Predict Survival Time Research Articles

Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics.

Glioblastoma multiforme (GBM) is the most common and deadly primary malignant brain tumor. As GBM tumor is aggressive and shows high biological heterogeneity, the overall survival (OS) time is extremely low even with the most aggressive treatment. If the OS time can be predicted before surgery, developing personalized treatment plans for GBM patients will be beneficial. Magnetic resonance imaging (MRI) is a commonly used diagnostic tool for brain tumors with high-resolution and sound imaging effects. However, in clinical practice, doctors mainly rely on manually segmenting the tumor regions in MRI and predicting the OS time of GBM patients, which is time-consuming, subjective and repetitive, limiting the effectiveness of clinical diagnosis and treatment. Therefore, it is crucial to segment the brain tumor regions in MRI, and an accurate pre-operative prediction of OS time for personalized treatment is highly desired. In this study, we present a multimodal MRI radiomics-based automatic framework for non-invasive prediction of the OS time for GBM patients. A modified 3D-UNet model is built to segment tumor subregions in MRI of GBM patients; then, the radiomic features in the tumor subregions are extracted and combined with the clinical features input into the Support Vector Regression (SVR) model to predict the OS time. In the experiments, the BraTS2020, BraTS2019 and BraTS2018 datasets are used to evaluate our framework. Our model achieves competitive OS time prediction accuracy compared to most typical approaches.

Identification and Validation of a Necroptosis-Related Prognostic Signature for Kidney Renal Clear Cell Carcinoma.

Necroptosis is progressively becoming an important focus of research because of its role in the pathogenesis of cancer and other inflammatory diseases. Our study is designed to anticipate the survival time of kidney renal clear cell carcinoma (KIRC) by constructing a prognostic signature of necroptosis-related genes. Clinical information and RNA-seq data were acquired from Renal Cell Cancer-European Union (RECA-EU) and The Cancer Genome Atlas- (TCGA-) KIRC, respectively. ConsensusClusterPlus was used to identify molecular subtypes, and the distribution of immune cell infiltration, anticancer drug sensitivity, and somatic gene mutations was studied in these subtypes. Subsequently, LASSO-Cox regression and univariate Cox regression were also carried out to construct a necroptosis-related signature. Cox regression, survival analysis, clinicopathological characteristic correlation analysis, nomogram, cancer stem cell analysis, and receiver operating characteristic (ROC) curve were some tools employed to study the prognostic power of the signature. Based on the expression patterns of 66 survival-related necroptosis genes, we classified the KIRC into three subtypes (C1, C2, and C3) that are associated with necroptosis, which had significantly different tumor stem cell components. Among these, C2 patients had a longer survival time and enhanced immune status and were more sensitive to conventional chemotherapeutic drugs. Moreover, in order to predict the prognosis of KIRC patients, five genes (BMP8A, TLCD1, CLGN, GDF7, and RARB) were used to develop a necroptosis-related prognostic signature, which had an acceptable predictive potency. The results from Cox regression and stratified survival analysis revealed that the signature was an independent prognostic factor, whereas the nomogram and calibration curve demonstrated satisfactory survival time prediction based on the risk score. Three molecular subtypes and five necroptosis-related genes were discovered in KIRC using data from TCGA-KIRC and RECA-EU. Thus, a new biomarker and a potentially effective therapeutic approach for KIRC patients were provided in the current study.

Relationship Between Malignant Brain Tumors and Values of Homocysteine, Folic Acid and Vitamin B12

Abstract Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. Homocysteine (Hcy) has a detrimental influence on human neurons, considering that human GBM cells undergo cell death already at D,L-Hcy concentrations in culture medium of 50 μM. This data demonstrate that Hcy is a potent gliotoxic agent capable of inducing the death of human glial cells already at concentrations reached in brain during hyperhomocysteinemia. The one retrospective study found that the serum vitamin B12 level can be used to predict survival time in metastatic cancer patients including neurological cancer. Cancer risk increases with elevated vitamin B12 level, mostly within the first year of the follow-up period, suggesting that vitamin B12 level could be used as a cancer diagnostic marker. In addition, the relationship between elevated vitamin B12 level and poor cancer survival time has been reported. Previous investigation suggests that the folate supplementation could be used as an adjuvant in antiglioma therapy to limit the low DNA methylation level because this confers a poor prognosis in glioblastoma multiforme patients. Taking into account all presented data, it can be concluded that effect of homocystein, folic acid and vitamin B12 on formation, development and outcome of treatment in patients with carcinoma is very intriguing question, whose response requires additional both experimental and clinical research. There lack of data in the literature on the incidence of elevated levels of Hcy in the blood, as well as the disorders of folic acid and vitamin B12, at malignant tumors of the brain.

Metastasis-Related Signature for Clinically Predicting Prognosis and Tumor Immune Microenvironment of Osteosarcoma Patients

Osteosarcoma is the most prevalent clinical malignant bone tumor in adolescents. The prognosis of metastatic osteosarcoma is still very poor. The aim of our study was to investigate the clinical diagnosis and prognostic significance of metastasis related genes (MRGs) in patients with osteosarcoma. Clinical information and RNA sequencing data with osteosarcoma patients were obtained and set as the training set from UCSC databases. GSE21257 were downloaded and chosen as the verification cohort. An eight gene metastasis related risk signature including MYC, TAC4, ABCA4, GADD45GIP1, TNFRSF21, HERC5, MAGEA11, and PDE1B was built to predict the overall survival of osteosarcoma patients. Based on risk assessments, patients were classified into high- and low-risk groups. The high-risk patients had higher risk score and shorter survival time. ROC curves revealed that this risk signature can accurately predict survival times of osteosarcoma patients at the 1-, 2-, 3-, 4- and 5- year. GSEA revealed that MYC targets, E2F targets, mTORC1 signaling, Wnt /β-catenin signaling and cell cycle were upregulated, and cell adhesion molecules, and primary immunodeficiency were decreased in high-risk group. MRGs were highly linked with the tumor immune microenvironment and ICB response. These results identified that MRGs as a novel prognostic and diagnostic biomarker in osteosarcoma.

Improving the Post-Operative Prediction of BCR-Free Survival Time with mRNA Variables and Machine Learning

Predicting the risk of, and time to biochemical recurrence (BCR) in prostate cancer patients post-operatively is critical in patient treatment decision pathways following surgical intervention. This study aimed to investigate the predictive potential of mRNA information to improve upon reference nomograms and clinical-only models, using a dataset of 187 patients that includes over 20,000 features. Several machine learning methodologies were implemented for the analysis of censored patient follow-up information with such high-dimensional genomic data. Our findings demonstrated the potential of inclusion of mRNA information for BCR-free survival prediction. A random survival forest pipeline was found to achieve high predictive performance with respect to discrimination, calibration, and net benefit. Two mRNA variables, namely ESM1 and DHAH8, were identified as consistently strong predictors with this dataset.

SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis

Recently, survival models have found vast applications in biostatistics, bioinformatics, reliability engineering, finance and related fields. But there are few R packages focusing on evaluating the predictive power of survival models. This lack of handy software on evaluating survival predictions hinders further applications of survival analysis for practitioners. In this research, we want to fill this gap by providing an \"all-in-one\" R package which implements most predictive evaluation metrics in survival analysis. In the proposed SurvMetrics R package, we implement concordance index for both untied and tied survival data; we give a new calculation process of Brier score and integrated Brier score; we also extend the applicability of integrated absolute error and integrated square error for real data. For models that can output survival time predictions, a simplified metric called mean absolute error is also implemented. In addition, we test the effectiveness of all these metrics on simulated and real survival data sets. The newly developed SurvMetrics R package is available on CRAN at <https://CRAN.R-project.org/package=SurvMetrics> and GitHub at <https://github.com/skyee1/SurvMetrics>.

Survival Time of Humans in Extreme Cold Climate: Experimental, Numerical and Parametric Study on Ambient Temperature, Fabric Insulation and Metabolic Heat

Abstract Prior knowledge of the survival time of a person clothed with extreme cold-weather clothing would be useful in designing an appropriate fabric ensemble. The survival time may depend on ambient temperature, fabric insulation, and metabolic heat and can be evaluated by designing a suitable experiment. To study the effect of different parameters on the survival time of humans, one such experiment is designed in an in-house developed guarded hot plate instrument placed inside a cold chamber capable of maintaining the ambient temperature between 210 K and 310 K. The experiments were performed at three different subzero temperatures, three different fabric insulation, and selecting three metabolic heats. The transient temperature profiles at different locations of the fabric assembly were measured continuously. Further, a one-dimensional heat transfer model was developed to carry out the numerical simulation. The experimental and numerical transient temperature profiles across the fabric assembly agree well. The correlations developed for theoretically predicted survival times compare well with actual experimental data. The parametric analysis has shown that the ambient temperature is the most influencing parameter affecting the survival time followed by fabric insulation. The metabolic heat does not have a significant effect on survival time.

ULK1 Depletion Protects Mice from Diethylnitrosamine-Induced Hepatocarcinogenesis by Promoting Apoptosis and Inhibiting Autophagy.

The uncoordinated-51 like kinase 1 (ULK1) is an important serine/threonine protein kinase involved in autophagy, especially for the initiation stage. Previous studies have shown that ULK1 could be used as a prognostic marker in predicting poor progression-free survival and a therapeutic target for hepatocellular carcinoma (HCC) when treated with sorafenib; however, its role during hepatocarcinogenesis remains to be elucidated. CCK8 and colony formation assay were used to detect cell growth ability. Western blotting was performed to determine expression level of protein. Data from public database were downloaded to analyze expression of ULK1 at mRNA level and predict survival time. RNA-seq was conducted to reveal disturbed gene profile orchestrated by ULK1 depletion. A diethylnitrosamine (DEN)-induced HCC mice model was used to uncover the role of ULK1 in hepatocarcinogenesis. ULK1 was up-regulated in liver cancer tissues and cell lines, and knockdown of ULK1 promoted apoptosis and suppressed proliferation of liver cancer cells. In in vivo experiments, Ulk1 depletion attenuated starvation-induced autophagy in mice liver, reduced diethylnitrosamine (DEN)-induced hepatic tumor number and size, and prevented tumor progression. Further, RNA-seq analysis revealed a close relationship between Ulk1 and immunity with significant changes in gene sets enriched in the interleukin and interferon pathways. ULK1 deficiency prevented hepatocarcinogenesis and inhibited hepatic tumor growth, and might be a molecular target for the prevention and treatment of HCC.

Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics.

Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical-pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes.

An indirect assessment of individual-level association between disease-free survival (DFS) and overall survival (OS) for resectable esophageal or gastroesophageal junction cancer (EC/GEJC): A pooled analysis of randomized controlled trials (RCTs) by an illness-death model.

369 Background: Validation of intermediate endpoints such as DFS as surrogates for overall survival (OS) in RCTs at individual-level remains as a challenge due to difficulty of collection of individual-level patient data. This study explores the individual-level correlation between DFS and OS indirectly using reconstructed Kaplan-Meier (KM) curves from the RCTs assessing therapies for (neo)adjuvant and perioperative treatment of EC/GEJC. Methods: An illness-death model previously validated in estimating individual-level association between DFS and OS for four correlation meta-analyses, two of which were published by the GASTRIC group, in early and late stage treatment of gastric cancer was employed. Reconstructed DFS and OS data from 25 RCTs published between 1994 - 2020 was pooled separately for each arm and each endpoint. Pooled data was analyzed to estimate the correlation between DFS and OS, as measured by Pearson’s r, Spearman’s rho, and Kendall’s tau. Predictive accuracy of the approach was evaluated by comparing its OS and restricted mean survival time (RMST) predictions to their counterparts obtained from the reported OS data from the RCTs. Sensitivity of correlation measures were assessed with respect to pre-specified variations in pre-recurrence death probability, post-recurrence mortality rate, and choice of the survival model used to extrapolate reported DFS curves from the RCTs. Results: Across all RCTs, the primary cancers covered were EC (n=13), EC/GEJC(n=9), GEJC (n=2) and GEJC/gastric cancer (n=1). According to the histology, 10 RCTs studied only squamous cell carcinoma (SCC) and 9 RCTs studied only adenocarcinoma (AC) of the disease, whereas the remaining 6 RCTs included varying mixtures of SCC and AC patients. The follow-up duration ranged between 27 and 72 months across RCTs. Comparator and intervention arms of the pooled data had 2499 and 2490 patients, respectively. Predicted Pearson’s r, Spearman’s rho, Kendall’s tau were 0.67, 0.79, and 0.70, respectively with negligibly narrow 95% CIs. Across both arms of the pooled data, on average, predicted OS curves laid within the 95% CIs of the pooled OS KM-curves 92% of the time. Average deviation between the RMSTs under the model-predicted OS curves and pooled OS KM curves was 2%. In sensitivity analyses, the ranges for r, r, and t were (0.56-0.88), (0.69-0.89), and (0.61-0.80), respectively. Conclusions: Results indicate moderate-to-strong correlation between DFS and OS in early-stage treatment of EC/GEJC providing supportive evidence for the validation of DFS as a surrogate for OS. Our approach mitigates the lack of patient level data in endpoint correlation assessment but may be prone to changes in the predicted value of pre-recurrence death probability.

Prognostic awareness in Japanese patients with advanced cancer: a follow-up cohort study.

Patients with advanced cancer have been reported to be more likely to receive goal-concordant care if they have accurate prognostic awareness. However, many patients do not have this awareness. This study aimed to examine the prognostic awareness among Japanese patients with advanced cancer. This single-center, follow-up cohort study included Japanese patients with advanced cancer who received chemotherapy at Tohoku University Hospital between January 2015 and January 2016. Patients were surveyed at enrollment and followed up for clinical events for 5years thereafter. We compared (i) the patients' prognostic awareness with both actual survival time and physician's prediction of survival and (ii) physician's prediction of survival time with actual survival. Factors associated with accurate prognostic awareness were identified by univariate analysis. Of the 133 patients eligible for the study, 57 patients were analyzed. Only 10 (17.5%) patients had accurate prognostic awareness. Forty-three patients (75.4%) were optimistic about their prognosis; >80% of patients were more optimistic than their physicians about their prognosis. The physicians' predictions were accurate in for patients (37.5%). Accurate prognostic awareness was associated with physician's explanation of the prognosis and patients' perception of a good death. A majority of the patients with advanced cancer in this study had prognostic awareness that was more optimistic in comparison with their actual survival, and most were more optimistic than their physicians about their prognosis. Further research is needed to develop programs to facilitate the discussion of life expectancy with patients in a manner that is consistent with their preferences.

Prediction of survival time after terminal extubation: the balance between critical care unit utilization and hospice medicine in the COVID-19 pandemic era

BackgroundWe established 1-h and 1-day survival models after terminal extubation to optimize ventilator use and achieve a balance between critical care for COVID-19 and hospice medicine.MethodsData were obtained from patients with end-of-life status at terminal extubation from 2015 to 2020. The associations between APACHE II scores and parameters with survival time were analyzed. Parameters with a p-value ≤ 0.2 in univariate analysis were included in multivariate models. Cox proportional hazards regression analysis was used for the multivariate analysis of survival time at 1 h and 1 day.ResultsOf the 140 enrolled patients, 76 (54.3%) died within 1 h and 35 (25%) survived beyond 24 h. No spontaneous breathing trial (SBT) within the past 24 h, minute ventilation (MV) ≥ 12 L/min, and APACHE II score ≥ 25 were associated with shorter survival in the 1 h regression model. Lower MV, SpO2 ≥ 96% and SBT were related to longer survival in the 1-day model. Hospice medications did not influence survival time.ConclusionAn APACHE II score of ≥ 25 at 1 h and SpO2 ≥ 96% at 1 day were strong predictors of disposition of patients to intensivists. These factors can help to objectively tailor pathways for post-extubation transition and rapidly allocate intensive care unit resources without sacrificing the quality of palliative care in the era of COVID-19.Trial registration They study was retrospectively registered. IRB No.: 202101929B0.

Molecular and Immunohistochemical assessment of Fibroblast Growth Factor Receptor 3 (FGFR3) gene as a survival time predictor in bladder cancer patients

BACKGROUND: Mutation signatures contributing to the tumorigenesis of bladder cancer (BC) are complex and heterogeneous, resulting in unpredictable progression, recurrence, and time survival. Clinically, useful prognostic and predictive biomarkers for both disease recurrence and surveillance are therefore needed. Activating fibroblast growth factor receptor 3 (FGFR3) mutations are regarded as early drivers in the molecular pathogenesis of BC. AIM: The aim of the present study is to evaluate the frequency and distribution pattern of FGFR3 mutation in urine sediments of BC patients in relation to its immunohistochemical (IHC) and molecular expression and to determine the prognostic and predictive value of FGFR3 relative to BC. PATIENTS AND METHODS: One hundred and sixty patients with diagnosed BC and 80 healthy controls were recruited. Urine samples were collected from all participants. DNA was extracted and FGFR3 mutations were examined in exons 7, 10, and 15 by polymerase chain reaction. IHC for FGFR3 expression and fluorescence in situ hybridization technique for assessment of gene amplification was also applied in tissue sections. RESULTS: Ninety-eight (61.3%) patients were mutant in exon 7, 82 (51.3%) were mutant in exon 10, while only 14 (8.8%) were mutant in exon 15. Univariate logistic regression analysis revealed that mutations in the three exons of FGFR3 were statistically associated with BC and could be used as predictor and/or prognostic parameters for BC. Receiver operating characteristic analysis showed that the mutation of exons 7 and 10 could be used as diagnostic biomarkers for BC. Our findings confirm that FGFR3 mutations are associated with tumors of low grade and stage. The prevalence of mutations was significantly associated with recurrence and survival time of patients for all exons. Kaplan–Meier analysis revealed a significant association between mutant patients in exon 10 and survival time. Our findings suggest that estimation of FGFR3 expression and gene amplification could serve as a prognostic indicator in the follow-up of BC patients. It could also be utilized for molecular targeted therapy in BC. CONCLUSION: Our data confirmed the feasibility of FGFR3 mutation detection in urine sediment. FGFR3 genetic mutations are independent prognostic factors for tumor recurrence and the genetic alternation of FGFR3 could be used for prediction of survival time of BC patients.

Survival rate prediction of nasopharyngeal carcinoma patients based on MRI and gene expression using a deep neural network.

To achieve a better treatment regimen and follow-up assessment design for intensity-modulated radiotherapy (IMRT)-treated nasopharyngeal carcinoma (NPC) patients, an accurate progression-free survival (PFS) time prediction algorithm is needed. We propose developing a PFS prediction model of NPC patients after IMRT treatment using a deep learning method and comparing that with the traditional texture analysis method. One hundred and fifty-one NPC patients were included in this retrospective study. T1-weighted, proton density and dynamic contrast-enhanced magnetic resonance (MR) images were acquired. The expression level of five genes (HIF-1α, EGFR, PTEN, Ki-67, and VEGF) and infection of Epstein-Barr (EB) virus were tested. A residual network was trained to predict PFS from MR images. The output as well as patient characteristics were combined using a linear regression model to provide a final PFS prediction. The prediction accuracy was compared with that of the traditional texture analysis method. A regression model combining the deep learning output with HIF-1α expression and Epstein-Barr infection provides the best PFS prediction accuracy (Spearman correlation R2 =0.53; Harrell's C-index=0.82; receiver operative curve [ROC] analysis area under the curve [AUC] = 0.88; log-rank test hazard ratio [HR]=8.45), higher than a regression model combining texture analysis with HIF-1α expression (Spearman correlation R2 =0.14; Harrell's C-index =0.68; ROC analysis AUC=0.76; log-rank test HR=2.85). The deep learning method does not require a manually drawn tumor region of interest. MR image processing using deep learning combined with patient characteristics can provide accurate PFS prediction for nasopharyngeal carcinoma patients and does not rely on specific kernels or tumor regions of interest, which is needed for the texture analysis method.

Development and validation of a nomogram for predicting survival time and making treatment decisions for clinical stage IA NSCLC based on the SEER database.

The aim of this study was to establish and validate a nomogram model for accurate prediction of patients' survival with T1aN0M0 none small cell lung cancer (NSCLC). The patients, diagnosed with the stage IA NSCLC from 2004-2015, were identified from the Surveillance, Epidemiology and End Results (SEER) database. The variables with a P-value < 0.05 in a multivariate Cox regression were selected to establish the nomogram. The discriminative ability of the model was evaluated by the concordance index (C-index). The proximity of the nomogram prediction to the actual risk was depicted by a calibration plot. The clinical usefulness was estimated by the decision curve analysis (DCA). Survival curves were made with Kaplan-Meier method and compared by Log-Rank test. Eight variables, including treatment, age, sex, race, marriage, tumor size, histology, and grade were selected to develop the nomogram model by univariate and multivariate cox regression. The C-index was 0.704 (95% CI, 0.694-0.714) in the training set and 0.713 (95% CI, 0.697-0.728) in the test set, which performed significantly better than 8th edition AJCC TNM stage system (0.550, 95% CI, 0.408-0.683, P < 0.001). The calibration curve showed that the prediction ability of 3-years and 5-years survival rate demonstrated a high degree of agreement between the nomogram model and the actual observation. The DCA curves also proved that the nomogram-assisted decisions could improve patient outcomes. We established and validated a prognostic nomogram to predict 3-years and 5-years overall survival in stage IA NSCLC.

Risk-aware survival time prediction from whole slide pathological images

Deep-learning-based survival prediction can assist doctors by providing additional information for diagnosis by estimating the risk or time of death. The former focuses on ranking deaths among patients based on the Cox model, whereas the latter directly predicts the survival time of each patient. However, it is observed that survival time prediction for the patients, particularly with close observation times, possibly has incorrect orders, leading to low prediction accuracy. Therefore, in this paper, we present a whole slide image (WSI)-based survival time prediction method that takes advantage of both the risk as well as time prediction. Specifically, we propose to combine these two approaches by extracting the risk prediction features and using them as guides for the survival time prediction. Considering the high resolution of WSIs, we extract tumor patches from WSIs using a pre-trained tumor classifier and apply the graph convolutional network to aggregate information across these patches effectively. Extensive experiments demonstrate that the proposed method significantly improves the time prediction accuracy when compared with direct prediction of the survival times without guidance and outperforms existing methods.

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

Comprehensive analysis of HHV-6 and HHV-7-related gene signature in prognosis and response to temozolomide of glioma.

Human herpesvirus (HHV)-6 and HHV-7 have been detected in central nervous system and glioma tissue, while their exact role in glioma remains uncertain. Omics profiles and clinical information were downloaded from public databases, including The Cancer Genome Atlas cohort for training set and the Chinese Glioma Genome Atlas cohorts for validation sets. Differentially expressed genes between HHV-6 and HHV-7 infected or noninfected glioma patients were screened for establishing the HHV-6 and HHV-7 infection (HI) model through Lasso regression analysis. Bioinformatics methods were used to analyze the correlation between HI scores and prognosis, metastasis in glioma patients. Predictable efficacy of HI in temozolomide-resistance and HI-related genetic signatures were also explored. The HI model was constructed as: Risk score = (0.014709*DIRAS3) + (0.029787*TEX26) + (0.223492*FBXO39) + (0.074951*MYBL1) + (0.060202*HILS1). The five gene signature showed good performance in predicting survival time for glioma patients, while higher HI score is correlated with malignant features. Moreover, DNA mismatch repair genes were augmented in glioma patients with higher HI score as well as nonresponse to temozolomide treatment, which was in parallel with the transcriptomic result of temozolomide-resistant glioma cell. Targeting the five gene signature is beneficial for prognosis of glioma patients, especially in glioma patients underwent temozolomide treatment.

A prognostic signature based on cuprotosis-related long non-coding RNAs predicts the prognosis and sensitivity to chemotherapy in patients with colorectal cancer.

Cuprotosis, a newly proposed mechanism of cell death, can trigger acute oxidative stress that leads to cell death by mediating protein lipidation in the tricarboxylic acid cycle. However, cuprotosis-related long non-coding RNAs (CRLNCs) and their relationship with prognosis and the immunological landscape of colorectal cancer (CRC) are unclear. We have developed a lncRNA signature to predict survival time, immune infiltration, and sensitivity to chemotherapy. CRLNCs were screened using the Cor function of the R software and the differentially expressed lncRNAs were collected with the limma package. Differentially expressed long non-coding RNAs (lncRNAs) associated with prognosis were selected using univariate regression analysis. A prognostic signature was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis. Patients with CRC were divided into two groups based on the risk score. The low-risk group had a more favorable prognosis, higher expression of immune checkpoints, and a higher level of immune cell infiltration compared with the high-risk group. Furthermore, there was a close association between the risk score and the clinical stage, tumor mutational burden, cancer stem cell index, and microsatellite instability. We also assessed chemotherapy response in the two risk groups. Our study analyzed the role of CRLNCs in CRC and provided novel targets and strategies for CRC chemotherapy and immunotherapy.

Metabolism-associated molecular classification of gastric adenocarcinoma.

Most gastric cancers (GC) are adenocarcinomas, whereas GC is a highly heterogeneous disease due to its molecular heterogeneity. However, traditional morphology-based classification systems, including the WHO classification and Lauren's classification, have limited utility in guiding clinical treatment. We performed nonnegative matrix factorization (NMF) clustering based on 2752 metabolism-associated genes. We characterized each of the subclasses from multiple angles, including subclass-associated metabolism signatures, immune cell infiltration, clinic10al characteristics, drug sensitivity, and pathway enrichment. As a result, four subtypes were identified: immune suppressed, metabolic, mesenchymal/immune exhausted and hypermutated. The subtypes exhibited significant prognostic differences, which suggests that the metabolism-related classification has clinical significance. Metabolic and hypermutated subtypes have better overall survival, and the hypermutated subtype is likely to be sensitive to anti-PD-1 immunotherapy. In addition, our work showed a strong connection with previously established classifications, especially Lei's subtype, to which we provided an interpretation based on the immune cell infiltration perspective, deepening the understanding of GC heterogeneity. Finally, a 120-gene classifier was generated to determine the GC classification, and a 10-gene prognostic model was developed for survival time prediction.