Stepwise Multivariate Cox Regression Analysis Research Articles

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

AbstractBackgroundNumerous 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.MethodsWe 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.ResultsThere 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.ConclusionOur 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.

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<0.001); conversely, a greater mean DES length (54.9±20 vs.42.5±27.1 mm, p<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.

A mitochondrial function-related LncRNA signature predicts prognosis and immune microenvironment for breast cancer

Mitochondrial function, as the core of the cell's energy metabolism, is firmly connected to cancer metabolism and growth. However, the involvement of long noncoding RNAs (lncRNAs) related to mitochondrial function in breast cancer (BRCA) has not been thoroughly investigated. As a result, the objective of this research was to dissect the prognostic implication of mitochondrial function-related lncRNAs and their link to the immunological microenvironment in BRCA. The Cancer Genome Atlas (TCGA) database was used to acquire clinicopathological and transcriptome information for BRCA samples. Mitochondrial function-related lncRNAs were recognized by coexpression analysis of 944 mitochondrial function-related mRNAs obtained from the MitoMiner 4.0 database. A novel prognostic signature was built in the training cohort using integrated analysis of mitochondrial function-related lncRNA and the corresponding clinical information through univariate analysis, lasso regression, and stepwise multivariate Cox regression analysis. The prognostic worth was judged in the training cohort and validated in the test cohort. In addition, functional enrichment and immune microenvironment analyses were performed to explore the risk score on the basis of the prognostic signature. An 8-mitochondrial function-related lncRNA signature was generated by integrated analysis. Individuals within the higher-risk category had a worse overall survival rate (OS) (training cohort: P < 0.001; validation cohort: P < 0.001; whole cohort: P < 0.001). The risk score was identified as an independent risk factor by multivariate Cox regression analysis (training cohort: HR 1.441, 95% CI 1.229–1.689, P < 0.001; validation cohort: HR 1.343, 95% CI 1.166–1.548, P < 0.001; whole cohort: HR 1.241, 95% CI 1.156–1.333, P < 0.001). Following that, the predictive accuracy of the model was confirmed by the ROC curves. In addition, nomograms were generated, and the calibration curves revealed that the model had excellent prediction accuracy for 3- and 5-year OS. Besides, the higher-risk BRCA individuals have relatively decreased amounts of infiltration of tumor-killing immune cells, lower levels of immune checkpoint molecules, and immune function. We constructed and verified a novel mitochondrial function-related lncRNA signature that might accurately predict the outcome of BRCA, play an essential role in immunotherapy, and might be exploited as a therapeutic target for precise BRCA therapy.

Preoperative Radiomics Nomogram Based on CT Image Predicts Recurrence-Free Survival After Surgical Resection of Hepatocellular Carcinoma

To construct preoperative models based on CT radiomics, radiologic and clinical features to predict recurrence-free survival (RFS) after liver resection (LR) of BCLC 0 to B stage hepatocellular carcinoma (HCC) and to classify the prognosis. This study retrospectively analyzed 161 HCC patients who underwent radical LR. Two methods, the least absolute shrinkage and selection operator and random survival forest analysis, were performed for radiomics signature (RS) construction. Univariate and multivariatestepwise Cox regression analyses were performed to establish a combined nomogram (RCN) of RS and clinical parameters and a clinical nomogram (CN). The performance of the models was assessed comprehensively using Harrell's concordance index (C-index), the calibration curve, and decision curve analysis. The discrimination accuracy of the models was compared using integrated discrimination improvement index (IDI). The risk stratification effect was assessed with Kaplan-Meier survival analysis and subgroup analysis. The RCN achieved a C-index of 0.792/0.758 in the training/validation set, which was higher than the CN, RS, and BCLC stage system. The discriminatory accuracy of the RCN was improved when compared to the CN, RS, and BCLC staging systems (IDI > 0). Decision curve analysis reflected the clinical net benefit of the RCN. The RCN allows risk stratification of patients in different clinical subgroups. The integrated model combining RS and clinical factors can more effectively predict RFS after LR of BCLC 0 to B stage HCC patients and can effectively stratify the prognostic risk.

Exploration of Influencing Factors for Postoperative Recurrence in Patients with Madelung's Disease on the Basis of Multivariate Stepwise Cox Regression Analysis.

Madelung's disease (MD) is a rare condition of massive deposits of fat accumulations between superficial and deep fascia at typical locations. There is an absence of systematic studies related to MD in the Chinese cohort. Thus, the objective of the study was to investigate the clinical features of the MD cases in our institution and to explore the clinical variables associated with postoperative recurrence. We retrospectively analyzed the clinical information of 21 individuals with MD from 2013 to 2021 enrolled in our institution. The paired t-test and χ 2 test were, respectively, used to determine the difference between continuous and classified variables. The univariate Kaplan-Meier analysis by log-rank and multivariate stepwise Cox regression analysis were used to explore variables possibly associated with postoperative recurrence in MD individuals. In the current study, 90.48% of the studied patients were male with a mean age of 48.76 years old. About 61.90% exhibited type I MD. MD patients who experienced postoperative recurrence had a higher age, BMI, incidence of chronic complications, and prevalence of alcoholism than the other MD patients without recurrence (P < 0.05). The univariate Kaplan-Meier analysis by log-rank identified that age, BMI, alcoholism, and comorbidities were influencing factors related with postoperative recurrence (P < 0.05). Demographic characteristics of the 21 studied Chinese cases with MD were generally in accordance with previously published data of other foreign populations. The factors possibly influencing the postoperative recurrence for patients with MD were age, BMI, alcoholism, and a combination of comorbidities. This is the first time that a summarization of clinical characteristics and postoperative recurrence variables of Chinese patients with MD has been reported.

Clinical Features and Prognosis of Uncommon Metastatic Breast Cancer: A Retrospective Analysis of 82 Cases.

With the improvement of diagnostic techniques, numerous uncommon metastases derived from breast cancer were reported. However, very few studies explored the clinical characteristics and prognostic patterns of these patients. A total of 82 cases of uncommon metastatic breast cancer (MBC) registered at our hospital from January 1, 2010, to July 1, 2022, were selected for this retrospective study. The diagnoses of uncommon metastases were based on pathology, and the potential prognostic indicators (overall survival [OS], uncommon disease-free interval [uDFI], and remaining survival [RS]) were estimated. The uncommon metastases involved distant soft tissue, parotid gland, thyroid, digestive system, urinary system, reproductive system, bone marrow, and pericardium. Stepwise multivariate Cox regression analysis indicates age ≤ 35 is an independent risk factor of poor outcome of OS, uDFI, and RS in uncommon MBC patients. Meanwhile, uncommon metastasis combined with common visceral metastasis is an independent risk factor for poor RS of uncommon MBC patients, with a hazard ratio of 6.625 (95% confidence interval = 1.490-29.455, P = .013). Post hoc pairwise comparisons showed that uncommon MBC patients who developed bone-only metastasis survived longer than those concomitant with common visceral metastasis (P = .029). Although the incidence is low, uncommon MBC may involve multiple metastatic sites. The delayed diagnosis of uncommon metastases could lead to systemic progression of the disease. However, patients who only develop uncommon metastasis have a significantly better prognosis than that of those combined with common visceral metastasis. Even for those complicated by bone-only metastasis, active treatment of bone metastases can still achieve substantially longer survival.

Dynamic monitoring of serum tumor markers as prognostic factors in patients with advanced non-small-cell lung cancer treated with first-line immunotherapy: a multicenter retrospective study

Background: To date, no specific studies have reported the use of dynamic serum tumor markers (STMs) as prognostic factors in patients with advanced non-small-cell lung cancer (NSCLC) who receive first-line immunotherapy. Therefore, it is unclear whether STMs can be used as a prognostic factor for first-line immunotherapy in advanced NSCLC. Objectives: To elucidate the role of STMs in monitoring immunotherapy response in advanced NSCLC. Patients were treated with first-line programmed cell death-1/programmed cell death ligand-1 inhibitors at four Chinese centers. Design: This was a multicenter retrospective study. Methods: Blood samples were collected at baseline and after 6–8 weeks of treatment. Computed tomography scans were used to evaluate treatment efficacy according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. Post-treatment drops in STMs [Serum carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin fragment 19 (CYFRA21-1), carbohydrate antigen 19-9 (CA19-9), and carbohydrate antigen 125 (CA125)] were decreased ⩾20% (Group C) over baseline was used as cutoff level for defining a marker response. If STMs were increased by ⩾20% after treatment, the therapeutic effect was limited (Group A). Patients with STM changes between a 20% increase or decrease were enrolled in Group B. In univariate and multivariate stepwise Cox regression analyses, STMs and RECIST responses were analyzed for their impact on progression-free survival (PFS) and overall survival (OS). Results: The analysis included 716 patients. By multivariate analysis, CEA, NSE, CYFRA21-1, CA19-9, and CA125 (Group A versus Group B and Group A versus Group C) were associated with significant differences in PFS. Similar results were observed in the OS analysis. Similar results were observed in the adenocarcinoma subgroup analyses. In squamous cell carcinoma subgroup analyses, there was no statistical difference in PFS ( p = 0.147) or OS ( p = 0.068) between Group A and Group B for CA125. Conclusion: The increase and decrease in serum levels of STMs might be reliable prognostic factors for immunotherapy efficacy in NSCLC patients.

TYK2 correlates with immune infiltration: A prognostic marker for head and neck squamous cell carcinoma.

Tyrosine kinase 2 (TYK2) is a member of the Janus kinase (JAK) family and is involved in immune and inflammatory signaling. TYK2 is overexpressed in several types of cancers and promotes the invasion and proliferation of cancer cells. Nevertheless, the roles of TYK2 in the prognosis and immune infiltration of head and neck squamous cell carcinoma (HNSCC) remain to be elucidated. In this study, the expression of TYK2 in HNSCC was evaluated based on the data retrieved from multiple databases and quantitative real-time polymerase chain reaction (qRT-PCR) analysis. The prognostic potential of TYK2 in patients with HNSCC was analyzed by Kaplan-Meier curves and Cox regression analysis. A TYK2-related risk assessment model was subsequently constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and stepwise multivariate Cox regression analysis. The association between the expression of TYK2 and the tumor immune microenvironment, immune checkpoints, and drug sensitivity was explored various packages in R. Cell function assays were finally used for exploring the effects of TYK2 on the growth and metastasis of HNSCC tumors. The expression of TYK2 was significantly upregulated in HNSCC and was found to be closely correlated with HPV status, gender, clinical grade, and TP53 mutation status. Survival analysis suggested that TYK2 is associated with better survival outcomes and acts as an independent prognostic indicator of HNSCC. The model constructed herein also performed well in terms of predicting patient prognosis. The expression of TYK2 was positively associated with the population of tumor-infiltrating immune cells, expression of immune checkpoint genes, and antitumor drug susceptibility. Functionally, TYK2 knockdown significantly promoted the proliferation, migration, and invasion of HNSCC cell lines in vitro. The findings demonstrated that TYK2 could serve as a suppressor of tumor growth and holds significant promise as a novel biomarker for assessing the prognosis of patients with HNSCC and aid in immunotherapy against HNSCC.