Stepwise Multivariate Cox Regression Analysis Research Articles

10-year risk scale of cardiovascular events for middle-aged and elderly people with type 2 diabetes mellitus

The purpose of this study was to create a scoring scale for assessing the 10-year risk of cardiovascular events for middle-aged and elderly people with type 2 diabetes mellitus (DM).Material and methods. The analysis included data from 314 people with T2DM: 142 men, 242 women, the average age for both sexes was 60.81 ± 6.38 years. The baseline survey was carried out within the framework of the HAPIEE project; the observation period lasted from 2003–2005. to December 31, 2013 and averaged 10 years. Persons who had myocardial infarction and/or cerebral stroke before the examination were excluded from the analysis. Non-fatal cardiovascular (CV) diseases and CV death were established in accordance with the codes of the International Classification of Diseases, 10th revision. As part of the study, two groups were formed: the main group, which included people with T2DM who “developed CV events” over 10 years of observation – 50 (15.9 %) people, and a comparison group – people with T2DM who “did not develop CV events” during the observation period (264 people). Anthropometric indicators, blood pressure, socio-demographic data, and some biochemical parameters were determined. To analyze the association of the studied factors with the risk of CV events, multivariate stepwise Cox regression analysis was used.Results. As a result of multivariate Cox regression analysis, associations with the risk of developing CV diseases were obtained for the following factors: gender (men), age (≥ 55 years), heart rate (≥ 80 beats/min), SBP (≥ 150 mmHg), marital status single, education (not higher). Individuals with a score above 9 are considered to be at high 10-year risk of cardiovascular events.Conclusions. A model has been created that predicts the risk of developing CV events in relation to individuals with T2DM living in a large industrial city in Western Siberia. Based on the data obtained, it will be possible to select priority preventive areas and therapeutic interventions.

Identification and Validation of an Invasion-Related Disease-Free Survival Prognostic Model for Tongue Squamous Cell Carcinoma

Introduction: Tongue squamous cell carcinoma (TSCC) is a common malignant tumour type with aggressive invasion and a poor prognosis. To date, invasion-related gene expression signatures for the prognostic stratification of TSCC patients are unavailable in clinical practice. This study aimed to assess the impact of invasion-related genes on the prognosis of TSCC patients. Methods: We obtained mRNA profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (TCGA-TSCC and GSE41116, respectively). The TSCC samples from the TCGA-TSCC cohort were randomly divided into TCGA training and TCGA test datasets at a 7:3 ratio. Next, a disease-free survival (DFS) prognostic risk model was established on the basis of univariate and stepwise multivariate Cox regression analyses of the TCGA training cohort. Moreover, prognostic genes were screened. The model was subsequently evaluated and validated using the TCGA test and GSE41116 datasets. In addition, the prognostic genes were validated in the human TSCC cell line UM1 and the human oral keratinocyte (HOK) cell line using quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Results: A total of 70 candidate genes related to invasion were identified in the TCGA-TSCC cohort. DFS data were subsequently constructed, and 6 prognostic genes, HMGN2, MYL12B, ACTB, PPP1CA, PSMB9, and IFITM3, were identified. The TSCC samples were divided into high- and low-risk groups in the TCGA training, TCGA test, and GSE41116 cohorts, respectively. In particular, patients with TSCC in the low-risk group had longer DFS than those in the high-risk group. Furthermore, qRT-PCR analysis confirmed that the expression levels of the 6 prognostic genes were significantly greater in the TSCC cell line UM1 than in the HOK cell line. Conclusion: This study identified new invasion-related target genes related to poor prognosis in TSCC patients, providing new insights into the underlying mechanisms of TSCC invasion.

Genetic Variants in p53 Pathway Genes Affect Survival of Patients with HBV-Related Hepatocellular Carcinoma.

P53 is a suppressor gene closely related to carcinogenesis. However, the associations between genetic variants in the p53 signaling pathway and prognosis in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) remain unknown. The current study aims to analyze associations between the single nucleotide polymorphisms (SNPs) in p53 pathway-related genes and survival of patients with HBV-HCC. We evaluated the associations between 4698 SNPs in 70 genes of the p53 pathway and overall survival (OS) of 866 patients in additive genetic models by using Cox proportional hazards regression analysis. Stepwise multivariable Cox regression analysis was conducted to determine the independent effects of identified SNPs in single-locus analyses. The expression of quantitative trait loci (eQTL) was also analyzed using data from GTEx and 1000 Genomes Project, and functional prediction of SNPs was performed by using RegulomeDB v2.2, 3DSNP v2.0, HaploReg v4.2 and VannoPortal. We found that two novel SNPs of CD82 rs7925603 A > G and PMAIP1 rs4396625 A > T, were significantly and independently associated with OS [adjusted hazards ratios (HRs) and 95% confidence intervals (CI) were 1.27 (1.10-1.48) and 0.77 (0.66-0.91), respectively; P = 0.001 and = 0.002, respectively] and that the combined risk genotypes of these SNPs showed a significant association with OS in patients with HBV-HCC (P trend < 0.001). Further eQTL analysis in the GTEx dataset showed that the rs7925603 G allele was associated with lower CD82 mRNA expression levels, while the rs4396625 T allele was associated with higher PMAIP1 mRNA expression levels in whole blood cells. We identified two observed survival-associated SNPs in CD82 and PMAIP1 in the p53 pathway, which influenced HBV-HCC survival possibly through a mechanism of altering mRNA expression. Large studies are warranted to validate our findings.

Potentially functional variants of CHMP4A and PANX1 in the pyroptosis-related pathway predict survival of patients with non-oropharyngeal head and neck squamous cell carcinoma.

Pyroptosis has been implicated in the advancement of various cancers. Triggering pyroptosis within tumors amplifies the immune response, thereby fostering an antitumor immune environment. Nonetheless, few published studies have evaluated associations between functional variants in the pyroptosis-related genes and clinical outcomes of patients with non-oropharyngeal head and neck squamous cell carcinoma (NON-ORO HNSCC). We conducted an association study of 985 NON-ORO HNSCC patients who were randomly divided into two groups: the discovery group of 492 patients and the replication group of 493 patients. We used Cox proportional hazards regression analysis to examine associations between genetic variants of the pyroptosis-related genes and survival of patients with NON-ORO HNSCC. Bayesian false discovery probability (BFDP) was used for multiple testing correction. Functional annotation was applied to the identified survival-associated genetic variants. There are 8254 single-nucleotide polymorphisms (SNPs) located in 82 pyroptosis-related genes, of which 202 SNPs passed multiple testing correction with BFDP < 0.8 in the discovery and six SNPs retained statistically significant in the replication. In subsequent stepwise multivariable Cox regression analysis, two independent SNPs (CHMP4A rs1997996 G > A and PANX1 rs56175344 C > G) remained significant with an adjusted hazard ratios (HR) of 1.31 (95% confidence interval [CI] = 1.09-1.57, p = 0.004) and 0.65 (95% CI = 0.51-0.83, p = 0.0005) for overall survival (OS), respectively. Further analysis of the combined genotypes revealed progressively worse OS associated with the number of unfavorable genotypes (ptrend < 0.0001 and 0.021 for OS and disease-specific survival, respectively). Moreover, both PANX1 rs56175344G and CHMP4A rs1997996A alleles were correlated with reduced mRNA expression levels. Genetic variants in the pyroptosis pathway genes may predict the survival of NON-ORO HNSCC patients, likely by reducing the gene expression, but our findings need to be replicated by larger studies.

Prognostic role of haemodynamics at follow-up in patients with pulmonary arterial hypertension: a challenge to current European Society of Cardiology/European Respiratory Society risk tools.

Haemodynamic variables like right atrial pressure (RAP), cardiac index (CI), stroke volume index (SVI) and mixed venous oxygen saturation (S vO2 ) predict survival in patients with pulmonary arterial hypertension (PAH). However, there is the need to identify further prognostic haemodynamic parameters as well as to redefine their role in PAH risk stratification compared to current risk tools and non-invasive parameters. This cohort study includes treatment-naïve patients assessed at baseline and after first-line PAH therapy with clinical, functional, exercise, laboratory and haemodynamic evaluations. Using a stepwise multivariate Cox regression analysis, independent prognostic haemodynamic parameters were identified and stratified according to cut-offs already defined in the European Society of Cardiology (ESC)/European Respiratory Society (ERS) risk table or defined based on the highest Chi-squared of the log-rank test. Their discriminatory power was tested for all-cause death and a combined end-point of death, hospitalisation and need of treatment escalation. 794 patients with PAH were enrolled. At first follow-up, RAP and pulmonary artery elastance were independently associated with death. Because of high correlations between haemodynamic parameters, different multivariable analyses were done identifying six other variables (pulmonary arterial compliance, cardiac efficiency, pulmonary vascular resistance, S vO2 , CI and SVI). Haemodynamic parameters were of no added prognostic value compared to ESC/ERS risk tools for the all-cause death end-point but they showed additional value to non-invasive parameters for the combined end-point and, when taken alone, had a discriminatory capacity comparable to ESC/ERS risk tools. Haemodynamics' discriminative ability for clinical worsening is comparable to current ESC/ERS risk tools and is of added value to non-invasive parameters.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy.

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

Clinical significance, molecular characterization, and immune microenvironment analysis of coagulation-related genes in clear cell renal cell carcinoma.

Numerous studies have revealed a tight connection between tumor development and the coagulation system. However, the effects of coagulation on the prognosis and tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) remain poorly understood. We employed the consensus clustering method to characterize distinct molecular subtypes associated with coagulation patterns. Subsequently, we examined variations in the overall survival (OS), genomic profiles, and TME characteristics between these subtypes. To develop a prognostic coagulation-related risk score (CRRS) model, we utilized the least absolute shrinkage and selection operator Cox regression and stepwise multivariate Cox regression analyses. We also created a nomogram to aid in the clinical application of the risk score, evaluating the relationships between the CRRS and the immune microenvironment, responsiveness to immunotherapy, and targeted treatment. The clinical significance of PLAUR and its biological function in ccRCC were also further analyzed. There were significant differences in clinical features, prognostic stratification, genomic variation, and TME characteristics between the two coagulation-related subtypes. We established and validated a CRRS using six coagulation-related genes that can be employed as an effective indicator of risk stratification and prognosis estimation for ccRCC patients. Significant variations in survival outcomes were observed between the high- and low-risk groups. The nomogram was proficient in predicting the 1-, 3-, and 5-year OS. Additionally, the CRRS emerged as a novel tool for evaluating the clinical effectiveness of immunotherapy and targeted treatments in ccRCC. Moreover, we confirmed upregulated PLAUR expression in ccRCC samples that was significantly correlated with poor patient prognosis. PLAUR knockdown notably inhibited ccRCC cell proliferation and migration. Our data suggested that CRRS may be employed as a reliable predictive biomarker that can provide therapeutic benefits for immunotherapy and targeted therapy in ccRCC.

Construction and evaluation of neonatal respiratory failure risk prediction model for neonatal respiratory distress syndrome

BackgroundNeonatal respiratory distress syndrome (NRDS) is a common respiratory disease in preterm infants, often accompanied by respiratory failure. The aim of this study was to establish and validate a nomogram model for predicting the probability of respiratory failure in NRDS patients.MethodsPatients diagnosed with NRDS were extracted from the MIMIC-iv database. The patients were randomly assigned to a training and a validation cohort. Univariate and stepwise Cox regression analyses were used to determine the prognostic factors of NRDS. A nomogram containing these factors was established to predict the incidence of respiratory failure in NRDS patients. The area under the receiver operating characteristic curve (AUC), receiver operating characteristic curve (ROC), calibration curves and decision curve analysis were used to determine the effectiveness of this model.ResultsThe study included 2,705 patients with NRDS. Univariate and multivariate stepwise Cox regression analysis showed that the independent risk factors for respiratory failure in NRDS patients were gestational age, pH, partial pressure of oxygen (PO2), partial pressure of carbon dioxide (PCO2), hemoglobin, blood culture, infection, neonatal intracranial hemorrhage, Pulmonary surfactant (PS), parenteral nutrition and respiratory support. Then, the nomogram was constructed and verified.ConclusionsThis study identified the independent risk factors of respiratory failure in NRDS patients and used them to construct and evaluate respiratory failure risk prediction model for NRDS. The present findings provide clinicians with the judgment of patients with respiratory failure in NRDS and help clinicians to identify and intervene in the early stage.

Long-Term Prognostic Value of Infarct Transmurality Determined by Contrast-Enhanced Cardiac Magnetic Resonance after ST-Segment Elevation Myocardial Infarction.

The long-term prognostic significance of maximal infarct transmurality evaluated by contrast-enhanced cardiac magnetic resonance (CE-CMR) in ST-segment elevation myocardial infarction (STEMI) patients has yet to be determined. This study aimed to see if maximal infarct transmurality has any additional long-term prognostic value over other CE-CMR predictors in STEMI patients, such as microvascular obstruction (MVO) and intramyocardial hemorrhage (IMH). The study included 112 consecutive patients who underwent CE-CMR after STEMI to assess established parameters of myocardial injury as well as the maximal infarct transmurality. The primary clinical endpoint was the occurrence of major adverse cardiac events (MACE), which included all-cause death, non-fatal reinfarction, and new heart failure hospitalization. The MACE occurred in 10 patients over a median follow-up of 7.9 years (IQR, 5.8 to 9.2 years) (2 deaths, 3 nonfatal MI, and 5 heart failure hospitalization). Patients with MACE had significantly higher rates of transmural extent of infarction, infarct size >5.4 percent, MVO, and IMH compared to patients without MACE. In stepwise multivariable Cox regression analysis, the transmural extent of infarction defined as 75 percent or more of infarct transmurality was an independent predictor of the MACE after correction for MVO and IMH (hazard ratio 8.7, 95% confidence intervals [CIs] 1.1-71; p=0.043). In revascularized STEMI patients, post-infarction CE-CMR-based maximal infarct transmurality is an independent long-term prognosticator. Adding maximal infarct transmurality to CE-CMR parameters like MVO and IMH could thus identify patients at high risk of long-term adverse outcomes in STEMI.

Drug coated balloons-based approach versus conventional percutaneous coronary intervention with drug eluting stents for left anterior descending revascularization

Abstract Introduction While representing the gold standard for percutaneous coronary interventions (PCI), drug eluting stents (DES) are associated with a progressively increasing risk of stent-related adverse events over time. (1) Left anterior descending (LAD) disease is a critical area for coronary stenting, due to the wide distribution with several side branches at risk of occlusion after stent implantation. Thus, the development of novel approaches for LAD PCI is warranted. Drug-coated balloons (DCB) used alone or in combination with DES as part of a hybrid procedure could allow to minimize stent burden and reduce the occurrence of stent-related events, but the evidence on their use for de novo lesions in large vessels is limited. (2-4) Methods Consecutive patients undergoing DCB implantation on the LAD in two Italian centers from 2018 to 2021 were retrospectively enrolled and compared to a historical cohort of LAD PCI with DES. Patients with in-stent restenosis were excluded. The primary endpoint was target lesion revascularization (TLR), while secondary endpoints included target vessel revascularization (TVR) and major adverse cardiovascular events (MACE), defined as the composite of cardiovascular death, TLR and TVR. Results 175 patients undergoing a DCB-based treatment on the LAD were included and compared to 1503 patients who received conventional PCI with DES. 83.9% of patients were male and 28.3% were diabetics, with no differences between the two groups. Total treated length was higher in the DCB group (54.9±20 for DES vs.66.2±35.2 mm for DCB, p&amp;lt;0.001); conversely, a greater mean DES length (54.9±20 vs.42.5±27.1 mm, p&amp;lt;0.001) was observed in the DES cohort. In the DCB group, mean DCB length was 36.8±20.2 mm; 32% of patients underwent DCB-only PCI and, in those who had hybrid PCI, DCB length was higher than stent length in 61.1% of cases. Large vessels (device diameter ≥3 mm) were treated in 81.6% and 77.1% of patients in the DES and DCB groups respectively, with no significant differences, while DCB with diameter ≥3 mm were used in 41.7% of patients in the DCB cohort. Mean follow-up duration was 2024 and 495 days in the DES and DCB groups respectively. At two years, the cumulative incidence of TLR was 9.1% (95% CI:7.6-10.9%) in the DES group vs. 4.8% (95% CI: 1.8-12.3%) in the DCB group (Log-rank p =0.225). After covariate adjustment through stepwise multivariable Cox regression analysis, DCB use was independently associated with lower TLR occurrence (HR 0.23, 95% CI: 0.06-0.98), alongside with large vessels involvement, while diabetes, dyslipidemia, and prior CABG correlated with greater TLR risk. No differences between DES and DCB were detected for secondary endpoints. Conclusions A DCB-based treatment approach allowed a significantly reduced stent burden for LAD PCI and led to a lower risk of target lesion failure at two years. Future randomized studies are warranted to demonstrate the effect of DCB-based LAD PCI on long term outcomes.Major adverse cardiovascular eventsTLR predictors at Cox regression

Prognostic value of coronary CT angiography and CT myocardial perfusion imaging among patients with and without Diabetes

ObjectivesWhether stress CT myocardial perfusion imaging (CT-MPI) improves risk assessment in patients with diabetes mellitus (DM) remains unexplored. We aimed to evaluate the prognostic value of coronary CT angiography (CCTA) and stress CT-MPI in suspected coronary artery disease (CAD) patients with and without DM. MethodsA total of 334 patients with suspected CAD who underwent CCTA and stress CT-MPI from May 2020 to July 2021 were retrospectively analyzed. The endpoint was major adverse cardiovascular events (MACEs). Multivariable Cox regression analysis was used to evaluate the risk factors for MACEs, including clinical risk factors, CCTA characteristics and CT-MPI characteristics. ResultsAfter a median follow-up of 21 months,15 patients of the DM group and 16 patients of the non-DM group experienced MACEs. Multivariate Cox stepwise regression analysis showed that abnormal perfusion myocardial segments ratio was associated with MACEs after adjusting for clinical risk factors and CCTA characteristics in all patients (HR:1.023, p < 0.001), DM group (HR:1.024, p = 0.008) and non-DM group (HR:1.028, p = 0.003). By adding CT-MPI characteristics to CCTA characteristics and clinical risk factors, the global chi-square for predicting MACEs increased from 62.24 to 78.84 in all patients (p < 0.001), from 19.18 to 27.30 in DM group (p = 0.004) and from 39.51 to 48.65 in non-DM group (p = 0.003); the increment of C-index in all patients, DM group and non-DM group were 0.018, 0.054 and 0.019, respectively. ConclusionIn all patients and those with and without DM, CT-MPI has incremental prognostic value over clinical risk factors alone or combined with CCTA characteristics in predicting MACEs.

Prognostic Factors and a Predictive Nomogram of Cancer-Specific Survival of Epithelial Ovarian Cancer Patients with Pelvic Exenteration Treatment.

The aim of this study was to explore prognostic factors, develop and internally validate a prognostic nomogram model, and predict the cancer-specific survival (CCS) of epithelial ovarian cancer (EOC) patients with pelvic exenteration (PE) treatment. A total of 454 EOC patients from the Surveillance, Epidemiology, and End Results (SEER) database were collected according to the inclusion criteria and randomly divided into the training (n = 317) and validation (n = 137) cohorts. Prognostic factors of EOC patients with PE treatment were explored by univariate and multivariate stepwise Cox regression analyses. A predictive nomogram was constructed based on selected risk factors. The predictive power of the constructed nomogram was assessed by the time-dependent receiver operating characteristic (ROC) curve. Kaplan-Meier (KM) curve stratified by patients' nomoscore was also plotted to assess the risk stratification of the established nomogram. In internal validation, the C index, calibration curve, and decision curve analysis (DCA) were employed to assess the discrimination, calibration, and clinical utility of the models, respectively. In the training cohort, age, histological type, Federation of Gynecology and Obstetrics (FIGO) stage, number of examined lymph nodes, and number of positive lymph nodes were found to be independent prognostic factors of postoperative CSS. A practical nomogram model of EOC patients with PE treatment was constructed based on these selected risk factors. Time-dependent ROC curves and KM curves showed the superior predictive capability and excellent clinical stratification of the nomogram in both training and validation cohorts. In the internal validation, the C index, calibration plots, and DCA in the training and validation cohorts confirmed that the nomogram presents a high level of prediction accuracy and clinical applicability. Our nomogram exhibited satisfactory survival prediction and prognostic discrimination. It is a user-friendly tool with high clinical pragmatism for estimating prognosis and guiding the long-term management of EOC patients with PE treatment.

Prediction of the survival of patients with advanced-stage ovarian cancer patients undergoing interval cytoreduction with the use of computed tomography reevaluation after neoadjuvant chemotherapy.

To predict ovarian cancer patients' survival by computed tomography (CT) reevaluation after neoadjuvant chemotherapy. In this retrospective single-center cohort study, all patients with advanced epithelial ovarian cancer underwent platinum-based chemotherapy followed by interval cytoreductive surgery. Assessment of abdominal and pelvic lesions before and after chemotherapy using CT scoring criteria. Meanwhile, the progression-free survival and overall survival times were obtained. The Kaplan-Meier method was used to estimate survival curves. Univariate analysis of continuous and categorical variables was performed for prognostic significance using the Cox proportional hazards model. Variables with p < 0.10 on univariate analysis were then included in a multivariate forward stepwise Cox regression analysis. A total of 162 patients were included, with a median age of 52 years (range, 20-72 years). One hundred seven patients (66.0%) underwent suboptimal cytoreduction, and there was no statistically significant difference in patient survival between surgical procedures (log-rank p = 0.092). Six radiographic features were hazard factors for suboptimal cytoreduction. Four features in the postchemotherapy CT images were assigned as predictive criteria by the stepwise regression model (area under the curve [AUC] = 0.689). As compared with a higher AUC (0.713) in the model involving two clinical variables (age and postsurgery CA-125) and two postchemotherapy CT features, the model considering the CT score changes before and after chemotherapy had the highest diagnostic accuracy (AUC = 0.843). CT reevaluation after neoadjuvant chemotherapy is essential for ovarian cancer, the changes of CT feature and score are potential great tools to predict patient survival.

Identification of EMT-associated LncRNA Signature for Predicting the Prognosis of Patients with Endometrial Cancer.

Endometrial cancer (EC) is one of the most normal malignancies globally. Growing evidence suggests epithelial-mesenchymal transition (EMT) related markers are closely correlated with poor prognosis of EC. However, the relationship between multiple EMT-associated long non-coding RNAs (lncRNAs) and the prognosis of EC has not yet been studied. The transcriptome data and clinical information of EC cases were obtained from The Cancer Genome Atlas (TCGA). Then, we identified differentially expressed EMT-associated lncRNAs between tumor and normal tissue. Univariate cox regression analysis and multivariate stepwise Cox regression analysis were applied to identify EMT-associated lncRNAs related to overall survival (OS). Kaplan-Meier curve, receiver operating characteristic (ROC), nomograms and multi-index ROC curves were further established to evaluate the performance of the prognostic signature. In addition, we also investigated the distribution of immune cell characteristics, sensitivity to immune checkpoint inhibitor (ICI) and chemotherapeutics, and tumor mutation burden (TMB) between high- and low-risk scores predicated on a prognostic model. We established nine EMT-associated lncRNA signatures to predict the OS of EC, the area under the ROC curve (AUC) of the risk score has better values than other clinical characteristics, indicating the accuracy of the prognostic signature. As revealed by multivariate Cox regression, the prognosis model independently predicted EC prognosis. Moreover, the signature and the EMTassociated lncRNAs showed significant correlations with other clinical characteristics,including. Multi-index ROC curves for estimating 1-, 3- and 5-year overall survival (OS) of EC patients showed good predictive accuracy with AUCs of 0.731, 0.791, and 0.782, respectively. The highrisk group had specific tumor immune infiltration, insensitive to ICI, higher chemotherapeutics sensitivity and higher expression of TP53 mutation. Finally, the five lncRNAs of signature were further verified by qRT-PCR. We constructed an EMT-associated lncRNA signature that can predict the prognosis of EC effectively, and the prognostic signature also played an essential role in the TME; thus, the establishment of an EMT-associated lncRNA signature may provide new perspectives for the treatment of EC.

Clinical and molecular analysis of cilia-associated gene signature for prognostic prediction in glioma.

Glioma is a highly malignant and unfavorable cancer in the brain. Recent evidence highlights the vital role of cilia-related pathways as novel regulators of glioma development. However, the prognostic potential of ciliary pathways in glioma is still ambiguous. In this study, we aim to construct a gene signature using cilia-related genes to facilitate the prognostication of glioma. A multi-stage approach was employed to build the ciliary gene signature for prognostication of glioma. The strategy involved the implementation of univariate, LASSO, and stepwise multivariate Cox regression analyses based on TCGA cohort, followed by independent validation in CGGA and REMBRANDT cohort. The study further revealed molecular differences at the genomic, transcriptomic, and proteomic levels between distinct groups. A prognostic tool utilizing a 9-gene signature based on ciliary pathways was developed to assess the clinical outcomes of glioma patients. The risk scores generated by the signature demonstrated a negative correlation with patient survival rates. The validation of the signature in an independent cohort reinforced its prognostic capabilities. In-depth analysis uncovered distinctive molecular characteristics at the genomic, transcriptomic, and protein-interactive levels in the high- and low-risk groups. Furthermore, the gene signature was able to predict the sensitivity of glioma patients to conventional chemotherapeutic drugs. This study has established the utility of a ciliary gene signature as a reliable prognostic predictor of glioma patient survival. Findings not only enhance our comprehension of the intricate molecular mechanisms of cilia pathways in glioma, but also hold significant clinical implications in directing chemotherapeutic strategies.

Prognostic factors in refractory metastatic colorectal cancer patients treated with Trifluridine/Tipiracil

PurposeThe systemic treatment options for metastatic colorectal cancer (mCRC) are unsatisfactory, and the disease recurs despite the use of numerous medications and their combinations. Trifluridine/Tipiracil is a relatively new drug used in refractory mCRC. Little is known about its real-world effectiveness and prognostic and predictive factors. Therefore, this study aimed to develop a prognostic model for refractory mCRC treated with Trifluridine/Tipiracil.MethodsWe retrospectively evaluated the data from 163 patients who had received Trifluridine/Tipiracil as a third or fourth line of treatment for refractory mCRC.ResultsAfter starting Trifluridine/Tipiracil, 21.5% of patients survived one year, and the median overall survival after Trifluridine/Tipiracil initiation was 251 days (SD: 17.855; 95%CI: 216–286). Median progression-free survival after Trifluridine/Tipiracil initiation was 56 days (SD: 4.826; 95%CI 47–65). Moreover, the median overall survival from diagnosis was 1333 days (SD: 82.84; 95%CI: 1170–1495). In forward stepwise multivariate Cox regression analysis, initial radical treatment (HR = 0.552, 95% CI 0.372–0.819, p < 0.003), the number of cycles of first-line chemotherapy (HR = 0.978, 95% CI 0.961–0.995, p < 0.011), the number of cycles of second-line chemotherapy (HR = 0.955, 95% CI 0.931–0.98, p < 0.011), BRAF mutation (HR = 3.016, 95% CI = 1.207–7.537, p = 0.018), and hypertension (HR = 0.64, 95% CI = 0.44–0.931, p = 0.02) were all associated with survival after Trifluridine/Tipiracil initiation. Our model and model-based nomogram displayed an AUC of 0.623 for one-year survival estimation in the testing cohort. The C-index for the prediction nomogram was 0.632.ConclusionWe have developed a prognostic model for refractory mCRC treated with Trifluridine/Tipiracil based on five variables. Moreover, we reported a nomogram which could be used by oncologists in clinic visits on a daily basis.Graphical abstract

Validating a Macrophage Marker Gene Signature (MMGS) in Lung Adenocarcinoma Prognosis and Response to Immunotherapy.

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. Tumor-associated macrophages play pivotal roles in the tumor microenvironment (TME) and prognosis of LUAD. We first used single-cell RNA sequencing data to identify macrophage marker genes in LUAD. Univariate, least absolute shrinkage and selection operator and stepwise multivariate Cox regression analyses were conducted to evaluate macrophage marker genes as prognostic factors and to construct the macrophage marker genes signature (MMGS). A novel 8-gene signature was constructed to predict prognosis based on 465 macrophage marker genes identified by an analysis of single-cell RNA sequencing data of LUAD, and was also verified in 4 independent GEO cohorts. The MMGS significantly classified patients into high-risk and low-risk groups in terms of OS. A prognostic nomogram based on independent risk factors was established to predict the 2-, 3- and 5-year survival, which indicated superior accuracy in predicting prognosis. The high-risk group was correlated to higher tumor mutational burden, number of neoantigens, T-cell receptor richness, and lower TIDE, which suggested that high-risk patients were more likely to benefit from immunotherapy. The prediction of the possibility of immunotherapy efficacy was also discussed. Analysis of an immunotherapy cohort further verified that patients with high-risk scores had better immunotherapy responses than low-risk patients. The MMGS is a promising signature for predicting prognosis and effectiveness of immunotherapy in patients with LUAD, and may be helpful for clinical decision-making.

Screening prognostic markers for hepatocellular carcinoma based on pyroptosis-related lncRNA pairs

BackgroundPyroptosis is closely related to cancer prognosis. In this study, we tried to construct an individualized prognostic risk model for hepatocellular carcinoma (HCC) based on within-sample relative expression orderings (REOs) of pyroptosis-related lncRNAs (PRlncRNAs).MethodsRNA-seq data of 343 HCC samples derived from The Cancer Genome Atlas (TCGA) database were analyzed. PRlncRNAs were detected based on differentially expressed lncRNAs between sample groups clustered by 40 reported pyroptosis-related genes (PRGs). Univariate Cox regression was used to screen out prognosis-related PRlncRNA pairs. Then, based on REOs of prognosis-related PRlncRNA pairs, a risk model for HCC was constructed by combining LASSO and stepwise multivariate Cox regression analysis. Finally, a prognosis-related competing endogenous RNA (ceRNA) network was built based on information about lncRNA–miRNA–mRNA interactions derived from the miRNet and TargetScan databases.ResultsHierarchical clustering of HCC patients according to the 40 PRGs identified two groups with a significant survival difference (Kaplan–Meier log-rank, p = 0.026). Between the two groups, 104 differentially expressed lncRNAs were identified (|log2(FC)|> 1 and FDR < 5%). Among them, 83 PRlncRNA pairs showed significant associations between their REOs within HCC samples and overall survival (Univariate Cox regression, p < 0.005). An optimal 11-PRlncRNA-pair prognostic risk model was constructed for HCC. The areas under the curves (AUCs) of time-dependent receiver operating characteristic (ROC) curves of the risk model for 1-, 3-, and 5-year survival were 0.737, 0.705, and 0.797 in the validation set, respectively. Gene Set Enrichment Analysis showed that inflammation-related interleukin signaling pathways were upregulated in the predicted high-risk group (p < 0.05). Tumor immune infiltration analysis revealed a higher abundance of regulatory T cells (Tregs) and M2 macrophages and a lower abundance of CD8 + T cells in the high-risk group, indicating that excessive pyroptosis might occur in high-risk patients. Finally, eleven lncRNA–miRNA–mRNA regulatory axes associated with pyroptosis were established.ConclusionOur risk model allowed us to determine the robustness of the REO-based PRlncRNA prognostic biomarkers in the stratification of HCC patients at high and low risk. The model is also helpful for understanding the molecular mechanisms between pyroptosis and HCC prognosis. High-risk patients may have excessive pyroptosis and thus be less sensitive to immune therapy.

Identification of an EMT-related gene-based prognostic signature in osteosarcoma.

The correlation between epithelial-mesenchymal transition (EMT) and osteosarcoma (OS) has been widely reported. Integration of the EMT-related genes to predict the prognosis is significant for investigating the mechanism of EMT in OS. Here, we aimed to construct a prognostic EMT-related gene signature for OS. Transcriptomic and survival data of OS patients were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO). We performed univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and stepwise multivariate Cox regression analysis to construct EMT-related gene signatures. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) were applied to evaluate its predictive performance. GSVA, ssGSEA, ESTIMATE, and scRNA-seq were performed to investigate the tumor microenvironment, and the correlation between IC50 of drugs and ERG score was investigated. Furthermore, Edu and transwell experiments were conducted to assess the malignancy of OS cells. We constructed a novel EMT-related gene signature (including CDK3, MYC, UHRF2, STC2, COL5A2, MMD, and EHMT2) for outcome prediction of OS. According to the signature, patients stratified into high- and low-ERG-score groups exhibited significantly different prognoses. ROC curves and Kaplan-Meier analysis revealed a promising performance of the signature with external validation. GSVA, ssGSEA, ESTIMATE algorithm, and scRNA-seq excavated EMT-related pathways and suggested the correlation between ERG score and immune activation. Notably, the pivotal gene CDK3 was upregulated in OS tissue and positively related to OS cell proliferation and migration. Our EMT-related gene signature might reference OS risk stratification and guide clinical strategies as an independent prognostic factor in OS.

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network.

Clear cell renal cell carcinoma's (ccRCC) occurrence and development are strongly linked to the metabolic reprogramming of tumors, and thus far, neither its prognosis nor treatment has achieved satisfying clinical outcomes. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively, provided us with information on the RNA expression of ccRCC patients and their clinical data. Cuproptosis-related genes (CRGS) were discovered in recent massive research. With the help of log-rank testing and univariate Cox analysis, the prognostic significance of CRGS was examined. Different cuproptosis subtypes were identified using consensus clustering analysis, and GSVA was used to further investigate the likely signaling pathways between various subtypes. Univariate Cox, least absolute shrinkage and selection operator (Lasso), random forest (RF), and multivariate stepwise Cox regression analysis were used to build prognostic models. After that, the models were verified by means of the C index, Kaplan-Meier (K-M) survival curves, and time-dependent receiver operating characteristic (ROC) curves. The association between prognostic models and the tumor immune microenvironment as well as the relationship between prognostic models and immunotherapy were next examined using ssGSEA and TIDE analysis. Four online prediction websites-Mircode, MiRDB, MiRTarBase, and TargetScan-were used to build a lncRNA-miRNA-mRNA ceRNA network. By consensus clustering, two subgroups of cuproptosis were identified that represented distinct prognostic and immunological microenvironments. A prognostic risk model with 13 CR-lncRNAs was developed. The immune microenvironment and responsiveness to immunotherapy are substantially connected with the model, which may reliably predict the prognosis of patients with ccRCC.