Multigene Model Research Articles

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer.

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Construction of prediction model for prognosis of uterine corpus endometrial carcinoma based on pyroptosis gene.

To assess the expression of genes that are relevant to pyroptosis and the relationship between these genes and prognosis in uterine corpus endometrial carcinoma (UCEC). The research identifies 16 pyroptosis regulators with different expressions in normal endometrium and UCEC. In accordance with the differentially expressed genes (DEGs), the various kinds of UCEC are classified into two sub-types. With the help of the Cancer Genome Atlas (TCGA), the prognostic value of all pyroptosis-related genes for survival was assessed, and a multigene model has constructed accordingly. Ten genes were modeled by applying the minimum criteria for determining risk score selection (LASSO) Cox regression method. Meanwhile, by referring to the TCGA atlas, UCEC patients were divided into the high- and low-risk subgroups. The effects of the gene with significant differences on the proliferation of two cancer cells were also verified. The survival rate of UCEC cases with higher risk was higher than that with lower risk (P < 0.001). Through the median risk score of TCGA atlas, UCEC cases were ranked as patients with higher risk and patients with lower risk. The low risk has a significant relationship with the prolongation of overall survival (OS) (p = 0.001) in the low-risk subgroup. Moreover, the KEGG and gene ontology (GO) enrichment models indicated that among the patients in the high-risk subgroup, their immune-related genes were concentrated but with decreased immune status. The apoptosis-related genes are crucial for the immunity of tumors and may forecast the prognosis of UCEC.

Association between tumor mutations and meningioma recurrence in Grade I/II disease.

Meningiomas are common intracranial tumors with variable prognoses not entirely captured by commonly used classification schemes. We sought to determine the relationship between meningioma mutations and oncologic outcomes using a targeted next-generation sequencing panel. We identified 184 grade I and II meningiomas with both >90 days of post-surgical follow-up and linked targeted next-generation sequencing. For mutated genes in greater than 5% of the sample, we computed progression-free survival Cox-regression models stratified by gene. We then built a multi-gene model by including all gene predictors with a p-value of less than 0.20. Starting with that model, we performed backward selection to identify the most predictive factors. ATM (HR = 4.448; 95% CI: 1.517-13.046), CREBBP (HR = 2.727; 95% CI = 1.163-6.396), and POLE (HR = 0.544; HR = 0.311-0.952) were significantly associated with alterations in disease progression after adjusting for clinical and pathologic factors. In the multi-gene model, only POLE remained a significant predictor of recurrence after adjusting for the same clinical covariates. Backwards selection identified recurrence status, resection extent, and mutations in ATM (HR = 7.333; 95% CI = 2.318-23.195) and POLE (HR = 0.413; 95% CI = 0.229-0.743) as predictive of recurrence. Mutations in ATM and CREBBP were associated with accelerated meningioma recurrence, and mutations in POLE were protective of recurrence. Each mutation has potential implications for treatment. The effect of these mutations on oncologic outcomes and as potential targets for intervention warrants future study.

EMILIN2 is associated with prognosis and immunotherapy in clear cell renal cell carcinoma.

Background: EMILIN2 is a platelet-associated elastin that regulates angiogenesis. It has recently been found to play an essential role in various tumors. Nevertheless, the mechanism of action of EMILIN2 in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods: Samples from 33 cancers were obtained from UCSC Xena and The Cancer Genome Atlas (TCGA) database. The relationship between EMILIN2 expression and the clinicopathological characteristics and immune infiltration of ccRCC was investigated. Nonnegative matrix factorization (NMF) was used to classify ccRCC patients. A multigene risk prediction model of ccRCC was constructed using LASSO regression and multivariate regression analysis. A nomogram survival probability prediction map and calibration curve were constructed based on clinical information. Results: EMILIN2 is significantly overexpressed in ccRCC, a phenomenon that is associated with poor prognosis. Meanwhile, EMILIN2 expression is closely related to tumor immune infiltration in ccRCC. Patients with clear cell renal cell carcinoma were divided into two subtypes using NMF, with subtype 2 showed poor prognosis. Next, we established a risk score model for ccRCC based on the common differentially expressed genes (DEGs) between subtypes and groups based on EMILIN2 expression. The results indicated poor prognosis in the high-risk group in the training set and were confirmed in the validation set. Conclusion: Our findings suggest that EMILIN2 expression is closely associated with immune infiltration in ccRCC. EMILIN2 expression is negatively correlated with the prognosis of ccRCC patients. Here, we developed a tool that could predict the prognosis of ccRCC patients.

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning.

Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma.

Comprehensive Analysis of Cuproptosis-Related Genes in Prognosis and Immune Infiltration of Hepatocellular Carcinoma Based on Bulk and Single-Cell RNA Sequencing Data.

Studies on prognostic potential and tumor immune microenvironment (TIME) characteristics of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) are limited. A multigene signature model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The cuproptosis-related multivariate cox regression analysis and bulk RNA-seq-based immune infiltration analysis were performed. The results were verified using two cohorts. The enrichment of CRGs in T cells based on single-cell RNA sequencing (scRNA-seq) was performed. Real-time polymerase chain reaction (RT-PCR) and multiplex immunofluorescence staining were performed to verify the reliability of the conclusions. A four-gene risk scoring model was constructed. Kaplan-Meier curve analysis showed that the high-risk group had a worse prognosis (p &lt; 0.001). The time-dependent receiver operating characteristic (ROC) curve showed that the OS risk score prediction performance was good. These results were further confirmed in the validation queue. Meanwhile, the Tregs and macrophages were enriched in the cuproptosis-related TIME of HCC. The CRGs-based signature model could predict the prognosis of HCC. Treg and macrophages were significantly enriched in cuproptosis-related HCC, which was associated with the depletion of proliferating T cells.

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients.

Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.

Identification of EGFR-Related LINC00460/mir-338-3p/MCM4 Regulatory Axis as Diagnostic and Prognostic Biomarker of Lung Adenocarcinoma Based on Comprehensive Bioinformatics Analysis and Experimental Validation.

Simple SummaryWhile the epidermal growth factor receptor (EGFR) is an important target for lung adenocarcinoma (LUAD) therapy, acquired resistance is still inevitable. A comprehensive bioinformatics analysis strongly suggested that the closely related LINC00460-mir-338-3p-MCM4 ceRNA network of EGFR plays an important role in the diagnosis and prognosis of LUAD. High expression of LINC00460 and MCM4 predicts shorter patient survival. Univariate and multivariate regression analyses demonstrated that higher expression of LINC00460 and MCM4 was significantly associated with tumor size, lymph node metastasis, distant metastasis and TNM stage. A multi-gene regulation model analysis revealed that the LINC00460 (downregulation)—mir-338-3p (upregulation))—MCM4 (downregulation) pattern significantly improved the overall survival of LUAD patients. We also verified the expression of these genes in LUAD cell lines and tumor tissues by RT-PCR and immunohistochemistry. Finally, the possible targeted drugs of MCM4 were queried through the drug database platform, hoping to solve its drug resistance problem by targeting EGFR-related genes.Background: Lung adenocarcinoma (LUAD) is one of the most aggressive and lethal tumor types and requires effective diagnostic and therapeutic targets. Though the epidermal growth factor receptor (EGFR) is an important target for LUAD therapy, acquired resistance is still inevitable. In recent years, the regulation of the EGFR by competing endogenous RNAs (ceRNAs) has been extensively studied and significant progress has been made. Therefore, we aim to find new targets for the diagnosis and treatment of LUAD by analyzing the EGFR-related ceRNA network in LUAD and expect to address the problem of EGFR resistance. Methods: We identified differentially expressed lncRNAs, miRNAs and mRNAs closely associated with the EGFR by analyzing transcriptome data from LUAD samples. Comprehensive bioinformatics analysis strongly suggests that the LINC00460—mir-338-3p—MCM4 ceRNA network plays an important role in the diagnosis and prognosis of LUAD. The effects of different patterns of the LINC00460/MCM4 axis on the overall survival of patients with LUAD were analyzed by a polygene regulation model. We also verified the expression of these genes in LUAD cell lines and tumor tissues by RT-PCR and immunohistochemistry. The functional enrichment analysis and targeted drug prediction of the MCM4 gene were performed. Results: Survival analysis indicated that high expressions of LINC00460 and MCM4 predict a shorter survival period for patients. Univariate and multivariate regression analyses demonstrated that higher expressions of LINC00460 and MCM4 were significantly associated with tumor size, lymph node metastasis, distant metastasis and TNM stage. A multi-gene regulation model analysis revealed that the LINC00460 (downregulation)—mir-338-3p (upregulation)—MCM4 (downregulation) pattern significantly improved the overall survival of LUAD patients (p = 0.0093). RT-PCR and immunohistochemical experiments confirmed our analytical results. In addition, the functional enrichment analysis indicated that MCM4-related genes were mainly enriched in the cell cycle and cell division. A functional association network analysis showed that MCM4 was closely related to the EGFR. Finally, the possible targeted drugs of MCM4 were queried through the drug database platform, hoping to solve its drug resistance problem by targeting EGFR-related genes. Conclusions: In summary, the LINC00460/MCM4 axis can be used as a potential new perspective for targeting EGFR genes in precision medicine and is expected to serve as a diagnostic, prognostic and drug target for LUAD.

Identification and validation of autophagy-related gene expression for predicting prognosis in patients with idiopathic pulmonary fibrosis.

BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal fibrotic pulmonary disease with unknow etiology. Owing to lack of reliable prognostic biomarkers and effective treatment measures, patients with IPF usually exhibit poor prognosis. The aim of this study is to establish a risk score prognostic model for predicting the prognosis of patients with IPF based on autophagy-related genes.MethodsThe GSE70866 dataset was obtained from the gene expression omnibus (GEO) database. The autophagy-related genes were collected from the Molecular Signatures Database (MSigDB). Gene enrichment analysis for differentially expressed genes (DEGs) was performed to explore the function of DEGs. Univariate, least absolute shrinkage and selection operator (LASSO), as well as multivariate Cox regression analyses were conducted to identify a multi-gene prognostic model. Receiver operating characteristic (ROC) curve was applied to assess the prediction accuracy of the model. The expression of genes screened from the prognostic model was validated in clinical samples and human lung fibroblasts by qPCR and western blot assays.ResultsAmong the 514 autophagy-related genes, a total of 165 genes were identified as DEGs. These DEGs were enriched in autophagy-related processes and pathways. Based on the univariate, LASSO, and multivariate Cox regression analyses, two genes (MET and SH3BP4) were included for establishing the risk score prognostic model. According to the median value of the risk score, patients with IPF were stratified into high-risk and low-risk groups. Patients in high-risk group had shorter overall survival (OS) than low-risk group in both training and test cohorts. Multivariate regression analysis indicated that prognostic model can act as an independent prognostic indicator for IPF. ROC curve analysis confirmed the reliable predictive value of prognostic model. In the validation experiments, upregulated MET expression and downregulated SH3BP4 expression were observed in IPF lung tissues and TGF-β1-activated human lung fibroblasts, which is consistent with results from microarray data analysis.ConclusionThese findings indicated that the risk score prognostic model based on two autophagy-related genes can effectively predict the prognosis of patients with IPF.

Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems

In this study, it was investigated that how machine learning (ML) methods show performance in different problems having different characteristics. Six ML approaches including Artificial neural networks (ANN), gaussian process regression (GPR), support vector machine regression (SVMR), long short-term memory (LSTM), multi-gene genetic programming (MGGP) and M5 model tree (M5Tree) were utilized to analyze three independent civil engineering problems belonging to construction management, geotechnical engineering, and hydrological engineering sub-disciplines. Mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), relative root means square error (RRMSE), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and overall index of model performance (OI) criteria were used to evaluate the performances of the models. Besides performance criteria, the relative performances of the six ML models were assessed using Taylor diagram, Violin diagram and One-Tailed Wilcoxon Signed-Rank Test. For each of the problem considered in this study, the effectiveness of the input parameters on the output parameter has been defined using the Relief Method and Correlation Coefficient. The results show that ANN and MGGP models yielded the most successful estimations for three different problems considered. The best prediction was achieved by MGGP model for hydrological engineering problem. For the construction management, geotechnical engineering problems, the best results were obtained using the ANN model. All models were reliable to solve the geotechnical engineering and hydrological engineering problems while LSTM and SVMR models are not reliable to solve the construction management problem. The most and least effective input parameters on output parameter were contract cost (CC) and work definition number (WDN) for the managerial data set. On the other hand, the most and least effective input parameters on the output parameters for the experimental and natural data sets have been obtained as width of the pile (B), rotation degree (R) and minimum temperature (Tmin), streamflow (Q) data, respectively. The number of data and data selection have a significant effect on the homogeneity of the data set and its representativeness of the problem. The error values obtained in test stage are affected from this condition. The equations to calculate the outputs of each of the problem considered were obtained using MGGP and M5Tree models.

Determination of Health Index of Insulating Oil of In-Service Transformer &amp; Reactors Based on IFT Predicted by Multigene Symbolic Regression

This article aims at developing a multigene symbolic regression model of interfacial tension (IFT) for determining the health index of insulating oil. The oil parameters considered for modeling of IFT are break down voltage, resistivity, acidity, tan delta and water. The unique study undertaken in this article attempts to develop a multigene symbolic regression model for generating and evaluating the relationship between IFT and other oil parameters. Data utilized in this study involves actual oil testing parameters of in-service transformers and reactors of age more than 15 years. A sensitivity analysis has been carried out to investigate the relative contribution of input variables to the model output. The result of sensitivity analysis has further been used to determine the weight of parameters for calculating the health index of insulating oil. Finally, the health index of 400 in-service transformer and reactors are evaluated and analyzed. This research will help in undertaking decisions for the replacement of oil based on health index, thereby increasing the life span of transformer and reactors. The health index so obtained will act as a reference for planning necessary maintenance activities of the equipment and thereby saving unprecedented failure of the oil-filled transformer and reactors.

A Cuproptosis-Related Gene Model For Predicting the Prognosis of Clear Cell Renal Cell Carcinoma.

Despite advances in its treatment, patients diagnosed with clear cell renal cell carcinoma (ccRCC) have a poor prognosis. The mechanism of cuproptosis has been found to differ from other mechanisms that regulate cell death, including apoptosis, iron poisoning, pyrophosphate poisoning, and necrosis. Cuproptosis is an essential component in the regulation of a wide variety of biological processes, such as cell wall remodeling and oxidative stress responses. However, cuproptosis-related genes’ expression in ccRCC patients and their association with the patient’s prognosis remain ambiguous. Evaluation of The Cancer Genome Atlas (TCGA) identified 11 genes associated with cuproptosis that were differently expressed in ccRCC and nearby nontumor tissue. To construct a multigene prognostic model, the prognostic value of 11 genes was assessed and quantified. A signature was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and this signature was used to separate ccRCC patients into different risk clusters, with low-risk patients having a much better prognosis. This five-gene signature, when combined with patients’ clinical characteristics, might serve as one independent predictor of overall survival (OS) in ccRCC patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that cuproptosis-related genes were enriched in patients with ccRCC. Then, quantitative real-time PCR (qPCR) was employed to verify these genes’ expression. Generally, research has indicated that cuproptosis-related genes are important in tumor immunity and can predict OS of ccRCC patients.

The prognostic value and clinical significance of mitophagy-related genes in hepatocellular carcinoma.

Background: Mitophagy has been found to play a significant part in the cancer process in a growing number of studies in recent years. However, there is still a lack of study on mitophagy-related genes’ (MRGs) prognostic potential and clinical significance in hepatocellular carcinoma (HCC). Methods: We employed bioinformatics and statistical knowledge to examine the transcriptome data of HCC patients in the TCGA and GEO databases, with the goal of constructing a multigene predictive model. Then, we separated the patients into high- and low-risk groups based on the score. The model’s dependability was determined using principal components analysis (PCA), survival analysis, independent prognostic analysis, and receiver operating characteristic (ROC) analysis. Following that, we examined the clinical correlations, pharmacological treatment sensitivity, immune checkpoint expression, and immunological correlations between patients in high and low risk groups. Finally, we evaluated the variations in gene expression between high- and low-risk groups and further analyzed the network core genes using protein-protein interaction network analysis. Results: Prognostic models were built using eight genes (OPTN, ATG12, CSNK2A2, MFN1, PGAM5, SQSTM1, TOMM22, TOMM5). During validation, the prognostic model demonstrated high reliability, indicating that it could accurately predict the prognosis of HCC patients. Additionally, we discovered that typical HCC treatment medicines had varying impacts on patients classified as high or low risk, and that individuals classified as high risk are more likely to fail immunotherapy. Additionally, the high-risk group expressed more immunological checkpoints. The immunological status of patients in different risk categories varies as well, and patients with a high-risk score have a diminished ability to fight cancer. Finally, PPI analysis identified ten related genes with potential for research. Conclusion: Our prognostic model had good and reliable predictive ability, as well as clinical diagnosis and treatment guiding significance. Eight prognostic MRGs and ten network core genes merited further investigation.

An Immunity-Related Gene Model Predicts Prognosis in Cholangiocarcinoma.

The prognosis of patients with cholangiocarcinoma (CCA) is closely related to both immune cell infiltration and mRNA expression. Therefore, we aimed at conducting multi-immune-related gene analyses to improve the prediction of CCA recurrence. Immune-related genes were selected from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Immunology Database and Analysis Portal (ImmPort). The least absolute shrinkage and selection operator (LASSO) regression model was used to establish the multi-gene model that was significantly correlated with the recurrence-free survival (RFS) in two test series. Furthermore, compared with single genes, clinical characteristics, tumor immune dysfunction and exclusion (TIDE), and tumor inflammation signature (TIS), the 8-immune-related differentially expressed genes (8-IRDEGs) signature had a better prediction value. Moreover, the high-risk subgroup had a lower density of B-cell, plasma, B-cell naïve, CD8+ T-cell, CD8+ T-cell naïve, and CD8+ T-cell memory infiltration, as well as more severe immunosuppression and higher mutation counts. In conclusion, the 8-IRDEGs signature was a promising biomarker for distinguishing the prognosis and the molecular and immune features of CCA, and could be beneficial to the individualized immunotherapy for CCA patients.

Identification of immune-related biomarkers for predicting neoadjuvant chemotherapy sensitivity in HER2 negative breast cancer via bioinformatics analysis.

Neoadjuvant chemotherapy (NAC) is an important treatment for breast cancer (BC) patients. However, due to the lack of specific therapeutic targets, only 1/3 of human epidermal growth factor receptor 2 (HER2)-negative patients reach pathological complete response (pCR). Therefore, there is an urgent need to identify novel biomarkers to distinguish and predict NAC sensitive in BC patients. The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database. Patients with pathological complete response (pCR) and those with residual disease (RD) were compared to obtain the differentially expressed genes (DEGs). Functional enrichment analyses were conducted on these DEGs. Then, we intersect the DEGs and immune-related genes to obtain the hub immune biomarkers, and then use the linear fitting model ("glm" package) to construct a prediction model composed of 9 immune biomarkers. Finally, the single sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze immune cell invasion in BC patients, and the correlation between immune cell content and immune gene expression levels was analyzed. Nine immune-related biomarkers were obtained in the intersection of DEGs and immune-related genes. Compared with RD patients, CXCL9, CXCL10, CXCL11, CXCL13, GZMB, IDO1, and LYZ were highly expressed in pCR patients, while CXCL14 and ESR1 were lowly expressed in pCR patients. After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844. Immunoinfiltration analysis showed that compared with RD patients, 15 of the 28 immune cell types examined showed high-infiltration in pCR patients, including activated CD8 T cells, effector memory CD8 T cells, and activated CD4 T cells. This investigation ultimately identified 9 immune-related biomarkers as potential tools for assessing the sensitivity of NAC in HER2-negative BC patients. These biomarkers have great potential for predicting pCR BC patients.

P-223 An eleven-lncRNA signature predicts overall survival in patients with gastric cancer

Gastric cancer (GC) is one of the most common malignancies worldwide. Multidisciplinary strategies including chemotherapy, radiotherapy and surgical resection are the basic treatment for stomach cancer and significantly improve the survival of patients with GC. However, the prognosis of patients with advanced GC remains poor. The high risk of disease relapse prompted the investigation of genomic factors which could be the predictive or prognostic biomarkers of GC. Thus, we established a multi-gene prognostic model for predicting the prognosis of GC and explored crucial molecular mechanisms associated with prognostic lncRNA expression signature.

Apoptosis-Related Signature Predicts Prognosis and Immune Microenvironment Infiltration in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD), a malignancy with high incidence and mortality rates worldwide, contains multiple genomic and epigenomic abnormalities. And the useful tumor markers associated with these abnormalities need further investigation. Whereas apoptosis is a form of programmed cell death, the expression of apoptosis-related genes in LUAD and its relationship with prognosis is unclear. In the present study, we identified 64 differentially expressed apoptosis-related genes (DEARGs) that were differentially expressed between LUAD tissue and normal lung tissue. Based on these DEARGs, all LUAD cases were classified into two subtypes using The Cancer Genome Atlas (TCGA) cohort to assess the prognostic value of apoptosis-related genes for survival. An 11-gene signature was established by applying the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression method to construct a multigene prediction model and classify all LUAD patients in the TCGA cohort into high or low AS-score groups. Patients in the low AS-score group had significantly higher survival and prognosis than those in the high AS-score group. Taking the median risk score of the AS-score, LUAD patients in the GSE68465 cohort were divided into two risk groups, low and high. The overall survival (OS) time was longer in the low AS-score group. Combined with clinical characteristics, the AS-score was an independent predictor of LUAD patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that the differential genes between the two groups were mainly enriched in cellular immunity. Further analysis revealed higher immune checkpoint protein expression and higher tumor mutational burden (TMB) in the high AS-score group, suggesting better efficacy of immunotherapy in the high AS-score group than the low AS-score group. And the high AS-score group was better in chemotherapy and targeted therapy efficiency. In conclusion, the AS-score constructed based on apoptosis-related genes can predict the prognosis of LUAD patients and provide some guidance for the antitumor treatment of LUAD patients.

A Novel Inflammatory Response-Related Gene Signature Improves High-Risk Survival Prediction in Patients With Head and Neck Squamous Cell Carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly prevalent and malignant tumor that is difficult to effectively prognosticate outcomes. Recent reports have suggested that inflammation is strongly related to tumor progression, and several biomarkers linked to inflammation have been demonstrated to be useful for making a prognosis. The goal of this research was to explore the relevance between the inflammatory-related genes and HNSCC prognosis. Methods: The clinical information and gene expression data of patients with HNSCC were acquired from publicly available data sources. A multigene prognostic signature model was constructed in The Cancer Genome Atlas and verified in the Gene Expression Omnibus database. According to the risk score calculated for each patient, they were divided into low- and high-risk groups based on the median. The Kaplan–Meier survival curve and receiver operating characteristic curve were applied to determine the prognostic value of the risk model. Further analysis identified the independent prognostic factors, and a prognostic nomogram was built. The relationship between tumor immune infiltration status and risk scores was investigated using Spearman correlation analysis. Finally, to confirm the expression of genes in HNSCC, quantitative real-time polymerase chain reaction (qRT-PCR) was performed. Results: A prognostic model consisting of 14 inflammatory-related genes was constructed. The samples with a high risk had an apparently shorter overall survival than those with a low risk. Independent prognostic analysis found that risk scores were a separate prognostic factor in HNSCC patients. Immune infiltration analysis suggested that the abundance of B cells, CD8 T cells, M2 macrophages, myeloid dendritic cells, and monocytes in the low-risk group was higher, while that of M0, M1 macrophages, and resting NK cells was obviously higher in the high-risk group. The risk scores were related to chemotherapeutic sensitivity and the expression of several immune checkpoint genes. Moreover, CCL22 and IL10 were significantly higher in HNSCC tissues, as determined by qRT-PCR. Conclusion: Taken together, we constructed a novel inflammatory response–related gene signature, which may be used to estimate outcomes for patients with HNSCC and may be developed into a powerful tool for forecasting the efficacy of immunotherapeutic and chemotherapeutic drugs for HNSCC.

Glycolysis-Related Gene Signature Can Predict Survival and Immune Status of Hepatocellular Carcinoma.

Concise and precise prognostic models are urgently needed due to the intricate genetic variations among hepatocellular carcinoma (HCC) cells. Disorder or change in glycolysis metabolism has been considered one of the "hallmarks" of cancer. However, the prognostic value of glycolysis-related genes in HCC remains elusive. A multigene prognostic model was constructed by least absolute shrinkage and selection operator Cox regression analysis in the The Cancer Genome Atlas (TCGA) cohort with 365 HCC patients and validated in the International Cancer Genome Consortium (ICGC) cohort with 231 HCC patients. The Kaplan-Meier methodology and time-dependent receiver operating characteristic curve were employed to confirm its predictive capability. A predictive nomogram was established based on the stepwise multivariate regression model. The differential expression of prognostic genes between HCC tissues and normal tissues was verified by quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry in an independent sample cohort with 30 HCC patients. The glycolysis-related gene signature and the nomogram model exhibited robust validity in predicting prognosis. The risk score was an independent predictor for overall survival (OS). Expression levels of immune checkpoint genes and cell cycle genes were significantly elevated in the high-risk group. The high-risk group presented high levels of immune exclusion. The risk score can distinguish the effect of immunotherapy in the IMvigor210 cohort. The prognostic gene expression showed a significant difference between HCC tissues and adjacent nontumorous tissues in the independent sample cohort. The currently established glycolysis-related gene signature can accurately predict prognosis and reflect immune status, which may be a therapeutic alternative.

821 Association Between Tumor Mutations and Meningioma Recurrence in Grade I/II Disease

INTRODUCTION: A subset of grade I and II meningiomas, in contrast to their predicted favorable oncologic course, recur. Recent interest has been paid to the development of predictive measures to identify which tumors may necessitate more aggressive initial management or more frequent post-resection surveillance. METHODS: Using our tissue bank of 255 meningiomas with linked targeted next-generation sequencing, we identified 173 with at &gt;90 days of post-surgical follow-up. We computed progression free survival cox-regression models for each of the 5% most prevalent tumor mutations corrected for tumor grade, recurrence status, and resection status. Grade III tumors were excluded due to issues with the proportional hazards assumption. We then built a multi-gene model by including all individual gene predictors with a p-value of less than 0.20. Starting with that model, we performed automated backwards selection. RESULTS: In the single gene analysis, ATM (HR = 5.22; 95%CI: 1.81 – 15.08), CREBBP (HR = 2.71; 95% CI = 1.15 – 6.38), and POLE (HR = 0.56; (95%CI = 0.32 – 0.97) were significantly associated with alterations in disease progression. In the multi-gene model, only POLE remained a significant predictor of recurrence after adjusting for the same clinical covariates and the genes ATM, ATR, CREBBP, RNF43, and SETD2. Backwards selection identified recurrence status, resection status, and mutations in ATM (HR = 8.14; 95%CI = 2.65 – 24.99) and POLE (HR = 0.45; 95% CI = 0.25 – 0.78) as predictive of recurrence. CONCLUSIONS: There is an association between ATM, CREBBP and POLE with progression free survival after accounting for tumor grade, resection extent, and recurrence status. In a muiti-gene model, POLE was the only gene found to alter survival. Backwards variable selection identified ATM as deleterious and POLE as protective.