Prognostic Risk Scoring System Research Articles

Potential shared gene signatures and molecular mechanisms between recurrent pregnancy loss and ovarian cancer.

Ovarian cancer (OV) is the second most prevalent gynecological tumor. Recurrent pregnancy loss (RPL) refers to two or more spontaneous abortions. However, the molecular mechanisms underlying both OV and RPL remain poorly understood. This article focuses on the exploration of the common genetic characteristics of OV and RPL and their molecular mechanisms. The 71 differentially expressed genes associated with RPL and 1427 genes associated with OV survival were analyzed, among which 7 common genes were both important in the pathogenesis of RPL and OV. Then stepAIC analysis was performed to simplify the model and decrease the number of genes, which yielded a final set of 5 prognostic genes with coefficients to construct a prognostic risk scoring system. Univariate and multivariate Cox analyses were conducted to verify the independent prognostic factor for OV patients. GSEA and GO analysis results showed enriched biological pathways in the high/low risk groups, thereby revealing their biological characteristics. The effect of immunotherapy is better in LR patients. There was a significantly higher enrichment score of stemness and higher tumor aneuploidy score in the HR group. A five-gene prognostic risk model provided a more accurate prognosis for OV, and this prognostic score system was validated using two external cohorts. The risk score was an independent prognostic index for OV patients. Based on levels of ICs, immune cell infiltration, and predicted response, low risk OV patients were more likely to benefit from immunotherapies. The 5-gene risk model can predict the prognosis of OV patients, which can draw the attention of clinicians and help stratify patients into high and low risk groups for management.

A novel nomogram based on the hematological prognosis risk scoring system can predict the overall survival of patients with hepatocellular carcinoma.

This study aimed to establish and validate a nomogram based on a hematological prognostic risk scoring system to predict the overall survival in patients with unresectable hepatocellular carcinoma (HCC). Patients diagnosed with unresectable HCC undergoing transcatheter arterial chemoembolization (TACE) in 2012-2016 and 2017-2018 were included in the development set and validation set, respectively. The clinical outcome was overall survival (OS). The LASSO regression analysis was used to construct a hematological prognostic risk scoring system (HPR) by using the 18 hematological markers of patients in the development set. Combining the features of oncology on the basis of HPR to construct a nomogram for OS. In the development set and validation sets, the C-index, calibration curve, and decision curve analysis (DCA) were used to evaluate the prediction performance of the nomogram. Multiple markers of immunity, coagulation, liver function, and nutrition, including red blood cell distribution width-coefficient of variation (RDW-CV), platelet (PLT), aspartate transferase (AST), alkaline phosphatase (ALP), prognostic nutritional index (PNI), and fibrinogen (Fib), construct the HPR. HPR was an independent risk factor for OS in patients with HCC. The C-index of the nomogram was 0.731 (95% confidence interval (CI) 0.712-0.749) and 0.696 (95% CI 0.668-0.725) in the development set and the validation set, respectively. HPR was a complement to the clinical features of patients with unresectable HCC. The nomogram based on HPR proved to be a practical and effective method for prognosticating HCC patients who undergo TACE.

Analysis of the genomic landscape of primary central nervous system lymphoma using whole-genome sequencing in Chinese patients.

Primary central nervous system lymphoma (PCNSL) is an uncommon non-Hodgkin's lymphoma with poor prognosis. This study aimed to depict the genetic landscape of Chinese PCNSLs. Whole-genome sequencing was performed on 68 newly diagnosed Chinese PCNSL samples, whose genomic characteristics and clinicopathologic features were also analyzed. Structural variations were identified in all patients with a mean of 349, which did not significantly influence prognosis. Copy loss occurred in all samples, while gains were detected in 77.9% of the samples. The high level of copy number variations was significantly associated with poor progression-free survival (PFS) and overall survival (OS). A total of 263 genes mutated in coding regions were identified, including 6 newly discovered genes (ROBO2, KMT2C, CXCR4, MYOM2, BCLAF1, and NRXN3) detected in ⩾ 10% of the cases. CD79B mutation was significantly associated with lower PFS, TMSB4X mutation and high expression of TMSB4X protein was associated with lower OS. A prognostic risk scoring system was also established for PCNSL, which included Karnofsky performance status and six mutated genes (BRD4, EBF1, BTG1, CCND3, STAG2, and TMSB4X). Collectively, this study comprehensively reveals the genomic landscape of newly diagnosed Chinese PCNSLs, thereby enriching the present understanding of the genetic mechanisms of PCNSL.

A signature of cuproptosis-related lncRNAs predicts prognosis and provides basis for future anti-tumor drug development in breast cancer.

Breast cancer is the most prevalent malignancy worldwide and the leading culprit for women's death. Cuproptosis is a novel and promising modality of tumor cell death and the relationship with long non-coding RNAs (lncRNAs) remains shrouded in a veil. Studies in cuproptosis-related lncRNAs can aid in the clinical management of breast cancer and provide a basis for anti-tumor drug development. RNA-Seq data, somatic mutation data, and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Patients were divided into high- and low-risk groups according to the risk score. Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to select prognostic lncRNAs to construct a risk score system. Its' prognostic value was confirmed in the training and validation cohorts subsequently. Functional analysis regarding cuproptosis-related lncRNAs was performed. Eighteen cuproptosis-related lncRNAs were identified and 11 of them including AL023882.1, AC091588.1, AC138028.2, AC027514.1, AL592301.1, LRRC8C-DT, MFF-DT, NIFK-AS1, MECOM-AS1, OTUD6B-AS1 and RNF32-AS1 were selected for risk score system construction. The risk score was confirmed as an independent prognostic factor and patients in the high-risk group had a worse prognosis. A nomogram based on the independent prognostic factors was constructed for clinical decision aids. Further analyses revealed that patients in the high-risk group faced a heavier tumor mutational burden (TMB) and suppressed anti-tumor immunity. Besides, cuproptosis-related lncRNAs were associated with the expression of immune checkpoint inhibitors, N6-adenylate methylation (m6a), and drug sensitivity in breast cancer. A prognostic risk score system with satisfactory predictive accuracy was constructed. Besides, cuproptosis-related lncRNAs can influence the immune microenvironment, TMB, m6a, and drug sensitivity in breast cancer, which may provide a basis for future anti-tumor drug development.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC.

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Age, creatinine, and ejection fraction (ACEF) score as predictive values for late non-valvular atrial fibrillation recurrence after radiofrequency ablation

ABSTRACT The clinical risk factors associated with late recurrence in patients with non-valvular atrial fibrillation (AF) (NVAF) undergoing radiofrequency catheter ablation (RFCA) remain unknown. Furthermore, the current prognostic risk score system is commonly used in such patients as a noninvasive method to assess late AF recurrence. According to recent research, the Age, creatinine, and ejection fraction (ACEF) score is a useful risk score for cardiovascular morbidity and mortality. As a result, we hypothesized that pre-ablation ACEF score could be used to assess late recurrence in patients with NVAF. We included 325 NVAF patients undergoing RFCA. During a median follow-up period of 12 months, patients with late AF recurrence had higher ACEF scores (P < .001). The pre-ablation ACEF score was a risk factor for late AF recurrence after RFCA (P = .027). The ACEF score was a predictor of late AF recurrence after RFCA, with an AUC of 0.624 (P = .001). Moreover, the AUC of left atrial diameter (LAD) was 0.7 (P < .001), which was higher than the ACEF score, but no significant difference was found (P = .104). The ACEF score was positively correlated with LAD, advanced age, and B-type natriuretic peptide. In patients with NVAF, the pre-ablation ACEF score is a valuable risk score for assessing late AF recurrence after RFCA, as with LAD.

Identification and validation of neurotrophic factor-related gene signatures in glioblastoma and Parkinson's disease.

Glioblastoma multiforme (GBM) is the most common cancer of the central nervous system, while Parkinson's disease (PD) is a degenerative neurological condition frequently affecting the elderly. Neurotrophic factors are key factors associated with the progression of degenerative neuropathies and gliomas. The 2601 neurotrophic factor-related genes (NFRGs) available in the Genecards portal were analyzed and 12 NFRGs with potential roles in the pathogenesis of Parkinson's disease and the prognosis of GBM were identified. LASSO regression and random forest algorithms were then used to screen the key NFRGs. The correlation of the key NFRGs with immune pathways was verified using GSEA (Gene Set Enrichment Analysis). A prognostic risk scoring system was constructed using LASSO (Least absolute shrinkage and selection operator) and multivariate Cox risk regression based on the expression of the 12 NFRGs in the GBM cohort from The Cancer Genome Atlas (TCGA) database. We also investigated differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between risk groups. Finally, the accuracy of the model genes was validated using multi-omics mutation analysis, single-cell sequencing, QT-PCR, and HPA. We found that 4 NFRGs were more reliable for the diagnosis of Parkinson's disease through the use of machine learning techniques. These results were validated using two external cohorts. We also identified 7 NFRGs that were highly associated with the prognosis and diagnosis of GBM. Patients in the low-risk group had a greater overall survival (OS) than those in the high-risk group. The nomogram generated based on clinical characteristics and risk scores showed strong prognostic prediction ability. The NFRG signature was an independent prognostic predictor for GBM. The low-risk group was more likely to benefit from immunotherapy based on the degree of immune cell infiltration, expression of immune checkpoints (ICs), and predicted response to immunotherapy. In the end, 2 NFRGs (EN1 and LOXL1) were identified as crucial for the development of Parkinson's disease and the outcome of GBM. Our study revealed that 4 NFRGs are involved in the progression of PD. The 7-NFRGs risk score model can predict the prognosis of GBM patients and help clinicians to classify the GBM patients into high and low risk groups. EN1, and LOXL1 can be used as therapeutic targets for personalized immunotherapy for patients with PD and GBM.

Analysis on echocardiographic data of fetal heart defects at high altitude in Yunnan province and surrounding high altitude areas

Objective: To explore the current situation of fetal heart defects in Yunnan Province and surrounding high altitude areas and the social factors affecting pregnancy outcome. Methods: This is a retrospective study. Pregnant woman who underwent fetal echocardiography and diagnosed as fetal cardiac defects in Yunnan Fuwai Cardiovascular Hospital from June 2017 to January 2021 were included. According to the clinical prognostic risk scoring system and grading criteria of fetal cardiac birth defects, the cases were divided into grade Ⅰ to Ⅳ. The disease distribution and proportion of each prognostic grade, pregnancy outcomes were analyzed and compared. The cases were divided into continued pregnancy group and terminated pregnancy group according to pregnancy outcome. The social factors that may affect the selection of pregnancy outcomes were analyzed by multivariate logistic regression analysis. Results: A total of 4 929 fetal echocardiography examination data were collected, and 4 464 cases (90.57%) were from Yunnan Province and surrounding high altitude areas. 2 166 cases of heart defects were finally analyzed, including 998 cases of congenital heart disease (CHD), 93 cases of cardiac tumors, cardiomyopathy and arrhythmia, 1 075 cases of foramen ovale, ductus arteriosus abnormalities and normal variations. The pregnant women were (29.2±5.0) years old with (25.6±3.8) gestational weeks. The number of cases with prognostic grade from Ⅰ to Ⅳ was 1 037 (47.88%), 620 (28.62%), 314 (14.50%), and 44 (2.03%), respectively. And 151 cases (6.97%) were not classified. The cases of normal variation and thin aortic arch development accounted for 42.66% (924/2 166), 5.22% (113/2 166), respectively. The top 3 diseases of grade Ⅱ were ventricular septal defect, coarctation of aorta and mild-moderate pulmonary stenosis, respectively, and their distribution was 11.63% (252/2 166), 3.92% (85/2 166) and 2.35% (51/2 166) respectively in all cases of heart defects, and 25.25% (252/998), 8.52% (85/998) and 5.11% (51/998) respectively in cases of CHD. Among the cases rated as grade Ⅲ and Ⅳ, most of them were complicated congenital heart disease, and the disease types are scattered. The more common cases in grade Ⅲ were complete transposition of great arteries (accounting for 2.40% (52/2 166) of all cases with heart defects, 5.21% (52/998) of all cases with CHD) and pulmonary artery occlusion (type Ⅰ to Ⅲ) with ventricular septal defect (accounting for 2.17% (47/2 166) of all cases with heart defects, and 4.71% (47/998) of all cases with CHD). In grade Ⅳ, single ventricle (0.74% (16/2 166) of all cases with heart defects, 1.60% (16/998) of all cases with CHD) and left ventricular dysplasia syndrome (0.65% (14/2 166) of all cases with heart defects, 1.40% (14/998) of all cases with CHD) are more common. A total of 1 084 cases were successfully followed up, and 675 cases were born, 392 cases were terminated, spontaneous abortion occurred in 17 cases. The proportion of terminated pregnancy cases was significantly increased from grade Ⅰ to Ⅳ, accounting for 5.24% (21/401), 27.78% (70/252), 89.54% (214/239) and 95.56% (43/45), respectively. Among the terminated pregnancy cases, those with grade Ⅲ accounted for the highest proportion (54.59% (214/392)). The distribution of terminated pregnancy cases was mainly complex congenital malformations or diseases with very poor prognosis (pregnancy outcome grade Ⅲ and Ⅳ), and proportion of terminated pregnancy with pregnancy outcome grade Ⅰ and Ⅱ cases (normal variation or good prognosis) accounted for 5.36% (21/392) and 17.86% (70/392), respectively. The results of multivariate logistic regression analysis showed that pregnant women with low education (high school and below: OR=2.73, 95%CI 1.26-5.93, P<0.001; illiteracy: OR=3.27, 95%CI 1.29-7.10, P<0.001) and low family income (Annual income<100 000 yuan: OR=2.47, 95%CI 1.69-5.12, P<0.001) were more likely to choose termination of pregnancy in case of fetal heart defect. Conclusion: In Yunnan province and the surrounding high altitude areas, the disease distribution of fetal heart defect is mainly simple or low-risk disease, but the complex malformation, especially the disease with poor pregnancy outcome, accounts for a relative high proportion. Pregnancy termination also occurs in some cases with good pregnancy outcome. The education level and family income of pregnant women may affect their choice of pregnancy outcome in case of fetal heart defect.

Establishment of a prognostic prediction system based on tumor microenvironment of pancreatic cancer

Pancreatic cancer (PC) is an inflammatory tumor. Tumor microenvironment (TME) plays an important role in the development of PC. This study aims to explore hub genes of TME and establish a prognostic prediction system for PC. High throughput RNA-sequencing and clinical data of PC were downloaded from The Cancer Genome Atlas and International Cancer Genome Consortium database, respectively. PC patients were divided into high- and low-score group by using stromal, immune scores system based on ESTIMATE. Differentially expressed genes between high- and low-score patients were screened and survival-related differentially expressed genes were identified as candidate genes by univariate Cox regression analysis. Final variables for establishment of the prognostic prediction system were determined by LASSO analysis and multivariate Cox regression analysis. The predictive power of the prognostic system was evaluated by internal and external validation. A total of 210 candidate genes were identified by stromal, immune scores system, and survival analyses. Finally, the prognostic risk score system was constructed by the following genes: FAM57B, HTRA3, CXCL10, GABRP, SPRR1B, FAM83A, and LY6D. In process of internal validation, Harrell concordance index (C-index) of this prognostic risk score system was 0.73, and the area under the receiver operating characteristic curve value of 1-year, 2-year, and 3-year overall survival period was 0.67, 0.76 and 0.86, respectively. In the external validation set, the survival prediction C-index was 0.71, and the area under the curve was 0.81, 0.72, and 0.78 at 1-year, 2-year, and 3-year, respectively. This prognostic risk score system based on TME demonstrated a good predictive capacity to the prognosis of PC. It may provide information for the treatment strategy and follow-up for patients with PC.

Identification and validation of neurotrophic factor-related genes signature in HNSCC to predict survival and immune landscapes.

Background: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer worldwide. Its highly aggressive and heterogeneous nature and complex tumor microenvironment result in variable prognosis and immunotherapeutic outcomes for patients with HNSCC. Neurotrophic factor-related genes (NFRGs) play an essential role in the development of malignancies but have rarely been studied in HNSCC. The aim of this study was to develop a reliable prognostic model based on NFRGs for assessing the prognosis and immunotherapy of HNSCC patients and to provide guidance for clinical diagnosis and treatment. Methods: Based on the TCGA-HNSC cohort in the Cancer Genome Atlas (TCGA) database, expression profiles of NFRGs were obtained from 502 HNSCC samples and 44 normal samples, and the expression and prognosis of 2601 NFRGs were analyzed. TGCA-HNSC samples were randomly divided into training and test sets (7:3). GEO database of 97 tumor samples was used as the external validation set. One-way Cox regression analysis and Lasso Cox regression analysis were used to screen for differentially expressed genes significantly associated with prognosis. Based on 18 NFRGs, lasso and multivariate Cox proportional risk regression were used to construct a prognostic risk scoring system. ssGSEA was applied to analyze the immune status of patients in high- and low-risk groups. Results: The 18 NFRGs were considered to be closely associated with HNSCC prognosis and were good predictors of HNSCC. The multifactorial analysis found that the NFRGs signature was an independent prognostic factor for HNSCC, and patients in the low-risk group had higher overall survival (OS) than those in the high-risk group. The nomogram prediction map constructed from clinical characteristics and risk scores had good prognostic power. Patients in the low-risk group had higher levels of immune infiltration and expression of immune checkpoints and were more likely to benefit from immunotherapy. Conclusion: The NFRGs risk score model can well predict the prognosis of HNSCC patients. A nomogram based on this model can help clinicians classify HNSCC patients prognostically and identify specific subgroups of patients who may have better outcomes with immunotherapy and chemotherapy, and carry out personalized treatment for HNSCC patients.

The types of tumor infiltrating lymphocytes are valuable for the diagnosis and prognosis of breast cancer.

This study aimed at constructing a diagnostic immune risk score (dIRS) system and a prognostic immune risk score (pIRS) system for diagnose and prognosis of breast cancer (BC). The gene expression data of BC were downloaded from TCGA dataset (training set), and from GSE65194, GSE29044, GSE42568, and GSE20685 (validation sets). Then, the immune cell type proportions in each dataset were assessed using EPIC tool, and the dIRS system was built based on the SVM-RFE and RF-VIMP algorithms. Subsequently, the pIRS system and the nomogram survival model were established separately using penalized and rms packages. Finally, the differential expressed genes (DEGs) between low and high pIRS groups were screened, and submitted for functional analysis. The dIRS system consisted of B cells, CD8 + T cells, endothelial cells, NK cells, and other cells had high accuracy in distinguishing BC patients from the healthy controls (AUROC >0.7). Subsequently, the pIRS system with the five prognosis-associated immune-infiltrating cell was constructed, and Kaplan-Meier analysis demonstrated that the survival rate of low pIRS group was significantly higher than that of high pIRS group (p < 0.05). Based on age, pathologic stage and the pIRS values, the nomogram survival model was built. The AUROC value, Specificity value, Sensitivity value and C-index of the nomogram survival model were higher than 0.7000, and had a good predictive ability for BC. Finally, a total of 539 DEGs were identified, and significantly enriched in six pathways. The dIRS system and the pIRS system composed of immune cells might be critical for the diagnosis and prognosis of BC patients.

Comprehensive Analysis of Gene Signatures of m6ARNA Methylation Regulators in Lung Adenocarcinoma and Development of a Risk Scoring System.

The recent application of targeted immunotherapy has greatly improved the clinical outcomes of patients with lung adenocarcinoma (LUAD), but drug resistance continues to emerge, and to evaluate and to improve patient prognosis are arduous. The diagnostic and prognostic value of N6-methyladenosine (M6A) in LUAD has attracted increasing attention. We systematically studied correlations among important M6A methylation regulators, tumor mutational burden (TMB), and immune infiltration in clinical and sequencing data from the LUAD cohort of the cancer genome map (TCGA). The molecular subtype clusters 1 and 2 were identified by the consensus clustering of 16 M6A regulatory factors. Clinical prognosis, M6A regulatory factor expression, TMB, pathway enrichment, and immune cell infiltration significantly differed between clusters 1 and 2. Compared with other clinical traits, a prognostic risk score system constructed using the M6A regulatory factors HNRNPA2B1 and HNRNPC can serve as an independent prognostic method for LUAD, with higher predictive sensitivity and specificity. Risk scores were significantly higher for cluster 2 than 1, which was consistent with the trend towards a better prognosis in cluster 1. Overall, our findings revealed an important role of M6A methylation regulators in LUAD, and our risk scoring system involving these regulators might help to screen groups at high risk for LUAD and provide important theoretical bioinformatic support for evaluating the prognosis of such patients.

Establishment of prognostic risk model and drug sensitivity based on prognostic related genes of esophageal cancer

At present, the treatment of esophageal cancer (EC) is mainly surgical and drug treatment. However, due to drug resistance, these therapies can not effectively improve the prognosis of patients with the EC. Therefore, a multigene prognostic risk scoring system was constructed by bioinformatics analysis method to provide a theoretical basis for the prognosis and treatment decision of EC. The gene expression profiles and clinical data of esophageal cancer patients were gathered from the Cancer Genome Atlas TCGA database, and the differentially expressed genes (DEGs) were screened by R software. Genes with prognostic value were screened by Kaplan Meier analysis, followed by functional enrichment analysis. A cox regression model was used to construct the prognostic risk score model of DEGs. ROC curve and survival curve were utilized to evaluate the performance of the model. Univariate and multivariate Cox regression analysis was used to evaluate whether the model has an independent prognostic value. Network tool mirdip was used to find miRNAs that may regulate risk genes, and Cytoscape software was used to construct gene miRNA regulatory network. GSCA platform is used to analyze the relationship between gene expression and drug sensitivity. 41 DEGs related to prognosis were pre-liminarily screened by survival analysis. A prognostic risk scoring model composed of 8 DEGs (APOA2, COX6A2, CLCNKB, BHLHA15, HIST1H1E, FABP3, UBE2C and ERO1B) was built by Cox regression analysis. In this model, the prognosis of the high-risk score group was poor (P < 0.001). The ROC curve showed that (AUC = 0.862) the model had a good performance in predicting prognosis. In Cox regression analysis, the comprehensive risk score can be employed as an independent prognostic factor of the EC. HIST1H1E, UBE2C and ERO1B interacted with differentially expressed miRNAs. High expression of HIST1H1E was resistant to trametinib, selumetinib, RDEA119, docetaxel and 17-AAG, High expression of UBE2C was resistant to masitinib, and Low expression of ERO1B made the EC more sensitive to FK866. We constructed an EC risk score model composed of 8 DEGs and gene resistance analysis, which can provide reference for prognosis prediction, diagnosis and treatment of the EC 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.

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.

New prognostic factors and scoring system for patients with acute myeloid leukemia

Acute myeloid leukemia (AML) is a malignant disease originating from myeloid hematopoietic stem or progenitor cells. It is important to identify molecules associated with the prognosis of AML and conduct an individual risk assessment for different patients. In the present study, the RNA expression profile of 132 patients with AML and 337 healthy individuals were downloaded from the University of California Santa Cruz Xena and the Genotype-Tissue Expression project databases. Differentially expressed mRNA (DEmRNA) transcripts between normal blood and AML blood were identified. Among these, prognosis-associated signature mRNA molecules were screened using univariate Cox and least absolute shrinkage and selection operator regression. A total of four genes, namely, family with sequence similarity 124 member B (FAM124B), 4-hydroxyphenylpyruvate dioxygenase-like protein (HPDL), myeloperoxidase (MPO) and purinergic receptor P2Y1 (P2RY1), were identified using multivariate Cox regression analysis and were used to construct a prognostic scoring system. Moreover, the expression levels of HPDL and MPO were higher in the samples with high immunity scores and estimate scores (sum of stromal score and immune score), compared with those with low scores. Reverse transcription-quantitative PCR and western blot analysis were used to confirm the upregulation of the four candidate genes in AML cell lines as well as in clinical AML samples. In summary, the present study identified a novel mRNA-based prognostic risk scoring system for patients with AML. The four genes used in this scoring system may also play an important role in AML.

Body Composition as an Independent Predictive and Prognostic Biomarker in Advanced Urothelial Carcinoma Patients Treated with Immune Checkpoint Inhibitors.

Several immune checkpoint inhibitors (ICIs) are approved for the treatment of advanced urothelial carcinoma (UC). There are limited biomarkers for ICI-treated patients with UC. We investigated the association between body composition and clinical outcomes in ICI-treated UC patients. We conducted a retrospective analysis of 70 ICI-treated patients with advanced UC at Winship Cancer Institute from 2015 to 2020. Baseline computed tomography images within 2 months of ICI initiation were collected at mid-L3 and muscle and fat compartments (subcutaneous, intermuscular, and visceral) were segmented using SliceOMatic v5.0 (TomoVision, Magog, Canada). A prognostic body composition risk score (high: 0-1, intermediate: 2-3, or low-risk: 4) was created based on the β coefficient from the multivariate Cox model (MVA) following best-subset variable selection. Our body composition risk score was skeletal muscle index (SMI) + 2 × attenuated skeletal muscle (SM) mean + visceral fat index (VFI). Concordance statistics (C-statistics) were used to quantify the discriminatory magnitude of the predictive model. Most patients (70%) were men and the majority received ICIs in the second- (46%) or third-line (21%) setting. High-risk patients had significantly shorter overall survival (OS; hazard ratio [HR], 6.72; p<.001), progression-free survival (HR, 5.82; p<.001), and lower chance of clinical benefit (odds ratio [OR], 0.02; p=.003) compared with the low-risk group in MVA. The C-statistics for our body composition risk group and myosteatosis analyses were higher than body mass index for all clinical outcomes. Body composition variables such as SMI, SM mean, and VFI may be prognostic and predictive of clinical outcomes in ICI-treated patients with UC. Larger, prospective studies are warranted to validate this hypothesis-generating data. This study developed a prognostic body composition risk scoring system using radiographic biomarkers for patients with bladder cancer treated with immunotherapy. The study found that the high-risk patients had significantly worse clinical outcomes. Notably, the study's model was better at predicting outcomes than body mass index. Importantly, these results suggest that radiographic measures of body composition should be considered for inclusion in updated prognostic models for patients with urothelial carcinoma treated with immunotherapy. These findings are useful for practicing oncologists in the academic or community setting, particularly given that baseline imaging is routine for patients starting on treatment with immunotherapy.

Identification and Validation of Immune-Related Methylation Clusters for Predicting Immune Activity and Prognosis in Breast Cancer.

The role of DNA methylation of breast cancer-infiltrating immune cells has not been fully explored. We conducted a cohort-based retrospective study analyzing the genome-wide immune-related DNA methylation of 1057 breast cancer patients from the TCGA cohort and GSE72308 cohort. Based on patients’ overall survival (OS), a prognostic risk score system using 18 immune-related methylation genes (IRMGs) was established and further validated in an independent cohort. Kaplan–Meier analysis showed a clear separation of OS between the low- and high-risk groups. Patients in the low-risk group had a higher immune score and stromal score compared with the high-risk group. Moreover, the characteristics based on 18-IRMGs signature were related to the tumor immune microenvironment and affected the abundance of tumor-infiltrating immune cells. Consistently, the 18-IRMGs signatures showed similar influences on immune modulation and survival in another external validation cohort (GSE72308). In conclusion, the proposed 18-IRMGs signature could be a potential marker for breast cancer prognostication.

Comprehensive Analysis of Autophagy-Associated lncRNAs Reveal Potential Prognostic Prediction in Pancreatic Cancer.

Pancreatic cancer (PC) is a highly malignant tumor in the digestive system. Both long noncoding RNAs (lncRNAs) and autophagy play vital roles in the development and progress of PC. Here, we constructed a prognostic risk score system based on the expression profile of autophagy-associated lncRNAs for prognostic prediction in PC patients. Firstly, we extracted the expression profile of lncRNA and clinical information from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The autophagy-associated genes were from The Human Autophagy Database. Through Cox regression and survival analysis, we screened out seven autophagy-associated lncRNAs and built the risk score system in which the patients with PC were distinguished into high- and low-risk groups in both training and validation datasets. PCA plot displayed distinct discrimination, and risk score system displayed independently predictive value for PC patient survival time by multivariate Cox regression. Then, we built a lncRNA and mRNA co-expression network via Cytoscape and Sankey diagram. Finally, we analyzed the function of lncRNAs in high- and low-risk groups by gene set enrichment analysis (GSEA). The results showed that autophagy and metabolism might make significant effects on PC patients of low-risk groups. Taken together, our study provides a new insight to understand the role of autophagy-associated lncRNAs and finds novel therapeutic and prognostic targets in PC.