Prognostic Risk Score Model Research Articles

Identification of pyroptosis-related signature and development of a novel prognostic model in diffuse large B-cell lymphoma.

Emerging evidence suggests that pyroptosis plays an essential role in the development and progression of multiple cancers. However, the role of pyroptosis remains elusive in diffuse large B-cell lymphoma (DLBCL). The expression profile data of DLBCL and normal samples of pyroptosis-related genes (PRGs) were analyzed, and the clinical characteristics of DLBCL patients were further investigated. A prognostic model was established using LASSO-Cox regression analysis. The expression of these PRGs was validated by qRT-PCR in DLBCL cell lines. Cell proliferation assay and flow cytometry were utilized to explore the impact of pyroptosis inhibitor (disulfiram, DSF) combined with PD1/PD-L1 inhibitor (BMS1166) on DLBCL cell proliferation. Most PRGs were dysregulated in DLBCL samples and associated with overall survival (OS). Six PRGs were selected to construct a prognostic risk score model. The qRT-PCR analysis revealed that CASP8, CASP9, NLRP1, NLRP6, and TIRAP are downregulated, while SCAF11 was significantly upregulated in DLBCL cell lines. This prognostic model divided DLBCL patients into low-risk and high-risk groups. Patients in the low-risk group exhibited lower mortality and longer OS than those in the high-risk group. The ROC curve and nomogram demonstrated this model's excellent predictive performance. GO and KEGG enrichment indicated that the differentially expressed genes (DEGs) between subgroups were associated with cellular protein modification processes and JAK-STAT signaling pathway regulation. Moreover, the risk score was correlated with the immune profile. Cell proliferation assay and flow cytometry further validated the synergistic anti-tumor effects of DSF and BMS1166 on DLBCL cells. In summary, we developed a comprehensive prognostic model based on PRGs characteristics, which accurately predicted the prognosis of DLBCL patients. Pyroptosis-targeting coupled with immunotherapies would be a promising therapeutic strategy for DLBCL.

Mechanistic insights into super-enhancer-driven genes as prognostic signatures in patients with glioblastoma.

Glioblastoma (GBM) is one of the most common malignant brain tumors in adults and is characterized by high aggressiveness and rapid progression, poor treatment, high recurrence rate, and poor prognosis. Although super-enhancer (SE)-driven genes haven been recognized as prognostic markers for several cancers, whether it can be served as effective prognostic markers for patients with GBM has not been evaluated. We first combined histone modification data with transcriptome data to identify SE-driven genes associated with prognosis in patients with GBM. Second, we developed a SE-driven differentially expressed genes (SEDEGs) risk score prognostic model by univariate Cox analysis, KM survival analysis, multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression. Its reliability in predicting was verified by two external data sets. Third, through mutation analysis, immune infiltration, we explored the molecular mechanisms of prognostic genes. Next, Genomics of Drug Sensitivity in Cancer (GDSC) and the Connectivity Map (cMap) database were employed to assess different sensitivities to chemotherapeutic agents and small-molecule drug candidates between high- and low-risk patients. Finally, SEanalysis database was chosen to identify SE-driven transcription factors (TFs) regulating prognostic markers which will reveal a potential SE-driven transcriptional regulatory network. First, we developed a 11-gene risk score prognostic model (NCF2, MTHFS, DUSP6, G6PC3, HOXB2, EN2, DLEU1, LBH, ZEB1-AS1, LINC01265, and AGAP2-AS1) selected from 1,154 SEDEGs, which is not only an independent prognostic factor for patients, but also can effectively predict the survival rate of patients. The model can effectively predict 1-, 2- and 3-year survival of patients and was validated in external Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) datasets. Second, the risk score was positively correlated with the infiltration of regulatory T cell, CD4 memory activated T cell, activated NK cell, neutrophil, resting mast cell, M0 macrophage, and memory B cell. Third, we found that high-risk patients showed higher sensitivity than low-risk patients to both 27 chemotherapeutic agents and 4 small-molecule drug candidates which might benefit further precision therapy for GBM patients. Finally, 13 potential SE-driven TFs imply how SE regulates GBM patient's prognosis. The SEDEG risk model not only helps to elucidate the impact of SEs on the course of GBM, but also provides a bright future for prognosis determination and choice of treatment for GBM patients.

Prognostic value of the S100 calcium binding protein family members in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains a crucial public health problem around the world, and the outlook remains bleak. More accurate prediction models are urgently needed because of thegreat heterogeneity of HCC.The S100 protein family contains over 20 differentially expressed members, which are commonly dysregulated in cancers. In this study, we analyzed the expression profile of S100 family members in patients with HCC based on the TCGA database. A novel prognostic risk score model,based on S100 family members, was developed using the LASSO regression algorithm, to analyze the clinicaloutcome. Our prediction model showed a powerful predictive value (1-year AUC: 0.738; 3-year AUC: 0.746; 5-year AUC: 0.813), while two former prediction models had less excellent performances than ours. And the S100 family members-based subtypes reveal the heterogeneity in many aspects, including gene mutations, phenotypic traits,tumor immune infiltration and predictive therapeutic efficacy. We further investigated the role of S100A9, one member with the highest coefficient in the risk score model, which was mainly expressed in para-tumoral tissues. Using the Single-Sample Gene Set Enrichment Analysis algorithm and immunofluorescence staining of tumor tissue sections,we found that S100A9 may be associated with macrophages. These findings provide a new potential risk score model for HCC and support further study of S100 family members in patients, especially S100A9.

Bioinformatics analysis of the endoplasmic reticulum stress-related prognostic model and immune cell infiltration in acute myeloid leukemia

ABSTRACTBackground:Acute myeloid leukemia (AML) is the most common malignant hematological disease originating from hematopoietic stem cells. Endoplasmic reticulum stress (ERs) has been reported to be involved in multiple tumor-related biological processes. However, the prognostic role of ERs-related genes in AML has not been fully investigated.Methods:The TCGA-LAML RNA-seq dataset was downloaded from the UCSC Xena website as the training cohort. Univariate Cox regression analysis was used to identify 42 ER stress-related genes associated with prognosis. Then, a ERs risk score prognostic model was established through LASSO regression analysis. AML patients were divided into high- and low-risk groups according to the median risk score. The Kaplan-Meier survival curve, time ROC curve analysis and univariate and multivariate independent prognostic analyses were presented for the high- and low-risk groups. Moreover, we verified the ERs risk model in the TARGET-AML and GSE37642 datasets. Next, we performed immune cell infiltration analysis, immune checkpoint gene expression analysis and drug sensitivity analysis.Results:We found 42 ER stress-related genes with prognostic significance, and a prognostic model consisting of 13 genes was constructed and verified. The survival rate of AML patients in the low-risk group was better than that in the high-risk group. The tumor microenvironment and immune cell infiltration results showed that immune cell infiltration was correlated with the survival status of patients.Conclusion:This research identified a ERs risk model with significant prognostic value. These genes are expected to be potential prognostic biomarkers in AML, providing a new theoretical basis for disease management.

Exploration of Identification and Prognostic Analysis of a Novel Immune-Related lncRNA Pair Signature and Immune Landscape in Esophageal Adenocarcinoma: A New Method Based on “Continuous Learning” Model

With the rapid development of information technology, many medical systems have emerged one after another with the support of continuous learning. A method of medical data privacy protection and resource utilization based on continuous learning is proposed to initialize the depth model of specific medical tasks. The depth model includes feature sampling model, data review model and task expression model, Finally, the depth model is trained according to the data from n institutions in turn. This method can overcome the obstacles of data sharing. The intelligent medical system of medical knowledge sharing will greatly improve the level of existing medical technology. An increasing body of evidence suggests that long non-coding RNAs (lncRNAs) participate in various physiological processes and pathological diseases. Esophageal adenocarcinoma develops rapidly with poor prognosis and high mortality in the near and long term. Immunotargeted therapy is a research hotspot. However, it is necessary to explore the immunomodulatory molecules of esophageal adenocarcinoma and analyze their relationship with clinicopathological characteristics and prognosis. We aimed to construct a robust immune-related lncRNA signature associated with survival outcomes in esophageal adenocarcinoma. We identified an immune-related lncRNA pairs signature with prognostic value from The Cancer Genome Atlas. Differentially expressed immune-related lncRNAs (DEirlncRNAs) were identified and paired, followed by prognostic assessment using univariate Cox regression analysis. We used least absolute shrinkage and selection operator penalized Cox analysis for constructing a risk score prognostic model and drew receiver operating characteristic (ROC) curves to predict overall survival. Then, we evaluated our signature in several settings: chemotherapy, tumor-infiltrating immune cells, and immune-mediated gene expression. In total, 339 DEirlncRNA pairs were identified, 11 of which were involved in the risk score prognostic signature. The area under ROC curves representing the predictive effect for 1-, 2-, and 3-year survival rates were 0.942, 0.987, and 0.977, respectively. The risk score model was confirmed as an independent prognostic factor and was significantly superior to clinicopathological characteristics. Correlation analyses showed disparities in drug sensitivity, tumor-infiltrating immune cells, and immune-related gene expression. We identified a novel prognostic immune-related lncRNA pair signature for esophageal adenocarcinoma. The risk score-based groups displayed different immune statuses, drug sensitivity, and immune-mediated gene expression. These findings may offer insights into the prognostic evaluation of esophageal adenocarcinoma and may provide a basis for creating personalized treatment plans.

A risk-scoring prognostic model of gastric cancer based on angiogenesis.

e16058 Background: Gastric cancer (GC) is a worldwide malignancy that represents a serious threat to human health. However, there is a lack of research on prognostic signatures of angiogenesis and prognosis, immune cell infiltration, and individualized treatment guidance in GC. Methods: Based on NCBI and MsigDB, Angiogenesis-related genes (ARGs) were obtained, and GO and KEGG enrichment analysis were performed. The risk scoring model was constructed by screening ARGs prognosis-related genes through a single-factor COX regression model, combined with Lasso regression to remove redundant factors. The prognostic value was assessed by combining TCGA and GEO databases. Immune infiltration difference analysis was performed. Furthermore, the variability of mutations was analyzed, and CNV differential display was performed. The predictive efficacy for immunotherapy was assessed by analogy to the melanoma immunotherapy suppression data from the GEO. The predictive efficacy of the TIDE assessment risk score for immunotherapy was additionally assessed. Finally, independent prognostic factors of risk scores were analyzed and a nomogram was construction. Results: We finally obtained 575 ARGs, and GO annotation showed that they were associated with angiogenesis, cell migration movement and ligand receptor activity. KEGG showed that it was related to Ras, MAPK and PI3K pathways. A risk score model containing 9 genes was constructed. It was found that the evaluation had a good prognostic value. Analysis of immunoinfiltration differences showed that there were differences. In addition, there was some variability in mutations between the high-risk and low-risk subgroups. It was found to have a better predictive effect of immunotherapy. TIDE assessment risk score was also a good predictor of immunotherapy efficacy. Finally, a nomogram was constructed based on clinical characteristics and risk scores. Conclusions: In this study, a prognostic risk score model for gastric cancer was established, and it was found to have a good predictive effect on immunotherapy effect.

Identification of New Prognostic Genes and Construction of a Prognostic Model for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a rapidly progressive malignancy, and its mortality rate is very high. In this study, we aimed at finding novel prognosis-related genes and constructing a credible prognostic model to improve the prediction for LUAD patients. Differential gene expression, mutant subtype, and univariate Cox regression analyses were conducted with the dataset from the Cancer Genome Atlas (TCGA) database to screen for prognostic features. These features were employed in the following multivariate Cox regression analysis and the produced prognostic model included the stage and expression of SMCO2, SATB2, HAVCR1, GRIA1, and GALNT4, as well as mutation subtypes of TP53. The exactness of the model was confirmed by an overall survival (OS) analysis and disease-free survival (DFS) analysis, which indicated that patients in the high-risk group had a poorer prognosis compared to those in the low-risk group. The area under the receiver operating characteristic curve (AUC) was 0.793 in the training group and 0.779 in the testing group. The AUC of tumor recurrence was 0.778 in the training group and 0.815 in the testing group. In addition, the number of deceased patients increased as the risk scores raised. Furthermore, the knockdown of prognostic gene HAVCR1 suppressed the proliferation of A549 cells, which supports our prognostic model that the high expression of HAVCR1 predicts poor prognosis. Our work created a reliable prognostic risk score model for LUAD and provided potential prognostic biomarkers.

Investigation of fatty acid metabolism-related genes in breast cancer: Implications for Immunotherapy and clinical significance

Breast cancer (BRCA) is a major global health issue, characterized by high mortality and low early diagnosis rates. The tumor immune microenvironment (TME) of BRCA is closely linked to fatty acid metabolism (FAM). This study aimed to identify FAM-related subtypes in BRCA based on gene expression and clinical data from the Cancer Genome Atlas (TCGA) database. The study found two distinct FAM-related subtypes, each with unique immune characteristics and prognostic implications. A FAM-related risk score prognostic model was developed and validated using TCGA and International Cancer Genome Consortium (GEO) cohorts, showing potential clinical applications for chemotherapy and immunotherapy. Additionally, a nomogram was established to facilitate clinical use of the risk score. These results highlight the significant correlation between FAM genes and TME in BRCA, and demonstrate the potential clinical utility of the FAM-related risk score in informing treatment decisions for BRCA patients.

Bacterial lipopolysaccharide-related genes are involved in the invasion and recurrence of prostate cancer and are related to immune escape based on bioinformatics analysis.

The composition of the tumor microbial microenvironment participates in the whole process of tumor disease. However, due to the limitations of the current technical level, the depth and breadth of the impact of microorganisms on tumors have not been fully recognized, especially in prostate cancer (PCa). Therefore, the purpose of this study is to explore the role and mechanism of the prostate microbiome in PCa based on bacterial lipopolysaccharide (LPS)-related genes by means of bioinformatics. The Comparative Toxicogenomics Database (CTD) was used to find bacterial LPS- related genes. PCa expression profile data and clinical data were acquired from TCGA, GTEx, and GEO. The differentially expressed LPS-related hub genes (LRHG) were obtained by Venn diagram, and gene set enrichment analysis (GSEA) was used to investigate the putative molecular mechanism of LRHG. The immune infiltration score of malignancies was investigated using single-sample gene set enrichment analysis (ssGSEA). Using univariate and multivariate Cox regression analysis, a prognostic risk score model and nomogram were developed. 6 LRHG were screened. LRHG were involved in functional phenotypes such as tumor invasion, fat metabolism, sex hormone response, DNA repair, apoptosis, and immunoregulation. And it can regulate the immune microenvironment in the tumor by influencing the antigen presentation of immune cells in the tumor. And a prognostic risk score and the nomogram, which were based on LRHG, showed that the low-risk score has a protective effect on patients. Microorganisms in the PCa microenvironment may use complex mechanism and networks to regulate the occurrence and development of PCa. Bacterial lipopolysaccharide-related genes can help build a reliable prognostic model and predict progression-free survival in patients with prostate cancer.

A Model of Basement Membrane-Associated Gene Signature Predicts Liver Hepatocellular Carcinoma Response to Immune Checkpoint Inhibitors.

Liver hepatocellular carcinoma (LIHC) is a highly lethal malignant tumor originating from the digestive system, which is a serious threat to human health. In recent years, immunotherapy has shown significant therapeutic effects in the treatment of LIHC, but only for a minority of patients. The basement membrane (BM) plays an important role in the occurrence and development of tumors, including LIHC. Therefore, this study is aimed at establishing a risk score model based on basement membrane-related genes (BMRGs) to predict patient prognosis and response to immunotherapy. First, we defined three patterns of BMRG modification (C1, C2, and C3) by consensus clustering of BMRG sets and LIHC transcriptome data obtained from public databases. Survival analysis showed that patients in the C2 group had a better prognosis, and Gene Set Variation Analysis (GSVA) revealed that the statistically significant pathways were mainly enriched in the C2 group. Moreover, we performed Weighted Correlation Network Analysis (WGCNA) on the above three subgroups and obtained 179 intersecting genes. We further applied function enrichment analyses, and the results demonstrated that they were mainly enriched in metabolism-related pathways. Furthermore, we conducted the LASSO regression analysis and obtained 4 BMRGs (MPV17, GNB1, DHX34, and MAFG) that were significantly related to the prognosis of LIHC patients. We further constructed a prognostic risk score model based on the above genes, which was verified to have good predictive performance for LIHC prognosis. In addition, we analyzed the correlation between the risk score and the tumor immune microenvironment (TIM), and the results showed that the high-risk scoring group tended to be in an immunosuppressed status. Finally, we investigated the relationship between the risk score and LIHC immune function. The results demonstrated that the risk score was closely related to the expression levels of multiple immune checkpoints. Patients in the low-risk group had significantly higher IPS scores, and patients in the high-risk group had lower immune escape and TIDE score. In conclusion, we established a novel risk model based on BMRGs, which may serve as a biomarker for prognosis and immunotherapy in LIHC.

Identification of immunotherapy-related lncRNA signature for predicting prognosis, immunotherapy responses and drug candidates in bladder cancer

BackgroundBladder cancer (BC) is one of the most common malignant diseases and the most common causes of cancer death worldwide. Immunotherapy has opened new avenues for precision treatment of bladder tumours, and immune checkpoint inhibitors (ICIs) have revolutionized the clinical treatment strategy of bladder tumours. In addition, long non-coding RNA (lncRNA) plays an important role in regulating tumour development and immunotherapy efficacy.MethodsWe obtained genes with significant differences between anti-PD-L1 response and non-response from the Imvogor210 data set and combined with the bladder cancer expression data in the TCGA cohort to obtain immunotherapy-related lncRNA. Based on these lncRNAs, the prognostic risk model of bladder cancer was constructed and verified by GEO external data set. The characterization of immune cell infiltration and immunotherapy effects between high-risk and low-risk groups were then analysed. We predicted the ceRNA network and performed molecular docking of key target proteins. The functional experiments verified the function of SBF2-AS1.ResultsThree immunotherapy-related lncRNAs were identified as independent prognostic biomarkers for bladder cancer and a prognostic model of immunotherapy-related prognosis was constructed. Prognosis, immune cell infiltration, and immunotherapy efficacy were significantly different between high- and low-risk groups based on risk scores. Additionally, we established a ceRNA network of lncRNA(SBF2-AS1)-miRNA(has-miR-582-5p)-mRNA (HNRNPA2B1). Targeting the protein HNRNPA2B1 identified the top eight small molecule drugs with the highest affinity.ConclusionWe developed a prognostic risk score model based on immune-therapy-related lncRNA, which was subsequently determined to be significantly associated with immune cell infiltration and immunotherapy response. This study not only helps to promote our understanding of immunotherapy-related lncRNA in the prognosis of BC, but also provides new ideas for clinical immunotherapy and the development of novel therapeutic drugs for patients.

An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases.

Background: Brain metastasis, with an incidence of more than 30%, is a common complication of non-small cell lung cancer (NSCLC). Therefore, there is an urgent need for an assessment method that can effectively predict brain metastases in NSCLC and help understand its mechanism. Materials and methods: GSE30219, GSE31210, GSE37745, and GSE50081 datasets were downloaded from the GEO database and integrated into a dataset (GSE). The integrated dataset was divided into the training and test datasets. TCGA-NSCLC dataset was regarded as an independent verification dataset. Here, the limma R package was used to identify the differentially expression genes (DEGs). Importantly, the RiskScore model was constructed using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Moreover, we explored in detail the tumor mutational signature, immune signature, and sensitivity to treatment of brain metastases in NSCLC. Finally, a nomogram was built using the rms package. Results: First, 472 DEGs associated with brain metastases in NSCLC were obtained, which were closely associated with cancer-associated pathways. Interestingly, a RiskScore model was constructed using 11 genes from 472 DEGs, and the robustness was confirmed in GSE test, entire GSE, and TCGA datasets. Samples in the low RiskScore group had a higher gene mutation score and lower immunoinfiltration status. Moreover, we found that the patients in the low RiskScore group were more sensitive to the four chemotherapy drugs. In addition, the predictive nomogram model was able to effectively predict the outcome of patients through appropriate RiskScore stratification. Conclusion: The prognostic RiskScore model we established has high prediction accuracy and survival prediction ability for brain metastases in NSCLC.

ALOX5AP is an Indicator for High CD8 Lymphocyte Infiltration and "Hot" Tumor Microenvironment in Osteosarcoma: A Bioinformatic Study.

Little progress has been made in the treatment and prognosis of osteosarcoma in the past 40years. Tumor microenvironment (TME) plays a critical role in the progression of osteosarcoma. This study aims to determine immune-associated prognostic biomarkers for osteosarcoma patients. With the help of analytical tools including ESTIMATE, differential gene expression, LASSO, and univariate cox and multivariate cox regression analysis, osteosarcoma gene expression data from Gene Expression Omnibus (GEO) databases were investigated. Following the establishment of a prognostic risk score model, internal and external validations using the GEO and TARGET databases were carried out. A total of 44 and 55 samples respectively in the GSE21257 and the TARGET databases were included. Our analysis found 93 differentially expressed genes (DEGs) between the high and low-ImmuneScore groups. Through univariate cox and LASSO analysis, ALOX5AP was identified as an indicator of TME in osteosarcomas. ALOX5AP was then used to construct a prognostic risk model. Internal and external verification revealed that higher expression of ALOX5AP was correlated with lower risk. Through the CIBERSORT algorithm, the level of CD8 T cells was found to negatively correlate with the risk score. This study revealed that ALOX5AP is an indicator for predicting high CD8 lymphocyte infiltration and "hot" tumor microenvironment in osteosarcomas. Thus, ALOX5AP has the potential to act as a biomarker for effective immunotherapies in osteosarcoma patients.

Characteristics of endoplasmic reticulum stress in colorectal cancer for predicting prognosis and developing treatment options.

An increasing body of evidence supports an essential role for endoplasmic reticulum stress (ERS) in colorectal cancer (CRC). In this study, we developed an ERS-related genes (ERSRGs) model to aid in the prognostic evaluation and treatment of CRC patients. The training set and validation set data were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. ERSRGs were obtained from the GeneCards database. A prognostic risk scoring model was constructed using the least absolute shrinkage and selection operator (LASSO) along with univariate Cox regression analysis. To further predict the probability of survival for patients at 1, 2, and 3 years, a nomogram was devised. The advantages of the prognostic risk score model in screening patients' sensitive to chemotherapy and immunotherapy were analyzed by drug sensitivity analysis and immune correlation analysis. Finally, hub genes associated with poor prognosis in the risk model were screened by Protein-protein interaction (PPI) network and their expression was validated using clinical specimens. A risk model for overall survival (OS) was developed using 16 ERSRGs associated with prognosis. Through analyses, we demonstrated a high degree of reliability for the prognostic risk scoring model. The constructed nomograms performed well in predicting patient survival over 1, 3, and 5 years. The calibration curve and decision curve analysis (DCA) supported a high degree of accuracy for the model. Patients in the low-risk group had a lower IC50 for the common chemotherapy drug, 5-FU, and responded better to immunotherapy. hub poor prognostic genes were validated in CRC clinical specimens. We have identified and validated a new ERS prognostic marker that can accurately predict the survival status of CRC patients for clinicians and better provide personalized treatment plans.

A novel scoring system for the quantitative prediction of prognosis in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy. Patient prognosis cannot be accurately assessed in National Comprehensive Cancer Network (NCCN) risk stratification subgroups based on the current criteria. This study aimed to develop a novel prognostic score model for the quantitative prediction of prognosis in AML. We developed a prognostic risk scoring model of AML using differentially expressed genes to predict prognosis in patients with AML. Furthermore, we evaluated the effectiveness and clinical significance of this prognostic model in 4 AML cohorts and 905 patients with AML. A prognostic risk scoring model of AML containing eight prognosis-related genes was constructed using a multivariate Cox regression model. The model had a higher predictive value for the prognosis of AML in the training and validation sets. In addition, patients with lower scores had significantly better overall survival (OS) and even-free survival (EFS) than those with higher scores among patients with intermediate-risk AML according to the NCCN guidelines, indicating that the model could be used to further predict the prognosis of the intermediate-risk AML populations. Similarly, patients with high scores had remarkably poor OS and EFS in the normal-karyotype populations, indicating that the scoring model had an excellent predictive performance for patients with AML having normal karyotype. Our study provided an individualized prognostic risk score model that could predict the prognosis of patients with AML.

Prediction of cardiovascular adverse events in newly diagnosed multiple myeloma: Development and validation of a risk score prognostic model.

Multiple myeloma (MM) is the second most common hematological malignancy, and the treatments markedly elevate the survival rate of the patients in recent years. However, the prevalence of cardiovascular adverse events (CVAEs) in MM had been increasing recently. CVAEs in MM patients are an important problem that we should focus on. Clinical tools for prognostication and risk-stratification are needed. This is a retrospective study that included patients who were newly diagnosed with multiple myeloma (NDMM) in Shanghai Changzheng Hospital and Affiliated Jinhua Hospital, Zhejiang University School of Medicine from June 2018 to July 2020.A total of 253 patients from two medical centers were divided into training cohort and validation cohort randomly. Univariable analysis of the baseline factors was performed using CVAEs endpoints. Multivariable analysis identified three factors for a prognostic model that was validated in internal validation cohorts. Factors independently associated with CVAEs in NDMM were as follows: age>61 years old, high level of baseline office blood pressure, and left ventricular hypertrophy (LVH). Age contributed 2 points, and the other two factors contributed 1 point to a prognostic model. The model distinguished the patients into three groups: 3-4 points, high risk; 2 points, intermediate risk; 0-1 point, low risk. These groups had significant difference in CVAEs during follow-up days in both training cohort (p<0.0001) and validation cohort (p=0.0018). In addition, the model had good calibration. The C-indexes for the prediction of overall survival of CVAEs in the training and validation cohorts were 0.73 (95% CI, 0.67-0.79) and 0.66 (95% CI, 0.51-0.81), respectively. The areas under the receiver operating characteristic curve (AUROCs) of the 1-year CVAEs probability in the training and validation cohorts were 0.738 and 0.673, respectively. The AUROCs of the 2-year CVAE probability in the training and validation cohorts were 0.722 and 0.742, respectively. The decision-curve analysis indicated that the prediction model provided greater net benefit than the default strategies of providing assessment or not providing assessment for all patients. A prognostic risk prediction model for predicting CVAEs risk of NDMM patients was developed and internally validated. Patients at increased risk of CVAEs can be identified at treatment initiation and be more focused on cardiovascular protection in the treatment plan.

Co-expression prognostic-related genes signature base on propofol and sevoflurane anesthesia predict prognosis and immunotherapy response in glioblastoma

Objectives Anesthetic drugs had been reported may impact the bio-behavior of the tumor. Propofol and sevoflurane are common anesthetics in the operation for glioblastoma (GBM). This study aims to establish a co-expression prognostic-related genes signature base on propofol and sevoflurane anesthesia to predict prognosis and immunotherapy response in GBM. Method GPM tissues with different anesthetics gene expression profiles (GSE179004) were obtained from the Gene Expression Omnibus (GEO) database. Core modules and central genes associated with propofol and sevoflurane anesthesia were identified by weighted gene coexpression network analysis (WGCNA) and establish a risk score prognostic model. Immune cell signature analysis in TCGA datasets was predicted via CIBERSORT. At last, serum methylation level of O6-methylguanine-DNA methyltransferase (MGMT) promoter was detected in GPM patient in different time during perioperative period. Results The burlywood1 group screened was significantly associated with sevoflurane-treated GBM tissue. 22 independent prognostic differential genes were construct a prognostic-related genes risk score in GBM, and showed good predictive ability. The risk score was strongly correlated with the age of the patients, but not with the sex of the patients. In addition, the differential responses to immunotherapy in high and low risk groups were analyzed, indicating that sevoflurane signature genes were consistent in the classification of gliomas. High-risk patients have high T-cell damage score and are less sensitive to immunotherapy. At last, serum methylation level of MGMT promoter was decreased in GBM patients during propofol and sevoflurane anesthesia. Conclusions Propofol and sevoflurane anesthesia associated impact on the gene expression of GBM, included the methylation level of MGMT promoter. Propofol and sevoflurane anesthesia-based risk score prognostic model, which has good prognostic power and is an independent prognostic factor in GBM patients. Therefore, this model can be used as a new biomarker for judging the prognosis of GBM patients. KEY MESSAGES Propofol and sevoflurane anesthesia-based risk score prognostic model has good prognostic power and is an independent prognostic factor in GBM patients. High Propofol and sevoflurane anesthesia-based risk score GBM patients have high T-cell damage scores and are less sensitive to immunotherapy. Serum methylation level of MGMT promoter decrease during propofol and sevoflurane anesthesia in GBM patients.

Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify Gene Signatures Related to Prognosis and Immunotherapy Response

The hallmark of pancreatic ductal adenocarcinoma (PDAC) is an exuberant tumor microenvironment (TME) comprised of diverse cell types that play key roles in carcinogenesis, chemo-resistance, and immune evasion. Here, we propose a gene signature score through the characterization of cell components in TME for promoting personalized treatments and further identifying effective therapeutic targets. We identified three TME subtypes based on cell components quantified by single sample gene set enrichment analysis. A prognostic risk score model (TMEscore) was established based on TME-associated genes using a random forest algorithm and unsupervised clustering, followed by validation in immunotherapy cohorts from the GEO dataset for its performance in predicting prognosis. Importantly, TMEscore positively correlated with the expression of immunosuppressive checkpoints and negatively with the gene signature of T cells' responses to IL2, IL15, and IL21. Subsequently, we further screened and verified F2R-like Trypsin Receptor1 (F2RL1) among the core genes related to TME, which promoted the malignant progression of PDAC and has been confirmed as a good biomarker with therapeutic potential in vitro and in vivo experiments. Taken together, we proposed a novel TMEscore for risk stratification and selection of PDAC patients in immunotherapy trials and validated effective pharmacological targets.

Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction

BackgroundThis study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients.MethodsFirstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model’s accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes’ mRNA and protein expressions were verified.ResultsA total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB).ConclusionThe new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future.

Predicting the Clinical Outcome of Triple-Negative Breast Cancer Based on the Gene Expression Characteristics of Necroptosis and Different Molecular Subtypes.

Necroptosis, a kind of programmed necrotic cell apoptosis, is the gatekeeper for the host to defend against the invasion of pathogens. It helps to regulate different biological processes regarding human cancer. Nevertheless, studies that determine the impact of death on triple-negative breast cancer (TNBC) are scarce. Therefore, this paper has comprehensively examined the expression as well as clinical significance of necroptosis in TNBC. ConsensusClusterPlus was used to establish a stable molecular classification that used the expression regarding the necroptosis-linked genes. The clinical and immune characteristics of different subclasses were evaluated. Then, the weighted gene coexpression network analysis (WGCNA) assisted in determining key modules, and we selected the genes exhibiting obvious association with necroptosis prognosis through the relationship with prognosis. The univariate Cox regression analysis together with least absolute shrinkage and selection operator (LASSO) techniques served for the construction of the necroptosis-related prognostic risk score (NPRS) model, and the pathway characteristics of NPRS model grouping were further studied. Finally, the NPRS, taking into account the clinicopathological features, used the decision tree model for enhancing the prognostic model as well as the survival prediction. First, two stable molecular subtypes with different prognosis and immune characteristics were identified using necroptosis marker genes. Then, the key modules were identified, and 10 genes significantly related to the prognosis of necroptosis were selected. Then, the clinical prognostic model of NPRS was developed considering the prognosis-linked necroptosis genes. Finally, the NPRS model, taking into account the clinicopathological features, adopted the decision tree model for enhancing the prognostic model as well as the survival prediction. Herein, two new molecular subgroups considering necroptosis-linked genes are proposed, and an NPRS model composed of 10 genes is developed, which maybe assist in the personalized treatment and clinical treatment guidance of TNBC patients.