Patient Risk Scores Research Articles

Predictive model of gene expression regulating invasion and migration of M2 macrophages in breast cancer: clinical prognosis and therapeutic implications.

Breast cancer (BRCA) has surpassed lung cancer to become the malignant tumor with the highest incidence in female population. It occurs in malignant cells in breast tissue and is common worldwide. An increasing body of research indicates that M2 macrophages are critical to the occurrence and progression of BRCA. The aim of this work is to build a predictive model of genes related to invasion and migration of M2 macrophages, forecast the prognosis of patients with BRCA, and then evaluate the efficacy of some targeted treatments. The Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database supplied the GSE20685 dataset, whereas the expression profile a clinical details of BRCA patients were obtained from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) database. The genes linked to M2 macrophages and the differentially elevated genes of invasion and migration were found in GSE20685. To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression. GSE58812 was utilized for external validation. After calculating each patient's risk score, the prognostic model was examined by analyses of immune infiltration, medication sensitivity, mutation, and enrichment of the risk score. The risk score had a strong correlation with both several immune cells and popular anti-tumor medications. Additionally, it was discovered that the risk score was a separate prognostic factor for BRCA. Based on invasion and migration-related M2 macrophage genes, we investigated and validated predictive characteristics in our study that may offer helpful insights into the progression and prognosis of BRCA.

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

BackgroundOsteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course.MethodsWe used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data.ResultsWe derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient’s risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922).ConclusionsThe consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient’s risk scores can be used in clinical settings.

Identification and Validation of a Necroptosis-Related Prognostic Model in Tumor Recurrence and Tumor Immune Microenvironment in Breast Cancer Management.

Breast cancer is the leading cause of cancer-related death in women. Necroptosis, a form of programmed necrotic cell death, occurs in many solid tumors, including breast cancer, and influences anti-tumor immunity. The role of necroptosis in managing breast cancer recurrence remains unclear. Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO (GSE20685, GSE21653, GSE25055) and TCGA databases. Data analysis and visualization were performed using R. Unsupervised Consensus Clustering and LASSO-COX regression stratified breast cancer patients. GO, KEGG, GSVA, ESTIMATE, and ROC analyses were used to investigate necroptotic signatures. In vitro and in vivo experiments validated necroptosis's role in breast cancer immunity. The potential function of necroptotic signature in immunity was first indicated with GO analysis in BRCA cohort. Next, two prognostic models based on the necroptotic profiles both suggested a link between low-risk group with a particular necroptotic immune signature. And a variety of immune cells and immune pathways were shown to be positively associated with a patient's risk score. As an altered immune checkpoint pattern was observed after regulating necroptotic genes, where TIM-3 and LAGLS9 elevated significantly in low-risk group, further validation in vitro and in vivo demonstrated that manipulating a subset of necroptotic gene set could sensitize tumor response to the co-blockade immunotherapy of anti-TIM-3 and anti-PD-1. We demonstrated two strategies to stratify breast cancer patients based on their necroptotic profiles and showed that necroptotic signature could assign patients with different tumor immune microenvironment patterns and different recurrence-related prognosis. A subset of necroptotic gene set, composed of TLR3, RIPK3, NLRP3, CASP1, ALDH2 and EZH2, was identified as a biomarker set for predicting immunotherapy-response and recurrence-related prognosis. Targeting necroptosis could helpfacilitate the development of novel breast cancer treatments and tailor personalized medical treatment.

Differences in adverse outcomes across race and ethnicity among Veterans with similar predicted risks of an overdose or suicide-related event.

To evaluate the degree to which differences in incidence of mortality and serious adverse events exist across patient race and ethnicity among Veterans Health Administration (VHA) patients receiving outpatient opioid prescriptions and who have similar predicted risks of adverse outcomes. Patients were assigned scores via the VHA Stratification Tool for Opioid Risk Mitigation (STORM), a model used to predict the risk of experiencing overdose- or suicide-related health care events or death. Individuals with the highest STORM risk scores are targeted for case review. Retrospective cohort study of high-risk veterans who received an outpatient prescription opioid between 4/2018-3/2019. All VHA medical centers. In total, 84 473 patients whose estimated risk scores were between 0.0420 and 0.0609, the risk scores associated with the top 5%-10% of risk in the STORM development sample. We examined the expected probability of mortality and serious adverse events (SAEs; overdose or suicide-related events) given a patient's risk score and race. Given a similar risk score, Black patients were less likely than White patients to have a recorded SAE within 6 months of risk score calculation. Black, Hispanic, and Asian patients were less likely than White patients with similar risk scores to die within 6 months of risk score calculation. Some of the mortality differences were driven by age differences in the composition of racial and ethnic groups in our sample. Our results suggest that relying on the STORM model to identify patients who may benefit from an interdisciplinary case review may identify patients with clinically meaningful differences in outcome risk across race and ethnicity.

A predictive model of immune infiltration and prognosis of head and neck squamous cell carcinoma based on cell adhesion-related genes: including molecular biological validation.

Focal adhesion serves as a bridge between tumour cells and the extracellular matrix (ECM) and has multiple roles in tumour invasion, migration, and therapeutic resistance. However, studies on focal adhesion-related genes (FARGs) in head and neck squamous cell carcinoma (HNSCC) are limited. Data on HNSCC samples were obtained from The Cancer Genome Atlas and GSE41613 datasets, and 199 FARGs were obtained from the Molecular Signatures database. The integrated datasets' dimensions were reduced by the use of cluster analysis, which was also used to classify patients with HNSCC into subclusters. A FARG signature model was developed and utilized to calculate each patient's risk score using least extreme shrinkage and selection operator regression analysis. The risk score was done to quantify the subgroups of all patients. We evaluated the model's value for prognostic prediction, immune infiltration status, and therapeutic response in HNSCC. Preliminary molecular and biological experiments were performed to verify these results. Two different HNSCC molecular subtypes were identified according to FARGs, and patients with C2 had a shorter overall survival (OS) than those with C1. We constructed an FARG signature comprising nine genes. We constructed a FARG signature consisting of nine genes. Patients with higher risk scores calculated from the FARG signature had a lower OS, and the FARG signature was considered an independent prognostic factor for HNSCC in univariate and multivariate analyses. FARGs are associated with immune cell invasion, gene mutation status, and chemosensitivity. Finally, we observed an abnormal overexpression of MAPK9 in HNSCC tissues, and MAPK9 knockdown greatly impeded the proliferation, migration, and invasion of HNSCC cells. The FARG signature can provide reliable prognostic prediction for patients with HNSCC. Apart from that, the genes in this model were related to immune invasion, gene mutation status, and chemosensitivity, which may provide new ideas for targeted therapies for HNSCC.

Learning Individual Survival Models from PanCancer Whole Transcriptome Data.

Personalized medicine attempts to predict survival time for each patient, based on their individual tumor molecular profile. We investigate whether our survival learner in combination with a dimension reduction method can produce useful survival estimates for a variety of patients with cancer. This article provides a method that learns a model for predicting the survival time for individual patients with cancer from the PanCancer Atlas: given the (16,335 dimensional) gene expression profiles from 10,173 patients, each having one of 33 cancers, this method uses unsupervised nonnegative matrix factorization (NMF) to reexpress the gene expression data for each patient in terms of 100 learned NMF factors. It then feeds these 100 factors into the Multi-Task Logistic Regression (MTLR) learner to produce cancer-specific models for each of 20 cancers (with >50 uncensored instances); this produces "individual survival distributions" (ISD), which provide survival probabilities at each future time for each individual patient, which provides a patient's risk score and estimated survival time. Our NMF-MTLR concordance indices outperformed the VAECox benchmark by 14.9% overall. We achieved optimal survival prediction using pan-cancer NMF in combination with cancer-specific MTLR models. We provide biological interpretation of the NMF model and clinical implications of ISDs for prognosis and therapeutic response prediction. NMF-MTLR provides many benefits over other models: superior model discrimination, superior calibration, meaningful survival time estimates, and accurate probabilistic estimates of survival over time for each individual patient. We advocate for the adoption of these cancer survival models in clinical and research settings.

A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas.

Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. In the context of the transfer learning approach, we aimed to train and test an MRI-based radiomics model for predicting survival in GBM patients and validate it in LGG patients. From each patient's 704 MRI-based radiomics features, we chose seventeen optimal radiomics signatures in the GBM training set (n = 71) and used these features in both the GBM testing set (n = 31) and LGG validation set (n = 107) for further analysis. Each patient's risk score, calculated based on those optimal radiomics signatures, was chosen to represent the radiomics model. We compared the radiomics model with clinical, gene status models, and combined model integrating radiomics, clinical, and gene status in predicting survival. The average iAUCs of combined models in training, testing, and validation sets were respectively 0.804, 0.878, and 0.802, and those of radiomics models were 0.798, 0.867, and 0.717. The average iAUCs of gene status and clinical models ranged from 0.522 to 0.735 in all three sets. The radiomics model trained in GBM patients can effectively predict the overall survival of GBM and LGG patients, and the combined model improved this ability.

Integrated analysis from multicentre studies identities m7G-related lncRNA-derived molecular subtypes and risk stratification systems for gastric cancer.

Gastric cancer (GC) is the fourth leading cause of cancer death worldwide. Due to the lack of effective chemotherapy methods for advanced gastric cancer and poor prognosis, the emergence of immunotherapy has brought new hope to gastric cancer. Further research is needed to improve the response rate to immunotherapy and identify the populations with potential benefits of immunotherapy. It is unclear whether m7G-related lncRNAs influence tumour immunity and the prognosis of immunotherapy. This study evaluated 29 types of immune cells and immune functions in gastric cancer patients, and m7G-related lncRNAs and their molecular subtypes were identified. In addition, we also studied the biological function characteristics of m7G-related lncRNA molecular subtypes. Finally, the patient's risk score was calculated based on m7G-related lncRNAs, and a nomogram of staging and risk groups was established to predict the prognosis. For experimental verification, RT-qPCR were preformed from the native cohort. After identifying m7G-related lncRNAs and their molecular subtypes, we found three molecular subtypes, the B subtype had the highest level of infiltration, and the B subtype may benefit more from immunotherapy. We divided GC patients into two regulator subtypes based on biological function. The two subtypes have significant immunological differences and can be used to judge ICI treatment. We established a risk score formula based on five lncRNAs, including LINC00924, LINC00944, LINC00865, LINC00702, and ZFAS1. Patients with poor prognoses were closely related to patients in the high-risk group. After comprehensive analysis of different risk groups, the efficacy of the high-risk group on bleomycin, cisplatin, docetaxel, doxorubicin and etoposide was better than that of the low-risk group, suggesting that risk subgroups based on risk scores play a guiding role in chemotherapy and that the high-risk group may benefit more from immunotherapy. RT-qPCR results showed that LINC00924, LINC00944, and LINC00865 were highly expressed in tumour tissues, while LINC00702 and ZFAS1 were expressed at low levels in tumour tissues. In conclusion, we were the first to discover that m7G-related lncRNAs play a vital role in the tumour immune microenvironment of gastric cancer, and a risk prediction model was established to identify patients with potential benefits from immunotherapy and predict the prognosis of GC patients.

Prediction of Hepatic Toxicity after Radiotherapy Using a Neural Network Including Blood Biomarkers and Liver Dose Distributions

<h3>Purpose/Objective(s)</h3> ASTRO recently published practical guidelines for treating unresectable hepatocellular carcinoma with radiotherapy, recommending hypofractionated dose escalated regimens when tolerated by the liver. This highlights the need for patient-specific prediction models of radiation-induced hepatic toxicities, which necessitates inclusion not only of radiation dose, but also baseline liver function biomarkers. <h3>Materials/Methods</h3> We developed a shallow neural network to predict declines in Child-Pugh score (CP) of 2 or more, with a focus on robustness, interpretability and ability to include both proton and photon radiation dose distributions. The network uses 1 convolution layer to extract 8 dosimetric features that are representative of the differential DVH (dDVH) and interactions of different dose levels within, which are then combined in a fully connected layer with baseline platelet counts and liver function biomarkers—CP and albumin-bilirubin grade. After registering a study analysis plan, we trained the model on 117 patients from a single institution and independently validated it on 88 patients from another institution, where 47%/36% of the training/validation cohorts received proton radiotherapy. We compared the model to logistic regression to evaluate the importance of feature interactions on predictive power, measured by the area under the receiver operating characteristics (AUC). <h3>Results</h3> The model prediction of CP decline had a cross-validation AUC = 0.86; 95%CI: 0.69-0.97 and independent validation AUC = 0.74 with 89% accuracy in 10% high-risk group. In contrast, logistic regression showed cross-validation AUC 0.73; 95%CI: 0.56-0.88 and validation AUC 0.56, demonstrating a significant importance of feature interactions. Sensitivity analysis of the neural network revealed that patients with low baseline platelet counts were most sensitive to high doses to the normal liver compared to other patient groups, indicating a potential benefit of liver-sparing therapies in these patients. <h3>Conclusion</h3> We developed a patient-specific hepatic toxicity model that can take into account both proton and photon dose distributions and the interaction of radiation dose to normal liver with baseline liver function. Robust model performance across institutions and particularly in high-risk patients could allow stratification of patients to standard, hypofractionated regimens, or proton therapy based on a patient's risk score.

Establishment and verification of a prognostic model of liver cancer by RNA-binding proteins based on the TCGA database.

BackgroundGlobally, liver cancer is one of the most common malignant tumors and is the third leading cause of cancer deaths. RNA-binding protein (RBP) is a general term for a class of proteins that bind to RNA to regulate metabolic processes. The expression of RNA-binding proteins is related to the prognosis of liver cancer patients.MethodsThe RBP gene expression data of liver cancer were extracted from the TCGA database. First, the differentially expressed RBPs (DE RBPs) were selected through enrichment analysis and volcano mapping. Then, the prognosis-related RBP genes were selected through single-factor Cox regression analysis. The key prognosis-related RBPs were further screened by multifactor Cox regression analysis, and a formula for the patient’s risk coefficient was obtained. Finally, based on the patient’s risk score, a nomogram was established and verified.ResultsWe extracted 374 cancer tissue samples and 50 normal tissue samples with the clinical information from each sample. Through enrichment analysis, we screened 208 upregulated RBPs and 122 downregulated RBPs. Prognosis-related high-risk genes were EEF1E1, NOP56, UPF3B, SF3B4, SMG5, CD3EAP, BRCA1, BARD1, XPO5, CSTF2, EZH2, EXO1, RRP12, PRIM1, LIN28B, NROB1 and TCOF1, and the low-risk genes were MRPL46, RCL1, MRPL54, CPEB3, IFIT5, PPARGC1A, EIF2AK4, SEPSECS, ACO1, SECISBP2 L and ZCCHC24. Further multivariate Cox regression analysis was performed on the prognosis-related RBPs, and the three key prognosis-related RBPs were screened out, which were BARD1, NR0B1 and EIF2AK4. A patient risk coefficient calculation formula was obtained: risk score = (1.207×BARD1 Exp) + (0.483×NR0B1 Exp) + (-0.720×EIF2AK4 Exp). Finally, a nomogram was established based on the risk score to predict the survival time of patients from 1 to 5 years.ConclusionsThe nomogram has good predictive value for the survival time of liver cancer patients.

Development and validation of an eight-gene signature based predictive model to evaluate the prognosis of hepatocellular carcinoma patients: a bioinformatic study.

BackgroundHepatocellular carcinoma (HCC) is a malignant tumor with a poor prognosis, however, biomarkers for the prognostic assessment of HCC remain suboptimal. Consequently, we aimed to develop a reliable tool for prognostic estimation of HCC.MethodsDifferentially expressed genes (DEGs) between HCC and adjacent normal tissues in 3 Gene Expression Omnibus (GEO) datasets were identified, followed by hub gene selection and least absolute shrinkage and selection operator (LASSO) Cox regression to develop a prognostic gene signature. Kaplan-Meier survival analysis, univariate and multivariate Cox regression, time-dependent area under the curve (AUC), and integrated value of time-dependent AUC (iAUC) were used to assess the relationship between predictors and clinical outcomes in the training and validation datasets. Then we built nomograms including gene signature and clinicopathological factors to forecast the probability of death. Moreover, we performed quantitative real-time PCR (qPCR) to compare the expression of prognostic genes between HCC and adjacent normal tissues. Finally, the relationship between prognostic genes and tumor microenvironment (TME) was investigated using immune cell infiltration algorithms and single cell transcriptomic database.ResultsEight prognostic genes (CDC20, PTTG1, TOP2A, CXCL2, CXCL14, CYP2C9, MT1F, and GHR) were finally identified to construct the gene signature. Each patient’s risk score was calculated according to the gene signature. Patients with high-risk scores showed worse outcomes in the training set [hazard ratio (HR) =3.404, P<0.001]. Risk score, age, body mass index (BMI), and TNM stage were identified as independent prognostic factors for overall survival (OS) in the training set. The nomogram including risk score and other independent prognostic factors showed better performance as opposed to the clinicopathological model. In the validation dataset, we obtained the similar results as well. Moreover, we found a close relationship between risk score and immune cell infiltration. Patients with high-risk scores had elevated expression of immune checkpoint genes, indicating that these patients may be more suitable for immunotherapy.ConclusionsWe have established and validated an eight-gene based prognostic model, which could be an effective tool for the prognostic evaluation of HCC patients.

A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients

ObjectiveThe Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed the capacity of healthcare resources and posed a challenge for worldwide hospitals. The ability to distinguish potentially deteriorating patients from the rest helps facilitate reasonable allocation of medical resources, such as ventilators, hospital beds, and human resources. The real-time accurate prediction of a patient's risk scores could also help physicians to provide earlier respiratory support for the patient and reduce the risk of mortality. MethodsWe propose a robust real-time prediction model for the in-hospital COVID-19 patients' probability of requiring mechanical ventilation (MV). The end-to-end neural network model incorporates the Multi-task Gaussian Process to handle the irregular sampling rate in observational data together with a self-attention neural network for the prediction task. ResultsWe evaluate our model on a large database with 9,532 nationwide in-hospital patients with COVID-19. The model demonstrates significant robustness and consistency improvements compared to conventional machine learning models. The proposed prediction model also shows performance improvements in terms of area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) compared to various deep learning models, especially at early times after a patient's hospital admission. ConclusionThe availability of large and real-time clinical data calls for new methods to make the best use of them for real-time patient risk prediction. It is not ideal for simplifying the data for traditional methods or for making unrealistic assumptions that deviate from observation's true dynamics. We demonstrate a pilot effort to harmonize cross-sectional and longitudinal information for mechanical ventilation needing prediction.

Construction and validation of a novel aging-related gene signature and prognostic nomogram for predicting the overall survival in ovarian cancer.

BackgroundOvarian cancer (OC) is the most lethal gynecological malignancy. The objective of this study was to establish and validate an individual aging‐related gene signature and a clinical nomogram that can powerfully predict independently the overall survival rate of patients with ovarian cancer.MethodsData on transcriptomic profile and relevant clinical information were retrieved from The Cancer Genome Atlas (TCGA) database as a training group, and the same data from three public Gene Expression Omnibus (GEO) databases as validation groups. Univariate Cox regression analysis, lasso regression analysis, and multiple multivariate Cox analysis were analyzed sequentially to select the genes to be included in the aging‐associated signature. A risk scoring model was established and verified, the predictive value of the model was evaluated, and a clinical nomogram was established.ResultsWe found eight genes that were most relevant to prognosis and constructed an eight‐mRNA signature. Based on the model, each OC patient's risk score was able to be calculated and patients were split into groups of low and high risks with a distinct outcome. Survival analysis confirmed that the outcome of patients in the high‐risk group was dramatically shorter than that of those in the low‐risk group, and the eight‐mRNA signature can be considered as a powerful and independent predictor that could predict the outcome of OC patient. Additionally, the risk score and age can be used to construct a clinical nomogram as a simpler tool for predicting prognosis. We also explored the association between the risk score and immunity and drug sensitivity.ConclusionThis study suggested that the aging‐related gene signature could be used as an intervention point and latent prognostic predictor in OC, which may provide new perceptions for postoperative treatment strategies.

Six mutator-derived lncRNA signature of genome instability for predicting the clinical outcome of colon cancer.

Colon adenocarcinoma (COAD) is one of the most common malignancies worldwide. Genomic instability is one of the hallmarks of colon cancer and is associated with prognosis. Nevertheless, the impact of genome instability-associated long non-coding RNAs (lncRNAs) along with their clinical significance in cancers has remained mostly unexplored. In this study, a mutator hypothesis-derived computational frame integrating the somatic mutation profiles and lncRNA expression profiles in a tumor genome was developed, which enabled the identification of 137 novel genomic instability-associated lncRNAs in colon cancer. Subsequently, a genome instability-derived lncRNA signature (GILncSig) segregated the patients into low- and high-risk groups with prominent differences in outcomes. Combined with the overall survival data, we established 6 six lncRNA-based signature to predict prognosis, which were LINC00896, AC007996.1, NKILA, AP003555.2, MIRLET7BHG, and AC009237.14. We found that the expression level of PD-L1 (CD274) and somatic mutations in the high-risk group were higher than those in the low-risk group. This suggests that high-risk patients may be sensitive to immunotherapy. We further found that the prognosis of patients in the high-risk group was significantly lower than that of patients in the low-risk group, and that patients' prognosis was likely to be worse as the patient's risk score increased. In conclusion, this study explores the role of lncRNAs in genomic instability and cancer prognosis and provides a new idea for the prognostic prediction of colon cancer.

Novel prognostic model established for patients with head and neck squamous cell carcinoma based on pyroptosis-related genes

We aimed at establishing a risk – score model using pyroptosis-related genes to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC). A total of 33 pyroptosis-related genes were selected. We then evaluated the data of 502 HNSCC patients and 44 normal patients from TCGA database. Gene expression was then profiled to detect differentially expressed genes (DEGs). Using the univariate, the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we generated a risk – score model. Tissue samples from neoplastic and normal sites of 44 HNSCC patients were collected. qRT-PCR were employed to analyze the mRNA level of the samples. Kaplan-Meier method was used to evaluate the overall survival rate (OS). Enrichment analysis was performed to elucidate the underlying mechanism of HNSCC patient's differentially survival status from the perspective of tumor immunology. 17 genes were categorized as DEGs. GSDME, IL-6, CASP8, CASP6, NLRP1 and NLRP6 were used to establish the risk – score model. Each patient's risk score in the TCGA cohort was calculated using the risk – score formula. The risk score was able to independently predict the OS of the HNSCC patients (P = 0.02). The OS analysis showed that the risk score model (P < 0.0001) was more reliable than single gene, a phenomenon verified by practical patient cohort. Additionally, enrichment analysis indicated more active immune activities in low-risk group than high-risk group. In conclusion, our risk – score model has provided novel strategy for the prediction of HNSCC patients’ prognosis.

Construction of a prognostic signature in Ewing's sarcoma: Based on metabolism-related genes

ObjectiveBy combining the expression profiles of metabolism-related genes (MRGS) with clinical information, the expression quantities of MRGS and the influence on development and prognosis were systematically analyzed, so as to provide a theoretical basis for the clinical study on the prognosis of Ewing's sarcoma. MethodsMRGs expression profiles of 64 patients with Ewing's sarcoma were obtained from GEO dataset. Univariate Cox regression analysis was used to identify metabolization-related differentially expressed genes (DEGs) related with prognosis in Ewing's sarcoma patients. Then, multivariate Cox analysis was used to calculate novel prognostic markers based on metabolism-related DEGs. Besides, We validate the model using ICGC datasets. Finally, the new prognostic index was verified on the basis of the prognostic models. ResultsMultivariate Cox regression analysis identified 74 metabolization-related DEGs, 25 of which were associated with Ewing's sarcoma patients' overall survival. Subsequently, we used 25 DEGs to construct metabolism-related prognostic signature for patients with Ewing's sarcoma. Based on the 18 DEGs regression coefficient, we propose the formula of each patient's risk score, and then divided the patients into high-risk group and low-risk group. The results indicated that the survival rate and survival time were higher in the low-risk group and lower in the high-risk group. Multivariate Cox analysis showed that risk score index was an independent prognostic factor for Ewing's sarcoma. ConclusionThe experimental results suggest that the 18 metabolism-related DEGs marker may be effective in predicting the prognosis of Ewing's sarcoma to some extent, helping to individualize treatment of patients at different risks.

Identification of a glycolysis-related gene signature associated with clinical outcome for patients with lung squamous cell carcinoma.

BackgroundLung squamous cell carcinoma (LUSC), one of the main types of lung cancer, has caused a huge social burden. There has been no significant progress in its therapy in recent years, Resulting in a poor prognosis. This study aims to develop a glycolysis‐related gene signature to predict patients’ survival with LUSC and explore new therapeutic targets.MethodsWe obtained the mRNA expression and clinical information of 550 patients with LUSC from the Cancer Genome Atlas (TCGA) database. Glycolysis genes were identified by Gene Set Enrichment Analysis (GSEA). The glycolysis‐related gene signature was established using the Cox regression analysis.ResultsWe developed five glycolysis‐related genes signature (HKDC1, AGL, ALDH7A1, SLC16A3, and MIOX) to calculate each patient's risk score. According to the risk score, patients were divided into high‐ and low‐risk groups and exhibited significant differences in overall survival (OS) between the two groups. The ROC curves showed that the AUC was 0.707 for the training cohort and 0.651 for the validation cohort. Additionally, the risk score was confirmed as an independent risk factor for LUSC patients by Cox regression analysis.ConclusionWe built a gene signature to clarify the connection between glycolysis and LUSC. This model performs well in evaluating patients’ survival with LUSC and provides new biomarkers for targeted therapy.

Prognostic Correlation of Glycolysis-Related Gene Signature in Patients with Laryngeal Cancer

BackgroundAerobic glycolysis is one of the metabolic characteristics of tumor cells, which is regulated by many genes. The aim of our study was to construct glycolysis-related gene signature to accurately predict the prognosis of laryngeal cancer (LC) patients. MethodsWe analyzed the mRNA expression profiles of LC patients from The Cancer Genome Atlas (TCGA). Eleven glycolysis-related gene sets were analyzed by gene set enrichment analysis (GSEA). In order to acquire the gene signature related to prognosis, we used univariate and multivariate Cox regression analysis. ResultsWe confirmed that a gene signature composed of two genes (STC2, LHPP) can predict the overall survival (OS) of patients with LC. Based on each patient's risk score, we found that the survival results of patients in the high-risk group were significantly lower than those in the low-risk group (log‐rank test P‐value=0.002). Multivariate Cox regression analysis confirmed that gene signature could independently predict OS in LC patients (HR = 1.981, 95% CI 1.446–2.714 P<0.001). In addition, a nomogram including the age, sex, grade and risk score was constructed. The nomogram demonstrated good accuracy for OS prediction, with a C-index of 0.752. ConclusionThe glycolysis-related two-gene risk score model could be used as a biomarker for LC prognosis.

Immunotherapy and tyrosine kinase inhibitors in first-line treatment of metastatic renal cell carcinoma-Which strategy when?

Immuntherapien mit Checkpoint-Inhibitoren haben beim metastasierten klarzelligen Nierenzellkarzinom (mRCC) zu einem Paradigmenwechsel geführt und einen neuen Standard in der Erstlinie etabliert. Einschließlich der bekannten Monotherapie mit Tyrosinkinaseinhibitoren ist das Spektrum an medikamentösen Therapieoptionen somit breiter geworden. In diesem Beitrag sollen anhand der aktuellen Studiendaten sowie Leitlinienempfehlungen mögliche Faktoren zur individuellen Therapieplanung in der Erstlinie des mRCC diskutiert werden. Hierbei ist das wichtigste Leitkriterium das Risikoprofil. Daneben sind Effektivität und Verträglichkeit der Substanzen, sowie Tumorlast, Alter und Präferenzen der Patienten sowie Überlegungen zur Sequenztherapie für die Therapiewahl ausschlaggebend. Real-world-Daten für die neuen Kombinationstherapien, Biomarker für eine personalisierte Medizin sowie Studien zur optimalen Sequenztherapie beim mRCC werden benötigt.

The Landscape of Iron Metabolism-Related and Methylated Genes in the Prognosis Prediction of Clear Cell Renal Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is characteristics of resistance to chemotherapy and radiotherapy. The prognosis of ccRCC was dismay with immense diversity. Iron metabolism disturbance is a common phenomenon in ccRCC. The purpose of our study is to identify and validate the candidate prognostic gene signature of iron metabolism and methylation closely related to the poor prognosis of ccRCC through comprehensive bioinformatics analysis in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases.Methods: The prognostic iron metabolism-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. We built a prognostic model using risk score method to predict OS, each ccRCC patient's risk score was calculated, and the resulting score can divide these patients into two categories according to the cut-point risk score. The prognostic significance of the hub genes was further evaluated with the Kaplan-Meier (KM) survival and Receiver Operating Characteristic (ROC) curve analysis. Univariate and multivariate Cox regression analysis was implemented to evaluate the impact of each variable on OS. Furthermore, the prediction power of the 25 gene signatures has been validated using an independent ccRCC cohort from the GEO database. The Gene Set Enrichment Analysis (GSEA) identified the characteristics of hub related oncogenes. Finally, we utilize Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the co-expression network based on these DEGs.Results: In this study, we identified and validated 25 iron metabolism-related and methylated genes as the prognostic signatures, which differentiated ccRCC patients into high and low risk subgroups. The KM analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 25 gene signatures could largely predict OS and DFS for 1, 3, and 5 years in patients with ccRCC.Conclusions: Taken together, we identified the key iron metabolism-related and methylated genes for ccRCC through a comprehensive bioinformatics analysis. This study provides a reliable and robust gene signature for the prognostic predictor of ccRCC patients and maybe provides a promising treatment strategy for this lethal disease.