A novel DNA repair-related nomogram predicts survival in low-grade gliomas

A Nomogram for Predicting Survival in Patients with SARS-CoV-2 Omicron Variant Pneumonia Based on Admission Data

Purpose

Patients with severe SARS-CoV-2 omicron variant pneumonia pose a serious challenge. This study aimed to develop a nomogram for predicting survival using chest computed tomography (CT) imaging features and laboratory test results based on admission data.

Patients and Methods

A total of 436 patients with SARS-CoV-2 pneumonia (323 and 113 in the training and validation groups, respectively) were enrolled. Pneumonitis volume, assessed on chest CT scans at admission, was used to identify low- and high-risk groups. Risk analysis was performed using clinical symptoms, laboratory findings, and chest CT imaging features. A predictive algorithm was developed using Cox multivariate analysis.

Results

The high-risk group had a shorter survival duration than the low-risk group. Significant differences in mortality rate, neutrophil and lymphocyte counts, C-reactive protein (CRP) concentration, and urea nitrogen level were observed between the two groups. In the training group, age, pneumonia volume, total bilirubin, and blood urea nitrogen were independent prognostic factors. In the validation group, age, pneumonia volume, neutrophil count, and CRP were independent prognostic factors. A personalized prediction model for survival outcomes was developed using independent predictors.

Conclusion

A personalized prediction model was created to forecast the 5-, 10-, 15-, 20-, and 30-day survival rates of patients with COVID-19 omicron variant pneumonia based on admission data, and can be used to determine the survival rate and early treatment of severe patients.

A novel predictive model utilizing retinal microstructural features for estimating survival outcome in patients with glioblastoma

PURPOSE

Glioblastoma is a highly aggressive brain tumor with poor prognosis despite surgery and chemoradiation. The visual sequelae of glioblastoma have not been well characterized. This study assessed visual outcomes in glioblastoma patients through neuro-ophthalmic exams, imaging of the retinal microstructures/microvasculature, and perimetry.

METHODS

A total of 19 patients with glioblastoma (9 male, 10 female, average age at diagnosis 69 years) were enrolled. Tumor characteristic, neuro-ophthalmic exam data, Optical Coherence Tomography (OCT) and OCT-Angiography data of all patient eyes were analyzed using Microsoft Excel and a Machine Learning algorithm.

RESULTS

Best-corrected visual acuity ranged from 20/20 - 20/50. Occipital tumors showed worse visual fields than frontal tumors (mean deviation -14.9 and -0.23, respectively, p < 0.0001). Those with overall survival (OS)< 15 months demonstrated thinner retinal nerve fiber layer and ganglion cell complex (p < 0.0001) and enlarged foveal avascular zone starting from 4 months post-diagnosis (p = 0.006). There was no significant difference between eyes ipsilateral and contralateral to radiation fields (average doses were 1370 cGy and 1180 cGy, respectively, p = 0.42). A machine learning algorithm using retinal microstructure and visual fields predicted patients with long (≥15 months) progression-free and overall survival with 78 % accuracy.

CONCLUSION

Glioblastoma patients frequently present with visual field defects despite normal visual acuity. Patients with poor survival duration demonstrated significant retinal thinning and decreased microvascular density. A machine learning algorithm predicted survival though further validation is warranted.

Integration of histone modification-based risk signature with drug sensitivity analysis reveals novel therapeutic strategies for lower-grade glioma

Background

Lower-grade glioma (LGG) exhibits significant heterogeneity in clinical outcomes, and current prognostic markers have limited predictive value. Despite the growing recognition of histone modifications in tumor progression, their role in LGG remains poorly understood. This study aimed to develop a histone modification-based risk signature and investigate its relationship with drug sensitivity to guide personalized treatment strategies.

Methods

We performed single-cell RNA sequencing analysis on LGG samples (n = 4) to characterize histone modification patterns. Through integrative analysis of TCGA-LGG (n = 513) and CGGA datasets (n = 693 and n = 325), we constructed a histone modification-related risk signature (HMRS) using machine learning approaches. The model's performance was validated in multiple independent cohorts. We further conducted comprehensive analyses of molecular mechanisms, immune microenvironment, and drug sensitivity associated with the risk stratification.

Results

We identified distinct histone modification patterns across five major cell populations in LGG and developed a robust 20-gene HMRS from 129 candidate genes that effectively stratified patients into high- and low-risk groups with significantly different survival outcomes (training set: AUC = 0.77, 0.73, and 0.71 for 1-, 3-, and 5-year survival; P < 0.001). Integration of HMRS with clinical features further improved prognostic accuracy (C-index >0.70). High-risk tumors showed activation of TGF-β and IL6-JAK-STAT3 signaling pathways, and distinct mutation profiles including TP53 (63% vs 28%), IDH1 (68% vs 85%), and ATRX (46% vs 20%) mutations. The high-risk group demonstrated significantly elevated immune and stromal scores (P < 0.001), with distinct patterns of immune cell infiltration, particularly in memory CD4+ T cells (P < 0.001) and CD8+ T cells (P = 0.001). Drug sensitivity analysis revealed significant differential responses to six therapeutic agents including Temozolomide and targeted drugs (P < 0.05).

Conclusion

Our study establishes a novel histone modification-based prognostic model that not only accurately predicts LGG patient outcomes but also reveals potential therapeutic targets. The identified associations between risk stratification and drug sensitivity provide valuable insights for personalized treatment strategies. This integrated approach offers a promising framework for improving LGG patient care through molecular-based risk assessment and treatment selection.

Multi-center real-world data-driven web calculator for predicting outcomes in IDH-mutant gliomas: Integrating molecular subtypes and treatment modalities

Background

Isocitrate dehydrogenase (IDH)-mutant gliomas generally have a better prognosis than IDH-wild-type glioblastomas, and the extent of resection significantly impacts prognosis. However, there is a lack of integrated tools for predicting outcomes based on molecular subtypes and treatment modalities. This study aimed to identify factors influencing gross total resection (GTR) rates and to develop a clinical prognostic tool for IDH-mutant gliomas.

Methods

We analyzed 650 patients with IDH-mutant gliomas from 3 Chinese medical centers (Shanghai, Hong Kong, and Zhengzhou). Data included age, sex, extent of resection, radiotherapy status, tumor grade, histology, and molecular markers (1p19q, TERT promoter, BRAF, EGFR, 10q). Patients were categorized based on GTR status, and a nomogram predicting 3-, 5-, and 10-year overall survival (OS) was developed using Cox proportional hazards regression and validated with time-dependent ROC and calibration plot analyses.

Results

Non-GTR was associated with diffuse astrocytoma (73.0% vs. 53.5%), 1p19q non-codeletion (67.9% vs. 48.7%), and wildtype TERT promoter (63.6% vs. 52.4%). The nomogram, incorporating age, TERT promoter status, extent of resection, grade, and radiotherapy status, demonstrated strong discriminatory ability (AUC > 0.75) and good calibration. Decision curve analysis indicated that it outperformed WHO grade-based classification in identifying high-risk patients. An online calculator was developed for clinical use (http://www.szflab.site/nomogram/).

Conclusion

We developed and validated a nomogram and online tool that integrates molecular and clinical factors for predicting outcomes in IDH-mutant gliomas, enhancing clinical decision-making.

SOCS1: A potential diagnostic and prognostic marker for aggressive gliomas and a new target for immunotherapy

Gliomas, the most common and deadly cancers of the central nervous system, present a unique immunological barrier that severely undermines the effectiveness of immunotherapies. Suppressor of cytokine signaling 1 (SOCS1), belonging to the SOCS protein family and playing a pivotal role in various cancer treatment strategies and is abundant in high-grade gliomas. This study conducted a comparative analysis of SOCS1 and glioma immune checkpoints. It underscores the feasibility of leveraging SOCS1 as a promising diagnostic and prognostic marker for aggressive gliomas, thus offering novel targets for glioma immunotherapy. Comprehensive gene expression analyses and clinical data validations were performed across multiple databases. The expression and biological functions of SOCS1 were examined through an array of techniques including pan-cancer analysis, functional enrichment, gene set variation analysis, and immune microenvironment examination. This was done alongside a comparison of the similarities between SOCS1 and various glioma immune checkpoints. Utilizing clinical information from patients, a bespoke predictive model was developed to further corroborate the prognostic capabilities of SOCS1. The investigation revealed considerable similarities between SOCS1 and several immune checkpoints such as CTLA4, demonstrating SOCS1's role as an independent prognostic factor positively influencing glioma patient outcomes. The inclusion of SOCS1 in the developed predictive model significantly enhanced its precision. Our findings highlight SOCS1's potential as an innovative target for glioma immunotherapy, providing a novel strategy to overcome the immunological barriers posed by gliomas. Furthermore, identifying SOCS1 as a viable diagnostic marker for aggressive gliomas improves the accuracy of prognostic predictions for affected patients.

Identification of a nomogram predicting overall survival based on ADAP2-related apoptosis genes in gliomas

BACKGROUND

Apoptosis continues to be a pivotal area of investigation in glioma research. ADAP2 mediates the malignant progression of gliomas through the inhibition of apoptosis and predicts the overall survival(OS) of glioma patients based on prognostic modeling of the apoptotic gene set.

METHODS

The study encompassed 686 glioma patients, with 413 allocated to the training group and 273 to the validation group. Differential expression of ADAP2 across various glioma subtypes was assessed through bioinformatics analysis and Western blotting. The correlation between ADAP2 and apoptosis was examined using Gene Set Enrichment Analysis (GSEA). Multivariate Cox regression analysis and LASSO dimension reduction analysis were employed to identify apoptosis-related genes with prognostic significance in glioma patients and to construct a nomogram. Biological functions and mechanisms associated with risk scores were explored via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA analyses, with validation through Western blotting, flow cytometry, and AM/PI staining.

RESULTS

ADAP2 was found to be enriched in more aggressive glioma subtypes and was closely linked to glioma cell apoptosis, modulating this process via the NF-κB and P53 signaling pathway. A nomogram for OS in glioma patients was constructed using thirteen apoptosis-related genes. Additionally, ROC curves, calibration curves, and C-indices confirmed the robust applicability of the nomogram.

CONCLUSION

ADAP2 functions as a prognostic biomarker for glioma patients, regulating glioma cell apoptosis through the NF-κB and P53 signaling pathway. Moreover, prognostic models based on apoptosis-related genes can accurately predict OS for glioma patients at 1, 2, 3, 5, and 10 years.

A Glycolysis and gluconeogenesis-related model for breast cancer prognosis

BackgroundBreast cancer is a malignant tumor with high morbidity and mortality, which seriously endangers the health of women around the world. Biomarker-based exploration will be effective for better diagnosis, prediction and targeted therapy.ObjectiveTo construct biomarker models related to glycolysis and gluconeogenesis in breast cancer.MethodsThe gene expression of 932 breast cancer patients in the Cancer Genome Atlas (TCGA) database was analyzed by Gene Set Variation Analysis (GSVA) using glycolysis and gluconeogenesis-related pathways. Differential expression genes were searched for by the T-test. Univariate Cox proportional hazards model (COX) regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Multivariate COX regression were used to find clinically significant genes for prognostic survival. After that, the constructed gene signature was externally validated through the Gene Expression Omnibus (GEO). Finally, a nomogram was constructed to predict the survival of patients. In addition, analyzing the role of biomarkers in pan-cancer.ResultsA risk scoring model associated with glycolysis and gluconeogenesis was developed and validated. A nomogram was created to predict 2-, 3-, and 5- survival.ConclusionsThe predictive model accurately predicted the prognosis of breast cancer patients.

Development and validation of a deep learning-based survival prediction model for pediatric glioma patients: A retrospective study using the SEER database and Chinese data

OBJECTIVE

Develop a time-dependent deep learning model to accurately predict the prognosis of pediatric glioma patients, which can assist clinicians in making precise treatment decisions and reducing patient risk.

STUDY DESIGN

The study involved pediatric glioma patients from the Surveillance, Epidemiology, and End Results (SEER) Registry (2000-2018) and Tangdu Hospital in China (2010-2018) within specific time frames. For training, we selected two neural network-based algorithms (DeepSurv, neural multi-task logistic regression [N-MTLR]) and one ensemble learning-based algorithm (random survival forest [RSF]). Additionally, a multivariable Cox proportional hazard (CoxPH) model was developed for comparison purposes. The SEER dataset was randomly divided into 80 % for training and 20 % for testing, while the Tangdu Hospital dataset served as an external validation cohort. Super-parameters were fine-tuned through 1000 repeated random searches and 5-fold cross-validation on the training cohort. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). Furthermore, the accuracy of predicting survival at 1, 3, and 5 years was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and the area under the ROC curves (AUC). The generalization ability of the model was assessed using the C-index of the Tangdu Hospital data, ROC curves for 1, 3, and 5 years, and AUC values. Lastly, decision curve analysis (DCA) curves for 1, 3, and 5-year time frames are provided to assess the net benefits across different models.

RESULTS

A total of 9532 patients with pediatric glioma were included in this study, comprising 9274 patients from the SEER database and 258 patients from Tangdu Hospital in China. The average age at diagnosis was 9.4 ± 6.2 years, and the average survival time was 96 ± 66 months. Through comprehensive performance comparison, the DeepSurv model demonstrated the highest effectiveness, with a C-index of 0.881 on the training cohort. Furthermore, it exhibited excellent accuracy in predicting the 1-year, 3-year, and 5-year survival rates (AUC: 0.903-0.939). Notably, the DeepSurv model also achieved remarkable performance and accuracy on the Chinese dataset (C-index: 0.782, AUC: 0.761-0.852). Comprehensive analysis of DeepSurv, N-MTLR, and RSF revealed that tumor stage, radiotherapy, histological type, tumor size, chemotherapy, age, and surgical method are all significant factors influencing the prognosis of pediatric glioma. Finally, an online version of the pediatric glioma survival predictor based on the DeepSurv model has been established and can be accessed through https://pediatricglioma-tangdu.streamlit.app.

CONCLUSIONS

The DeepSurv model exhibits exceptional efficacy in predicting the survival of pediatric glioma patients, demonstrating strong performance in discrimination, calibration, stability, and generalization. By utilizing the online version of the pediatric glioma survival predictor, which is based on the DeepSurv model, clinicians can accurately predict patient survival and offer personalized treatment options.

Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients

Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) representing the most malignant form, while low-grade glioma (LGG) is a less severe. The prognosis for glioma remains poor even after various treatments, such as chemotherapy and immunotherapy. Cuproptosis is a newly defined form of programmed cell death, distinct from ferroptosis and apoptosis, primarily caused by the accumulation of the copper within cells. In this study, we compared the difference between the expression of cuproptosis-related genes in GBM and LGG, respectively, and conducted further analysis on the enrichment pathways of the exclusive expressed cuproptosis-related mRNAs in GBM and LGG. We established two prediction models for survival status using xgboost and random forest algorithms and applied the ROSE algorithm to balance the dataset to improve model performance.

Prediction model for poor short-term prognosis in patients with chronic subdural hematoma after burr hole drainage: a retrospective cohort study

Chronic subdural hematoma (CSDH) is a common condition in neurosurgery. With an aging population, there is increasing attention on the prognosis of patients following surgical intervention. We developed a postoperative short-term prognostic prediction model using preoperative clinical indicators, aiming to assist in perioperative medical decision-making and management. The dataset was randomly divided into training and validation cohorts. An mRS score greater than 2 one month after discharge was considered indicative of a poor prognosis. In the training cohort, the least absolute shrinkage and selection operator (LASSO) regression analysis was used for multivariate analysis to identify independent risk factors and construct a prediction nomogram for poor prognosis one month after discharge. The performance of the nomogram was assessed using the Receiver Operating Characteristic (ROC) curve and calibration curve. A Decision Curve Analysis (DCA) was also conducted to determine the net benefit threshold of the prediction model. Among the 505 participants, 18.8% (95/505) had a poor prognosis one month after discharge. The baseline characteristics did not significantly differ between the training cohort and the validation cohort. LASSO regression analysis in the training cohort reduced the predictors to four potential factors. Further multivariate logistic analyses in the training cohort identified four independent predictors: age, admission Glasgow Coma Scale (GCS) score, hemiparesis, and hemoglobin count. These predictors were incorporated into the nomogram prediction model. Internal validation using ROC analysis, calibration curves, and other methods demonstrated a strong correlation between the observed and predicted likelihood of poor prognosis one month after discharge. The visualized nomogram prediction model we developed for short-term postoperative prognosis of chronic subdural hematoma after burr hole drainage aids in predicting short-term outcomes and guiding clinical treatment decisions. Further external validation is needed in the future to confirm its effectiveness.

The PTPRZ1-MET/STAT3/ISG20 axis in glioma stem-like cells modulates tumor-associated macrophage polarization

Recent studies have revealed that PTPRZ1-MET (ZM) fusion plays a pivotal role in the progression of glioma to glioblastoma multiforme (GBM), thus serving as a biomarker to distinguish between primary GBM and secondary GBM (sGBM). However, the mechanisms through which ZM fusion influences this progression remain to be elucidated. GBMs with ZM showed poorer prognoses and greater infiltration of tumor-associated macrophages (TAMs) than those without ZM. Glioma stem-like cells (GSCs) and TAMs play complex roles in glioma recurrence, glioma progression and therapy resistance. In this study, we analyzed RNA-seq data from sGBM patients' glioma tissues with or without ZM fusion, and found that stemness and macrophage markers were more highly expressed in sGBM patients harboring ZM than in those without ZM fusion. ZM enhanced the self-renewal and proliferation of GSCs, thereby accelerating glioma progression. In addition, ZM-positive GSCs facilitated the infiltration of TAMs and drove their polarization toward an immunosuppressive phenotype, which was primarily accomplished through the extracellular secretion of ISG20. Our research identified the MET-STAT3-ISG20 axis within GSCs, thus demonstrating the critical role of ZM in GBM initiation and progression. Our study demonstrated that, in contrast to ZM-positive differentiated glioma cells, ZM-positive GSCs upregulated ISG20 expression through the MET-STAT3-ISG20 axis. The extracellular secretion of ISG20 recruited and induced M2-like polarization in macrophages, thereby promoting tumor progression. Our results reveal a novel mechanism involved in ZM-positive GBM pathogenesis and identify potential therapeutic targets.

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 TNFAIP6 as a reliable prognostic indicator of low-grade glioma

Glioma is the most common primary malignant tumor in the brain, characterizing by high disability rate and high recurrence rate. Although low-grade glioma (LGG) has a relative benign biological behavior, the prognosis of LGG patients still varies greatly. Glioma stem cells (GSCs) are considered as the chief offenders of glioma cell proliferation, invasion and resistance to therapies. Our study screened a series of glioma stem cell-related genes (GSCRG) based on mDNAsi and WCGNA, and finally established a reliable single-gene prognostic model through 101 combinations of 10 machine learning methods. Our result suggested that the expression level of TNFAIP6 is negatively correlated with the prognosis of LGG patients, which may be the result of pro-cancer signaling pathways activation and immunosuppression. In general, this study revealed that TNFAIP6 is a robust and valuable prognostic factor in LGG, and may be a new target for LGG treatment.

Value analysis of ITLN1 in the diagnostic and prognostic assessment of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) remains the leading cause of cancer death worldwide. Less than half of the patients are diagnosed when the cancer is locally advanced. Several studies have shown that intelectin-1 (ITLN1) can serve as a key prognostic and therapeutic target for CRC. The purpose of this study was to investigate the clinical value of ITLN1 in CRC and to analyse its potential as a predictive biomarker for CRC.

METHODS

Colon adenocarcinoma (COAD) is the main type of CRC. COAD project in The Cancer Genome Atlas (TCGA) database served as the training cohort, and GSE39582 series in the Gene Expression Omnibus (GEO) database served as the external independent validation cohort. First, the difference in the expression level of ITLN1 between COAD tissue and normal tissue was analysed, and the results were verified via immunohistochemistry. The relationship between ITLN1 expression and the prognosis of COAD patients was evaluated via the heatmap and the Kaplan-Meier (KM) curve. The ITLN1 coexpressed gene set obtained by Pearson correlation analysis was used. The prognostic signatures that were significantly correlated with survival status were screened by Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Finally, a nomogram related to ITLN1 was constructed based on the risk score of the prognostic signature and routine clinicopathological variables.

RESULTS

ITLN1 is significantly underexpressed in tumour tissues and can be used as a valuable tool to distinguish COAD. The high-expression group of ITLN1 was verified to have a greater survival rate. ITLN1 is significantly associated with a good prognosis in COAD patients. Six candidate genes (ITLN1 and MORC2, SH2D7, LGALS4, ATOH1, and NAT2) were selected for use in the Cox-LASSO regression analysis to calculate the risk score. Finally, a nomogram was constructed with a comprehensive risk score and clinicopathologic factors to successfully predict and verify the 1-year, 3-year, and 5-year survival probability.

CONCLUSIONS

Our study established ITLN1 as an effective tool for CRC screening, diagnosis, and prognostic assessment, provided a basis for further study of the molecular function of ITLN1, and provided new insights for the mechanistic exploration and treatment of CRC.

A Novel Predictive Model Utilizing Retinal Microstructural Features for Estimating Survival Outcome in Patients with Glioblastoma

Glioblastoma is a highly aggressive brain tumor with poor prognosis despite surgery and chemoradiation. The visual sequelae of glioblastoma have not been well characterized. This study assessed visual outcomes in glioblastoma patients through neuro-ophthalmic exams, imaging of the retinal microstructures/microvasculature, and perimetry. A total of 19 patients (9 male, 10 female, average age at diagnosis 69 years) were enrolled. Best-corrected visual acuity ranged from 20/20-20/50. Occipital tumors showed worse visual fields than frontal tumors (mean deviation - 14.9 and - 0.23, respectively, p < 0.0001). Those with overall survival (OS) < 15 months demonstrated thinner retinal nerve fiber layer and ganglion cell complex (p < 0.0001) and enlarged foveal avascular zone starting from 4 months post-diagnosis (p = 0.006). There was no significant difference between eyes ipsilateral and contralateral to radiation fields (average doses were 1370 cGy and 1180 cGy, respectively, p = 0.42). A machine learning algorithm using retinal microstructure and visual fields predicted patients with long (≥ 15 months) progression free and overall survival with 78% accuracy. Glioblastoma patients frequently present with visual field defects despite normal visual acuity. Patients with poor survival duration demonstrated significant retinal thinning and decreased microvascular density. A machine learning algorithm predicted survival; further validation is warranted.

Machine learning unveils immune-related signature in multicenter glioma studies

In glioma molecular subtyping, existing biomarkers are limited, prompting the development of new ones. We present a multicenter study-derived consensus immune-related and prognostic gene signature (CIPS) using an optimal risk score model and 101 algorithms. CIPS, an independent risk factor, showed stable and powerful predictive performance for overall and progression-free survival, surpassing traditional clinical variables. The risk score correlated significantly with the immune microenvironment, indicating potential sensitivity to immunotherapy. High-risk groups exhibited distinct chemotherapy drug sensitivity. Seven signature genes, including IGFBP2 and TNFRSF12A, were validated by qRT-PCR, with higher expression in tumors and prognostic relevance. TNFRSF12A, upregulated in GBM, demonstrated inhibitory effects on glioma cell proliferation, migration, and invasion. CIPS emerges as a robust tool for enhancing individual glioma patient outcomes, while IGFBP2 and TNFRSF12A pose as promising tumor markers and therapeutic targets.

The mitotic cell cycle-associated nomogram predicts overall survival in lung adenocarcinoma

BACKGROUND

This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) associated with mitotic cell cycle. The model will predict the probability of survival at different time points and serve as a reference tool to evaluate the effectiveness of LUAD treatment.

METHODS

A cohort of 442 patients with LUAD from the gene expression omnibus (GEO) database was randomly divided into a training group (n = 299) and a validation group (n = 99). The least absolute shrinkage and selection operator (LASSO)-COX algorithm was used to reduce the number of predictors based on the clinicopathological and RNA sequencing data to establish mutant characteristics that could predict patient survival. Additionally, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) analyses were conducted on the mutant characteristics. The performance of the developed nomogram was evaluated using calibration curves and the C-index.

RESULTS

The mutant characteristics had prognostic value for LUAD and acted as an independent prognostic factor. The mutant characteristics profile derived from the LASSO-COX algorithm demonstrated a significant association with overall survival in patients with LUAD. Functional annotation based on the mutant score, its involvement in the phase transition of the mitotic cell cycle, and its regulatory processes. The nomogram, which combined the mutant score with clinical factors associated with prognosis, showed robust accuracy in both the training and validation groups.

CONCLUSION

This study presents the first individualized model that establishes a mutant score for predicting survival in LUAD. This model can be used as a predictive tool for determining 1-, 2-, 3-, and 5-year survival probabilities in patients with LUAD.

A gene signature predicting prognosis of patients with lower-grade gliomas receiving temozolomide therapy

Temozolomide (TMZ) has been used as a first-line therapy against lower-grade gliomas (LGGs) combined with other chemotherapy drugs. However, there has been no reliable index predicting TMZ response of patients with LGGs. In this study, we aim to investigate the relationship between gene expressions and the prognosis of TMZ therapy in LGGs. We integrated transcriptome and clinical data of 171 LGGs from the Chinese Glioma Genome Atlas (CGGA). Consensus LASSO Cox regression was used to identify 14 key genes related to different clinical outcomes under TMZ chemotherapy. We constructed and evaluated a risk score based on the 14 genes. Patients with LGGs of lower risk scores (low-risk group) generally had better survival than those LGGs of higher risk scores (high-risk group), which is independent of clinicopathological factors. High-risk patients showed activation of innate and humoral-type immunity. The prognostic contribution of the risk score was validated in an independent validation cohort of 65 patients. Besides, combined with three independent predictors (grade, IDH1 mutation status, and chr1p19q co-deletion status), we further developed a nomogram to predict the benefit of TMZ treatment in LGGs. Our results indicate that a transcriptome-based index can optimize the treatment strategy for patients with LGGs under TMZ therapy.

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

Background

This study aimed to establish and validate an easy-to-use nomogram for predicting long-term mortality among ischemic stroke patients.

Methods

All raw data were obtained from the Medical Information Mart for Intensive Care IV database. Clinical features associated with long-term mortality (1-year mortality) among ischemic stroke patients were identified using least absolute shrinkage and selection operator regression. Then, binary logistic regression was used to construct a nomogram, the discrimination of which was evaluated by the concordance index (C-index), integrated discrimination improvement (IDI), and net reclassification index (NRI). Finally, a calibration curve and decision curve analysis (DCA) were employed to study calibration and net clinical benefit, compared to the Glasgow Coma Scale (GCS) and the commonly used disease severity scoring system.

Results

Patients who were identified with ischemic stroke were randomly assigned into developing (n = 1,443) and verification (n = 646) cohorts. The following factors were associated with 1-year mortality among ischemic stroke patients, including age on ICU admission, marital status, underlying dementia, underlying malignant cancer, underlying metastatic solid tumor, heart rate, respiratory rate, oxygen saturation, white blood cells, anion gap, mannitol injection, invasive mechanical ventilation, and GCS. The construction of the nomogram was based on the abovementioned features. The C-index of the nomogram in the developing and verification cohorts was 0.820 and 0.816, respectively. Compared with GCS and the commonly used disease severity scoring system, the IDI and NRI of the constructed nomogram had a statistically positive improvement in predicting long-term mortality in both developing and verification cohorts (all with p < 0.001). The actual mortality was consistent with the predicted mortality in the developing (p = 0.862) and verification (p = 0.568) cohorts. Our nomogram exhibited greater net clinical benefit than GCS and the commonly used disease severity scoring system.

Conclusion

This proposed nomogram has good performance in predicting long-term mortality among ischemic stroke patients.

A novel DNA repair-related nomogram predicts survival in low-grade gliomas

Aims

We aimed to create a tumor recurrent‐based prediction model to predict recurrence and survival in patients with low‐grade glioma.

Methods

This study enrolled 291 patients (188 in the training group and 103 in the validation group) with clinicopathological information and transcriptome sequencing data. LASSO‐COX algorithm was applied to shrink predictive factor size and build a predictive recurrent signature. GO, KEGG, and GSVA analyses were performed for function annotations of the recurrent signature. The calibration curves and C‐Index were assessed to evaluate the nomogram's performance.

Results

This study found that DNA repair functions of tumor cells were significantly enriched in recurrent low‐grade gliomas. A predictive recurrent signature, built by the LASSO‐COX algorithm, was significantly associated with overall survival and progression‐free survival in low‐grade gliomas. Moreover, function annotations analysis of the predictive recurrent signature exhibited that the signature was associated with DNA repair functions. The nomogram, combining the predictive recurrent signature and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups.

Conclusion

An individualized prediction model was created to predict 1‐, 2‐, 3‐, 5‐, and 10‐year survival and recurrent rate of patients with low‐grade glioma, which may serve as a potential tool to guide postoperative individualized care.