Univariate Cox Analysis Research Articles

Prognostic factors and etiological insights in constrictive pericarditis

Abstract Constrictive pericarditis (CP) is an infrequent disease that poses a diagnostic and therapeutic challenge, with mortality often linked to underlying etiological factors. Our unicentric retrospective research aims to elucidate prognostic indicators and unravel the diverse etiological spectrum of this condition. The study is accepted by the Ethical Committee of our Center. Between January 2010 and December 2023, we have identified a total of 112 patients diagnosed with CP, with an average follow-up time of 6.42 years (IQR 1.98 - 11.45). We present the baseline variables of the patients, etiology and prescribed treatments in the table 1, comparing patients who survive with those who do not survive. 66.1% of patients were males and median age was 60.8 years old. We present two Kaplan-Meier curves (Figures 1 and 2) comparing the survival rates between CP etiologies. In Figure 1, all causes are represented separately. In Figure 2, CP related to cardiac surgery, radiotherapy, purulent pericarditis, tuberculous pericarditis, mixed and other causes are combined. Our results suggest a better prognosis in patients with idiopathic CP and worse prognosis in patients with oncological related CP. The significant predictors in the univariate Cox analysis were age, cachexia, ascites, oncology etiology, hemoglobin, sodium, Euroscore II and surgical pericardiectomy. The best predictive model (selected with a lower AIC) to predict mortality throughout follow-up, with a Harrell's C of 0.835, included the variables age, cachexia, oncological cause and hyponatremia as risk factors for mortality and surgical treatment by pericardiectomy as a protective factor (Figure 3). In conclusion, our study suggests that CP etiology has an impact on survival with oncological cause having the worst prognosis. Furthermore, other factors as hyponatremia, cachexia, shock and age at diagnosis have also resulted in indicators of poor outcome.Figure 1.Kaplan meierFigure 3.Predictive model

Myocardial work and aortic valve stenosis cardiac damage stages are predictors of all-cause mortality in patients undergoing transcatheter aortic valve replacement

Abstract In aortic stenosis (AS) patients, assessment of left ventricular (LV) systolic function is complicated due to the significant impact of increased afterload on conventional parameters. Myocardial work (MW) analysis, a novel echocardiographic method adjusts myocardial deformation to instantaneous LV pressure which may better reflect the LV contractile state. Importantly, long-term LV pressure overload has marked backward effects beyond the LV: classification of this extravalvular cardiac damage effectively represents the involvement of the cardiopulmonary system in AS. Both MW and cardiac damage staging could hold remarkable prognostic power in the clinically complex population of transcatheter aortic valve replacement (TAVR) candidates. Accordingly, our aim was to assess the prognostic value of MW analysis and cardiac damage staging in patients undergoing TAVR. We enrolled 289 patients (79±6 years, 41% female) prior to TAVR. Echocardiographic measurements were performed one day before the procedure. We calculated LV ejection fraction (EF) and by speckle-tracking echocardiography global longitudinal strain (GLS), as well. Global constructive work (GCW) was quantified with dedicated software and LV pressure was estimated using transaortic mean gradient and systolic blood pressure. Based on the echocardiographic data, we also determined the extent of cardiac damage associated with AS: patients were classified as Stage 0 (no cardiac damage), Stage 1 (LV damage), Stage 2 (mitral valve or left atrial damage), Stage 3 (pulmonary artery vasculature or tricuspid valve damage), or Stage 4 (right ventricular damage). Our primary outcome was all-cause mortality, reached by 68 patients (median follow-up: 26 months). In our cohort, preprocedural EF was 47±13 %, GLS was -12.3±4 %, while GCW was 2043±791 mmHg%. 14 (5%) patients were classified as Stage 0, 61 (21%) as Stage 1, 128 (44%) as Stage 2, 15 (5%) as Stage 3, and 71 (25%) as Stage 4. GCW showed a continuous decline through the AS Stages (from Stage 0-4: 2963±652 vs. 2154±652 vs. 2168±718 vs. 1988±969 vs. 1552±772 mmHg%; p<0.001). Using univariate Cox analysis GCW (HR 0.968 [95% CI 0.938-0.998] per 100 unit change; p=0.034) and AS Staging (HR 1.236 [95% CI 1.015-1.505]; p=0.035) were both associated with all-cause mortality, while EF (HR 0.984 [95% CI 0.966-1.002]; p=0.085) and GLS (HR 1.042 [95% CI 0.984-1.103]; p=0.160) were not. In multivariate Cox regression models, both GCW (HR 0.957 [95% CI 0.922-0.992] per 100 unit change; p=0.018) and AS cardiac damage staging (HR 1.263 [95% CI 1.029-1.549]; p=0.025) were proven to be significant independent predictors of all-cause mortality. In TAVR patients, preoperative GCW values exhibited a persistent decrease across AS Stages. As opposed to EF and GLS, GCW and AS Staging both showed strong association with all-cause mortality in our cohort. Moreover, GCW had higher prognostic value than any other conventional or advanced measure of LV function.

AI-based cluster analysis enables outcomes prediction among patients with increased LVM

Abstract Background The traditional classification of left ventricular hypertrophy (LVH) solely based on left ventricular geometry disregards associated comorbidities and clinical characteristics. We aimed to identify unique clinical phenotypes of LVH using unsupervised cluster analysis and explore their association with clinical outcomes. Methods From UK Biobank (UKBB) participants with available left ventricular mass (LVM) estimations, we identified those meeting CMR-based criteria for increased LVM: LVM index ≥ 72 g/m2 for males and LVM index ≥ 55 g/m2 for females. Baseline demographic, clinical, and laboratory data were collected. Using Ward's method, patients were clustered based on 27 variables. The primary outcome was a composite of all-cause mortality with heart failure (HF) admissions, ventricular arrhythmia, and atrial fibrillation (AF). Cox proportional hazard model and Kaplan-Meier survival analysis were applied. Results Among CMR-assessed UKBB participants, 4,255 individuals exhibited increased LVM, with an average age of 64 ± 7 years; 2,447 (58%) were females. Cluster analysis identified four distinct subgroups. Over a median 5-year follow-up (IQR: 4-6), 100 patients (2%) died, 118 (2.8%) were hospitalized due to HF, 29 (0.7%) were hospitalized due to VA, and 208 (5%) were hospitalized due to AF. Univariate Cox analysis revealed that compared to the 1st cluster (n=1,578), patients in the 2nd (n=1,296), 3rd (n=824) and 4th (n=557) clusters had 60%, 104%, and 164% increased risk of a major event, respectively (95% CI 1.2-2.16, p<.001 for cluster 2; 95% CI 1.49-2.78, p<.001 for cluster 3; 95% CI 1.92-3.62, p<.001 for cluster 4). Each cluster manifested different phenotypes: cluster 2 comprised mainly overweight females, with the highest prevalence of chronic lung disease; cluster 3, predominantly composed of males, had the highest burden of comorbidities and cardiovascular risk factors; and cluster 4, mostly males, presented with the most abnormal cardiac measures, including left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), and left ventricular ejection fraction (LVEF). Conclusions Unsupervised cluster analysis identified four subgroups among patients with increased LVM. These subgroups bear notable relevance to overall mortality risk and clinical outcomes. This phenotypic classification holds promise in offering valuable insights regarding clinical course and outcomes in LVH patients.

Treatment indicators and prognostic factors in colorectal neuroendocrine neoplasms and adenocarcinoma with neuroendocrine differentiation: a single center retrospective study.

This study compared survival and metastasis occurrence between colorectal neuroendocrine neoplasms (cNEN) and colorectal adenocarcinoma with neuroendocrine differentiation (cNED) and further explored their prognostic factors and treatment indicators. Patients diagnosed as cNEN and cNED in West China Hospital from January 2009 to December 2020 were enrolled. The diagnosis and metastasis rates were calculated. Univariate and multivariate Cox analyses were conducted for progression-free survival (PFS) in cNEN surgical patients, and generalized linear regression was used for metastatic disease. The study enrolled 435 patients, including 257 neuroendocrine tumors (NET), 52 neuroendocrine carcinomas (NEC), 29 mixed neuroendocrine-non-neuroendocrine neoplasms (MiNEN), and 97 NED patients, of whom 202 received local resection, and 233 received radical resection. Metastasis rates were higher in MiNEN and NEC groups compared to other groups (NED: 28.9%, MiNEN: 58.6%, NEC: 65.4%, NET: 8.6%, p < 0.001). The liver is the main metastatic site in cNEN, whereas cNED metastasized to various sites. For NEC and MiNEN patients, colon location (p = 0.002) and T stage > 2 (p = 0.040) were associated with disease progression separately. Independent risk factors for metastatic NET included tumor grade G2/G3 (p < 0.001), colon location (p = 0.001), size ≥ 1cm (p = 0.005), and CK20 partial positive (p < 0.001). cNEN show high metastatic capacity and are challenging to diagnose. More aggressive treatment and follow-up strategies are necessary for those patients. NET tumor grade higher than G2, size larger than 1cm, or located in the colon should be managed with radical surgery.

Identification of a pyroptosis-related long noncoding RNA signature for determining the prognosis and immune status of hepatocellular carcinoma patients.

Despite improvements in cancer screening and diagnosis, hepatocellular carcinoma (HCC) is still diagnosed at an advanced stage, and the prognosis is worse than that of early HCC patients. Therefore, better molecular markers and therapeutic targets in HCC are required. We investigated the predictive value of pyroptosis-related long noncoding RNAs (lncRNAs) in HCC and the effects of these lncRNAs on the immune microenvironment of HCC. RNA sequencing data of HCC patients were extracted from The Cancer Genome Atlas (TCGA) database to identify differentially expressed pyroptosis-related lncRNAs related to overall survival (OS). A model was established to verify the character of pyroptosis-associated lncRNAs in the tumor microenvironment, and their prognostic value was evaluated. A total of 721 PR lncRNAs were identified based on the analysis of the TCGA database. Univariate Cox analysis revealed 37 survival-related PRlncRNAs with prognostic values. As a result of least absolute shrinkage and selection operator (LASSO) regression analysis, 'ELFN-AS1', AC099850.3, AC073389.3, 'HPN-AS1', AC009283.1, and AL139289.1 showed prognostic value. Kaplan-Meier analysis indicated that the OS of the high-risk set was worse than those of the low-risk set in both the training and testing cohorts. Univariate and multivariate analyses revealed that the risk score was a better independent prognostic factor than the stage. The precision of the lncRNA signature was confirmed using receiver operating characteristic curve (ROC) analysis. Immuneand metabolism-related pathways were enriched in both the lowand high-risk groups. Gene set enrichment analysis suggested that the identified lncRNAs regulate HCC tumorigenesis and prognosis by modulating metabolism. Various algorithms were used to confirm the significant differences in immune cells between these 2 groups. These findings could contribute to the development and validation of favorable biomarkers, improve the prognosis and survival of HCC, and help in developed individualized treatment plans for HCC.

Construction and validation of a prognostic model based on immune-metabolic-related genes in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC), a significant type of head and neck cancer, has witnessed increasing incidence and mortality rates. Immune-related genes (IRGs) and metabolic-related genes (MRGs) play essential roles in the pathogenesis, metastasis, and progression of OSCC. This study exploited data from The Cancer Genome Atlas (TCGA) to identify IRGs and MRGs related to OSCC through differential analysis. Univariate Cox analysis was utilized to determine immune-metabolic-related genes (IMRGs) associated with patient prognosis. A prognostic model for OSCC was constructed using Lasso-Cox regression and subsequently validated with datasets from the Gene Expression Omnibus (GEO). Non-Negative Matrix Factorization (NMF) clustering identified three molecular subtypes of OSCC, among which the C2 subtype showed better overall survival (OS) and progression-free survival (PFS). A prognostic model based on nine IMRGs was developed to categorize OSCC patients into high- and low-risk groups, with the low-risk group demonstrating significantly longer OS in both training and testing cohorts. The model showed strong predictive capabilities, and the risk score served as an independent prognostic factor. Additionally, expression levels of programmed death 1 (PD1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) differed between the risk groups. Gene Set Enrichment Analysis (GSEA) indicated distinct enriched pathways between high-risk and low-risk groups, highlighting the crucial roles of immune and metabolic processes in OSCC. The nine IMRGs prognostic model presented excellent predictive performance and has potential for clinical application.

Identification of Immune-Related Gene Signature Model for Predicting Lung Cancer Survival and Response to Immunotherapy

Introduction: Studies have shown that immune-related genes play a crucial role in tumor development and treatment. However, the specific roles and potential value of these genes in lung cancer patients are still not fully understood. Therefore, this study aims to establish a novel risk model based on immune-related genes for evaluating the prognostic risk and response to immune therapy in lung cancer patients. Methods: Gene expression and clinical data of lung cancer patients were retrieved from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, while immune-related genes were obtained from the ImmPort database. A risk signature model was developed using univariate Cox analysis and LASSO regression analysis. The prognostic value of the model and its response to immunotherapy were analyzed by survival analysis, immune infiltration analysis, and immunotherapy response analysis. Results: We have developed a risk signature model based on eight key immune-related genes, which can classify patients into high-risk and low-risk groups. The prognosis of the high-risk group was significantly lower than that of the low-risk group and was validated in multiple GEO datasets. The mutation frequency was lower in the low-risk group compared to the high-risk group (TP53: 55% vs. 65%; TTN: 52% vs. 60%; CSMD3: 34% vs. 45%). Futhermore, CD274 expression was lower in the low-risk patients, and the high-risk patients in the IMvigor210 cohort had lower survival. Immune infiltration analyses showed that the high-risk group was negatively correlated with the infiltration level of B cells, CD4+ T cells, and NK cells. Importantly, patients in the low-risk group exhibit significantly lower TIDE scores, suggesting that they are more responsive to immunotherapy. Conclusion: Our study has established a novel and robust immune-related gene risk model that can assist in evaluating the prognostic risk and immune therapy response of lung cancer patients.

Draw on advantages and avoid disadvantages: CT-derived individualized radiomic signature for predicting chemo-radiotherapy sensitivity in unresectable advanced non-small cell lung cancer

BackgroundPresently, the options of concurrent chemo-radiotherapy (CCR) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) are controversial and there is no reliable prediction tool to stratify poor- and good-responders. Although radiomic analysis has provided new opportunities for personalized medicine in oncological practice, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This study aimed to develop a qualitative radiomic signature based on the within-sample rank of radiomics features, and to use this novel method to predict CCR sensitivity in LA-NSCLC, avoiding the variability of quantitative signatures to multicenter effect.MethodsWe retrospectively analyzed 125 patients with stage III NSCLC who received treatment from our hospital. Radiomic features were extracted from pretreatment plain CT scans and constructed as feature pairs based on their within-sample rank. Fisher and univariate Cox analyses were performed to select feature pairs significantly associated with patients’ overall survival (OS). NSCLC-Radiomic (R422) cohort including 104 NSCLC patients was used as an independent testing cohort. NSCLC-Radiogenomic (RG211) cohort with matched RNA sequencing profiles, was used for functional enrichment analysis to reveal the underlying biological mechanism reflected by the signature.ResultsA qualitative signature, consisting of 15 radiomic feature pairs (termed as 15-RiFPS), was developed based on the Genetic Algorithm, which could optimally distinguish responder from non-responder with significantly improved OS if they received CCR treatment (log-rank P = 0.0009, HR = 13.79, 95% CIs 1.83–104.1). The performance of 15-RiFPS was validated in an independent public cohort (log-rank P = 0.0037, HR = 2.40, 95% CIs 1.30–4.40). Furthermore, the transcriptomic analyses provided biological pathways (‘glutathione metabolic process’, ‘cellular oxidant detoxification’) underlying the signature.ConclusionsWe developed a CT-derived 15-RiFPS, which could potentially help predict individualized therapeutic benefit of CCR in patients with LA-NSCLC. Additionally, we investigated the underlying intra-tumoral biological characteristics behind 15-RiFPS which would accelerate its clinical application. This approach could be applied to a wider range of treatments and cancer types.

Hypoxia-related lncRNA correlates with prognosis and immune microenvironment in uveal melanoma

BackgroundHypoxia-related genes are linked to the prognosis of various solid malignant tumors. However, the role of hypoxia-related long non-coding RNAs (HRLs) in uveal melanoma (UVM) remains unclear. This study aimed to identify HRLs associated with UVM prognosis and develop a novel risk signature to predict patient outcomes.MethodsData from 80 UVM samples were obtained from The Cancer Genome Atlas. Prognostic HRLs were screened using Cox univariate and Pearson correlation analyses. HRL signature were constructed using Lasso analysis, and gene enrichment analysis was performed to explore the association between HRLs and immune features. Cell Counting Kit-8 assay was used to measure the propagation of human uveal melanoma (MuM2B) cells, while tumor invasion and migration were evaluated using Transwell and wound-healing experiments. Inflammatory factors and macrophage polarization were evaluated using quantitative PCR.ResultsIn total, 621 prognostic HRLs were screened and constructed in 12 HRLs. The risk score showed a significant correlation with the survival time of patients with UVM. Additionally, HRL correlated with diverse key immune checkpoints, revealing possible targets for immunotherapy. Immune-related pathways were highly enriched in the high-risk group. LINC02367, a protective HRL, was associated with the tumor microenvironment and survival time of patients with UVM. In vitro, LINC02367 significantly influenced MuM2B proliferation and migration. It also modulated macrophage polarization by regulating inflammatory factor levels, thereby affecting the immune microenvironment.ConclusionsWe developed a novel HRL signature to predict prognosis in patients with UVM. HRLs are potential biomarkers and therapeutic targets for the treatment of UVM.

Identification and validation of an m7G-related lncRNAs signature for predicting prognosis, immune response and therapy landscapes in ovarian cancer.

Ovarian cancer is the most mortality malignancy in gynecology. N7-methylguanosine (m7G) is one of the most prevalent RNA modifications in the development and progression of cancer. The aim of this study is to investigate the effect of m7G-related lncRNA on ovarian cancer in terms of instruction prognosis and immunotherapy. After integrating and processing the RNA expression profiles with the clinical sample information in the TCGA database, we initially screened to the m7G-related lncRNAs by Spearman correlation analysis, and subsequently obtained a prognostic model constructed by five m7G-related lncRNAs with Univariate Cox analysis, LASSO regression analysis, and Multivariate Cox regression analysis, after which we further evaluated and validated the prognostic value of the model using Kaplan-Meier survival analysis, Principal component analysis, Nomogram, and ROC curve. In addition, based on this risk model, we explored the differentially enriched pathways and functions of the high and low risk groups, and characterized the immune cells, immune functions, gene mutations, and drug sensitivity between the two groups. After a series of rigorous filtering, we finally attained a prognostic risk model consisting of KRT7-AS, USP30-AS1, ZFHX4-AS1, ACAP2-IT1, and TWSG1-DT which is excellent in predicting the prognostic survival of ovarian cancer patients as well as existing as an independent prognostic factor. Moreover, the model has certain relevance in the immune cells and functions between high and low risk groups, and simultaneously, the signature has the role of guiding the option of immunotherapy and chemotherapeutic drugs. Altogether, our study established a tight connection between m7G-associated lncRNAs and ovarian cancer, with potential that the prognostic patterns contribute to steering the prognosis of ovarian cancer patients, measuring the efficacy of immunotherapeutic approaches, and detecting effective chemotherapeutic agents.

Thulium laser en bloc resection reduces recurrence rates in NMIBC patients with tumor diameters ≥3cm compared to transurethral resection: a non-randomized controlled study

PurposeThe advantages of en bloc resection of bladder tumors (ERBT) over transurethral resection of bladder tumors (TURBT) in terms of patient prognosis are not yet clear, and there are some technical limitations. We aimed to compare the tumor recurrence in non-muscle invasive bladder cancer (NMIBC) patients with tumor diameter ≥ 3 cm undergoing either TURBT or thulium laser ERBT.MethodsThe patients included were those diagnosed with NMIBC based on pathological confirmation and underwent TURBT or modified thulium laser ERBT in the Department of Urology at Tongji Hospital from 2019 to 2024. The patients’ medical records were meticulously collected and postoperative follow-up was diligently conducted by trained personnel. Recurrence-free survival curves were generated utilizing the Kaplan–Meier method, and group comparisons were performed using the log-rank trend test. To minimize biases, we employed stratified survival analysis, alongside univariate and multivariate Cox regression analysis.ResultsThis study included a total of 396 patients with NMIBC, with 214 undergoing TURBT and 182 undergoing ERBT. For all patients, there was no significant difference (P = 0.180) in RFS between the TURBT and ERBT groups. For patients with tumor diameter ≥ 3 cm, stratified analysis revealed that the RFS of the ERBT group was significantly better than that of the TURBT group (P = 0.033). However, in patients with tumor diameter < 3 cm, there was no significant difference (P = 0.150) between the two groups. Univariate (HR: 0.52, 95% CI 0.28–0.96, P = 0.036) and multivariate (HR: 0.49, 95% CI 0.25–0.93, P = 0.031) Cox analyses revealed that ERBT was an independent protective factor for recurrence in NMIBC patients with tumor diameter ≥3cm.ConclusionThis study found that thulium laser ERBT may offer advantages in managing NMIBC patients with tumor diameters ≥ 3 cm. This could potentially drive the clinical application of thulium laser ERBT.Trial registrationProtocol was registered at Chinese Clinical Trial Register (ChiCTR) with number ChiCTR2000035407 on 12 August 2020.

Phenotype remodelling of HNSCC cells in the muscle invasion environment

BackgroundTumour invading muscle in head and neck squamous cell carcinoma (HNSCC) is often associated with destructive growth and poor prognosis. However, the phenotypic functions and pathological mechanisms of muscle-invasive cancer cells in tumour progress remains unknown. In this study, we aimed to investigate the phenotypic functions of muscle-invasive cancer cells of HNSCC and their potential crosstalk with tumour microenvironment.MethodsWe obtained scRNA-seq data (SC) from GSE103322 (N = 18) and GSE181919 (N = 37), spatial RNA-seq data (ST) from GSE208253 and GSE181300 (N = 4), transcriptomics of human HNSCC samples from GSE42743 (N = 12) and GSE41613 (N = 97). Utilizing the TCGA-HNSC dataset, we conducted univariate and multivariate Cox analyses to investigate the prognostic impact of muscle-invasion in HNSCC, with validation in an additional cohort. Through Stutility and AUCell approaches, we identified and characterized muscle-invasive cancer cell clusters, including their functional phenotypes and gene-specific profiles. Integration of SC and ST data was achieved using Seurat analysis, multimodal intersection analysis, and spatial deconvolution. The results were further validated via in vitro and in vivo experiments.ResultsOur analyses of the TCGA-HNSC cohort revealed the presence of muscle-invasion was associated with a poor prognosis. By combining ST and SC, we identified muscle-invasive cancer cells exhibiting epithelial-to-mesenchymal transition (EMT) and myoepithelial-like transcriptional programs, which were correlated with a poor prognosis. Furthermore, we identified G0S2 as a novel marker of muscle-invasive malignant cells that potentially promotes EMT and the acquisition of myoepithelium-like phenotypes. These findings were validated through in vitro assays and chorioallantoic membranes experiments. Additionally, we demonstrated that G0S2-overexpressing cancer cells might attract human ECs via VEGF signalling. Subsequent in vitro and in vivo experiments revealed G0S2 plays key roles in promoting the proliferation and invasion of cancer cells.ConclusionsIn this study, we profiled the transcriptional programs of muscle-invasive HNSCC cell populations and characterized their EMT and myoepithelial-like phenotypes. Furthermore, our findings highlight the presence of muscle-invasion as a predictive marker for HNSCC patients. G0S2 as one of the markers of muscle-invasive cancer cells is involved in HNSCC intravasation, probably via VEGF signalling.

Clinicopathological characteristics and prognosis of combined hepatocellular cholangiocarcinoma

There is limited research on the clinicopathological characteristics of combined hepatocellular-cholangiocarcinoma (cHCC-CCA) currently. The aim of this study is to summerize the clinicopathological factors and prognosis of cHCC-CCA, which could help us understand this disease. 72 cases of cHCC-CCA from West China Hospital of Sichuan University were collected. Tissue components were reviewed by pathologists. Immunohistochemistry was used to detect the status of mismatch repair (MMR) and human epidermal growth factor receptor 2 (HER2) in cHCC-CCA, as well as the quantity and distribution of CD3+ T cells and CD8+ T cells. Fluorescence in situ hybridization was used to detect fibroblast growth factor receptor 2 (FGFR2) gene alteration. COX univariate and multivariate analyses were used to evaluate risk factors, and survival curves were plotted. 49 cases were classified as classic type cHCC-CCA and 23 cases as intermediate cell carcinoma. The cut-off value for diagnosing classic type was determined to be ≥ 30% for the cholangiocarcinoma component based on prognostic calculations. All tumors were MMR proficient. The rate of strong HER2 protein expression (3+) was 8.3%, and the frequency of FGFR2 gene alteration was 26.4%. CD3+ T cells and CD8+ T cells were mainly distributed at the tumor margin, and were protective factors for patients with cHCC-CCA. The overall survival of the 72 patients was 18.9 months, with a median survival of 12 months. Tumor size, TNM stage, and serum AFP level were prognostic factors for cHCC-CCA. The proportion of cholangiocarcinoma component reaching the threshold of 30%, may provide a reference for future pathology diagnosis. FGFR2 gene alteration was 26.4%, providing a clue for anti-FGFR2 therapy. However, more data is needed for further verification.

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type’s preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs’ risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

Selenoprotein S (SELENOS) is a potential prognostic biomarker for brain lower grade glioma

BackgroundSelenium, an essential micronutrient, primarily exists as selenocysteine in various selenoproteins. Selenoprotein S (SELENOS) is crucial in the development of human cancer. This study aimed to explore the correlation between SELENOS gene expression and the prognosis of brain lower-grade glioma (LGG). MethodsSELENOS protein and mRNA expression in human normal and tumor tissues were explored through the HPA database. SELENOS expression differences between normal and tumor tissues, along with its prognostic significance in gliomas, were analyzed using the TCGA, GTEx datasets, while the CGGA dataset was used to further assess its prognostic potential in a Chinese cohort. The association between SELENOS expression and tumor immune infiltration was also assessed. Multivariate and univariate Cox models were used to screen for clinicopathological parameters associated with SELENOS expression. The GDSC datasets was utilized to explore the connection between SELENOS and chemotherapeutic responses in LGG. A protein-protein interaction network for SELENOS was created. SELENOS expression in LGG cell lines were determined by Western blotting and qRT-PCR, and its functions were ascertained by routine in vitro experiments. ResultsSELENOS was upregulated in 11 cancers and downregulated in 10 cancers relative to the corresponding normal tissues, and correlated significantly with the prognosis, especially for GBM, LGG and GBMLGG. Furthermore, It displayed a positive correlation with immune cell infiltration levels in LGG. Multivariate and Univariate Cox analyses confirmed that the impact of SELENOS on the prognosis of LGG is the combined result of factors such as age and tumor grade. The expression of SELENOS was significantly negatively correlated with temozolomide IC50 in LGG. We found that SELENOS interacts with 10 proteins, which are upregulated in LGG compared to human normal tissues. The expression of these interactors is positively correlated with SELENOS expression and LGG survival/prognosis. In vitro experiments confirmed the aberrant expression of SELENOS in LGG cell lines, and siRNA-mediated knockdown of SELENOS reduced the proliferation, viability, invasion and migration of LGG cells, and induced apoptosis. ConclusionsSELENOS is a potential prognostic marker and therapeutic target for LGG, and its low expression is associated with favorable prognosis in LGG.

Construction and validation of a machine learning-based immune-related prognostic model for glioma.

Glioma stands as the most prevalent primary brain tumor found within the central nervous system, characterized by high invasiveness and treatment resistance. Although immunotherapy has shown potential in various tumors, it still faces challenges in gliomas. This study seeks to develop and validate a prognostic model for glioma based on immune-related genes, to provide new tools for precision medicine. Glioma samples were obtained from a database that includes the ImmPort database. Additionally, we incorporated ten machine learning algorithms to assess the model's performance using evaluation metrics like the Harrell concordance index (C-index). The model genes were further studied using GSCA, TISCH2, and HPA databases to understand their role in glioma pathology at the genomic, molecular, and single-cell levels, and validate the biological function of IKBKE in vitro experiments. In this study, a total of 199 genes associated with prognosis were identified using univariate Cox analysis. Subsequently, a consensus prognostic model was developed through the application of machine learning algorithms. In which the Lasso + plsRcox algorithm demonstrated the best predictive performance. The model showed a good ability to distinguish two groups in both the training and test sets. Additionally, the model genes were closely related to immunity (oligodendrocytes and macrophages), and mutation burden.The results of in vitro experiments showed that the expression level of the IKBKE gene had a significant effect on the apoptosis and migration of GL261 glioma cells. Western blot analysis showed that down-regulation of IKBKE resulted in increased expression of pro-apoptotic protein Bax and decreased expression of anti-apoptotic protein Bcl-2, which was consistent with increased apoptosis rate. On the contrary, IKBKE overexpression caused a decrease in Bax expression an increase in Bcl-2 expression, and a decrease in apoptosis rate. Tunel results further confirmed that down-regulation of IKBKE promoted apoptosis, while overexpression of IKBKE reduced apoptosis. In addition, cells with down-regulated IKBKE had reduced migration in scratch experiments, while cells with overexpression of IKBKE had increased migration. This study successfully constructed a glioma prognosis model based on immune-related genes. These findings provide new perspectives for glioma prognosis assessment and immunotherapy.

Identifying a CD8T cell signature in the tumor microenvironment to forecast gastric cancer outcomes from sequencing data.

The tumor microenvironment (TME) could be critical in carcinogenesis, immune evasion, and treatment response. TME-related genes are limited in their ability to predict gastric cancer (GC) outcomes. We utilized data from The Cancer Genome Atlas (TCGA) to investigate the functional roles of TME-related genes in GC. We acquired single-cell data, bulk sequencing data, and clinical characteristics of GC patients from the TCGA database. The CD8T cell genes associated with the TME were selected for bioinformatic analysis in GC. Tumor classification of GC was established through consistent cluster analysis. We then evaluated the prognosis and immune cell infiltration in connection with a CD8T cell-related model for GC. The single-cell messenger RNA (mRNA) sequencing (scRNA-Seq) dataset of GSE134520 was utilized to investigate the pathogenesis and disease-specific cell types in GC. Interestingly, compared to healthy tissue, the proportions of CD8Tex cells, malignant cells, and gland mucous increased in GC, whereas the proportion of pit mucous decreased in GC. Since CD8Tex cells may play a vital role in pancreatic adenocarcinoma (PAAD), based on the 612 differentially expressed genes (DEGs) involved in CD8Tex cells, TCGA-GC patients were stratified into low- and high-risk groups. The downregulated DEGs in the low-risk G1 group were associated with proteoglycans in cancer, the cGMP-PKG signaling pathway, focal adhesion, and cell adhesion molecules (CAMs), whereas the upregulated DEGs were associated with viral protein interaction with cytokine and cytokine receptors, the tumor necrosis factor (TNF) signaling pathway, the interleukin (IL)-17 signaling pathway, and the chemokine signaling pathway. Combined with univariate Cox analysis, we ultimately identified 23 CD8T cell-related prognostic genes: TCIM, AADAC, SLC2A3, ZNF331, TSC22D3, CMTM3, ZFP36, VIM, CLDND1, GABARAPL1, SOCS3, RGS1, TCEAL9, RGS2, CD59, SPRY1, EMP3, ZEB2, PDE4B, GLIPR1, ERRFI1, and LBH. Using the Cox regression model to prioritize the 23 CD8T cell-related genes, we finally selected 7 genes: CXCR4, AADAC, SLC2A3, CMTM3, RGS2, CD59, and ZEB2. CD8T cell-related genes have a strong association with tumor classification and immune response in GC patients. A CD8T cell-related signature demonstrated robust prognostic predictive performance for GC. Our findings may reveal novel insights into the diagnosis and treatment of GC.

Identification of anoikis-related long non-coding RNA signature as a novel prognostic model in lung adenocarcinoma.

Anoikis, as a specific form of programmed cell death, involves in tumor metastasis. However, there is still lacking of anoikis-related long non-coding RNA (lncRNA) risk signature in the diagnosis and prognosis of lung adenocarcinoma (LUAD). This study constructed a prognostic risk model by comprehensively analyzing anoikis-related lncRNAs which could effectively diagnose and predict the outcomes of LUAD patients. A list of anoikis-related genes (ARGs) was retrieved from literatures. Anoikis-related lncRNAs were selected using co-expression analysis from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate regression analyses were used to construct a prognostic model. The performance of the risk signature in predicting the prognosis and clinical significance were determined by Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, univariate and multivariate regression analyses. Moreover, the differences of tumor immune microenvironment between the high- and low-risk groups were explored. Finally, a novel nomogram was developed by combining the signature and clinicopathological factors, and the association between lncRNAs and differential N6-methyladenosine (m6A) genes was analyzed by Spearman's analysis. A total of 1,694 anoikis-related lncRNAs were identified from 479 cases of LUAD. According to the univariate and multivariate Cox analyses, we established a prognostic risk model consisting of seven lncRNAs (AC026355.2, AL606489.1, AL031667.3, LINC02802, LINC01116, AC018529.1, and AP000844.2). This prognostic risk model could efficiently classify low- and high-risk patients. The area under the curve (AUC) value was 0.717, which indicated more powerful predictive capability than commonly used clinicopathological factors. The high- and low-risk groups demonstrated different immune microenvironment. Moreover, the nomogram also demonstrated good performance in predicting the prognosis. Twelve differential m6A regulators were identified, and RBM15 was found to be correlated positively with the hub lncRNA AL606489.1. Our study constructed a prognostic risk model based on anoikis-related lncRNAs, which could provide novel perspective on the prognosis of LUAD patients.

Lymph node ratio–based model for predicting survival and assessing the benefit of adjuvant chemotherapy in postoperative duodenal adenocarcinoma

BackgroundThis study aimed to evaluate the prognostic significance of lymph node ratio in postoperative duodenal adenocarcinoma and develop a nomogram-based model for prognosis assessment and treatment optimization. MethodsClinical information of patients with duodenal adenocarcinoma were retrieved from the Surveillance, Epidemiology, and End Results database, and prognostic factors were identified by univariate and multivariable analyses. Prognostic factors influencing patient outcomes were identified using univariate and multivariable Cox analyses. Subsequently, a novel nomogram and risk stratification system were developed based on these identified factors. ResultsA total of 943 eligible patients were included, with 656 in the training cohort and 287 in the validation cohort. Lymph node ratio ≥0.12 were associated with poorer overall survival (hazard ratio 1.562, 95% confidence interval 1.195–2.041, and P = .001 for lymph node ratio = 0.12–0.30; hazard ratio 2.431, 95% confidence interval 1.847–3.199, and P < .001 for lymph node ratio >0.30). Prognostic factors including age at diagnosis, race, T stage, lymph node ratio, and tumor size were integrated into the nomogram. Patients in the low-risk group demonstrated significantly better overall survival compared with those in the high-risk group. Additionally, adjuvant chemotherapy significantly improved overall survival in the high-risk subgroup, whereas low-risk patients might be exempt from adjuvant chemotherapy. ConclusionsThis study represented the pioneering endeavor in introducing a lymph node ratio–based nomogram model for prognosis stratification and adjuvant chemotherapy decision-making protocol for patients with duodenal adenocarcinoma, thereby guiding personalized treatment strategies and minimizing overtreatment.

Pathomics signatures and cuproptosis-related genes signatures for prediction of prognosis in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor with high heterogeneity and poor prognosis, so early prediction and treatment are still difficult. Cuproptosis is a newly discovered type of programmed cell death that has been shown to be closely related to the occurrence and progression of HCC. Cancer morphology is influenced by genetic drivers, and computational pathology methods typically use tissue images such as entire slide images as input to predict clinical or genetic features. Therefore, the comprehensive analysis of pathological features and genomic data provides a feasible way to explore the potential mechanism of the tumor. The objective of this study was to develop a prediction model for HCC prognosis based on the pathomics signatures (PS) and the genomics signatures (GS). A dataset comprising 315 HCC patients was randomly divided into a training set (n=200) and a validation set (n=115). Prognostic models related to PS and GS were constructed by univariate and multivariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, univariate and multivariate Cox analyses, and nomogram were used to evaluate the predictive performance of the prognostic model. The prognostic value of the model was internally validated. A prognostic model incorporating clinical features, PS, and GS was developed using Cox regression analysis and LASSO regression analyses. Kaplan-Meier survival analysis revealed statistically significant differences in survival time between high-risk and low-risk subgroups in both the training and validation datasets (PS: P=0.003 and <0.001, respectively; GS: P=0.008 and 0.004, respectively). The time-dependent ROC curve showed favorable predictive value for survival in both the training and validation sets. The area under the ROC curves at 1, 3, and 5 years was 0.750, 0.830, and 0.870 in the training set, and 0.780, 0.810, and 0.760 in the validation set, respectively. A nomogram model based on the risk model score could effectively predict the survival probability of HCC patients. The calibration curves further demonstrated the good predictive capability of the nomogram model. The prognostic model incorporating PS and GS could effectively predict the prognosis of HCC patients.