Prognostic Model Research Articles

A Novel Prognostic Nomogram Based on TIGIT and NKG2A Can Predict Relapse-Free Survival of Hepatocellular Carcinoma After Hepatectomy.

Hepatocellular carcinoma (HCC) is a major global health concern, and emerging evidence suggests that TIGIT and NKG2A are potential immune checkpoints with implications for HCC progression. This study aimed to evaluate the prognostic significance of TIGIT and NKG2A expression in HCC patients who underwent radical liver resection. We conducted a retrospective analysis of 144 HCC patients who underwent radical liver resection. TIGIT and NKG2A expression levels were assessed using the immunoreactive score. Cox proportional hazards models were utilized to analyze the association between TIGIT/NKG2A expression and clinical characteristics, relapse-free survival (RFS), and overall survival (OS). Prognostic models for OS and RFS was developed and validated using concordance index and calibration curves. Additionally, the random forest algorithm was employed to identify independent risk factors for OS and RFS and their correlation with predicted survival. TIGIT and NKG2A expression were identified as independent risk factors for RFS, while TIGIT expression alone significantly impacted OS. The prognostic models showed good discriminative ability, with concordance indices exceeding 0.7 for predicting 1-, 3-, and 5-year OS or RFS. Calibration curves confirmed the reliability of the nomograms for OS and RFS prediction. The areas under the ROC curve consistently exceeded 0.7 for predicting OS and RFS. Elevated levels of TIGIT and NKG2A expression were associated with diminished RFS, highlighting their importance as prognostic factors. Our study establishes the prognostic significance of TIGIT and NKG2A expression in predicting OS and RFS following radical liver resection for HCC patients. The developed prognostic models incorporating TIGIT and NKG2A expression hold promise for improving risk stratification and clinical management of HCC patients.

Identification of Vesicle-Mediated Transport-Related Genes for Predicting Prognosis, Immunotherapy Response, and Drug Screening in Cervical Cancer.

Cervical cancer is one of the most common malignancies among women. Vesicle-mediated transport mechanisms significantly influence tumor cell behavior through intercellular material exchange. However, prognostic significance in CC patients remains underexplored. We identified differentially expressed vesicle-mediated transport-related genes from TCGA and GeneCards datasets through differential expression analysis. We constructed a prognostic model using Cox regression and LASSO regression, categorized patients into high- and low-risk groups, and validated the model in the GEO data set. A nomogram integrating clinical features and risk scores demonstrated the model's independent prognostic capability. We analyzed tumor immune cell infiltration, immune checkpoints, and predicted immunotherapy responses in the high- and low-risk groups. Finally, we screened potential drugs for targeting CC and conducted drug-sensitivity analysis. We successfully established a 10-gene prognostic model based on VMTRGs. The low-risk group exhibited favorable prognosis, significant immune cell infiltration, and promising immunotherapy response, whereas the high-risk group showed higher sensitivity to chemotherapeutic agents such as Docetaxel and Paclitaxel. Potential drugs identified for targeting CC patients included Megestrol acetate, Lenvatinib, Adavosertib, and Barasertib. The VMTRG-based prognostic model demonstrates reliable clinical prognostic value and enhances understanding of vesicle-mediated transport mechanisms in CC.

Identification and Validation of a Novel PANoptosis-related Gene Signatured for Osteosarcoma as Prognostic Model.

To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes. Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024. Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves. This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators. The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways. PANoptosis-related genes, Osteosarcoma, Prognosis, Bioinformatics, Tumour micro-environment.

Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III-IV Colorectal Cancer Patients.

Most colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis. A total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1-, 3-, 5-, 8- and 10-year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan-Meier curves were used to plot the survival probabilities for different strata of each risk factor. There was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1-, 3-, 5-, 8- and 10-year OS, which in the training group were 0.826 (95% CI: 0.807-0.845), 0.836 (0.819-0.853), 0.839 (0.820-0.859), 0.835 (0.809-0.862) and 0.825 (0.779-0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786-0.852), 0.831 (0.804-0.858), 0.830 (0.799-0.861), 0.815 (0.774-0.857) and 0.802 (0.723-0.882) respectively. Finally, the 1-, 3-, 5-, 8- and 10-year OS rates for advanced stage CRC patients were 73.4 (71.8-75.0), 49.5 (47.8-51.4), 43.3 (41.5-45.2), 40.1 (38.1-41.9) and 38.6 (36.6-40.8) respectively. We constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high-risk patients.

Single-cell and machine learning approaches uncover intrinsic immune-evasion genes in the prognosis of hepatocellular carcinoma

Background and aimsHepatocellular carcinoma (HCC) is a tumor of high heterogeneity and complexity, which poses significant challenges to effective treatment and patient prognosis because of its immune evasion characteristics. To address these issues, single-cell technology and machine learning methods have emerged as a promising approach to identify genes associated with immune escape in HCC. This study aimed to develop a prognostic risk score model for HCC by identifying intrinsic immune-evasion genes (IIEGs) through single-cell technology and machine learning, providing insights into immune infiltration, enhancing predictive accuracy, and facilitating the development of more effective treatment strategies. Materials and methodsThe study utilized data from The Cancer Genome Atlas database to analyze gene expression profiles and clinical data related to intrinsic immune evasion in patients with HCC. Various tools, including the Human Protein Atlas, cBioPortal, single-cell analysis, machine learning, and Kaplan-Meier plot, were used to analyze IIEGs. Functional enrichment analysis was conducted to explore potential mechanisms. In addition, the abundance of infiltrating cells in the tumor microenvironment was investigated using single-sample gene set enrichment analysis, CIBERSORT, xCELL, and tumor immunophenotype algorithms. The expression of glycosylphosphatidylinositol anchor attachment 1 (GPAA1) was examined in the clinical sample of HCC by quantitative real-time polymerase chain reaction, Western blotting, and immunohistochemical staining. ResultsUnivariate Cox analysis identified 63 IIEGs associated with the prognosis of HCC. Using random forest, least absolute shrinkage and selection operator regression analysis, and support vector machine, a risk score model consisting of six IIEGs (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), phosphatidylinositol glycan anchor biosynthesis class U (PIGU), endoplasmic reticulum membrane protein complex subunit 3 (EMC3), centrosomal protein 55 (CEP55), autophagy-related 10 (ATG10), and GPAA1) developed, which was validated using 10 pairs of HCC and adjacent non-cancerous samples. Based on the calculated median risk score, HCC samples were categorized into high- and low-risk groups. The Kaplan-Meier curve analysis showed that the high-risk group had a worse prognosis compared with the low-risk group. Time-dependent receiver operating characteristic analysis demonstrated the accurate predictive capability of the risk score model for HCC prognosis. Furthermore, immune infiltration analysis showed a positive correlation between the risk score model and 40 immune checkpoint genes as well as Th2 cells. ConclusionsA prognostic risk score model was formulated by six IIEG signatures and showed promise in predicting the prognosis of patients diagnosed with HCC. The utilization of the IIEG risk score as a novel prognostic index, together with its significance as a valuable biomarker for immunotherapy in HCC, provides benefit for patients with HCC in determining therapeutic strategies for clinical application.

Integrated bioinformatics analysis and in vivo validation of potential immune-related genes linked to diabetic nephropathy

BackgroundDiabetic nephropathy (DN) is a common microvascular complication of diabetes mellitus and the main cause of chronic renal failure. This study explored the potential immunomodulation-related genes (IRGs) in DN using bioinformatics. MethodsIRGs were identified using GeneCards, and differentially expressed genes were identified using the GSE99339, GSE96804, and GSE30122 datasets. We conducted enrichment analyses using Gene Ontology, gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes to identify the associated signaling pathways. Prognostic models were constructed using Least Absolute Shrinkage and Selection Operator regression. The predictive power of IRGs was evaluated using receiver operating characteristic (ROC) curves. Furthermore, we utilized ssGSEA to determine the relative abundance of immune cell infiltration. The expression of five significant IRGs was further validated using immunohistochemistry (IHC) in individuals with DN and real-time PCR (RT-PCR) in animal experiments. ResultsIn total, 17 immunomodulation-related differentially expressed genes were identified, which were enriched in immune-associated pathways and inflammation. GSEA unveiled substantial enrichments in metabolic irregularities and the structural composition of the extracellular matrix. ROC analysis results revealed that the diagnostic efficacy of IFNAR2 and CASP3 was comparatively high. Notably, we identified potential IRGs for DN, including CASP3, LGALS9, and SST, using IHC and RT-PCR. ConclusionsCASP3, LGALS9, and SST are potential IRGs in patients with DN. Our findings may offer a theoretical basis for developing more focused and innovative immunotherapy approaches for patients with DN.

Prognostic nomogram models for elderly patients with differentiated thyroid carcinoma: A population-based study.

This study aimed to develop and validate a prognostic model for elderly patients with differentiated thyroid carcinoma (DTC) based on various demographic and clinical parameters in order to accurately predict patient outcomes. Patients who were diagnosed with DTC and were over 55 years old between 2010 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The patients were then randomly divided into a training set and a validation set in a 7:3 ratio, and patients from our center were included as an external validation group. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify independent prognostic factors, which were then utilized to develop nomograms for predicting the prognosis. The discriminative ability of the nomograms was evaluated using the concordance index, and the calibration was assessed using calibration plots. The clinical usefulness and benefits of the predictive models were determined through decision curve analysis. The findings of the stepwise Cox regression analysis revealed that several variables, including age, marital status, sex, multifocality, T stage, N stage, and M stage, were significantly associated with overall survival in elderly patients with DTC. Additionally, age, tumor size, multifocality, T stage, N stage, and M stage were identified as the primary determinants of cancer specific survival in elderly patients with DTC. Using these predictors, nomograms were constructed to estimate the probability of overall survival and cancer specific survival. The nomograms demonstrated a high level of predictive accuracy, as evidenced by the concordance index, and the calibration plots indicated that the predicted outcomes were consistent with the actual outcomes. Furthermore, the decision curve analysis demonstrated that the nomograms provided substantial clinical net benefit, indicating their utility in clinical practice.

Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Non-Metastatic Paediatric Wilms' Tumour Patients Undergoing Upfront Radical Nephrectomy.

To analyse the relationship between the preoperative systemic immune-inflammation index (SII) and the relapse-free survival (RFS) of paediatric patients with Wilms' tumour (WT) after radical surgery, and to establish and validate a prognostic survival model. Observational study. Place and Duration of the Study: Department of Oncologic Surgery, Anhui Children's Hospital of Fudan University, Hefei, China, from January 2013 to August 2023. A retrospective analysis was conducted on 79 WT patients treated with radical resection, with their preoperative SII values computed. The best cut-off for SII was determined through the ROC curve, categorising patients into high and low SII groups. The Kaplan-Meier method and Cox-regression were used for survival analysis. A survival prognostic model was constructed and its predictive capability gauged (AUC of the ROC). The study included 79 WT patients with a median RFS of 65 months and an average of 75.5 ± 3.4 months. The optimal cut-off value for SII was 534.95. The low SII group had a higher RFS (Log-rank: χ2 = 9.380, p = 0.002). Preoperative SII (HR = 3.277, 95% CI: 1.167 - 9.200, p = 0.024), clinical staging (HR = 8.408, 95% CI: 2.604 - 27.147, p <0.001), and tissue differentiation (HR = 2.237, 95% CI: 1.043 - 5.828, p = 0.039) were independent risk factors for RFS. The model's diagnostic performance was 0.749 (95% CI: 0.636 - 0.861). Internal validation showed an AUC of 0.723 (95% CI: 0.608 - 0.838). Lower preoperative SII suggests a more favourable prognosis. The SII-based nomogram efficiently forecasts post-radical surgery prognosis for WT. Wilms' Tumour, Systemic immune-inflammation index, Relapse-free survival, Nomogram.

Integrating Machine Learning with Web-Based Tools for Personalized Prognosis in Oral Adenoid Cystic Carcinoma

BackgroundAdenoid cystic carcinoma (ACC) of the oral cavity is a rare head and neck cancer. This rarity contributes to the paucity of comprehensive research on this cancer thereby complicating the development of evidence-based treatment strategies. This study aims to use machine learning (ML) techniques to analyze survival outcomes and optimize treatment approaches of ACC. MethodsThe SEER database (2000-2020) was used in this study. Cox regression analysis was used to identify the prognostic variables; prognostic models using five ML algorithms were constructed to predict the 5-year survival rates. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. Also, Kaplan-Meier survival analysis was performed. ResultsThis study's sample included 645 patients. The most common primary site for ACC was the hard palate, followed by the cheek mucosa. Survival rates varied across treatment groups, with the highest rates observed in patients who underwent surgery only. ML models revealed that the most significant prognostic factors were age, metastasis, and surgery. ConclusionsThis study contributes evidence and knowledge to the limited literature on ACC and emphasizes the importance of adjuvant radiotherapy. This study highlights that metastasis and age are key prognostic factors. Furthermore, the developed ML-based web tool offers a novel approach for the personalized prognosis of these rare cancer types.

Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Non-Metastatic Paediatric Wilms' Tumour Patients Undergoing Upfront Radical Nephrectomy.

To analyse the relationship between the preoperative systemic immune-inflammation index (SII) and the relapse-free survival (RFS) of paediatric patients with Wilms' tumour (WT) after radical surgery, and to establish and validate a prognostic survival model. Observational study. Place and Duration of the Study: Department of Oncologic Surgery, Anhui Children's Hospital of Fudan University, Hefei, China, from January 2013 to August 2023. A retrospective analysis was conducted on 79 WT patients treated with radical resection, with their preoperative SII values computed. The best cut-off for SII was determined through the ROC curve, categorising patients into high and low SII groups. The Kaplan-Meier method and Cox-regression were used for survival analysis. A survival prognostic model was constructed and its predictive capability gauged (AUC of the ROC). The study included 79 WT patients with a median RFS of 65 months and an average of 75.5 ± 3.4 months. The optimal cut-off value for SII was 534.95. The low SII group had a higher RFS (Log-rank: χ2 = 9.380, p = 0.002). Preoperative SII (HR = 3.277, 95% CI: 1.167 - 9.200, p = 0.024), clinical staging (HR = 8.408, 95% CI: 2.604 - 27.147, p <0.001), and tissue differentiation (HR = 2.237, 95% CI: 1.043 - 5.828, p = 0.039) were independent risk factors for RFS. The model's diagnostic performance was 0.749 (95% CI: 0.636 - 0.861). Internal validation showed an AUC of 0.723 (95% CI: 0.608 - 0.838). Lower preoperative SII suggests a more favourable prognosis. The SII-based nomogram efficiently forecasts post-radical surgery prognosis for WT. Wilms' Tumour, Systemic immune-inflammation index, Relapse-free survival, Nomogram.

Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer

BackgroundAbnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.MethodsSingle-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.ResultsA total of 153 SMRGs were acquired, of which 48 were linked to PC patients’ prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.ConclusionThis study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

Establishment of a Novel Risk Stratification System Integrating Clinical and Pathological Parameters for Prognostication and Clinical Decision‐Making in Early‐Stage Cervical Cancer

ABSTRACTBackgroundHighly heterogeneity and inconsistency in terms of prognosis are widely identified for early‐stage cervical cancer (esCC). Herein, we aim to investigate for an intuitional risk stratification model for better prognostication and decision‐making in combination with clinical and pathological variables.MethodsWe enrolled 2071 CC patients with preoperative biopsy‐confirmed and clinically diagnosed with FIGO stage IA‐IIA who received radical hysterectomy from 2013 to 2018. Patients were randomly assigned to the training set (n = 1450) and internal validation set (n = 621), in a ratio of 7:3. We used recursive partitioning analysis (RPA) to develop a risk stratification model and assessed the ability of discrimination and calibration of the RPA‐derived model. The performances of the model were compared with the conventional FIGO 2018 and 9th edition T or N stage classifications.ResultsRPA divided patients into four risk groups with distinct survival: 5‐year OS for RPA I to IV were 98%, 95%, 85.5%, and 64.2%, respectively, in training cohort; and 99.5%, 93.2%, 85%, and 68.3% in internal validation cohort (log‐rank p &lt; 0.001). Calibration curves confirmed that the RPA‐predicted survivals were in good agreement with the actual survivals. The RPA model outperformed the existing staging systems, with highest AUC for OS (training: 0.778 vs. 0.6–0.717; internal validation: 0.772 vs. 0.595–0.704; all p &lt; 0.05), and C‐index for OS (training: 0.768 vs. 0.598–0.707; internal validation: 0.741 vs. 0.583–0.676; all p &lt; 0.05). Importantly, there were associations between RPA groups and the efficacy of treatment regimens. No obvious discrepancy was observed among different treatment modalities in RPA I (p = 0.922), whereas significant survival improvements were identified in patients who received adjuvant chemoradiotherapy in RPA II–IV (p value were 0.028, 0.036, and 0.024, respectively).ConclusionWe presented a validated novel clinicopathological risk stratification signature for robust prognostication of esCC, which may be used for streamlining treatment strategies.

Identification and Experimental Validation of Prognostic miRNA Signature and Ferroptosis-Related Key Genes in Cervical Squamous Cell Carcinoma.

This study aimed to investigate the prognostic value of miRNAs and ferroptosis-related genes in cervical squamous cell carcinoma. We mined data from public databases for differentially expressed miRNAs, ferroptosis-related genes, and clinical parameters and constructed a prognostic risk model. The predictive performance of the model was evaluated using survival and receiver operating characteristic curve analyses. We combined the clinicopathological features to construct a nomogram and evaluated its efficacy using calibration and clinical decision curves. The correlation between miRNA characteristics, risk score, and the tumor microenvironment was also studied. Next, consensus and key genes were screened, and their biological functions were analyzed using KEGG, GO, GSEA, and drug sensitivity analysis. Finally, the expression of miRNAs and key genes was detected using qRT-PCR and western blotting to verify the prediction results. Seven miRNA signatures (miR-100-3p, miR-301a-5p, miR-331-3p, miR-425-5p, miR-502-3p, miR-505-5p, and miR-629-3p) were generated, and prognostic risk and nomogram models were successfully constructed. These models exhibited good accuracy. miRNA signatures correlated with the tumor microenvironment. Twelve consensus genes and three key genes (SLC2A1, ANO6, and TXNIP) were screened and their biofunctional diversity was identified using various analytical methods. qRT-PCR and western blotting were used to verify the expression of miR-301a-5p, miR-505-5p, SLC2A1, and TXNIP in cervical squamous carcinoma. The results were consistent with those of bioinformatics analyses. Seven miRNAs may serve as prognostic biomarkers of cervical squamous cell carcinoma. SLC2A1, ANO6, and TXNIP are associated with cervical squamous cell carcinoma and may serve as ferroptosis-related markers of the disease.

Prognostic nomogram model based on quantitative metrics of subregions surrounding residual cavity in glioblastoma patients

BackgroundThe hyperintensity area surrounding the residual cavity on postoperative fluid-attenuated inversion recovery (FLAIR) image is a potential site for glioblastoma (GBM) recurrence. This study aimed to develop a nomogram using quantitative metrics from subregions of this area, prior to chemoradiotherapy (CRT), to predict early GBM recurrence.MethodsAdult patients with GBM diagnosed between October 2018 and October 2022 were retrospectively analyzed. Quantitative metrics, including the mean, maximum, minimum, median values, and standard deviation of FLAIR signal intensity (SI) (measured using 3D-Slicer software), were extracted from the following subregions surrounding the residual cavity on post-contrast T1-weighted (CE-T1WI)-FLAIR fusion images: the enhancing region (ER), non-enhancing region (NER), and combined ER + NER. Independent prognostic factors were identified using Cox regression and least absolute shrinkage and selection operator (LASSO) analyses and were incorporated into the prediction nomogram model. The model’s performance was evaluated using the C-index, calibration curves, and decision curves.ResultsA total of 129 adult GBM patients were enrolled and randomly assigned to a training (n = 90) and a validation cohorts (n = 39) in a 7:3 ratio. Sixty-nine patients experienced postoperative recurrence. Cox regression analysis identified subventricular zone involvement, the median FLAIR intensity in the ER, the rFLAIR (relative FLAIR intensity compared to the contralateral normal region) of ER + NER, and corpus callosum involvement as independent prognostic factors. For predicting recurrence within 1 year after surgery, the nomogram model had a C-index of 0.733 in the training cohort and 0.746 in the validation cohort. Based on the nomogram score, post-operative GBM patients could be stratified into high- and low-risk for recurrence.ConclusionsNomogram models which based on quantitative metrics from FLAIR hyperintensity subregions may serve as potential markers for assessing GBM recurrence risk. This approach could enhance clinical decision-making and provide an alternative method for recurrence estimation in GBM patients.

Identification of oxidative stress-related hub genes for predicting prognosis in diffuse large B-cell lymphoma

BackgroundOxidative stress is a cellular characteristic that might induce the proliferation and differentiation of tumor cells and promote tumor progression in diffuse large B-cell lymphoma (DLBCL). MethodsThe DLBCL gene sequencing dataset, tumor mutation burden data, copy number variation data of Somatic cell mutation data in TCGA were downloaded for data training analysis, along with four DLBCL datasets in GEO for validation analysis. The known oxidative stress related genes (OSRGs) were collected from websites. The weighted gene co-expression network analysis (WGCNA) was conducted on the TCGA DLBCL dataset to obtain gene modules related to oxidative stress and intersected with the known OSRGs to obtain the hub genes, which were used to perform consensus clustering on the samples to obtain new phenotypes. Next, the prognosis related OSRGs were selected through regression analysis algorithms and key genes were identified. These genes were used to establish the prognostic risk model and predictive model, and to compare functional and pathway differences among different risk groups. ResultsThrough website search, we obtained 297 known OSRGs, and after intersecting with WGCNA results, we obtained 26 OSRGs. The TCGA-DLBC samples were clustered into 2 subtypes with these genes and there were significant differences in immune infiltration between subtypes. After regression analysis, we obtained a total of four key genes, BMI1, CDKN1A, NOX1, and SESN1. The risk prediction model established with these four genes as variables has accurate prognostic prediction ability. The key genes interact with 65 miRNAs, 57 TFs, 47 RBPs, and 62 drugs, respectively, and are closely related to immune infiltration of the disease. Among them, CDKN1A and SESN1 had the highest variability. ConclusionsThe key genes involved in oxidative stress could predict the prognosis of DLBCL and potentially become therapeutic targets.

Comprehensive gene set enrichment and variation analyses identify SUV39H1 as a potential prognostic biomarker for glioblastoma immunorelevance

Glioblastoma (GBM) is the most common intracranial malignancy. SUV39H1 encodes a histone H3 lysine 9 methyltransferase that acts as an oncogene in several cancers; however, its role in GBM remains unknown. We obtained GBM transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database on the UCSC Xena platform to perform differential and enrichment analyses of genes in the SUV39H1 high- and low-expression groups to construct a prognostic risk model. Analysis of SUV39H1 related biological processes in GBM was performed by gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). High- and low-risk subgroup mutation signatures were analyzed using maftools. Immune infiltration was evaluated using IOBR and CIBERSORT algorithms. We analyzed the cell types and intercellular communication networks in glioma stem cells (GSCs) using scRNA-seq. The effects on GBM cells and GSCs after inhibition of SUV39H1 were investigated in vitro. SUV39H1 was significantly overexpressed in GBM and associated with poor prognosis. SUV39H1-related differentially expressed genes were enriched in immune and inflammation related pathways, and GSEA revealed that these genes were significantly enriched in signaling pathways such as IL-18, oxidative phosphorylation, and regulation of TP53 activity. Mutational analysis revealed frequent alterations in TP53 and PTEN expression. In addition, the infiltration abundances of the five immune cell types were significantly different between the high- and low-expression groups. Analysis of cellular communication networks by scRNA-seq revealed a strong interaction between CRYAB-GSC and PTPRZ1-GSC in GSCs. In vitro experiments verified that knockdown of SUV39H1 inhibited the viability and proliferation of U87 and U251 glioblastoma cells and downregulated the expression of stemness markers Nestin and SOX2 in CSC1589 and TS576 GSC lines. Increased SUV39H1 expression is associated with immune cell infiltration and poor prognosis in patients with GBM. Inhibition of SUV39H1 restrains GBM growth and reduces the stem cell properties of GSC. Thus, SUV39H1 might be a prognostic predictor and immunotherapeutic target in patients with GBM.

"The definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study".

Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting. For this scoping review, MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched. Studies developed within a general ICU population reporting on prediction models with mortality as a primary or secondary outcome were eligible. The selection criteria were adopted from a review by Keuning et al. Predictor and outcome variables, variable characteristics (defined as units, definitions, moments of measurement and methods of measurement), and publication details (defined as first author, year of publication and title) were extracted from the included studies. Predictor and outcome variable categories were demographics, chronic disease, care logistics, acute diagnosis, clinical examination and physiological derangement, laboratory assessment, additional diagnostics, support and therapy, risk scores, and (mortality) outcomes. A total of 56 mortality prediction models containing 204 unique predictor and outcome variables were included. The predictor variables most frequently included in the models were age (40 times), admission type (27 times), and mechanical ventilation (21 times). We observed that single variables were measured with different units, according to different definitions, at a different moment, and with a different method of measurement in different studies. The reporting of the unit was mostly complete (98% overall, 95% in the laboratory assessment category), whereas the definition of the variable (74% overall, 63% in the chronic disease category) and method of measurement (70% overall, 34% in the demographics category) were most often lacking. Accurate and transparent reporting of predictor and outcome variables is paramount to enhance reproducibility, model performance in different contexts, and validity. Since unclarity about the required input data may introduce bias and thereby affect model performance, this study advocates that prognostic ICU models can be improved by transparent and clear reporting of predictor and outcome variables and their characteristics.

Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer.

Concurrent chemoradiotherapy (CCRT) is used as the primary treatment modality for currently limited cervical cancer and lacks non-invasive quantitative parameters to assess clinical outcomes of treatment for cervical cancer treatment. To develop nomograms based on clinical prognostic factors and apparent diffusion coefficient (ADC) in predicting downstaging and progression-free survival (PFS) after CCRT for cervical cancer. X-tile was used to calculate the optimal threshold for ΔADCmean(%) for prognostic stratification. Kaplan-Meier curves were used to calculate the difference in PFS between high- and low-risk groups. Univariate and multivariate Cox proportional risk regression models were used to identify clinical and radiological risk factors for prognosis and construct a prognostic nomogram model. ΔADCmean(%) was significantly correlated with tumor downstaging; the area under the receiver operating characteristic curve (AUC) was 0.868. X-tile showed that the optimal threshold for ΔADCmean(%) to diagnose prognosis was 40.8. Kaplan-Meier curves showed that the low-risk population in the training group had significantly longer PFS within 3 years (P < 0.001). Multivariate Cox regression showed that ΔADC (%) is independent risk factor for PFS. The C-index of ΔADC(%) predicting 3-year PFS in the training set is 0.761 and the C-index of the nomogram model is 0.862. ΔADCmean(%) is a non-invasive biomarker for predicting tumor downstaging in cervical cancer after CCRT. The nomograms based on ΔADCmean(%) predict PFS of patients with cervical cancer with moderate accuracy.

Comprehensive utilization of in silico approach and in vitro experiment to unveil the molecular mechanisms of mono (2-ethylhexyl) phthalate-induced lung adenocarcinoma

Mono (2-ethylhexyl) phthalate (MEHP), the main bioactive metabolite of commonly used plasticizer Di (2-ethylhexyl) phthalate, has received increasing attention due to its carcinogenic toxicity. This study aims to systematically explore the molecular mechanisms underlying MEHP-induced lung adenocarcinoma (LUAD). Firstly, network toxicology was employed to construct the interaction network of MEHP-targeted LUAD-related proteins and identify core proteins. Subsequently, functional analyses were used to determine the key pathways of these proteins enriched. Next, expression and survival analyses of multiple public datasets were conducted to emphasize the importance of core genes, and an optimized prognostic model was constructed based on independent prognostic genes to explore the relationship of gene risk with immune infiltration and immunotherapy. Ultimately, molecular docking and dynamics simulation were used to predict the binding modes and affinities of MEHP with core proteins, and surface plasmon resonance experiments were utilized to further validate their direct interactions. The findings demonstrated that MEHP targets 167 LUAD-related proteins, including 28 core target proteins. These proteins form the critical networks that regulate cancer and immune-associated pathways to induce the occurrence and development of LUAD, and further coordinate patient prognosis and treatment by altering the immune microenvironment. Most importantly, their direct interactions (especially PTGS2) lay the structural foundation of MEHP regulating core proteins, greatly supporting its LUAD toxicity. In conclusion, this study introduces a novel approach for evaluating the safety of plasticizers and elucidates the molecular mechanisms behind MEHP-induced LUAD, thus offering new and effective targets and strategies for cancer prevention and treatment.

Weakly supervised deep learning-based prognostic model for AI-assisted prospective clinical investigation of cervical cancer

Weakly supervised deep learning-based prognostic model for AI-assisted prospective clinical investigation of cervical cancer