Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis / 浙江大学学报·医学版

OBJECTIVES@#To classify bladder cancer based on immune cell infiltration score and to construct a risk assessment model for prognosis of patients.@*METHODS@#The transcriptome data and data of breast cancer patients were obtained from the TCGA database. The single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was realized by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were extracted. A risk scoring model and a nomogram for risk assessment of prognosis for bladder cancer patients were constructed and verified.@*RESULTS@#The immune cell infiltration scores of normal tissues and tumor tissues were calculated, and B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. Breast cancer patients were clustered into two groups (Cluster 1 and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). WGCNA screened out 35 genes related to key immune cells, and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients.@*CONCLUSIONS@#According to the immune cell infiltration score, bladder cancer patients can be classified. And the bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Prognostic Value and Immune Infiltration of Anoikis-related LncRNAs in Lung Adenocarcinoma / 肿瘤防治研究

Objective To explore the prognostic value and immune infiltration landscape of anoikis-related long noncoding RNAs (arlncRNAs) in lung adenocarcinoma. Methods RNA-seq and clinical data of lung adenocarcinoma were downloaded from the TCGA database, and anoikis-related genes were obtained from the GeneCards and Harmonizome databases. Coexpression, differential, and WGCNA analyses were performed to screen differentially expressed arlncRNAs closely related to the occurrence of lung adenocarcinoma. A prognostic risk model was then constructed based on the arlncRNAs, and its predictive efficacy was further validated. Finally, consensus clustering was used to identify the molecular subtypes associated with anoikis in lung adenocarcinoma. Results Seven prognostic arlncRNAs were identified, and the prognostic risk models established based on them had AUC values of ROC curves greater than 0.7. Survival and immune infiltration analyses revealed that low-risk patients had high overall survival and immune infiltration, implying that they experienced good immune treatment effects. Drug sensitivity analysis showed that the high-risk patients were more sensitive to commonly used chemotherapeutic agents than the low-risk patients. According to the expression of model genes, subtypes C1 and C2 were identified through consensus clustering, and C1 showed a good prognosis. Conclusion The prognostic risk model based on the seven arlncRNAs can effectively predict the prognosis of lung adenocarcinoma patients. The results of immune-related and drug sensitivity analyses provide a reference for the precise individualized treatment of patients with lung adenocarcinoma.

Applicability of the acute leukemia (AL) - EBMT score as a prognostic model for allogeneic hematopoietic stem cell transplantation: a single-center, prospective, cohort study at a reference transplant center in Brazil

Abstract Introduction The Acute Leukemia-European Society for Blood and Marrow Transplantation (AL-EBMT) risk score was recently developed and validated by Shouval et al. Objective To assess the ability of this score in predicting the 2-year overall survival (OS-2), leukemia-free survival (LFS-2) and transplant-related mortality (TRM) in acute leukemia (AL) adult patients undergoing a first allogeneic hematopoietic stem cell transplant (HSCT) at a transplant center in Brazil. Methods In this prospective, cohort study, we used the formula published by Shouval et al. to calculate the AL-EBMT score and stratify patients into three risk categories. Results A total of 79 patients transplanted between 2008 and 2018 were analyzed. The median age was 38 years. Acute myeloid leukemia was the most common diagnosis (68%). Almost a quarter of the cases were at an advanced stage. All hematopoietic stem cell transplantations (HSCTs) were human leukocyte antigen-matched (HLA-matched) and the majority used familial donors (77%). Myeloablative conditioning was used in 92% of the cases. Stratification according to the AL-EBMT score into low-, intermediate- and high-risk groups yielded the following results: 40%, 12% and 47% of the cases, respectively. The high scoring group was associated with a hazard ratio of 2.1 (p= 0.007), 2.1 (p= 0.009) and 2.47 (p= 0.01) for the 2-year OS, LFS and TRM, respectively. Conclusion This study supports the ability of the AL-EBMT score to reasonably predict the 2-year post-transplant OS, LFS and TRM and to discriminate between risk categories in adult patients with AL, thus confirming its usefulness in clinical decision-making in this setting. Larger, multicenter studies may further help confirm these findings.

Prognostic Model Based on Preoperative FAR and SII Versus TNM Staging System in Evaluating Prognosis of Patients with Pancreatic Cancer After Radical Resection / 肿瘤防治研究

Objective To investigate the predictive value of preoperative fibrinogen/albumin ratio (FAR) and systemic immune inflammation index (SII) on the postoperative prognosis of patients with pancreatic ductal adenocarcinoma. Methods An ROC curve was used in determining the best cutoff values of FAR and SII and then grouped. The Cox proportional hazards model was used in analyzing the prognostic factors of radical pancreatic cancer surgery, and then a Nomogram prognostic model was established. C-index, AUC, and calibration curve were used in evaluating the discrimination and calibration ability of the Nomogram. DCA curves were used in assessing the clinical validity of the Nomograms. Results The optimal cutoff values for preoperative FAR and SII were 0.095 and 532.945, respectively. FAR≥ 0.095, SII≥ 532.945, CA199≥ 450.9 U/ml, maximum tumor diameter≥ 4 cm, and the absence of postoperative chemotherapy were independent risk factors for the poor prognosis of pancreatic cancer (P<0.05). The discrimination ability, calibration ability, and clinical effectiveness of Nomogram prognostic model were better than those of the TNM staging system. Conclusion The constructed Nomogram prognostic model has higher accuracy and level of discrimination and more clinical benefits than the TNM staging prognostic model.

Expression and Prognostic Value of Cuprotosis-related Genes in Liver Cancer / 肿瘤防治研究

Objective To explore the relationship of cuprotosis-related genes with survival rate and prognosis in patients with liver cancer. Methods By collecting clinical information and corresponding RNA-seq data of patients with liver cancer in the TCGA database, the differential expression levels of 10 cuprotosis-related genes in liver cancer and normal tissues was analyzed. Novel liver cancer subtypes were identified through consistent clustering, and differences in overall survival and clinicopathological factors were compared between the two subtypes. Univariate Cox regression analysis was used in screening cuprotosis genes associated with prognosis, and LASSO regression analysis was used in constructing a risk model. Results FDX1 was down-regulated, and the other nine genes were up-regulated in HCC tissues compared with normal tissues. Cluster analysis showed that the prognosis of Cluster1 was poor. Five prognostic genes (LIPT1, DLAT, MTF1, GLS, and CDKN2A) were screened out through univariate Cox regression analysis and LASSO regression analysis for risk model construction. The risk score of this prognostic model was identified as an independent prognostic factor compared with other clinical features. Conclusion Through bioinformatics analysis, a liver cancer prognosis model of five cuprotosis-related genes is constructed, which may be used as molecular markers for tumor diagnosis and are potential therapeutic targets.

Construction of a prognostic model for hepatocellular carcinoma based on pyroptosis-related genes / 中华肝脏病杂志

Objective: To study the construction of a prognostic model for hepatocellular carcinoma (HCC) based on pyroptosis-related genes (PRGs). Methods: HCC patient datasets were obtained from the Cancer Genome Atlas (TCGA) database, and a prognostic model was constructed by applying univariate Cox and least absolute shrinkages and selection operator (LASSO) regression analysis. According to the median risk score, HCC patients in the TCGA dataset were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox analysis, and nomograms were used to evaluate the predictive ability of the prognostic models. Functional enrichment analysis and immune infiltration analysis were performed on differentially expressed genes between the two groups. Finally, two HCC datasets (GSE76427 and GSE54236) from the Gene Expression Omnibus database were used to externally validate the prognostic value of the model. Univariate and multivariate Cox regression analysis or Wilcoxon tests were performed on the data. Results: A total of 366 HCC patients were included after screening the HCC patient dataset obtained from the TCGA database. A prognostic model related to HCC was established using univariate Cox regression analysis, LASSO regression analysis, and seven genes (CASP8, GPX4, GSDME, NLRC4, NLRP6, NOD2, and SCAF11). 366 cases were evenly divided into high-risk and low-risk groups based on the median risk score. Kaplan-Meier survival analysis showed that there were statistically significant differences in the survival time between patients in the high-risk and low-risk groups in the TCGA, GSE76427, and GSE54236 datasets (median overall survival time was 1 149 d vs. 2 131 d, 4.8 years vs. 6.3 years, and 20 months vs. 28 months, with P = 0.000 8, 0.034 0, and 0.0018, respectively). ROC curves showed good survival predictive value in both the TCGA dataset and two externally validated datasets. The areas under the ROC curves of 1, 2, and 3 years were 0.719, 0.65, and 0.657, respectively. Multivariate Cox regression analysis showed that the risk score of the prognostic model was an independent predictor of overall survival time in HCC patients. The risk model score accurately predicted the survival probability of HCC patients according to the established nomogram. Functional enrichment analysis and immune infiltration analysis showed that the immune status of the high-risk group was significantly decreased. Conclusion: The prognostic model constructed in this study based on seven PRGs accurately predicts the prognosis of HCC patients.

Construction of a prediction model for postoperative prognosis in patients with resectable cholangiocarcinoma based on SIRT2 expression / 浙江大学学报·医学版

OBJECTIVES@#To develop a prediction model for postoperative prognosis in patients with cholangiocarcinoma (CCA) based on the expression of silence information regulator 2 (SIRT2).@*METHODS@#The differential expression of SIRT2 between CCA and normal tissues was analyzed using TCGA and GEO databases. Gene set enrichment analysis (GSEA) was used to explore potential mechanisms of SIRT2 in CCA. The expression of SIRT2 protein in CCA tissues and normal tissues (including 44 pairs of specimens) was also detected by immunohistochemistry (IHC) staining in 89 resectable CCA patients who underwent surgical treatment in The First Affiliated Hospital of Bengbu Medical College between January 2016 and December 2021. The relationship between SIRT2 expression and clinicopathological characteristics and prognosis of CCA patients was analyzed. A survival prediction model for patients with resectable CCA was constructed with COX regression results, the calibration curve and the time-dependent receiver operating characteristic curve (ROC) were used to evaluate the performance of the constructed model, and the predictive power between this model and the AJCC/TNM staging system (8th Edition) was compared.@*RESULTS@#SIRT2 mRNA was overexpressed in CCA tissues as shown in TCGA and GEO databases. IHC staining showed that SIRT2 protein expression in CCA tissues was significantly higher than that in adjacent non-tumor tissues. GSEA results showed that elevated SIRT2 expression may be involved in multiple metabolism-related signaling pathway, such as fatty acid metabolism, oxidative phosphorylation, amino acid metabolism, etc. SIRT2 expression level was related to serum triglycerides level, tumor size and lymph node metastasis (all P<0.05). The survival analysis results showed that the patients with higher SIRT2 expression had a significant lower overall survival (OS) than patients with lower SIRT2 expression (P<0.05). Univariate COX regression analysis suggested that pathological differentiation, clinical stage, postoperative treatment and SIRT2 expression level were associated with the prognosis of CCA patients (all P<0.05). Multivariate regression analysis confirmed that clinical stage and SIRT2 expression level were independent predictors of OS in postoperative CCA patients (both P<0.05). A nomogram based on SIRT2 for prediction of survival in postoperative CCA patients was constructed. The C-index of the model was 0.675, and the area under the time-dependent ROC curve (AUC) for predicting survival in the first, second, and third years was 0.879, 0.778, and 0.953, respectively, which were superior to those of AJCC/TNM staging system (8th Edition).@*CONCLUSIONS@#SIRT2 is highly expressed in CCA tissues, which is associated with poor prognosis in patients with resectable CCA. The nomogram developed based on SIRT2 may have better predictive power than the AJCC/TNM staging system (8th Edition) in prediction of survival of postoperative CCA patients.

Development and validation of a prognostic model for patients with sepsis in intensive care unit / 中华危重病急救医学

Objective:To analyze the risk factors related to the prognosis of patients with sepsis in intensive care unit (ICU), construct a nomogram model, and verify its predictive efficacy.Methods:A retrospective cohort study was conducted using data from Medical Information Mart for Intensive Care-Ⅳ 0.4 [MIMIC-Ⅳ (version 2.0)]. The information of 6 500 patients with sepsis who meet the diagnostic criteria of Sepsis-3 were collected, including demography characteristics, complications, laboratory indicators within 24 hours after ICU admission, and final outcome. Using a simple random sampling method, the patients were divided into a training set and a validation set at a ratio of 7∶3. The restricted cubic spline (RCS) was used to explore whether there was a linear relationship between each variable and the prognosis, and the nonlinear variables were truncated into categorical variables. All variables were screened by LASSO regression and included in multivariate Cox regression analysis to analyze the death risk factors in ICU patients with sepsis, and construct a nomograph. The consistency index, calibration curve and receiver operator characteristic curve (ROC curve) were used to evaluate the prediction efficiency of nomogram model. The decision curve analysis (DCA) was used to validate the clinical value of the model and its impact on actual decision-making.Results:Among 6 500 patients with sepsis, 4 551 were in the training set and 1 949 were in the validation set. The 28-day, 90-day and 1-year mortality in the training set were 27.73% (1?262/4?551), 34.76% (1?582/4?551), and 42.98% (1?956/4?551), respectively, those in the validation set were 27.24% (531/1?949), 33.91% (661/1?949), and 42.23% (823/1?949), respectively. Both in training set and the validation set, compared with the final survival patients, the death patients were older, and had higher sequential organ failure assessment (SOFA) score and simplified acute physiology scoreⅡ (SAPSⅡ), more comorbidities, less urine output, and more use of vasoactive drugs, kidney replacement therapy, and mechanical ventilation. By RCS analysis, the variables with potential nonlinear correlation with the prognosis risk of septic patients were transformed into categorical variable. The variables screened by LASSO regression were enrolled in the multivariate Cox regression model. The results showed that age [hazard ratio ( HR) = 1.021, 95% confidence interval (95% CI) was 1.018-1.024], SOFA score ( HR = 1.020, 95% CI was 1.000-1.040), SAPSⅡ score > 44 ( HR = 1.480, 95% CI was 1.340-1.634), mean arterial pressure (MAP) ≤ 75 mmHg (1 mmHg ≈ 0.133 kPa; HR = 1.120, 95% CI was 1.026-1.222), respiratory rate (RR; HR = 1.044, 95% CI was 1.034-1.055), cerebrovascular disease ( HR = 1.620, 95% CI was 1.443-1.818), malignant tumor ( HR = 1.604, 95% CI was 1.447-1.778), severe liver disease ( HR = 1.330, 95% CI was 1.157-1.530), use of vasoactive drugs within 24 hours ( HR = 1.213, 95% CI was 1.101-1.336), arterial partial pressure of oxygen (PaO 2; HR = 0.999, 95% CI was 0.998-1.000), blood lactic acid (Lac; HR = 1.066, 95% CI was 1.053-1.079), blood urea nitrogen (BUN) > 8.9 mmol/L ( HR = 1.257, 95% CI was 1.144-1.381), total bilirubin (TBil; HR = 1.023, 95% CI was 1.015-1.031), and prothrombin time (PT) > 14.5 s ( HR = 1.232, 95% CI was 1.127-1.347) were associated with the death of ICU patients with sepsis (all P < 0.05). Based on the above factors, a nomogram model was constructed, and the model validation results showed that the consistency index was 0.730. The calibration curve showed a good consistency between the predicted results of the nomogram model and observed results in the training and validation sets. ROC curve analysis showed that the area under the ROC curve (AUC) predicted by the nomogram model in the training set and the validation set for 28-day, 90-day and 1-year death risk was 0.771 (95% CI was 0.756-0.786) and 0.761 (95% CI was 0.738-0.784), 0.777 (95% CI was 0.763-0.791) and 0.765 (95% CI was 0.744-0.787), 0.677 (95% CI was 0.648-0.707) and 0.685 (95% CI was 0.641-0.728), respectively. DCA analysis showed that the nomogram model had significant net benefits in predicting 28-day, 90-day, and 1-year death risk, verifying the clinical value of the model and its good impact on actual decision-making. Conclusions:The death risk factors related to ICU patients with sepsis include age, SOFA score, SAPSⅡ score > 44, MAP ≤ 75 mmHg, RR, cerebrovascular disease, malignant tumors, severe liver disease, use of vasoactive drugs within 24 hours, PaO 2, Lac, BUN, TBil, PT > 14.5 s. The nomogram model constructed based on this can predict the death risk of ICU patients with sepsis.

Screening ferroptosis related genes influencing prognosis of colon cancer through bioinformatics analysis / 解剖学报

Objective To explore ferroptosis-related long non-coding RNAs (lncRNAs) with prognostic significance in colon cancer (CC), and then construct a prognosis-related predictive scoring model. To search for ferroptosis-related differential expressed genes co-expressed with prognosis-related lncRNAs. Methods Ferroptosis-related genes (FGs) were downloaded from FerrDb database; The expression data of 41 adjacent normal tissues and 473 tumor tissues, and clinical data of 452 patients were successfully downloaded. Co-expression and differential expression analysis was performed to identify differentially expressed ferroptosis-related lncRNAs (DEFlncRNAs), and univariate Cox regression analysis was used to screen statistically significant prognosis-related DEFlncRNAs, and then multivariate Cox regression analysis was used to construct a prognostic model, calculate risk score among CC patients and divide patients by the median risk score. Kaplan-Meier curves, univariate and multivariate Cox regression analyses, and receiver operationg characteristic(ROC) curve were used to reveale great accuracy of the model. Then, a nomogram was drawed to predict the survival among CC patients. Finally, the differentially expressed ferroptosis-related genes regulating DEFlncRNAs were found by co-expression analysis, and the different expression was verified by immunohistochemical experiments. Result Expression and clinical data among colon cancer (CC) patients were downloaded from TCGA database. A risk prognostic model containing 28 lncRNAs to predict the prognosis among CC patients was successfully constructed. An effective clinical nomogram for predicting the overall survival of CC patients was successfully constructed. Finally, the co-expression analysis of DEFlncRNAs and differentially expressed ferroptosis-related genes (DEFGs) was preformed to obtain a co-expression network, including17 key DEFGs, with the correlation coefficient filter criteria (| corFilter |) > 0.4 and P value filter criteria (P value filter) < 0.05. Immunohistochemical experiments confirmed ANGPTL7 was highly expressed in the adjacent tissues among CC patients. Conclusion Successfully constructed a prognostic-related model among CC patients containing 28 DEFlncRNAs, and 17 DEFGs was finally obtained.

Construction of a Prognostic Model of Multiple Myeloma Based on Metabolism-Related Genes / 中国实验血液学杂志

OBJECTIVE@#To screen the prognostic biomarkers of metabolic genes in patients with multiple myeloma (MM), and construct a prognostic model of metabolic genes.@*METHODS@#The histological database related to MM patients was searched. Data from MM patients and healthy controls with complete clinical information were selected for analysis.The second generation sequencing data and clinical information of bone marrow tissue of MM patients and healthy controls were collected from human protein atlas (HPA) and multiple myeloma research foundation (MMRF) databases. The gene set of metabolism-related pathways was extracted from Molecular Signatures Database (MSigDB) by Perl language. The biomarkers related to MM metabolism were screened by difference analysis, univariate Cox risk regression analysis and LASSO regression analysis, and the risk prognostic model and Nomogram were constructed. Risk curve and survival curve were used to verify the grouping effect of the model. Gene set enrichment analysis (GSEA) was used to study the difference of biological pathway enrichment between high risk group and low risk group. Multivariate Cox risk regression analysis was used to verify the independent prognostic ability of risk score.@*RESULTS@#A total of 8 mRNAs which were significantly related to the survival and prognosis of MM patients were obtained (P<0.01). As molecular markers, MM patients could be divided into high-risk group and low-risk group. Survival curve and risk curve showed that the overall survival time of patients in the low-risk group was significantly better than that in the high risk group (P<0.001). GSEA results showed that signal pathways related to basic metabolism, cell differentiation and cell cycle were significantly enriched in the high-risk group, while ribosome and N polysaccharide biosynthesis signaling pathway were more enriched in the low-risk group. Multivariate Cox regression analysis showed that the risk score composed of the eight metabolism-related genes could be used as an independent risk factor for the prognosis of MM patients, and receiver operating characteristic curve (ROC) showed that the molecular signatures of metabolism-related genes had the best predictive effect.@*CONCLUSION@#Metabolism-related pathways play an important role in the pathogenesis and prognosis of patients with MM. The clinical significance of the risk assessment model for patients with MM constructed based on eight metabolism-related core genes needs to be confirmed by further clinical studies.

Analysis on tumor immune microenvironment and construction of a prognosis model for immune-related skin cutaneous melanoma / 中南大学学报(医学版)

OBJECTIVES@#Malignant melanoma is a highly malignant and heterogeneous skin cancer. Although immunotherapy has improved survival rates, the inhibitory effect of tumor microenvironment has weakened its efficacy. To improve survival and treatment strategies, we need to develop immune-related prognostic models. Based on the analysis of the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Sequence Read Archive (SRA) database, this study aims to establish an immune-related prognosis prediction model, and to evaluate the tumor immune microenvironment by risk score to guide immunotherapy.@*METHODS@#Skin cutaneous melanoma (SKCM) transcriptome sequencing data and corresponding clinical information were obtained from the TCGA database, differentially expressed genes were analyzed, and prognostic models were developed using univariate Cox regression, the LASSO method, and stepwise regression. Differentially expressed genes in prognostic models confirmed by real-time reverse transcription PCR (real-time RT-PCR) and Western blotting. Survival analysis was performed by using the Kaplan-Meier method, and the effect of the model was evaluated by time-dependent receiver operating characteristic curve as well as multivariate Cox regression, and the prognostic model was validated by 2 GEO melanoma datasets. Furthermore, correlations between risk score and immune cell infiltration, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) score, immune checkpoint mRNA expression levels, tumor immune cycle, or tumor immune micro-environmental pathways were analyzed. Finally, we performed association analysis for risk score and the efficacy of immunotherapy.@*RESULTS@#We identified 4 genes that were differentially expressed in TCGA-SKCM datasets, which were mainly associated with the tumor immune microenvironment. A prognostic model was also established based on 4 genes. Among 4 genes, the mRNA and protein levels of killer cell lectin like receptor D1 (KLRD1), leukemia inhibitory factor (LIF), and cellular retinoic acid binding protein 2 (CRABP2) genes in melanoma tissues differed significantly from those in normal skin (all P<0.01). The prognostic model was a good predictor of prognosis for patients with SKCM. The patients with high-risk scores had significantly shorter overall survival than those with low-risk scores, and consistent results were achieved in the training cohort and multiple validation cohorts (P<0.001). The risk score was strongly associated with immune cell infiltration, ESTIMATE score, immune checkpoint mRNA expression levels, tumor immune cycle, and tumor immune microenvironmental pathways (P<0.001). The correlation analysis showed that patients with the high-risk scores were in an inhibitory immune microenvironment based on the prognostic model (P<0.01).@*CONCLUSIONS@#The immune-related SKCM prognostic model constructed in this study can effectively predict the prognosis of SKCM patients. Considering its close correlation to the tumor immune microenvironment, the model has some reference value for clinical immunotherapy of SKCM.

Prognostic Prediction Value and Biological Functions of Non-Apoptotic Regulated Cell Death Genes in Lung Adenocarcinoma / 中国医学科学杂志(英文版)

Objective To explore the potential biological functions and prognostic prediction values of non-apoptotic regulated cell death genes (NARCDs) in lung adenocarcinoma.Methods Transcriptome data of lung adenocarcinoma were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. We identified differentially expressed NARCDs between lung adenocarcinoma tissues and normal tissues with R software. NARCDs signature was constructed with univariate Cox regression analysis and the least absolute shrinkage and selection operator Cox regression. The prognostic predictive capacity of NARCDs signature was assessed by Kaplan-Meier survival curve, receiver operating characteristic curve, and univariate and multivariate Cox regression analyses. Functional enrichment of NARCDs signature was analyzed with gene set variation analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes. In addition, differences in tumor mutational burden, tumor microenvironment, tumor immune dysfunction and exclusion score, and chemotherapeutic drug sensitivity were analyzed between the high and low NARCDs score groups. Finally, a protein-protein interaction network of NARCDs and immune-related genes was constructed by STRING and Cytoscape software. Results We identified 34 differentially expressed NARCDs associated with the prognosis, of which 16 genes (ATIC, AURKA, CA9, ITGB4, DDIT4, CDK5R1, CAV1, RRM2, GAPDH, SRXN1, NLRC4, GLS2, ADRB2, CX3CL1, GDF15, and ADRA1A) were selected to construct a NARCDs signature. NARCDs signature was identified as an independent prognostic factor (P < 0.001). Functional analysis showed that there were significant differences in mismatch repair, p53 signaling pathway, and cell cycle between the high NARCDs score group and low NARCDs score group (all P < 0.05). The NARCDs low score group had lower tumor mutational burden, higher immune score, higher tumor immune dysfunction and exclusion score, and lower drug sensitivity (all P < 0.05). In addition, the 10 hub genes (CXCL5, TLR4, JUN, IL6, CCL2, CXCL2, ILA, IFNG, IL33, and GAPDH) in protein-protein interaction network of NARCDs and immune-related genes were all immune-related genes. Conclusion The NARCDs prognostic signature based on the above 16 genes is an independent prognostic factor, which can effectively predict the clinical prognosis of patients of lung adenocarcinoma and provide help for clinical treatment.

Pyroptosis-related lncRNAs Predict Prognosis of Laryngeal Cancer / 肿瘤防治研究

Objective To construct a prognostic model of laryngeal cancer based on pyroptosis-related lncRNAs. Methods Transcriptome expression and clinical data of patients with laryngeal cancer were downloaded from TCGA database. Differentially-expressed pyroptosis-related lncRNAs were selected using Wilcox rank sum test and Spearman correlation analysis. LncRNAs associated with patients' prognosis were further selected using univariate Cox analysis (P < 0.05), and a prognostic model was established using multivariate Cox regression. ROC curve was used to assess the sensitivity and specificity of this model. Results There were a total of 483 differentially-expressed pyroptosis-related lncRNAs in laryngeal cancer tissues, compared with normal laryngeal tissues (|logFC|≥1, FDR < 0.05). Univariate Cox analysis showed that 23 differentially-expressed lncRNAs were associated with prognosis. Multivariate Cox regression analysis finally obtained a prognostic model based on 10 lncRNAs for predicting the survival of laryngeal carcinoma patients. AUC showed that the model had a good predictive ability (AUC > 0.8). Conclusion Pyroptosis-related lncRNAs can be used to predict the prognosis of patients with laryngeal cancer.

Construction and Validation of A Nomogram Prognostic Model for Patients with Lung Adenocarcinoma / 肿瘤防治研究

Objective To construct a nomogram prognostic model for predicting the survival of patients with lung adenocarcinoma based on the large sample data from the SEER database. Methods We retrospectively analyzed the clinical data of patients who were diagnosed with lung adenocarcinoma from 2010 to 2015 in the SEER database. A nomogram model was created based on independent parameters influencing the prognosis of patients with lung adenocarcinoma using Lasso Cox regression analysis. The C-index and calibration curve were utilized to assess the ability to distinguish and calibrate the nomogram. NRI and DCA curves were used to evaluate the prediction ability and net benefit of the nomogram. Results A total of 15 independent risk factors affecting the prognosis of lung adenocarcinoma were identified and integrated into the nomogram model. The C-index of the prediction model was 0.819 in the training cohort and 0.810 in the validation cohort. The predicted specific survival rate of the 1-, 3- and 5-year calibration curves of the training cohort and the validation cohort were consistent with the actual specific survival rate. In comparison to the 7th edition of the AJCC TNM staging system, the NRI and DCA curves demonstrated a considerable boost to the predictive capacity and net benefits achieved by the nomogram model. The risk stratification model constructed with this nomogram model was able to distinguish the patients with different risks well (P < 0.0001). Conclusion A nomogram prognostic model is successfully developed and validated, which provides a simple and reliable tool for the survival prediction of the patients with lung adenocarcinoma. Meanwhile, the risk stratification model constructed by the prediction model can conveniently screen patients with different risks, which is important for the individualized treatment of lung adenocarcinoma patients.

Construction and validation of lncRNA prognostic model for bladder cancer / 中华检验医学杂志

Objective:This study aims to construct a prognostic model of bladder cancer (BLCA) based on lncRNA.Methods:BLCA lncRNA expression data and clinical information were downloaded from TCGA. Univariate Cox regression was used to evaluate the correlation between the expression level of each lncRNA and overall survival (OS), and the lncRNAs with a corrected P-value<0.01 were selected as candidate predictors. In the training queue, the prediction model is constructed by methods such as least absolute shrinkage and selection operator, and multi-factor stepwise Cox regression, and verified in the verification queue at the same time.. Evaluation the area under the curve of time-dependent receiver operating characteristic (tROC) and Harrel C index. According to the median risk score of the prediction model, patients were divided into high-risk group and low-risk group and the differences in clinicopathological characteristics between the two groups were compared by t-test or chi-square test. Results:Establish a BLCA prognostic model based on 13 lncRNAs, of which LINC01465, ARHGAP5-AS1, ZFHX4-AS1, MAFG-AS1 are prognostic risk factors (β regression coefficients are 0.32, 0.16, 0.06, 0.20, respectively, all>0), and the rest are protection factors (β regression coefficients are all<0); the prediction model of the overall survival in the first year, the third year, and the fifth year in the complete cohort has an area under the tROC curve of 0.79, 0.82, and 0.80 respectively, and the Harrell C index is 0.74. Its predictive ability is better than the previously published BLCA prognostic model based on lncRNA. Adjusting for confounding factors including age and tumor stage found that the risk score of this model was an independent poor prognostic factor for overall survival in BLCA patients (hazard ratio 4.05; P<0.001). Comparison of clinicopathological characteristics of patients in the high-risk and low-risk groups showed that in the high-risk group, there were more old patints (70.0 vs. 66.1, P<0.001), more non-papillary patients (74.2% vs. 61.2, P=0.005), more high-stage patients (37.6% vs. 28.0%, P<0.001 for stage Ⅳ patients), and more high-grade tumors (98.0% vs. 92.0%, P=0.005). Conclusion:In this study, a prognostic model of bladder cancer based on 13 lncRNAs was constructed. This model has good predictive ability and can provide value for clinical decision-making and patient consultation.

Development and validation of a predictive model for the risk of 30-day death in emergency department patients / 中华危重病急救医学

Objective:To explore the risk factors for 30-day death in emergency department patients, and then construct a prediction model and validate it using nomogram.Methods:A retrospective cohort study was conducted. The clinical data of 1 091 patients admitted to the emergency department of the First People's Hospital of Changde from January 1 to June 30, 2021 was collected, including 741 patients from January 1 to March 31 in the development group and 350 patients from April 1 to June 30 in the validation group. General information, first vital signs admitted to the emergency department, and laboratory results were collected, the modified early warning score (MEWS) was calculated, and 30-day outcomes were recorded. Univariate and multivariate Logistic regression analysis was used to screen out the risk factors of 30-day death. According to the results of multivariate analysis, the nomogram was used to construct a 30-day death prediction model. The receiver operator characteristic curve (ROC curve) was used to evaluate the consistency of the prediction model, the calibration of the prediction model was evaluated by the Hosmer-Lemeshow goodness of fit test.Results:A total of 1 091 patients were enrolled. There were 741 patients in the development group, including 356 males and 385 females, aged (51.42±17.33) years old, and the 30-day mortality was 28.88%. There were 350 patients in the validation group, including 188 males and 162 females, aged (52.88±16.11) years old, and the 30-day mortality was 24.00%. The results of the univariate analysis showed that age, primary diagnosis on admission, consciousness, respiratory rate (RR), systolic blood pressure (SBP), heart rate (HR), pulse oxygen saturation (SpO 2), MEWS score, erythrocyte sedimentation rate (ESR), procalcitonin (PCT) and body mass index (BMI) might be the risk factors for 30-day death in patients in the emergency department. The results of the multivariate analysis showed that the MEWS score [odds ratio ( OR) = 14.22, 95% confidence interval (95% CI) was 1.46-138.12], ESR ( OR = 46.71, 95% CI was 20.48-106.53), PCT ( OR = 4.97, 95% CI was 2.46-10.02), BMI (24.0-27.9 kg/m 2: OR = 37.82, 95% CI was 14.69-97.36; ≥28.0 kg/m 2: OR = 62.11, 95% CI was 25.77-149.72) were independent risk factors for 30-day death in the emergency department (all P < 0.05). Using the four variables with the results of multivariate analysis to construct a nomogram prediction model, the area under the ROC curve (AUC) was 0.974 (95% CI was 0.753-0.983) for the development group, and the AUC was 0.963 (95% CI was 0.740-0.975) for the validation group. The Hosmer-Lemeshow test showed no statistically significant difference between the predicted outcome of the nomogram prediction model and the actual occurrence ( χ2 = 1.216, P = 1.270). Conclusion:The prediction model developed by the MEWS score combined with BMI, ESR and PCT can scientifically and effectively predict the 30-day outcome of emergency department patients.

Construction of a prognostic model of transcription factors for colon cancer / 中华内分泌外科杂志

Objective:To investigate the relationship between transcription factors (TFs) and the prognosis of colon cancer, and to construct a prognosis model through TCGA and GEO dual databases, so as to quantify the risk of patients and guide clinical treatment decisions.Methods:The transcriptome and clinical data of colon cancer in TCGA and GEO databases were used in this study. The transcriptome data were annotated and the gene expression was calculated. The difference analysis of TFs in TCGA and GEO (log2FC > 1, P-value (Fdr) < 0.05) was performed. The difference TFs of double data intersection were used for correlation prognosis analysis ( P<0.01). The risk coefficient and risk value of prognosis-related TFs were calculated by COX multivariate analysis, and the prognosis model of TFs was constructed by COX model with "survival" and "glmnet" package. The survival curve ( P<0.001) and ROC curve (AUC>0.75) of the sequence set and verification set were drawn, and the distribution of risk value was visualized. After grouping according to risk value, GSEA enrichment analysis was calculated, gene set grid was constructed, target genes were predicted, and finally, pathway enrichment analysis of GO and KEGG was carried out. Results:387 TFs with different expressions in TCGA and GEO databases were used to draw heat map, volcanic map and TFs-related forest map, and the prognosis model of colon cancer was constructed according to COX multivariate analysis=0.310×HSF4+0.137×IRX3-0.127×ATOH1+0.290×OVOL3+0.137×HOXC6+0.155×SIX2+0.092×ZNF556-0.444×CXXC5+0.429×TIGD1+0.413×TCF7L1. Through enrichment analysis, our results showed that these prognostic factors may directly or indirectly act on cancer pathways, such as basic cell carcinoma and cancer signaling pathway, local tissue-cell adhesion, and extracellular matrix.Conclusions:The constructed TFs prognosis model of colon cancer can quantify the prognostic risk of colon cancer, and its high-risk group is an independent risk factor of colon cancer prognosis. This model is a new way to evaluate the prognosis of colon cancer.

m7G-lncRNAs are potential biomarkers for prognosis and tumor microenvironment in patients with colon cancer / 南方医科大学学报

OBJECTIVE@#To assess the value of m7G-lncRNAs in predicting the prognosis and microenvironment of colorectal cancer (CRC).@*METHODS@#We screened m7G-lncRNAs from TCGA to construct an m7G-lncRNAs risk model using multivariate Cox analysis, which was validated using ROC and C-index curves. Calibration and nomogram were used to predict the prognosis of CRC patients. Point-bar charts and K-M survival curves were used to assess the correlation of risk scores with the patients' clinical staging and prognosis. CIBERSORT and ESTIMATE were used to explore the association between the tumor microenvironment and immune cell infiltration in patients in high and low risk groups and the correlation of risk scores with microsatellite instability, stem cell index and immune checkpoint expression. A protein-protein interaction network was constructed, and the key targets regulated by m7G-lncRNAs were identified and validated in paired samples of CRC and adjacent tissues by immunoblotting.@*RESULTS@#We identified a total of 1722 m7G-lncRNAs from TCGA database, from which 12 lncRNAs were screened to construct the risk model. The AUCs of the risk model for predicting survival outcomes at 1, 3 and 5 years were 0.727, 0.747 and 0.794, respectively. The AUC of the nomogram for predicting prognosis was 0.794, and the predicted results were consistent with actual survival outcomes of the patients. The patients in the high-risk group showed more advanced tumor stages and a greater likelihood of high microsatellite instability than those in the low-risk group (P < 0.05). The tumor stemness index was negatively correlated with the risk score (r=-0.19; P=7.3e-05). Patients in the high-risk group had higher stromal cell scores (P=0.0028) and higher total scores (P=0.007) with lowered expressions of activated mast cells (r=-0.11; P=0.045) and resting CD4+ T cells (r=-0.14; P=0.01) and increased expressions of most immune checkpoints (P < 0.05). ATXN2 (P= 0.006) and G3BP1 (P=0.007) were identified as the key targets regulated by m7G-lncRNAs, and their expressions were both higher in CRC than in adjacent tissues.@*CONCLUSION@#The risk model based on 12 m7G-lncRNAs has important prognostic value for CRC and can reflect the microenvironment and the efficacy of immunotherapy in the patients.

Prognosis prediction model for hepatocellular carcinoma based on autophagy related genes / 生物医学工程学杂志

Autophagy is a programmed cell degradation process that is involved in a variety of physiological and pathological processes including malignant tumors. Abnormal induction of autophagy plays a key role in the development of hepatocellular carcinoma (HCC). We established a prognosis prediction model for hepatocellular carcinoma based on autophagy related genes. Two hundred and four differentially expressed autophagy related genes and basic information and clinical characteristics of 377 registered hepatocellular carcinoma patients were retrieved from the cancer genome atlas database. Cox risk regression analysis was used to identify autophagy-related genes associated with survival, and a prognostic model was constructed based on this. A total of 64 differentially expressed autophagy related genes were identified in hepatocellular carcinoma patients. Five risk factors related to the prognosis of hepatocellular carcinoma patients were determined by univariate and multivariate Cox regression analysis, including TMEM74, BIRC5, SQSTM1, CAPN10 and HSPB8. Age, gender, tumor grade and stage, and risk score were included as variables in multivariate Cox regression analysis. The results showed that risk score was an independent prognostic risk factor for patients with hepatocellular carcinoma ( HR = 1.475, 95% CI = 1.280-1.699, P < 0.001). In addition, the area under the curve of the prognostic risk model was 0.739, indicating that the model had a high accuracy in predicting the prognosis of hepatocellular carcinoma. The results suggest that the new prognostic risk model for hepatocellular carcinoma, established by combining the molecular characteristics and clinical parameters of patients, can effectively predict the prognosis of patients.

Factors related to swallowing recovery for nil per os patients after stroke and prediction model / 中国康复理论与实践

@#Objective To explore the factors related to the recovery of nil per os (NPO) patients after stroke by retrospective data analysis, and to establish a predictive model.Methods The information of demographics, evaluation and treatment of 141 stroke patients admitted to the Hearing and Language Department in Beijing Bo'ai Hospital from April, 2017 to November, 2020 were selected. The predictive model was established by univariate analysis and Logistic regression. The fitting degree and discriminant validity of the model were evaluated by Hosmer-Lemeshow (H-L) test and receiver operating characteristic (ROC) curve. Other 121 patients with post-stroke dysphagia from December, 2020 to November, 2021 were used as the validation set to verify the model.Results For univariate analysis, National Institute of Health Stroke Scale (NIHSS) score, drinking water test results, autonomous cough ability, cough after swallowing, movement ability of tongue and jaw, and electrical stimulation treatment were significantly associated with the outcome (H=65.803, χ2 > 4.623, P<0.05). Multivariate Logistic regression analysis showed that NIHSS score (X1, OR=0.772, 95%CI 0.64 to 0.82, P<0.001), spontaneous cough ability (X2, OR=5.116, 95%CI 1.28 to 20.41, P=0.021), and electrical stimulation during treatment (X3, OR=94.718, 95%CI 5.65 to 1589.26, P=0.002) were independent factors for the outcome of swallowing function. Thus, the predictive model was P=11+e−(2.368−0.325X1+1.632X2+4.551X3) P = 1 1 + e - ( 2.368 - 0.325 X 1 + 1.632 X 2 + 4.551 X 3 ) , which was well fitting (P=0.845), with the largest area under curve (0.884). The overall accuracy of the model in the validation set was 91.7%.Conclusion The patients with dysphagia would like to recover well if he/she was with lower NIHSS scores and normal autonomous cough ability; meanwhile, the addition of electrical stimulation therapy in comprehensive rehabilitation may be helpful. A predictive model has been established, which needs a further research.