An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer.

Aim: This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.Methods: Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.Results: The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.Conclusion: The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.

[Box: see text].

A nomogram and heat map based on LASSO-Cox regression for predicting the risk of early-stage severe fever with thrombocytopenia syndrome patients developing into critical illness at 7-day and 14-day.

Severe fever with thrombocytopenia syndrome (SFTS) represents an emerging infectious disease characterized by a substantial mortality risk. Early identification of patients is crucial for effective risk assessment and timely interventions. In the present study, least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was conducted to identify key risk factors associated with progression to critical illness at 7-day and 14-day. A nomogram was constructed and subsequently assessed for its predictive accuracy through evaluation and validation processes. The risk stratification of patients was performed using X-tile software. The performance of this risk stratification system was assessed using the Kaplan-Meier method. Additionally, a heat map was generated to visualize the results of these analyses. A total of 262 SFTS patients were included in this study, and four predictive factors were included in the nomogram, namely viral copies, aspartate aminotransferase (AST) level, C-reactive protein (CRP), and neurological symptoms. The AUCs for 7-day and 14-day were 0.802 [95% confidence interval (CI): 0.707-0.897] and 0.859 (95% CI: 0.794-0.925), respectively. The nomogram demonstrated good discrimination among low, moderate, and high-risk groups. The heat map effectively illustrated the relationships between risk groups and predictive factors, providing valuable insights with high predictive and practical significance.

Hub metastatic gene signature and risk score of breast cancer patients with small tumor sizes using WGCNA.

BACKGROUND: Breast cancer (BC) is the most common cancer in women and accounts for approximately 15% of all cancer deaths among women globally. The underlying mechanism of BC patients with small tumor size and developing distant metastasis (DM) remains elusive in clinical practices. METHODS: We integrated the gene expression of BCs from ten RNAseq datasets from Gene Expression Omnibus (GEO) database to create a genetic prediction model for distant metastasis-free survival (DMFS) in BC patients with small tumor sizes (≤ 2 cm) using weighted gene co-expression network (WGCNA) analysis and LASSO cox regression. RESULTS: ABHD11, DDX39A, G3BP2, GOLM1, IL1R1, MMP11, PIK3R1, SNRPB2, and VAV3 were hub metastatic genes identified by WGCNA and used to create a risk score using multivariable Cox regression. At the cut-point value of the median risk score, the high-risk score (≥ median risk score) group had a higher risk of DM than the low-risk score group in the training cohort [hazard ratio (HR) 4.51, p < 0.0001] and in the validation cohort (HR 5.48, p = 0.003). The nomogram prediction model of 3-, 5-, and 7-year DMFS shows good prediction results with C-indices of 0.72-0.76. The enriched pathways were immune regulation and cell-cell signaling. EGFR serves as the hub gene for the protein-protein interaction network of PIK3R1, IL1R1, MMP11, GOLM1, and VAV3. CONCLUSION: Prognostic gene signature was predictive of DMFS for BCs with small tumor sizes. The protein-protein interaction network of PIK3R1, IL1R1, MMP11, GOLM1, and VAV3 connected by EGFR merits further experiments for elucidating the underlying mechanisms.

Establishing and Validating a novel Prognostic Model in the Initial Diagnosis of Non-small Cell Lung Cancer with Bone Metastases.

Background: The aim of this research is to establish and validate a prognostic model for predicting prognosis in non-small cell lung cancer (NSCLC) patients with bone metastases. Methods: Overall, 176 NSCLC patients with bone metastases were retrospectively evaluated in the research. We employed the LASSO-Cox regression method to select the candidate indicators for predicting the prognosis among NSCLC patients complicated with bone metastases. We employed the receiver operating characteristic curve (ROC) and the concordance index (C-index) to assess the discriminative ability. Results: Based on the LASSO-Cox regression analysis, 9 candidate indicators were screened to build the prognostic model. The prognostic model had a higher C-index in the training cohort (0.738, 95% CI: 0.680-0.796) and the validation cohort (0.660, 95% CI: 0.566-0.754) than the advanced lung cancer inflammation index (ALI). Furthermore, the AUCs of the 1-, 2-, and 3-year OS predictions for the prognostic model were higher than ALI in both cohorts. Kaplan-Meier curves and the estimated restricted mean survival time (RMST) values showed that the patients in the low-risk subgroup had the lower probabilities of cancer-specific mortality than high-risk subgroup. Conclusions: The prognostic model could provide clinicians with precise information and facilitate individualized treatment for patients with bone metastases.

Development and validation of a nomogram to predict mortality of patients with DIC in ICU.

Background: Disseminated intravascular coagulation (DIC) is a devastating condition, which always cause poor outcome of critically ill patients in intensive care unit. Studies concerning short-term mortality prediction in DIC patients is scarce. This study aimed to identify risk factors contributing to DIC mortality and construct a predictive nomogram. Methods: A total of 676 overt DIC patients were included. A Cox proportional hazards regression model was developed based on covariates identified using least absolute shrinkage and selection operator (LASSO) regression. The prediction performance was independently evaluated in the MIMIC-III and MIMIC-IV Clinical Database, as well as the 908th Hospital Database (908thH). Model performance was independently assessed using MIMIC-III, MIMIC-IV, and the 908th Hospital Clinical Database. Results: The Cox model incorporated variables identified by Lasso regression including heart failure, sepsis, height, SBP, lactate levels, HCT, PLT, INR, AST, and norepinephrine use. The model effectively stratified patients into different mortality risk groups, with a C-index of >0.65 across the MIMIC-III, MIMIC-IV, and 908th Hospital databases. The calibration curves of the model at 7 and 28 days demonstrated that the prediction performance was good. And then, a nomogram was developed to facilitate result visualization. Decision curve analysis indicated superior net benefits of the nomogram. Conclusion: This study provides a predictive nomogram for short-term overt DIC mortality risk based on a Lasso-Cox regression model, offering individualized and reliable mortality risk predictions.

Identification of Novel Prognostic Signature of Recurrent Low-Grade Glioma.

OBJECTIVES: The prognosis of patients with recurrent low-grade glioma (rLGG) varies greatly. Some patients can survive >10 years after recurrence, whereas other patients have <1 year of survival. METHODS: To identify the related risk factors affecting the prognosis of patients with rLGG, we performed a series of bioinformatics analyses on RNA sequencing data of rLGG based on the Chinese Glioma Genome Altas database. RESULTS: We constructed a 12-gene prognostic signature, dividing all the patients with rLGG into high- and low-risk subgroups. The result showed an excellent predictive effect in both the training cohort and the validation cohort using LASSO-Cox regression. Moreover, multivariate Cox analysis identified 4 independent prognostic factors of rLGG; among them, ZCWPW1 is identified as a high-value protective factor. CONCLUSIONS: In all, this prognostic model displayed robust predictive capability for the overall survival of patients with rLGG, providing a new monitoring method for rLGG. The 4 independent prognostic factors, especially ZCWPW1, can be potential targets for rLGG, bringing new possibilities for the treatment of patients with rLGG.

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer.

Background: Thyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet. Methods: We identified PANoptosis-related differentially expressed genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas. Results: A LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P < 0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95 % CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue. Conclusion: A potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation.

Introduction: To analyze the risk factors affecting recurrence in early-stage hepatocellular carcinoma (HCC) patients treated with ablation and then establish a nomogram to provide a clear and accessible representation of the patients' recurrence risk. Methods: Collect demographic and clinical data of 898 early-stage HCC patients who underwent ablation treatment at Beijing You'an Hospital, affiliated with Capital Medical University from January 2014 to December 2022. Patients admitted from 2014 to 2018 were included in the training cohort, while 2019 to 2022 were in the validation cohort. Lasso and Cox regression was used to screen independent risk factors for HCC patients recurrence, and a nomogram was then constructed based on the screened factors. Results: Age, gender, Barcelona Clinic Liver Cancer (BCLC) stage, tumor size, globulin (Glob) and γ-glutamyl transpeptidase (γ-GT) were finally incorporated in the nomogram for predicting the recurrence-free survival (RFS) of patients. We further confirmed that the nomogram has optimal discrimination, consistency and clinical utility by the C-index, Receiver Operating Characteristic Curve (ROC), calibration curve and Decision Curve Analysis (DCA). Moreover, we divided the patients into different risk groups and found that the nomogram can effectively identify the high recurrence risk patients by the Kaplan-Meier curves. Conclusion: This study developed a nomogram using Lasso-Cox regression to predict RFS in early-stage HCC patients following ablation, aiding clinicians in identifying high-risk groups for personalized follow-up treatments.

A systematic computational analysis of the endosomal recycling pathway in glioblastoma.

Glioblastoma (GBM) is the most common and aggressive brain cancer in adults. The standard treatment is brutal and has changed little in 20 years, and more than 85% of patients will die within two years of their diagnosis. There is thus an urgent need to identify new drug targets and develop novel therapeutic strategies to increase survival and improve quality of life. Using publicly available genomics, transcriptomics and proteomics datasets, we compared the expression of endosomal recycling pathway regulators in non-tumour brain tissue with their expression in GBM. We found that key regulators of this pathway are dysregulated in GBM and their expression levels can be linked to survival outcomes. Further analysis of the differentially expressed endosomal recycling regulators allowed us to generate an 8-gene prognostic signature that can distinguish low-risk from high-risk GBM and potentially identify tumours that may benefit from treatment with endosomal recycling inhibitors. This study presents the first systematic analysis of the endosomal recycling pathway in glioblastoma and suggests it could be a promising target for the development of novel therapies and therapeutic strategies to improve outcomes for patients.

A novel exosome-related prognostic risk model for thyroid cancer.

AIM: The aim was to build an exosome-related gene (ERG) risk model for thyroid cancer (TC) patients. METHODS: Note that, 510 TC samples from The Cancer Genome Atlas database and 121 ERGs from the ExoBCD database were obtained. Differential gene expression analysis was performed to get ERGs in TC (TERGs). Functional enrichment analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on the TERGs. Then we constructed a model based on LASSO Cox regression analysis. Kaplan-Meier survival analysis was applied and a Nomogram model was also built. The immune landscape was evaluated by CIBERSORT. RESULTS: Thirty-eight TERGs were identified and their functions were enriched on 591 GO terms and 30 KEGG pathways. We built a Risk Score model based on FGFR3, ADRA1B, and POSTN. Risk Scores were significantly higher in T4 than in other stages, meanwhile, it didn't significantly differ in genders and TNM N or M classifications. The nomogram model could reliably predict the overall survival of TC patients. The mutation rate of BRAF and expression of cytotoxic T-lymphocyte-associated protein 4 were significantly higher in the high-risk group than in the low-risk group. The risk score was significantly correlated to the immune landscape. CONCLUSION: We built a Risk Score model using FGFR3, ADRA1B, and POSTN which could reliably predict the prognosis of TC patients.

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci.

Backgrounds: Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population. Methods: A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes. Results: Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable. Conclusion: We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

Immune landscape of hepatocellular carcinoma tumor microenvironment identifies a prognostic relevant model.

Background: Various studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression. Methods: We integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients. Results: To describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration. Conclusions: Taken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma.

OBJECTIVE: To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it. METHODS: Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group. RESULTS: The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model. CONCLUSION: The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.

A novel model for predicting prognosis and response to immunotherapy in nasopharyngeal carcinoma patients.

Blood-based biomarkers of immune checkpoint inhibitors (ICIs) response in patients with nasopharyngeal carcinoma (NPC) are lacking, so it is necessary to identify biomarkers to select NPC patients who will benefit most or least from ICIs. The absolute values of lymphocyte subpopulations, biochemical indexes, and blood routine tests were determined before ICIs-based treatments in the training cohort (n = 130). Then, the least absolute shrinkage and selection operator (Lasso) Cox regression analysis was developed to construct a prediction model. The performances of the prediction model were compared to TNM stage, treatment, and Epstein-Barr virus (EBV) DNA using the concordance index (C-index). Progression-free survival (PFS) was estimated by Kaplan-Meier (K-M) survival curve. Other 63 patients were used for validation cohort. The novel model composed of histologic subtypes, CD19+ B cells, natural killer (NK) cells, regulatory T cells, red blood cells (RBC), AST/ALT ratio (SLR), apolipoprotein B (Apo B), and lactic dehydrogenase (LDH). The C-index of this model was 0.784 in the training cohort and 0.735 in the validation cohort. K-M survival curve showed patients with high-risk scores had shorter PFS compared to the low-risk groups. For predicting immune therapy responses, the receiver operating characteristic (ROC), decision curve analysis (DCA), net reclassifcation improvement index (NRI) and integrated discrimination improvement index (IDI) of this model showed better predictive ability compared to EBV DNA. In this study, we constructed a novel model for prognostic prediction and immunotherapeutic response prediction in NPC patients, which may provide clinical assistance in selecting those patients who are likely to gain long-lasting clinical benefits to anti-PD-1 therapy.

Construction of a prognostic risk model for Stomach adenocarcinoma based on endoplasmic reticulum stress genes.

OBJECTIVE: Stomach adenocarcinoma (STAD) is caused by malignant transformation of gastric glandular cells and is characterized by a high incidence rate and a poor prognosis. This study was designed to establish a prognostic risk model for STAD according to endoplasmic reticulum (ER) stress feature genes as cancer cells are susceptible to ER stress. METHODS: The TCGA-STAD dataset was downloaded to screen differentially expressed genes (DEGs). By intersecting DEGs with ER stress genes retrieved from GeneCards, ER stress-related DEGs in STAD were obtained. Kmeans cluster analysis of STAD subtypes and Single sample gene set enrichment analysis (ssGSEA) analysis of immune infiltration were performed. Cox regression analysis was utilized to construct a risk prognostic model. Samples were split into high-risk and low-risk groups according to the median risk score. Survival analysis and Receiver Operating Characteristic (ROC) curves were conducted to assess the validity of the model. Gene set enrichment analysis (GSEA) was performed to investigate differential pathways in the two risk groups. Cox analysis was performed to verify the independence of the risk model, and a nomogram was generated. RESULTS: A total of 162 ER stress-related DEGs in STAD were identified by bioinformatics analysis. Kmeans cluster analysis showed that STAD was divided into 3 subgroups. The ssGSEA showed that the levels of immune infiltration in subgroups 2 and 3 were significantly higher than subgroup 1. With 12 prognostic genes (MATN3, ATP2A1, NOX4, AQP11, HP, CAV1, STARD3, FKBP10, EGF, F2, SERPINE1, CNGA3) selected from ER stress-related DEGs using Cox regression analysis, we then constructed a prognostic model. Kaplan-Meier (K­M) survival curves and ROC curves showed good prediction performance of the model. Significant enrichment of genes in the high-risk group was found in extracellular matrix (ECM) receptor interaction. Cox regression analysis combined with clinical factors showed that the risk model could be used as an independent prognostic factor. The prediction correction curve showed that the good prediction ability of the nomogram. CONCLUSION: The STAD could be divided into three subgroups, and the 12-gene model constructed by ER stress signatures had a good prognostic performance for STAD patients.

A practical recurrence risk model based on Lasso-Cox regression for gastric cancer.

INTRODUCTION: Gastric cancer remains huge cancer threat worldwide. Detecting the recurrence of gastric cancer after treatment is especially important in improving the prognosis of patients. We aim to fit different risk models with different clinical variables for patients with gastric cancer, which further provides applicable guidance to clinical doctors for their patients. METHODS: We collected the primary data from the medical record system in Lanzhou University Second Hospital and further cleaned the primary data via assessing data integrity artificially; meanwhile, detailed conclusion criteria and exclusion criteria were made. We used R software (version 4.1.3) and SPSS 25.0 to analyze data and build models, in which SPSS was used to analyze the correlation and difference of different items in the training set and testing set, and different R packages were used to run LASSO regression, Cox regression and nomogram for variable selection, model construction and model validation. RESULT: A total of 649 patients were included in our data analysis and model building. In LASSO regression selection, seven variables, pathological stage, tumor size, the number of total lymph nodes, the number of metastatic lymph nodes, intraoperative blood loss (IBL), the level of AFP and CA199, showed their correlation to the dependent variable. The multivariable Cox regression model fitted using these seven variables showed medium prediction ability, with an AUC of 0.840 in the training set and 0.756 in the testing set. CONCLUSIONS: Pathological stage, tumor size, the number of total lymph nodes, the number of metastatic lymph nodes, IBL, the level of AFP and CA199 are significant in identifying recurrence risk for gastric cancer patients after radical gastrectomy.

A prognostic model based on regulatory T-cell-related genes in gastric cancer: Systematic construction and validation.

Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg-related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg-related prognostic genes were screened from Treg-related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg-related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6-gene Treg-related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low-risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1-, 3- and 5-year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low-risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high-risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6-gene Treg-associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients.

Development and validation of an [18F]FDG-PET/CT radiomic model for predicting progression-free survival for patients with stage II - III thoracic esophageal squamous cell carcinoma who are treated with definitive chemoradiotherapy.

BACKGROUND: Radiomics is a method for extracting a large amount of information from images and used to predict treatment outcomes, side effects and diagnosis. In this study, we developed and validated a radiomic model of [18F]FDG-PET/CT for predicting progression-free survival (PFS) of definitive chemoradiotherapy (dCRT) for patients with esophageal cancer. MATERIAL AND METHODS: Patients with stage II - III esophageal cancer who underwent [18F]FDG-PET/CT within 45 days before dCRT between 2005 and 2017 were included. Patients were randomly assigned to a training set (85 patients) and a validation set (45 patients). Radiomic parameters inside the area of standard uptake value ≥ 3 were calculated. The open-source software 3D slicer and Pyradiomics were used for segmentation and calculating radiomic parameters, respectively. Eight hundred sixty radiomic parameters and general information were investigated.In the training set, a radiomic model for PFS was made from the LASSO Cox regression model and Rad-score was calculated. In the validation set, the model was applied to Kaplan-Meier curves. The median value of Rad-score in the training set was used as a cutoff value in the validation set. JMP was used for statistical analysis. RStudio was used for the LASSO Cox regression model. p < 0.05 was defined as significant. RESULTS: The median follow-up periods were 21.9 months for all patients and 63.4 months for survivors. The 5-year PFS rate was 24.0%. In the training set, the LASSO Cox regression model selects 6 parameters and made a model. The low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.019). In the validation set, the low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.040). CONCLUSIONS: The [18F]FDG-PET/CT radiomic model could predict PFS for patients with esophageal cancer who received dCRT.

Transcriptomic Profiling Reveals an Enhancer RNA Signature for Recurrence Prediction in Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) is one of the most fatal malignancies worldwide, and this is in part due to high rates of tumor recurrence in these patients. Currently, TNM staging remains the gold standard for predicting prognosis and recurrence in CRC patients; however, this approach is inadequate for identifying high-risk patients with the highest likelihood of disease recurrence. Recent evidence has revealed that enhancer RNAs (eRNAs) represent a higher level of cellular regulation, and their expression is frequently dysregulated in several cancers, including CRC. However, the clinical significance of eRNAs as recurrence predictor biomarkers in CRC remains unexplored, which is the primary aim of this study. RESULTS: We performed a systematic analysis of eRNA expression profiles in colon cancer (CC) and rectal cancer (RC) patients from the TCGA dataset. By using rigorous biomarker discovery approaches by splitting the entire dataset into a training and testing cohort, we identified a 22-eRNA panel in CC and a 19-eRNA panel in RC for predicting tumor recurrence. The Kaplan-Meier analysis showed that biomarker panels robustly stratified low and high-risk CC (p = 7.29 × 10-5) and RC (p = 6.81 × 10-3) patients with recurrence. Multivariate and LASSO Cox regression models indicated that both biomarker panels were independent predictors of recurrence and significantly superior to TNM staging in CC (HR = 11.89, p = 9.54 × 10-4) and RC (HR = 3.91, p = 3.52 × 10-2). Notably, the ROC curves demonstrated that both panels exhibited excellent recurrence prediction accuracy in CC (AUC = 0.833; 95% CI: 0.74-0.93) and RC (AUC = 0.834; 95% CI: 0.72-0.92) patients. Subsequently, a combination signature that included the eRNA panels and TNM staging achieved an even greater predictive accuracy in patients with CC (AUC = 0.85). CONCLUSIONS: Herein, we report a novel eRNA signature for predicting recurrence in patients with CRC. Further experimental validation in independent clinical cohorts, these biomarkers can potentially improve current risk stratification approaches for guiding precision oncology treatments in patients suffering from this lethal malignancy.

The Prognostic Value of a lncRNA Risk Model Consists of 9 m6A Regulator-Related lncRNAs in Hepatocellular Carcinoma (HCC).

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver. Although the RNA modification N6-methyladenine (m6A) has been reported to be involved in HCC carcinogenesis, early diagnostic markers and promising personalized therapeutic targets are still lacking. In this study, we identified that 19 m6A regulators and 34 co-expressed lncRNAs were significantly upregulated in HCC samples; based on these factors, we established a prognostic signal of HCC associated with 9 lncRNAs and 19 m6A regulators using LASSO Cox regression analysis. Kaplan-Meier survival estimate revealed correlations between the risk scores and patients' OS in the training and validation dataset. The ROC curve demonstrated that the risk score-based curve has satisfactory prediction efficiency for both training and validation datasets. Multivariate Cox's proportional hazard regression analysis indicated that the risk score was an independent risk factor within the training and validation dataset. In addition, the risk score could distinguish HCC patients from normal non-cancerous samples and HCC samples of different pathological grades. Eventually, 232 mRNAs were co-expressed with these 9 lncRNAs according to GSE101685 and GSE112790; these mRNAs were enriched in cell cycle and cell metabolic activities, drug metabolism, liver disease-related pathways, and some important cancer related pathways such as p53, MAPK, Wnt, RAS and so forth. The expression of the 9 lncRNAs was significantly higher in HCC samples than that in the neighboring non-cancerous samples. Altogether, by using the Consensus Clustering, PCA, ESTIMATE algorithm, LASSO regression model, Kaplan-Meier survival assessment, ROC curve analysis, and multivariate Cox's proportional hazard regression model analysis, we established a prognostic marker consisting of 9 m6A regulator-related lncRNAs that markers may have prognostic and diagnostic potential for HCC.