%), the 1-year and 2-year OS rates were 95.2% (95%CI: 88.9% to 100.0%) and 58.2% (95%CI: 25.6% to 81.8%), and the 1-year and 2-year PFS rates were 83.9% (95%CI: 74.2% to 94.9%) and 53.5% (95%CI: 32.1% to 89.1%). Adverse events associated with the neoadjuvant therapy were mainly myelosuppression (45.3%), gastrointestinal reactions (37.7%) and hypothyroidism (20.8%). Conclusion: The neoadjuvant treatment of locally advanced hypopharyngeal squamous cell carcinoma using PD-1 inhibitors combined with paclitaxel and cisplatin can provide with a higher survival rate with a improved laryngeal organ function preservation rate.

Deep learning prediction of survival in patients with heart failure using chest radiographs.

Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our studyisto develop and validate a deep learning survival prediction model using chest X-ray (DLSPCXR) in patients with HF. The study retrospectively enrolled a cohort of 353 patients with HF who underwent chest X-ray (CXR) at our institution between March 2012 and March 2017. The dataset was randomly divided into training (n = 247) and validation (n = 106) datasets. Univariate and multivariate Cox analysis were conducted on the training dataset to develop clinical and imaging survival prediction models. The DLSPCXR was trained and the selected clinical parameters were incorporated into DLSPCXR to establish a new model called DLSPinteg. Discrimination performance was evaluated using the time-dependent area under the receiver operating characteristic curves (TD AUC) at 1, 3, and 5-years survival. Delong's test was employed for the comparison of differences between two AUCs of different models. The risk-discrimination capability of the optimal model was evaluated by the Kaplan-Meier curve. In multivariable Cox analysis, older age, higher N-terminal pro-B-type natriuretic peptide (NT-ProBNP), systolic pulmonary artery pressure (sPAP) > 50mmHg, New York Heart Association (NYHA) functional class III-IV and cardiothoracic ratio (CTR) ≥ 0.62 in CXR were independent predictors of poor prognosis in patients with HF. Based on the receiver operating characteristic (ROC) curve analysis, DLSPCXR had better performance at predicting 5-year survival than the imaging Cox model in the validation cohort (AUC: 0.757 vs. 0.561, P = 0.01). DLSPinteg as the optimal model outperforms the clinical Cox model (AUC: 0.826 vs. 0.633, P = 0.03), imaging Cox model (AUC: 0.826 vs. 0.555, P < 0.001), and DLSPCXR (AUC: 0.826 vs. 0.767, P = 0.06). Deep learning models using chest radiographs can predict survival in patients with heart failure with acceptable accuracy.

Construction and validation of prognostic signature for transcription factors regulating T cell exhaustion in hepatocellular carcinoma.

In the tumor microenvironment (TME), CD8+ T cells showed stage exhaustion due to the continuous stimulation of tumor antigens. To evaluate the status of CD8+ T cells and reverse the exhaustion is the key to evaluate the prognosis and therapeutic effect of tumor patients. The aim of this study was to establish a prognostic signature that could effectively predict prognosis and response to immunotherapy in patients with hepatocellular carcinoma (HCC). We used univariate Cox analysis to obtain transcription factors associated with CD8+ T cell exhaustion from The Cancer Genome Atlas dataset. Then, the prognostic signature for transcription factors basic leucine zipper ATF-like transcription factor, Eomesodermin, and T-box protein 21 regulating T cell exhaustion was constructed using LASSO Cox regression. The relative expression levels of the mRNA of the 3 transcription factors were detected by reverse transcription-quantitative polymerase chain reaction in 23 pairs of HCC and paracancer tissues, and verified internally in The Cancer Genome Atlas dataset and externally in the International Cancer Genome Consortium dataset. Cox regression analysis showed that risk score was an independent prognostic variable. The overall survival of the high-risk group was significantly lower than that of the low-risk group. The low-risk group had higher immune scores, matrix scores, and ESTIMATE scores, and significantly increased expression levels of most immune checkpoint genes in the low-risk group. Therefore, patients with lower risk scores benefit more from immunotherapy. The combination of the 3 transcription factors can evaluate the exhaustion state of CD8+ T cells in the TME, laying a foundation for evaluating the TME and immunotherapy efficacy in patients with HCC.

Identification of a combined hypoxia and lactate metabolism prognostic signature in lung adenocarcinoma

BackgroundIn the tumor microenvironment (TME), a bidirectional relationship exists between hypoxia and lactate metabolism, with each component exerting a reciprocal influence on the other, forming an inextricable link. The aim of the present investigation was to develop a prognostic model by amalgamating genes associated with hypoxia and lactate metabolism. This model is intended to serve as a tool for predicting patient outcomes, including survival rates, the status of the immune microenvironment, and responsiveness to therapy in patients with lung adenocarcinoma (LUAD).MethodsTranscriptomic sequencing data and patient clinical information specific to LUAD were obtained from comprehensive repositories of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A compendium of genes implicated in hypoxia and lactate metabolism was assembled from an array of accessible datasets. Univariate and multivariate Cox regression analyses were employed. Additional investigative procedures, including tumor mutational load (TMB), microsatellite instability (MSI), functional enrichment assessments and the ESTIMATE, CIBERSORT, and TIDE algorithms, were used to evaluate drug sensitivity and predict the efficacy of immune-based therapies.ResultsA novel prognostic signature comprising five lactate and hypoxia-related genes (LHRGs), PKFP, SLC2A1, BCAN, CDKN3, and ANLN, was established. This model demonstrated that LUAD patients with elevated LHRG-related risk scores exhibited significantly reduced survival rates. Both univariate and multivariate Cox analyses confirmed that the risk score was a robust prognostic indicator of overall survival. Immunophenotyping revealed increased infiltration of memory CD4 + T cells, dendritic cells and NK cells in patients classified within the high-risk category compared to their low-risk counterparts. Higher probability of mutations in lung adenocarcinoma driver genes in high-risk groups, and the MSI was associated with the risk-score. Functional enrichment analyses indicated a predominance of cell cycle-related pathways in the high-risk group, whereas metabolic pathways were more prevalent in the low-risk group. Moreover, drug sensitivity analyses revealed increased sensitivity to a variety of drugs in the high-risk group, especially inhibitors of the PI3K-AKT, EGFR, and ELK pathways.ConclusionsThis prognostic model integrates lactate metabolism and hypoxia parameters, offering predictive insights regarding survival, immune cell infiltration and functionality, as well as therapeutic responsiveness in LUAD patients. This model may facilitate personalized treatment strategies, tailoring interventions to the unique molecular profile of each patient’s disease.

Risk factors for mid- and long-term mortality in lung transplant recipients aged 70 years and older.

With increased lung transplantation in those aged 70 and older, limited literature addresses risk factors affecting their survival. Our study aims to identify independent factors impacting mid- and long-term mortality in this elderly population. This study analyzed lung transplant patients over 70 from May 2005 to December 2022 using United Network for Organ Sharing data. The 3- or 5-year cohort excluded multi-organ, secondary transplantation and loss to follow-up. Univariable Cox analysis was conducted to assess recipient, donor and transplant factors. Factors with a significance level of P < 0.2 were subsequently included in a multivariable Cox model to identify correlations with 3- and 5-year mortality in patients aged over 70. Multivariable analysis has identified key factors affecting 3- and 5-year mortality in elderly lung transplant patients over 70. Common notable factors include recipient total bilirubin, intensive care unit status at the time of transplantation, donor diabetes, Cytomegalovirus(CMV) mismatch and single lung transplantation. Additionally, Hispanic/Latino patients and ischaemia time of the transplant significantly impact the 3-year mortality, while recipient age, diabetes, nitric oxide use before transplantation and creatinine were identified as unique independent risk factors affecting the 5-year morality. The study identified several independent risk factors that impact the mid- and long-term survival of lung transplantation for individuals over 70 years. These findings can contribute to the optimization of lung transplant treatment strategies and perioperative management in elderly patients, thereby enhancing the survival rate of this age group.

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.

A five-gene prognosis model based on lysine β-hydroxybutyrylation site genes to predict the survival and therapy response in pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is one of the most malignancy diseases. Lysine β-hydroxybutyrylation (Kbhb) has been reported to involve various metabolism and cancer progression. MethodsData from online databases (TCGA and GEO) were retrieved for the selection of differential expressed Kbhb site genes (DTRGs). Univariate cox and LASSO analysis were performed to identify the prognostic DTRGs. Based on the optimal DTRGs, a prognostic risk score model was established. Kaplan-Meier and Receiver operator characteristic analysis were conducted to evaluate the predicting ability of the prognosis model. Generated with clinical data, independent analysis and nomogram model were performed. Finally, the differences of survival, immune cell levels, immunotherapy response, drug sensitivity between high- and low-risk groups were explored. ResultsA total of 63 DTRGs were identified in PAAD, and these genes were related to cell division and apoptosis biological functions. Through univariate cox regression and LASSO analysis, 30 DTRGs were selected to be related to prognosis and five (KRT18, ANLN, ECT2, RBM5, and RBM6) were identified as the optimal DTRGs in PAAD. Based on the five optimal DTRGs, a prognostic risk score model was constructed, with promising predictive ability in PAAD survival (AUC >0.70). High-risk group showed lower survival rate (P < 0.05). Moreover, based on the risk score, a nomogram model was also established, which possessed perfect stability. Finally, lower risk score was related to higher immune cell levels, indicating an immune activation in low-risk status, which maybe the reason for the better survival in low-risk group. Furthermore, the immunotherapy response and drug sensitivity were all higher than that in low-risk groups (P < 0.05). ConclusionA five-gene prognosis risk model which exhibit promising predictive ability in survival is constructed for patients with PAAD.