Prognostic Risk Score Research Articles

A New Prognostic Risk Score: Based on the Analysis of Autophagy-Related Genes and Renal Cell Carcinoma.

Introduction: Clear cell renal cell carcinoma (ccRCC) patients suffer from its high recurrence and metastasis rate, and a new prognostic risk score to predict individuals with high possibility of recurrence or metastasis is in urgent need. Autophagy has been found to have a dual influence on tumorigenesis. In this study we aim to analyze autophagy related genes (ATGs) and ccRCC patients and find a new prognostic risk score. Method: Analyzing differential expression genes (DEGs) in TCGA-KIRC dataset, and took intersection with ATGs. Through lasso, univariate, and multivariate cox regression, DEGs were chosen, and the coefficients and expression levels of them were components constructing the formula of risk score. We analyzed mRNA expression of DEGs in tumor and normal tissue in ONCOMINE database and TCGA-KIRC dataset. The Human Protein Atlas (HPA) was used to analyze protein levels of DEGs. The protein-protein interaction (PPI) network was examined in STRING and visualized in cytoscape. Functional enrichment analysis was performed in RStudio. To prove the ability and practicibility of risk score, we analyzed univariate and multivariate cox regression, Kaplan-Meier curve (K-M curve), risk factor association diagram, receiver operating characteristic curve (ROC curve) of survival and nomogram, and the performance of nomogram was evaluated by calibration curve. Then we further explored functional enrichment related to risk groups through Gene Set Enrichment Analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and Metascape database. At last, we investigated immune cell infiltration of DEGs and two risk groups through TIMER database and “Cibersort” algorithm. Result: We identified 7 DEGs (BIRC5, CAPS, CLDN7, CLVS1, GMIP, IFI16, and TCIRG1) as components of construction of risk score. All 7 DEGs were differently expressed in ccRCC and normal tissue according to ONCOMINE database and TCGA-KIRC dataset. Functional enrichment analysis indicated DEGs, and their most associated genes were shown to be abundant in autophagy-related pathways and played roles in tumorigenesis and progression processes. A serious analysis proved that this risk score is independent from the risk signature of ccRCC patients. Conclusion: The risk score constructed by 7 DEGs had the ability of predicting prognosis of ccRCC patients and was conducive to the identification of novel prognostic molecular markers. However, further experiment is still needed to verify its ability and practicability.

A novel 12-gene prognostic signature in breast cancer based on the tumor microenvironment.

BackgroundThe progression of breast cancer (BC) is highly dependent on the tumor microenvironment. Inflammation, stromal cells, and the immune landscape have been identified as significant drivers of BC in multiple preclinical studies. Therefore, this study aimed to clarify the predictive relevance of stromal and immune cell-associated genes in patients suffering from BC.MethodsWe employed the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm to calculate the stromal and immunological scores, which were then used to evaluate differentially expressed genes (DEGs) in BC samples using The Cancer Genome Atlas (TCGA) database. Univariate analyses were conducted to identify the DEGs linked to survival in BC patients. Next, the prognostic DEGs (with a log-rank P<0.05) were used to create a risk signature, and the least absolute shrinkage and selection operator (LASSO) regression method was used to analyze and optimize the risk signature. The following formula was used to compute the prognostic risk score values: Risk score = Gene 1 * β1 + Gene 2 * β2 +… Gene n * βn. The median prognostic risk score values were used to divide BC patients into the low-risk (LR) and high-risk (HR) groups. The patient samples of the validation cohort were then assessed using this formula. We used principal component analysis (PCA) to determine the expression patterns of the different patient groups. Gene Set Enrichment Analysis (GSEA) was used to determine whether there were significant variations between the groups in the evaluated gene sets.ResultsThe present study revealed that DEGs linked with survival were closely associated with immunological responses. A prognostic signature was constructed that consisted of 12 genes (ASCL1, BHLHE22, C1S, CLEC9A, CST7, EEF1A2, FOLR2, KLRB1, MEOX1, PEX5L, PLA2G2D, and PPP1R16B). According to their survival, BC patients were separated into LR and HR groups using the identified 12-gene signature. The immunological status and immune cell infiltration were observed differently in the LR and HR groups.ConclusionsOur results provide novel insights into several microenvironment-linked genes that influence survival outcomes in patients with BC, which suggests that these genes could be candidate therapeutic targets.

Exploiting Interdata Relationships in Prostate Cancer Proteomes: Clinical Significance of HO-1 Interactors.

Prostate cancer (PCa) cells display abnormal expression of proteins resulting in an augmented capacity to resist chemotherapy and colonize distant organs. We have previously shown the anti-tumoral role of heme oxygenase 1 (HO-1) in this disease. In this work, we undertook a mass spectrometry-based proteomics study to identify HO-1 molecular interactors that might collaborate with its modulatory function in PCa. Among the HO-1 interactors, we identified proteins with nuclear localization. Correlation analyses, using the PCa GSE70770 dataset, showed a significant and positive correlation between HMOX1 and 6 of those genes. Alternatively, HMOX1 and YWHAZ showed a negative correlation. Univariable analyses evidenced that high expression of HNRNPA2B1, HSPB1, NPM1, DDB1, HMGA1, ZC3HAV1, and HMOX1 was associated with increased relapse-free survival (RFS) in PCa patients. Further, PCa patients with high HSPB1/HMOX1, DDB1/HMOX1, and YWHAZ/HMOX1 showed a worse RFS compared with patients with lower ratios. Moreover, a decrease in RFS for patients with higher scores of this signature was observed using a prognostic risk score model. However, the only factor significantly associated with a higher risk of relapse was high YWHAZ. Multivariable analyses confirmed HSPB1, DDB1, and YWHAZ independence from PCa clinic-pathological parameters. In parallel, co-immunoprecipitation analysis in PCa cells ascertained HO-1/14-3-3ζ/δ (protein encoded by YWHAZ) interaction. Herein, we describe a novel protein interaction between HO-1 and 14-3-3ζ/δ in PCa and highlight these factors as potential therapeutic targets.

An integrative multi-omics analysis based on liquid\u2013liquid phase separation delineates distinct subtypes of lower-grade glioma and identifies a prognostic signature

BackgroundEmerging evidences have indicated that the aberrant liquid–liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG).MethodsWe integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment.ResultsBased on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4.We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent.ConclusionsThis study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.

Improved survival of patients with myelofibrosis in the last decade: Single‐center experience

The management of myelofibrosis (MF) has changed over the last several years and could have an impact on patient outcome. This study evaluates the survival of patients with MF at the authors' institution to determine whether it changed in the last decade. This retrospective study consists of 844 patients (64% male; median age, 66 years; range, 20-90 years) who were examined between 2000 and 2020 with a new diagnosis of MF. Only patients with available marrow biopsy who had reticulin fibrosis of grade 2 or higher were included. Patients were compared by year of presentation: 2000-2010 (n = 373) and 2011-2020 (n = 471). A statistically significant improvement in median survival in the last decade was noted: from 48 months (95% CI, 42-54 months) to 63 months (95% CI, 55-71 months) (P < .001; HR, 0.78 [95% CI, 0.64-0.95]). Improved survival was observed also in patients 65 years old or older and those having intermediate 2 or high-risk Dynamic International Prognostic Scoring System (DIPSS) or DIPSS-Plus risk scores. Among 532 patients treated with MF-directed therapy, patients exposed to JAK inhibitor ruxolitinib had superior outcomes with median overall survival of 84 months (95% CI, 70-94 months). The results demonstrate that survival of patients with MF has improved in the last decade. This improvement is likely due to increased disease awareness, advances in supportive care, and the development of effective treatments.

A Prognostic Risk Score Based on Hypoxia-, Immunity-, and Epithelialto-Mesenchymal Transition-Related Genes for the Prognosis and Immunotherapy Response of Lung Adenocarcinoma.

Background: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. However, current stage-based clinical methods are insufficient for survival prediction and decision-making. This study aimed to establish a novel model for evaluating the risk of LUAD based on hypoxia, immunity, and epithelial-mesenchymal transition (EMT) gene signatures. Methods: In this study, we used data from TCGA-LUAD for the training cohort and GSE68465 and GSE72094 for the validation cohorts. Immunotherapy datasets GSE135222, GSE126044, and IMvigor210 were obtained from a previous study. Using bioinformatic and machine algorithms, we established a risk model based on hypoxia, immune, and EMT gene signatures, which was then used to divide patients into the high and low risk groups. We analyzed differences in enriched pathways between the two groups, following which we investigated whether the risk score was correlated with stemness scores, genes related to m6A, m5C, m1A and m7G modification, the immune microenvironment, immunotherapy response, and multiple anti-cancer drug sensitivity. Results: Overall survival differed significantly between the high-risk and low-risk groups (HR = 4.26). The AUCs for predicting 1-, 3-, and 5-year survival were 0.763, 0.766, and 0.728, respectively. In the GSE68465 dataset, the HR was 2.03, while the AUCs for predicting 1-, 3-, and 5-year survival were 0.69, 0.651, and 0.618, respectively. The corresponding values in the GSE72094 dataset were an HR of 2.36 and AUCs of 0.653, 0.662, and 0.749, respectively. The risk score model could independently predict OS in patients with LUAD, and highly correlated with stemness scores and numerous m6A, m5C, m1A and m7G modification-related genes. Furthermore, the risk model was significantly correlated with multiple immune microenvironment characteristics. In the GSE135222 dataset, the HR was 4.26 and the AUC was 0.702. Evaluation of the GSE126044 and IMvigor210 cohorts indicated that PD-1/PD-LI inhibitor treatment may be indicated in patients with low risk scores, while anti-cancer therapy with various drugs may be indicated in patients with high risk scores. Conclusion: Our novel risk model developed based on hypoxia, immune, and EMT gene signatures can aid in predicting clinical prognosis and guiding treatment in patients with LUAD.

Comprehensive Analysis of Immune-Related Prognosis of TK1 in Hepatocellular Carcinoma.

Increased expression of TK1 is associated with the progression of a variety of tumors. However, the relationship of TK1 expression with immune cell infiltration and its prognostic value in hepatocellular carcinoma (HCC) are still unknown. In this study the TCGA database was used to evaluate TK1 expression and its impact on survival in patients with HCC. Compared with normal tissue, TK1 in the liver tissue of patients with HCC was significantly up-regulated at both the mRNA and protein levels. Furthermore, TK1 expression was significantly related to pathological stage, tumor stage and lymph node metastasis, with high TK1 expression being an unfavorable prognostic factor for HCC. TK1 expression was also significantly associated with the infiltration of B cells, T cells, and dendritic cells in HCC. Single-cell sequencing analysis revealed that TK1 was associated with relatively large changes in T cells, especially gamma-delta T cells. A prognostic risk score based on TK1-related immune genes (CD40LG and TNFRSF4) was established using COX regression analysis. By integrating the immune-related risk score model with clinical features, a nomogram was constructed to predict the survival rate of HCC patients (1 year, 3-year and 5-year AUC of 0.782, 0.783 and 0.771, respectively). Knockdown of the target gene for TK1 was found to have significant anti-apoptosis and pro-proliferation effects on HepG2 cells. The level of TK1 in the serum and liver tissue of patients with HCC was significantly increased relative to healthy controls. These findings highlight the role of TK1 in the tumor immune response of HCC patients and in the proliferation and apoptosis of HepG2 cells. TK1 could therefore be a potential immunotherapy target for HCC patients, while the two immune genes related to TK1 (CD40LG And TNFRSF4) may be promising prognostic biomarkers in HCC.

Comprehensive Analysis of Cell Cycle-Related Genes in Patients With Prostate Cancer.

This study aimed to identify critical cell cycle-related genes (CCRGs) in prostate cancer (PRAD) and to evaluate the clinical prognostic value of the gene panel selected. Gene set variation analysis (GSVA) of dysregulated genes between PRAD and normal tissues demonstrated that the cell cycle-related pathways played vital roles in PRAD. Patients were classified into four clusters, which were associated with recurrence-free survival (RFS). Moreover, 200 prognostic-related genes were selected using the Kaplan–Meier (KM) survival analysis and univariable Cox regression. The prognostic CCRG risk score was constructed using random forest survival and multivariate regression Cox methods, and their efficiency was validated in Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70770. We identified nine survival-related genes: CCNL2, CDCA5, KAT2A, CHTF18, SPC24, EME2, CDK5RAP3, CDC20, and PTTG1. Based on the median risk score, the patients were divided into two groups. Then the functional enrichment analyses, mutational profiles, immune components, estimated half-maximal inhibitory concentration (IC50), and candidate drugs were screened of these two groups. In addition, the characteristics of nine hub CCRGs were explored in Oncomine, cBioPortal, and the Human Protein Atlas (HPA) datasets. Finally, the expression profiles of these hub CCRGs were validated in RWPE-1 and three PRAD cell lines (PC-3, C4-2, and DU-145). In conclusion, our study systematically explored the role of CCRGs in PRAD and constructed a risk model that can predict the clinical prognosis and immunotherapeutic benefits.

Identification and Validation of Constructing the Prognostic Model With Four DNA Methylation-Driven Genes in Pancreatic Cancer

Background: Pancreatic cancer (PC) is a highly aggressive gastrointestinal tumor and has a poor prognosis. Evaluating the prognosis validly is urgent for PC patients. In this study, we utilized the RNA-sequencing (RNA-seq) profiles and DNA methylation expression data comprehensively to develop and validate a prognostic signature in patients with PC. Methods: The integrated analysis of RNA-seq, DNA methylation expression profiles, and relevant clinical information was performed to select four DNA methylation-driven genes. Then, a prognostic signature was established by the univariate, multivariate Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses in The Cancer Genome Atlas (TCGA) dataset. GSE62452 cohort was utilized for external validation. Finally, a nomogram model was set up and evaluated by calibration curves. Results: Nine DNA methylation-driven genes that were related to overall survival (OS) were identified. After multivariate Cox and LASSO regression analyses, four of these genes (RIC3, MBOAT2, SEZ6L, and OAS2) were selected to establish the predictive signature. The PC patients were stratified into two groups according to the median risk score, of which the low-risk group displayed a prominently favorable OS compared with the high-risk group, whether in the training (p < 0.001) or validation (p < 0.01) cohort. Then, the univariate and multivariate Cox regression analyses showed that age, grade, risk score, and the number of positive lymph nodes were significantly associated with OS in PC patients. Therefore, we used these clinical variables to construct a nomogram; and its performance in predicting the 1-, 2-, and 3-year OS of patients with PC was assessed via calibration curves. Conclusion: A prognostic risk score signature was built with the four alternative DNA methylation-driven genes. Furthermore, in combination with the risk score, age, grade, and the number of positive lymph nodes, a nomogram was established for conveniently predicting the individualized prognosis of PC patients.

A prognostic model of non small cell lung cancer based on TCGA and ImmPort databases

Bioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC. The transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database (http://www.immport.org/); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed. A total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P < 0.05). After including 19 differentially expressed immune genes with P < 0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan–Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic model curve confirmed that the prediction model had a certain accuracy (AUC = 0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment. The NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.

Identification of proteins associated with treatment response of neoadjuvant chemoradiotherapy in rectal mucinous adenocarcinoma by co-expression network analysis based on proteomic analysis

For rectal mucinous adenocarcinoma (MAC), identifying biomarkers of neoadjuvant chemoradiotherapy (NCRT) response has become imperative. This study applied label-free mass spectrometry and weighted gene co-expression network analysis to identify hub proteins in association with the NCRT response in 20 rectal MAC patients. We identified 131 differentially abundant proteins and 7 candidate proteins associated with the NCRT response. The immunostaining expressions of six proteins (ENOA, ILEU, MDHM, RM11, PTGDS, and RL3) were significantly associated with the NCRT response. Logistic regression analysis revealed that ENOA (OR = 6.275, P = 0.006) was independent risk hub protein for the NCRT response. Tow hub proteins (ENOA and PTGDS) were identified as significant risk factors by Cox regression analysis. A prognostic risk score system was constructed: risk score = (0.910 × EXPENOA) + (−1.519 × EXPPTGDS), and found to be an independent predictor of DFS in rectal MAC patients (HR = 10.308, P < 0.001). Our study suggested that ENOA may be a novel biomarker for the NCRT response and prognosis in rectal MAC patients. A two-hub-protein-based risk score system might be used for predicting tumor recurrence in rectal MAC patients. SignificanceNCRT resistance is a major problem in the treatment of rectal MAC patients. Identifying robust predictive biomarkers for NCRT resistance is beneficial to the stratified treatment of rectal MAC patients. In this study, label-free mass spectrometry and weighted gene co-expression network analysis identified ENOA as a potential novel biomarker for the NCRT response and prognosis. ENOA may be involved in the process of the NCRT resistance and tumor recurrence through the carbon metabolism pathway.

Four m6A RNA Methylation Gene Signatures and Their Prognostic Values in Lung Adenocarcinoma.

Introduction: Evidence demonstrates that N6-methyladenosine (m6A) modification plays an increasingly important role in the development of tumors. The aim of this study is to explore the expression of m6A-related regulators in lung adenocarcinoma, identify the effect of altered key factors modified by m6A on the prognosis of patients with lung adenocarcinoma. Methods: A comprehensive analysis of m6A-related gene expressions in patients with lung adenocarcinoma based on The Cancer Genome Atlas database (TCGA) and the CBioPortal database. A prognostic risk score was established based on a linear combination of 4 key gene expression levels using the regression coefficients of the multivariate Cox regression models. Immunohistochemical staining analysis was performed to validate the relationship between the protein expression level of m6A regulators and the prognosis of patients retrospectively. The possible mechanism and prospective therapeutic targets of these key m6A molecules were explored by the M6A2Target database and the CMAP database. Results: Mutation pattern analysis revealed that 32% of 656 patients had genetic alterations. Four genes (writer: methyltransferase like 3 [METTL3] and three readers: insulin like growth factor 2 mRNA binding protein 2 [IGF2BP2], heterogeneous nuclear ribonucleoprotein C [HNRNPC], and heterogeneous nuclear ribonucleoprotein A2/B1 [HNRNPA2B1]) were selected to construct a survival risk prediction model and the results of immunohistochemical staining showed that the expression of these four m6A genes was significantly different between lung adenocarcinoma tissues and normal lung tissues (p < .01). The possible downstream genes and prospective therapeutic targets of these four m6A key molecules were discovered. Conclusion: These four m6A RNA methylation regulators may be effective prognostic and diagnostic factors which can provide auxiliary diagnosis and prognosis of lung adenocarcinoma.

Construction of a Prognostic Nomogram Based on Autophagy-Related Genes for Children With Neuroblastoma.

Neuroblastoma (NB) is the most common solid malignancy in children. MYCN gene amplification is the most relevant genetic alteration in patients with NB and is associated with poor prognosis. Autophagy plays specific roles in the occurrence, development, and progression of NB. Here, we aimed to identify and assess the prognostic effects of autophagy-related genes (ARGs) in patients with NB and MYCN gene amplification. Differentially expressed ARGs were identified in patients with NB with and without MYCN gene amplification, and the ARG expression patterns and related clinical data from the Therapeutically Applicable Research to Generate Effective Treatments database were used as the training cohort. Least absolute shrinkage and selection operator analyses were used to identify prognostic ARGs associated with event-free survival (EFS), and a prognostic risk score model was developed. Model performance was assessed using the Kaplan–Meier method and receiver operating characteristic (ROC) curves. The prognostic ARG mode l was verified using the validation cohort dataset, GSE49710. Finally, a nomogram was constructed by combining the ARGbased risk score with clinicopathological factors. Three ARGs (GABARAPL1, NBR1, and PINK1) were selected to build a prognostic risk score model. The EFS in the low-risk group was significantly better than that in the high-risk group in both the training and validation cohorts. A nomogram incorporating the prognostic risk score, age, and International Neuroblastoma Staging System stage showed a favorable predictive ability for EFS rates according to the area under the ROC curve at 3 years (AUC = 0.787) and 5 years (AUC = 0.787). The nomogram demonstrated good discrimination and calibration. Our risk score model for the 3 ARGs can be used as an independent prognostic factor in patients with NB and MYCN gene amplification. The model can accurately predict the 3- and 5-year survival rates.

Identification of Ten-Gene Related to Lipid Metabolism for Predicting Overall Survival of Breast Invasive Carcinoma.

Background Predicting the risk of poor prognosis of breast cancer is crucial to treating breast cancer. This study investigated the prognostic assessment of 10 lipid metabolism-related genes constructed as breast cancer models based on this study. Methods The TCGA database was used to obtain clinical information and expression data of breast cancer patients, and GSEA analysis and univariate and multivariate Cox proportional risk regression models were performed to identify lipid metabolism genes closely associated with overall survival (OS) of breast cancer patients and to construct a prognostic risk score model based on lipid metabolism gene markers. The Kaplan–Meier method was used to analyze the survival status of patients with high and low-risk scores, and ROC curves assessed the accuracy of this risk score. Finally, the relationship between this risk score and clinicopathological characteristics of BRCA was analyzed in a stratified manner, and the validity of this risk score as an independent prognostic factor was determined using univariate and multivariate Cox regression analyses. Results One hundred and forty-four differentially expressed lipid metabolism-related genes were identified in cancer and paracancerous tissues in BRCA, 21 of which were associated with overall survival (OS) in BRCA (P < 0.05). Univariate and multivariate Cox analyses revealed that age, grade, and risk score were independent prognostic factors for BRCA. Multivariate Cox regression analysis further identified APOL4, NR1H3, SLC25A5, APOL3, OSBPL1A, DYNLT1, IMMT, MAP2K6, ZDHHC8, and RAB2A lipid metabolism-related genes as independent prognostic markers for BRCA. A prognostic risk score model was developed by labeling lipid metabolism genes with these 10 genes, and patients with BRCA with high-risk scores in the model sample had significantly worse OS than those with low-risk (P < 0.01). The ROC curve area (AUC) of this risk score model was 0.712. Conclusion By mining the TCGA database, we identified 10 lipid metabolism-related genes APOL4, NR1H3, SLC25A5, APOL3, OSBPL1A, DYNLT1, IMMT, MAP2K6, ZDHHC8, and RAB2A, which are closely related to the prognosis of BRCA patients, and constructed a prognostic risk scoring system based on 10 lipid metabolism genes tags.

A novel cuproptosis-related lncRNA prognostic risk score model for gastric cancer based on bioinformatics

Objective To establish an effective cuproptosis-related long non-coding ribonucleic acid (lncRNA) (CRL) prognostic risk score (RS) model (CRLPRSM) for the prediction of the outcomes of patients with gastric cancer (GC). Introduction Cuproptosis is an up-to-date mode of cell death, and its action mechanism is different from all other known mechanisms that regulate cell death. LncRNAs are RNA species that are over 200 nt long and do not encode proteins. They have prominent actions in tumor onset and development, and their involvement in a variety of intracellular regulatory processes is vital for cell proliferation and differentiation, so they may serve as prognostic biomarkers in tumor patients. Methods We retrieved cuproptosis-associated clinical and lncRNA expression data of GC cases from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD). Then a CRLPRSM was built based on univariate and multivariate Cox regression (UCR and MCR) analyses. As per the RS, the patients fell into high- and low-risk group. Later, the predictive efficacy of the CRLPRSM was confirmed with the aid of Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve analysis. Next, combining independent prognostic factors in clinical characteristics, we plotted a prognosis-related nomogram to predict one-year, three-year and five-year overall survival (OS) in GC patients. Finally, we implemented Gene Ontology enrichment analysis (GOEA) and Kyoto Encyclopedia of Genes and Genomes enrichment analysis (KEGGEA) for clarifying the possible biological actions and molecular mechanisms. Results The constructed CRLPRSM consisted of 3 CRLs, namely, AC092574.1, MAGI2-AS3, AC090204.1. It was found that the hazard ratio (HR) was 1.911 (1.337–2.731) ( p &lt; 0.001) in UCR analysis and 1.852 (1.286–1.668) ( p &lt; 0.001) in MCR analysis, and the AUC of the CRLPRSM was 0.649. Moreover, the KM analysis showed a pronounced intergroup difference in survival, and the nomogram illustrated some clinical benefits of CRLPRSM. Furthermore, GO terms and KEGG pathways were unveiled to be significantly enriched. Conclusion The constructed CRLPRSM has a significant predicted value for GC patient prognosis, and CRLs may become novel hallmarks for clinical treatment of GC.

Performance of prognostic scores in prediction of 30-day postoperative mortality in COVID-19 patients after emergency surgery: A retrospective cohort study.

: Risk assessment with prognostic scoring, though important, is scarcely studied in emergency surgical patients with COVID-19 infection. We conducted a retrospective cohort study on adult emergency surgical patients with COVID-19 infection in our institute from 1 May 2020 to 31 October 2021 to find the 30-day postoperative mortality and predictive accuracy of prognostic scores. We assessed the demographic data, prognostic risk scores (American Society of Anesthesiologists-Physical Classification (ASA-PS), Sequential Organ Failure Assessment (SOFA), Quick SOFA (qSOFA), Physiologic and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Portsmouth-POSSUM (P-POSSUM) scores), surgical and anesthetic factors. We assessed the postoperative morbidity using the Clavien-Dindo scale and recorded the 30-day mortality. Correlation of prognostic scores and mortality was evaluated using Univariate Cox proportional hazards regression, receiver operating characteristic curve (ROC), Youden's index and Hosmer- Lemeshow goodness of fit model. Emergency surgery was performed in 67 COVID-19 patients with postoperative complication and 30-day mortality rate of 33% and 19%, respectively. A positive qSOFA and ASAPS IIIE/IVE had a 9.03- and 12.7-times higher risk of mortality compared to a negative qSOFA and ASA-PS IE/IIE (P &lt; 0.001), respectively. Every unit increase of SOFA, POSSUM and P-POSSUM scores was associated with a 50%, 18% and 17% higher risk of mortality, respectively. SOFA, POSSUM and P-POSSUM AUCROC curves showed good discrimination between survivors and non-survivors (AUC 0.8829, 0.85 and 0.86, respectively). SOFA score has a higher sensitivity to predict 30-day postoperative mortality as compared to POSSUM and P-POSSUM. However, in absence of a control group of non-COVID-19 patients, actual risk attributable to COVID-19 infection could not be determined.

Poor prognosis indicators of type-2 diabetic COVID-19 patients.

Diabetes is associated with a worse prognosis and a high risk of morbidity and mortality in COVID-19 patients. We aimed to evaluate the main factors involved in the poor prognosis in diabetic patients. A total of 984 patients diagnosed with COVID-19 admitted to the hospital were included in this study. Patients were first divided into type-2 diabetic (DM+) and non-diabetic (DM–) groups. The participants were analyzed based on the National Early Warning Score (NEWS) and on the Quick-Sequential Organ Failure Assessment (qSOFA) to find the best prognostic risk score for our study. The DM+ and DM– groups were divided into non-severe and severe groups. Comparative and correlative analyses were used to identify the physiological parameters that could be employed for creating a potential risk indicator for DM+ COVID-19 patients. We found a poorer prognosis for the DM+ COVID-19 patients with a higher ICU admission rate, mechanical ventilation rate, vasopressor use, dialysis, and longer treatment times compared with the DM– group. DM+ COVID-19 patients had increased plasma glucose, lactate, age, urea, NEWS, and D-dimer levels, herein referred to as the GLAUND set, and worse prognosis and outcomes when compared with infected DM– patients. The NEWS score was a better indicator for assessing COVID-19 severity in diabetic patients than the q-SOFA score. In conclusion, diabetic COVID-19 patients should be assessed with the NEWS score and GLAUND set for determining their prognosis COVID-19 prognosis.

Development of a prognostic signature for esophageal cancer based on a novel 7-DNA damage repair genes signature

Esophageal cancer (EC) was an aggressive malignant neoplasm characterized by high morbidity and poor prognosis. Identifying the changes in DNA damage repair genes helps to better understand the mechanisms of carcinoma progression. In this study, by comparing EC samples and normal samples, we found a total of 132 DDR expression with a significant difference. Moreover, we revealed higher expression of POLN, PALB2, ATM, PER1, TOP3B and lower expression of HMGB1, UBE2B were correlated to longer OS in EC. In addition, a prognostic risk score based on 7 DDR gene expression (POLN, HMGB1, TOP3B, PER1, UBE2B, ATM, PALB2) was constructed for the prognosis of EC. Meanwhile, EC cancer samples were divided into 3 subtypes based on 132 DDR genes expressions. Clinical profile analysis showed cluster C1 and C2 showed a similar frequency of T2, which was remarked higher than that in cluster 3. Moreover, we found the immune cell inflation levels were significantly changed in different subtypes of EC. The infiltration levels of T cell CD8+, B cell and NK cells were greatly higher in cluster 2 than that in cluster 1 and cluster 3. The results showed T cell CD4+ infiltration levels were dramatically higher in cluster 1 than that in cluster 2 and cluster 3. Finally, we perform bioinformatics analysis of DEGs among 3 subtypes of EC and found DDR genes may be related to multiple signaling, such as Base excision repair, Cell cycle, Hedgehog signaling pathway, and Glycolysis/Gluconeogenesis. These results showed DDR genes may serve as new target for the prognosis of EC and prediction of the potential response of immune therapy in EC.

Risk stratification of acute myeloid leukemia: Assessment using a novel prediction model based on ferroptosis-immune related genes.

In acute myeloid leukemia (AML), the link between ferroptosis and the immune microenvironment has profound clinical significance. The objective of this study was to investigate the role of ferroptosis-immune related genes (FIRGs) in predicting the prognosis and therapeutic sensitivity in patients with AML. Using The Cancer Genome Atlas dataset, single sample gene set enrichment analysis was performed to calculate the ferroptosis score of AML samples. To search for FIRGs, differentially expressed genes between the high- and low-ferroptosis score groups were identified and then cross-screened with immune related genes. Univariate Cox and LASSO regression analyses were performed on the FIRGs to establish a prognostic risk score model with five signature FIRGs (BMP2, CCL3, EBI3, ELANE, and S100A6). The prognostic risk score model was then used to divide the patients into high- and low-risk groups. For external validation, two Gene Expression Omnibus cohorts were employed. Overall survival was poorer in the high-risk group than in the low-risk group. The novel risk score model was an independent prognostic factor for overall survival in patients with AML. Infiltrating immune cells were also linked to high-risk scores. Treatment targeting programmed cell death protein 1 may be more effective in high-risk patients. This FIRG-based prognostic risk model may aid in optimizing prognostic risk stratification and treatment of AML.

Construction of a prognostic classifier and prediction of the immune landscape and immunosuppressive molecules in gliomas based on combination of inflammatory response-related genes and angiogenesis-associated genes

Objective:We aimed to combine inflammatory response-related genes (IRRGs) and angiogenesis-associated genes (AAGs) to build a prognostic classifier and to predict immune landscapes and immunosuppressive molecules in gliomas. Introduction: Gliomas, the commonest primary brain tumors, account for about 80% of cancerous tumors in the central nervous system (CNS), featuring rapid progression, high malignancy, and extremely poor prognosis. The induction of inflammatory responses and angiogenesis have been considered to be closely related to tumors. However, there are little publications systematically elaborating on their impacts on gliomas. Methods: We downloaded the data of IRRGs and AAGs from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and retrieved 68 differentially expressed genes (DEGs), of which 13 DEGs pertained to the prognosis of glioma cases. Next, 9 DEGs were screened from the 13 major DEGs with predictive significance and utilized to build a 9-gene signature as a prognostic risk score model (PRSM) with the aid of univariate Cox regression analyses (CRA) and least absolute shrinkage and selection operator (LASSO)-CRA. On this basis, glioma patients fell into high-risk (HR) group and low-risk (LR) group. Later, we implemented Gene Set Enrichment Analysis (GSEA, Gene Set: WP_ANGIOGENESIS) and calculate the scores of cell infiltration and immune-associated function by harnessing single-sample GSEA (ssGSEA). Results: The prognosis was compared between the two groups by introducing Kaplan-Meier (KM) analysis, which yielded that HR group exhibited poorer prognosis. Additionally, the predictive capacity and independent characteristics were proven by the receiver operating characteristic curve (ROC) and multivariate CRA. Further, We took an evaluation of immune profiles, which unraveled that immunosuppressive cell count was distinctively larger in HS group. Finally, a protein-protein interaction (PPI) network of DEGs was built, and 10 hub genes were obtained, of which epidermal growth factor receptor (EGFR) was closely related to poor prognosis. Conclusion: A 9-gene signature was established on the strength of IRRGs and AAGs for predicting glioma prognosis, tumor microenvironment (TME), immune landscapes and immunosuppressive molecules. However, the molecular mechanism developed by this signature to function in tumor immunity needs further experimental research in the future and is expected to be a research target for glioma immunotherapy strategies.