High Risk Score Group Research Articles

Identification of an Immune-Related Gene Signature Based on Immunogenomic Landscape Analysis to Predict the Prognosis of Adult Acute Myeloid Leukemia Patients.

Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by highly heterogeneous molecular lesions and cytogenetic abnormalities. Immune disorders in AML and impaired immune cell function have been found to be associated with abnormal karyotypes in AML patients. Immunotherapy has become an alternative therapeutic method that can improve the outcomes of AML patients. For solid tumors, the expression patterns of genes associated with the immune microenvironment provide valuable prognostic information. However, the prognostic roles of immune genes in AML have not been studied as yet. In this study, we identified 136 immune-related genes associated with overall survival in AML patients through a univariate Cox regression analysis using data from TCGA-AML and GTEx datasets. Next, we selected 24 hub genes from among the 136 genes based on the PPI network analysis. The 24 immune-related hub genes further underwent multivariate Cox regression analysis and LASSO regression analysis. Finally, a 6 immune-related gene signature was constructed to predict the prognosis of AML patients. The function of the hub IRGs and the relationships between hub IRGs and transcriptional factors were investigated. We found that higher levels of expression of CSK, MMP7, PSMA7, PDCD1, IKBKG, and ISG15 were associated with an unfavorable prognosis of AML patients. Meanwhile, patients in the TCGA-AML datasets were divided into a high risk score group and a low risk score group, based on the median risk score value. Patients in the high risk group tended to show poorer prognosis [P = 0.00019, HR = 1.89 (1.26–2.83)]. The area under the curve (AUC) was 0.6643. Multivariate Cox Regression assay confirmed that the 6 IRG signature was an independent prognostic factor for AML. The prognostic role of the immune related-gene signature was further validated using an independent AML dataset, GSE37642. In addition, patients in the high risk score group in the TCGA dataset were found to be of an advanced age, IDH mutation, and M5 FAB category. These results suggested that the proposed immune related-gene signature may serve as a potential prognostic tool for AML patients.

A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer.

BackgroundWe attempted to develop a prognostic model and characterize molecular subtypes for gastric cancer on the basis of ribonucleic acid (RNA)-binding proteins (RBPs).Material/MethodsRNA sequence data of gastric cancer were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen survival-related RBPs, followed by least absolute shrinkage and selection operator Cox modeling. Overall and stratified survival analysis was carried out between high and low risk score groups, followed by receiver operator characteristic curve construction. Univariate and multivariate survival analysis was applied to assess its independent prognostic potential. A nomogram was constructed by combining age and the risk score, which was verified by calibration curves and decision curve analyses for 1-, 3-, and 5-year survival. Molecular subtypes were identified using nonnegative matrix factorization method. Clinical features of the identified subtypes were characterized on prognosis, drug sensitivity, and immune infiltration. An external Gene Expression Omnibus dataset was used to verify the above findings.ResultsOn the basis of 44 survival-related RBPs, a robust prognostic 15-RBP signature was constructed. Patients with high risk score had a poorer prognosis than those with low risk score. The risk score had good performance in predicting clinical outcomes for 1-, 3-, and 5-year survival. The signature was effectively independent of other clinical features. The nomogram model combining age and the 15-RBP prognostic model exhibited better practicality and reliability for prognosis. RBP expression data were utilized to define 2 distinct molecular subtypes obviously related to survival outcomes, chemotherapeutic drug sensitivity, and immune infiltration.ConclusionsOur study provides a nomogram model that consists of age and a 15-RBP signature and identifies 2 molecular subtypes for gastric cancer that possess potential value for preclinical, clinical, and translational research on gastric cancer.

Splenic Marginal Zone Lymphoma (SMZL) - Outcomes, Prognostic Factors and Risk-Adapted Therapy in Resource-Poor Settings: Data from a Latin American Retrospective Cohort

Introduction: Splenic marginal zone lymphoma (SMZL) is a rare B-cell lymphoid malignancy with primary involvement of the white pulp of the spleen. It is a chronic disease, of indolent behavior and with prolonged survival. However, approximately 25% of cases have greater biological aggressiveness, a propensity for histological transformation to high-grade B-cell non-Hodgkin's lymphoma and shortened survival. The recognition of these cases of particular outcome is important for the selection of a risk-adapted therapeutic approach in resource-poor settings. Methods: In this single-center, retrospective and observational study, we describe the clinical and epidemiological characteristics, analysis of survival, determination of prognostic factors and propose a risk-adapted therapeutic approach, based on data from a cohort of 39 SMZL patients, treated in the largest cancer center in Latin America, from January 1992 to December 2016. Factors with prognostic implications were determined by Cox's univariate analysis, survival curves were estimated by the Kaplan-Meier method and the log-rank test was used to compare curves of survival and to verify the association between categorical variables and survival curves. Results: The clinical and epidemiological characteristics of the 39 SMZL patients are summarized inTable 1. There was predominance of female patients (71.8% - 28-29), with a median age of 63 years (28-76) and a higher incidence of B symptoms (56.4% - 22-39) and extra-splenic involvement (87.1% - 34-39) than the results described in series in Europe and North America. With a median follow-up of 8.7 years (0.6-20.2 years), the overall survival (OS) estimated at 5 years and progression-free survival (PFS) were 76.9% and 63.7%, respectively. Factors with an adverse prognostic impact on OS included: LDH ≥ 480 UL (HR: 4.55, 95% CI 1.05-19.70, p = 0.043), albumin < 3.5 g-dl (HR: 7.32, 95% CI 1.46-36.32, p = 0.015), hemoglobin < 100 gL (HR: 4.27, 95% CI 1.01-17.99, p = 0.048), platelets < 100 x 109 (HR: 7.32, 95% CI 2.26-60.54, p = 0.003), high-risk Arcaini score (HR: 14.26, 95% CI 1.65-123.20, p = 0.002) and high-risk SMZL-Working Group score (HR: 6.66, 95% CI 1.48-30.05, p = 0.004). Factors with adverse prognostic impact on PFS were: LDH ≥ 480 UL (HR: 4.47, 95% CI 1.42-13.73, p = 0.005), albumin < 3.5 g-dl (HR: 3.39, 95% CI 1.07-10.72, p = 0.007), hemoglobin < 100 gL (HR: 2.80, 95% CI 0.95-8.10, p = 0.048), platelets < 100 x 109 (HR: 2.78, 95% CI 0.95-8.02, p = 0.050), high-risk Arcaini score (HR: 7.32, 95% CI 1.96-27.34, p = 0.0007) and SMZL-Working Group high-risk score (HR: 7.29, 95% CI 2.25-23.56, p = 0.001). Despite the relatively low number of patients, no superiority was observed between the therapeutic regimens used, including rituximab monotherapy, splenectomy and low-intensity cytotoxic therapy, in relation to the outcomes 5 year-OS (p = 0.565) and PFS (p = 0.530) -Table 2. However, baseline characteristics differed between therapeutic groups in a statistically significant manner, and patients treated with rituximab or low-intensity cytotoxic therapy had a median LDH (p = 0.050) and B2 microglobulin (p = 0.048) higher than the group treated with splenectomy, as well as lower albumin value (p = 0.005), higher rate of comorbidities (p = 0.0002), worse performance status by ECOG (p = 0.050) and greater number of individuals with high-risk SMZL-Working Group score (p = 0.030) -Table 3, thus justifying the results described. Conclusion: Therefore, in resource-poor settings, where access to anti-CD20 immunotherapy is not universal for patients with SMZL, we suggest that the first-line treatment should consist of monotherapy with rituximab for elderly patients, with high surgical risk or with at least one risk factor identified in our study. Remainders can be safely managed with splenectomy. Disclosures No relevant conflicts of interest to declare.

Assessing the prognostic value of stemness-related genes in breast cancer patients

Breast cancer (BC) is currently one of the deadliest tumors worldwide. Cancer stem cells (CSCs) are a small group of tumor cells with self-renewal and differentiation abilities and high treatment resistance. One of the reasons for treatment failures is the inability to completely eliminate tumor stem cells. By using the edgeR package, we identified stemness-related differentially expressed genes in GSE69280. Via Lasso-penalized Cox regression analysis and univariate Cox regression analysis, survival genes were screened out to construct a prognostic model. Via nomograms and ROC curves, we verified the accuracy of the prognostic model. We selected 4 genes (PSMB9, CXCL13, NPR3, and CDKN2C) to establish a prognostic model from TCGA data and a validation model from GSE24450 data. We found that the low-risk score group had better OS than the high-risk score group, whether using TCGA or GSE24450 data. A prognostic model including four stemness-related genes was constructed in our study to determine targets of breast cancer stem cells (BCSCs) and improve the treatment effect.

Lipid metabolism gene-wide profile and survival signature of lung adenocarcinoma

BackgroundLung cancer has high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. Disordered lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on the diagnosis and prognostic biomarkers of LUAD.MethodsIn this study, expression analysis of 1045 lipid metabolism-related genes was performed between LUAD tumors and normal tissues derived from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differentially expressed genes (DEGs) was constructed to identify the hub genes. The association between hub genes and overall survival (OS) was evaluated and formed a model to predict the prognosis of LUAD using a nomogram. The model was validated by another cohort, GSE13213.ResultsA total of 217 lipid metabolism-related DEGs were detected in LUAD. Genes were significantly enriched in glycerophospholipid metabolism, fatty acid metabolic process, and eicosanoid signaling. Through network analysis and cytoHubba, 6 hub genes were identified, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. High expression of CYP2C9, UGT1A6, and INS, and low expressions of DGAT1, HPGDS, and LPL, were associated with worse overall survival for 1925 LUAD patients. The model showed that the high-risk score group had a worse OS, and the validated cohort showed the same result.ConclusionsIn this study, a signature of 6 lipid metabolism genes was constructed, which was significantly associated with the diagnosis and prognosis of LUAD patients. Thus, the gene signature can be used as a biomarker for LUAD.

Prognostic value of immune related genes in lung adenocarcinoma.

Lung cancer has the highest incidence and mortality rates of all cancers in China. Immune-related genes and immune infiltrating lymphocytes are involved in tumor growth, and in the past decade, immunotherapy has become increasingly important in the treatment of lung cancer. Using the edgeR package, differentially expressed genes and immune-related genes (DEIRGs) were identified in patients with lung adenocarcinoma (LUAD). Functional enrichment analysis of DEIRGs was performed using Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Survival-associated immune-related genes (IRGs) were selected using univariate Cox regression analysis and the prognostic model was assessed using multivariate Cox regression analysis. Overall, 273 DEIRGs were identified in LUAD, and KEGG pathway analysis of IRGs showed that ‘cytokine-cytokine receptor interaction’ was the most significantly enriched pathway. Furthermore, six survival associated IRGs were screened to establish a prognostic model; patients in the high risk score group had less favorable survival times, and the prognostic model was negatively associated with B cell infiltration. The present study established a prognostic model using analysis of survival-related immune-related genes, which were associated with B cell infiltration.

Abstract 5960: Machine learning assisted prognostication model based on genomic expression in the tumor microenvironment of stage II and III breast cancer

Abstract Background: Immune cells and stroma in the tumor microenvironment (TME) has an important role in patient prognosis and responses to therapy. Only few mathematical models exist to prognosticate patients, based on mRNA expressivity in the TME. Methods: Clinical outcomes data and mRNA-seq of 246 patients with stage 2 and 98 patients with stage 3 estrogen receptor (ER) positive (+) and HER2 negative (-) breast cancer were obtained from TCGA. 26 gene groups composed of 191 genes* enriched in cellular and non-cellular elements of TME, mutational burden (MB), and clinical data were analyzed by Kaplan-Meier (KM) analysis and multivariate nonlinear regression assisted by machine learning to achieve confined optimization with model-data minimization among multiple distribution functions. *Due to character limit, more details about these genes will be at actual presentation. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis in stage 2 and 3 ER+HER2- breast cancer. In stage 2 patients, six genes (C15orf53, PDGFB, IL10, HS3ST2, GPNMB, PADI4) and seven genes (FCRL3, IFNGR2, ICAM2, CXCR4, HLA-DMB, LGMN, ICOSLG) out of 191 genes associated with poor prognosis were identified (p<0.05 and 0.05<p<0.1, respectively). Genomic expression of the six and seven gene groups were labeled as P1 and P2, respectively. RS = -0.173 + 0.151 × (Age at diagnosis0.334) + 0.080 × (P10.528) + 0.156 × (P2−0.116). Based on RS, patients were clustered into 2 groups; high and low RS groups, showing two KM curves with P<0.001, HR=3.762 (95% CI 2.914 - 4.939), confirming the validity of RS modeling. Similarly, in stage 3 patients, 15 genes (CD8A, CD8B, FCRL3, GZMK, CD3E, CCL5, TP53, ICAM3, CD247, IFNG, IFNGR1, ICAM4, SHH, HLA-DOB, CXCR3) and 5 genes (LOXL2, PHEX, ACTA2, MEGF9, TNFSF4) out of 191 genes associated with good and poor prognosis were identified. Genomic expression of the 15 and 5 gene groups were labeled as G and P, respectively. RS = 9.3185 - 0.3250 × (Age at diagnosis0.0001) - 8.2979 × (P/G0.0051). Based on RS, patients were clustered into 2 groups; high and low RS groups, showing two KM curves with P = 0.05, HR = 2.878 (95% CI 1.903 - 3.471), confirming the validity of RS modeling. MB was not associated with survival in both groups. Conclusions: Machine learning-assisted mathematical modeling of RS and gene analysis identified TME-related genes and gene groups that are strongly associated with worse prognosis in stage 2 and 3 ER+HER2- breast cancer. RS could potentially prognosticate patients in the clinic with available genomic profiles. Citation Format: Yara Abdou, Jessica Jerez, Sunyoung Lee. Machine learning assisted prognostication model based on genomic expression in the tumor microenvironment of stage II and III breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5960.

Systematic Analysis of Autophagy-Related Signature Uncovers Prognostic Predictor for Acute Myeloid Leukemia.

Autophagy, a highly conserved cellular protein degradation process, has been involved in acute myeloid leukemia (AML). The present study aims to establish a novel, autophagy-related prognostic signature for prediction of AML prognosis. Differentially expressed autophagy-related genes in AML and healthy samples were screened using GSE1159. Univariate Cox regression analysis was applied to determine survival-associated autophagy-related genes in The Cancer Genome Atlas (TCGA) AML cohort. Lasso regression was performed to develop multiple-gene prognostic signatures. A novel six-gene signature (including CASP3, CHAF1B, KLHL24, OPTN, VEGFA, and VPS37C) DC was established for AML prognosis prediction. The Kaplan-Meier survival analysis revealed that patients in the high-risk score group had poorer overall survival (OS). The receiver operating characteristic (ROC) curve validated its good performance in survival prediction in TCGA AML cohort, and the area under the curve value was 0.817. Moreover, our signature could independently predict OS. A nomogram was constructed, including the six-gene signature and other clinical parameters, and predictive efficiency was confirmed using the ROC curve and calibration curve. Furthermore, gene set enrichment analyses identified several tumor-associated pathways that may contribute to explain the potential molecular mechanisms of our signature. Overall, we developed a new autophagy-associated gene signature and nomogram to predict OS of AML patients, which may help in clinical decision-making for AML treatment.

Effect of serum uric acid level and Multinational Association for Supportive Care in Cancer risk score on febrile neutropenia mortality.

The study was aimed to evaluate the effect of uric acid (UA) on the 30-day mortality of patients admitted to the tertiary referral hospital with a complaint of febrile neutropenia (FEN). The secondary aim was to evaluate the use of combining serum UA levels with the Multinational Association for Supportive Care in Cancer (MASCC) risk score. A retrospective study in which the MASCC score and serum UA levels were used to evaluate the mortality risk within 30days among patients with FEN. A total of 118 FEN episodes were included in the study and 17 (14%) of these patients died. While this rate is 23% in the high-risk group according to the MASCC score, it is 7% in the low-risk group (p= 0.011). In multivariate analysis of the parameters that significantly affect the 30-day FEN mortality, MASCC risk score (OR, 4.28; CI 95% 1.19-15.39, p= 0.013) and having a level of serum UA > 7mg/dL (OR, 4.46; CI 95% 1.19-15.38, p= 0.032) was significantly increased the risk of in 30-day mortality of FEN. The rate of 30-day mortality of FEN was 0% in patients with a low MASCC risk score and UA level compared with 50% in the high MASCC risk score and high UA level group, and the difference was statistically significant (p< 0.001). Increased level of UA at the time of FEN diagnosis was independently associated with an increased rate of 30-day mortality of FEN. The combination of the MASCC risk score and serum UA level might thoroughly predict the 30-day mortality of FEN.

Clustering of Lifestyle Factors and Its Association with Low Back Pain: A Cross-Sectional Study of Over 400,000 Japanese Adults.

PurposeAlthough many studies have indicated the association between low back pain (LBP) and lifestyle factors, the combined effect of lifestyle factors on LBP has not been adequately investigated. We aimed to investigate the association between a cluster of unhealthy lifestyle behaviors and LBP using a large cohort of Japanese adults.MethodsWe included 419,003 adults aged over 20 years who underwent an annual health checkup between April 2013 and March 2014 in Japan. Information on the following lifestyle factors was collected using the standardized questionnaire: smoking, alcohol intake, exercise, physical activity, walking speed, weight control, eating habits, and sleep. Each factor was evaluated as a dichotomous variable (1: health risk, 0: no health risk). A lifestyle risk score was calculated by summing the score of each lifestyle factor (range: 0–12) and was categorized into three groups (low, moderate, high). LBP was defined as self-reported LBP under treatment. Logistic regression analysis was conducted to calculate the odds ratio (OR) and 95% confidence interval (CI) for LBP.ResultsIn multivariable logistic regression analysis, the OR for LBP was significantly higher in the moderate-risk score group (adjusted OR: 1.33 [95% CI: 1.23–1.44] in men; 1.40 [95% CI: 1.27–1.54] in women) and the high-risk score group (adjusted OR: 1.54 [95% CI: 1.43–1.67] in men; 1.83 [95% CI: 1.64–2.03] in women) than in the low-risk score group. A trend of higher risk of LBP associated with higher lifestyle risk score was observed in both sexes (p for trend < 0.001). These results were similar even in subgroup analysis by age and body mass index (BMI).ConclusionClustering of unhealthy lifestyles was associated with increased risk of LBP regardless of age and BMI. These results may provide implications for better prevention and management of LBP, considering modifiable lifestyle factors.

Risk score and prognostication modeling based on mRNA expressivity in the tumor microenvironment of hepatocellular carcinoma.

e16658 Background: Stromal elements in the tumor microenvironment (TME) impact prognosis and response to therapy. Advances in mRNA-sequencing improved understanding of gene expressivity, but few models exist to model prognosis in association with mRNA expression. Methods: Clinical data and mRNA-seq of 256 patients (pts) with hepatocellular carcinoma (HCC) were obtained from TCGA. The expressivity of 191 genes enriched in cellular and structural components of the TME and clinical data were analyzed using machine learning, multivariable COX model, and Kaplan-Meier (KM) analysis to model risk score (RS) for prediction of prognosis. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis. Gene expression associated with poor (P) and good (G) in stage 1 and 2 HCC was identified (refer to presentation). RS (stage 1) = 5.997 - 0.589 × (Age at diagnosis−7.979E-06) - 4.818 × (P/G−0.009); RS (stage 2) = -5.704 - 0.780 × (Age at diagnosis−9.383E-06) + 7.228 × (P/G−0.004). Based on RS, pts were clustered into 2 groups in each stage - high and low RS groups, showing two KM curves with P &lt; 0.05, HR = 3.213 (95% CI 2.212 – 4.347) in stage 1; HR = 2.733 (95% CI 2.131 – 3.426) in stage 2, confirming the validity of RS modeling. Analysis of immune profiles in high and low RS groups shows that expression of genes associated with immunosuppressive factors, desmoplastic reaction, neutrophils, and co-inhibitory factors of T-cells are higher in high RS group in both stages (p &lt; 0.05). Conclusions: Machine learning-assisted mathematical modeling of RS and gene analysis identified TME-related genes and gene groups that are strongly associated with worse prognosis in stage 1 and 2 of HCC. RS could potentially prognosticate pts in the clinic with available genomic profiles.

A novel immune-related genes prognosis biomarker for melanoma: associated with tumor microenvironment.

Background: Melanoma is a cancer of the skin with potential to spread to other organs and is responsible for most deaths due to skin cancer. It is imperative to identify immune biomarkers for early melanoma diagnosis and treatment.Results: 63 immune-related genes of the total 1039 unique IRGs retrieved were associated with overall survival of melanoma. A multi-IRGs classifier constructed using eight IRGs showed a powerful predictive ability. The classifier had better predictive power compared with the current clinical data. GSEA analysis showed multiple signaling differences between high and low risk score group. Furthermore, biomarker was associated with multiple immune cells and immune infiltration in tumor microenvironment.Conclusions: The immune-related genes prognosis biomarker is an effective potential prognostic classifier in the immunotherapies and surveillance of melanoma.Methods: Melanoma samples of genes were retrieved from TCGA and GEO databases while the immune-related genes (IRGs) were retrieved from the ImmPort database. WGCNA, Cox regression analysis and LASSO analysis were used to classify melanoma prognosis. ESTIMATE and CIBERSORT algorithms were used to explore the relationship between risk score and tumor immune microenvironment. GSEA analysis was performed to explore the biological signaling pathway.

Application of decision rules on diagnosis and prognosis of renal colic: a systematic review and meta-analysis.

Renal colic is a prevalent emergency department presentation resulting from urolithiasis. Clinical decision rules for the diagnosis of urolithiasis were developed to help clinicians with better judgment. In this systematic review, we assessed the performance of prediction rules on urolithiasis diagnosis and prognosis. MEDLINE, Embase, Web of Science, and Scopus were searched for studies on the performance of a clinical decision tool for diagnosis or prognosis of urolithiasis. Performance and accuracy of the rules were the key outcomes of interest. Databases were searched from inception to March 2019. Of the 4980 articles reviewed, 28 studies were included in the present analysis. Twenty-one studies were on urolithiasis diagnosis (including eight studies on STONE rule), and 10 studies reported urolithiasis outcomes. Studies were at low to moderate risk of bias. The pooling of data on STONE showed that the prevalence of urolithiasis in low, moderate, and high risk groups were: 12% (95% confidence interval 9%-15%), 53% (95% confidence interval 43%-62%), and 83% (95% confidence interval 75%-91%), respectively. In the high risk score group, prevalence of clinically important alternative diagnosis was 1% (95% confidence interval 0%-2%) and 11% (95% confidence interval 8%-13%) of patients needed intervention. STONE scoring system is useful in estimating the prevalence of urolithiasis but high heterogeneity among the studies makes it unsuitable for application. Other decision tools were poorly studied and cannot be recommended for clinical use.

Integrated Analysis Identifies a Nine-microRNA Signature Biomarker for Diagnosis and Prognosis in Colorectal Cancer.

BackgroundColorectal cancer (CRC) is the third most lethal and malignant type of cancer in the world. Abnormal expression of human microRNA-200a (hsa-miRNA-200a or miR-200a) has previously been characterized as a clinically noticeable biomarker in several cancers, but its role in CRC is still unclear.MethodsThree CRC miRNA expression datasets were integratively analyzed by Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Nine candidate miRNAs were identified and validated for diagnostic and prognostic capability with the prediction model. The potential roles of the tumor suppressor miR-200a-3p in invasion, migration, and epithelial-mesenchymal transition of CRC cells were elaborated by in vitro studies.ResultsNine miRNAs (miR-492, miR-200a, miR-338, miR-29c, miR-101, miR-148a, miR-92a, miR-424, and miR-210) were identified as potentially useful diagnostic biomarkers in the clinic. The overall accuracy rate of the nine miRNAs in the diagnostic model was 0.94, 0.89, and 0.978 in the testing, validation, and independent validation dataset, respectively. CRC patients in the GSE29622 cohort were separated by the prognostic model into the low-risk score group and the high-risk score group. The area under the receiver operating characteristic curve (AUC) was 0.872 and 0.783 for predicting the 1- to 10-year survival of CRC patients. The performance of the prognostic model was validated by an independent TCGA-Colon Adenocarcinoma (COAD) dataset with AUC values between 0.911 and 0.796 in predicting 1- to 10-year survival. Nomograms comprising risk scores, tumor stage, and TNM staging were generated for predicting 1-, 3-, and 5-year overall survival (OS) in the GSE29622 and TCGA-COAD datasets. Colony formation, invasion, and migration in DLD1 and SW480 cells were suppressed by overexpression of miR-200a-3p. Inhibition of miR-200a-3p function contributed to abnormal colony formation, migration, invasion, and epithelial–mesenchymal transition (EMT). miR-200a-3p binding sites were located within the 3′-untranslated region (3′-UTR) of the Forkhead box protein A1 (FOXA1) mRNA.ConclusionWe developed and validated a diagnostic and prognostic prediction model for CRC. miR-200a-3p was determined to be a potential diagnostic and prognostic biomarker for CRC.

Prognostication model based on genomic expression in the tumor microenvironment of ER-positive, HER2-negative stage III breast cancer via machine learning.

3 Background: Stroma in the tumor microenvironment (TME) is known to impact prognosis and responses to therapy. Few mathematical models exist to prognosticate patients, based on mRNA expressivity in the TME. Methods: Clinical outcomes data and mRNA-seq of 98 patients with stage III estrogen receptor (ER) positive (+) and HER2 negative (-) breast cancer were obtained from TCGA. Twenty six gene groups composed of 191 genes (refer to presentation) enriched in cellular and non-cellular elements of TME, mutational burden (MB), and clinical data were analyzed by Kaplan-Meier (KM) analysis and multivariate nonlinear regression assisted by machine learning to achieve confined optimization with model-data minimization among multiple distribution functions. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis in stage 3 ER+HER2- breast cancer. Fifteen genes (CD8A, CD8B, FCRL3, GZMK, CD3E, CCL5, TP53, ICAM3, CD247, IFNG, IFNGR1, ICAM4, SHH, HLA-DOB, CXCR3) and five genes (LOXL2, PHEX, ACTA2, MEGF9, TNFSF4) out of 191 genes associated with good and poor prognosis were identified. Genomic expression of the fifteen and five gene groups were labeled as G and P, respectively. RS = 9.3185 – 0.3250 × (Age at diagnosis0.0001) – 8.2979 × (P/G−0.0051). Based on RS, patients were clustered into two groups; high and low RS groups, showing two KM curves with P = 0.05, HR = 2.878 (95% CI 1.903 – 3.471), confirming the validity of RS modeling. Analysis of immune profiles in high and low RS groups shows that expression of genes associated with desmoplastic reaction, neutrophils, and immunosuppressive cytokines are higher in high RS groups; and those related to immune system activation are higher in low RS groups (p &lt; 0.05). Conclusions: Machine learning-assisted mathematical modeling of RS and gene analysis identified TME-related genes and gene groups that are strongly associated with worse prognosis in stage 3 ER+HER2- breast cancer. RS could potentially prognosticate patients in the clinic with available genomic profiles.

The Role of the Platelet/Lymphocyte Ratio and Neutrophil/Lymphocyte Ratio in Predicting High-Risk Heart Score in Patients Admitted with Non-ST Elevation Acute Coronary Syndrome

Background: For cardiologists, management of acute chest pain continues to be a challenge. Physicians struggle to avoid unnecessary admissions and at the same time not to miss high-risk patients needing urgent intervention. Therefore, diagnostic strategies focus on identifying patients in whom an acute coronary syndrome can be safely ruled out based on findings from history, physical examination, and early cardiac marker measurement. The HEART score, a clinical prediction rule, was developed to provide the clinician with a simple and reliable predictor of cardiac risk. Aim: This study aimed to investigate the role of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) as independent laboratory biomarkers when associated with the HEART risk score. Method: A cross-sectional study of 120 patients who attended the emergency department with acute chest pain. NLR and PLR were both measured. In addition, the HEART score was the valid instrument used in evaluating and risk stratifying patients into low-, intermediate-, and high-risk group. Results: There was a positive correlation between the HEART score and the mean PLR and NLR (p = 0.000*). PLR and NLR were found to be significantly higher in the high-risk HEART score group (p = 0.05 and 0.0001*, respectively). A PLR of 115.5 and above had a sensitivity of 73% and specificity of 78%, while an NLR of 3.95 and above had a sensitivity of 75% and specificity of 86% to detect high-risk HEART score patients. Conclusion: PLR and NLR proved to be a useful tool to identify high-risk patients when validated against the HEART score.

Bioinformatic Analysis Reveals an Immune/Inflammatory-Related Risk Signature for Oral Cavity Squamous Cell Carcinoma.

High-throughput gene expression profiling has recently emerged as a promising technique that provides insight into cancer subtype classification and improved prediction of prognoses. Immune/inflammatory-related mRNAs may potentially enrich genes to allow researchers to better illustrate cancer microenvironments. Oral cavity squamous cell carcinoma (OC-SCC) exhibits high morbidity and poor prognosis compared to that of other types of head and neck squamous cell carcinoma (HNSCC), and these differences may be partially due to differences within the tumor microenvironments. Based on this, we designed an immune-related signature to improve the prognostic prediction of OC-SCC. A cohort of 314 OC-SCC samples possessing whole genome expression data that were sourced from The Cancer Genome Atlas (TCGA) database was included for discovery. The GSE41613 database was used for validation. A risk score was established using immune/inflammatory signatures acquired from the training dataset. Principal components analysis, GO analysis, and gene set enrichment analysis were used to explore the bioinformatic implications. When grouped by the dichotomized risk score based on the signature, this classifier could successfully discriminate patients with distinct prognoses within the training and validation cohorts (P < 0.05 in both cohorts) and within different clinicopathological subgroups. Similar somatic mutation patterns were observed between high and low risk score groups, and different copy number variation patterns were also identified. Further bioinformatic analyses suggested that the lower risk score group was significantly correlated with immune/inflammatory-related biological processes, while the higher risk score group was highly associated with cell cycle-related processes. The analysis indicated that the risk score was a robust predictor of patient survival, and its functional annotation was well established. Therefore, this bioinformatic-based immune-related signature suggested that the microenvironment of OC-SCC could distinguish among patients with different underlying biological processes and clinical outcomes, and the use of this signature may shed light on future OC-SCC classification and therapeutic design.

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network.

Background and Goals. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. We constructed a weighted gene coexpression network (WGCN) using the consensus DEGs and identified the module significantly associated with pathological M stage and consisted of 61 genes. After univariate Cox regression analysis and subsequent stepwise model selection by the Akaike information criterion (AIC) and multivariate Cox hazard model analysis, an mRNA signature model which calculated prognostic score was generated: prognostic score = (−0.2491 × EXPRRAGB) + (−0.0679 × EXPRSPH9) + (−0.2317 × EXPRPS6KL1) + (−0.1035 × EXPRXFP1) + 0.1571 × EXPRRM2 + 0.1104 × EXPRTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model. The prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. Higher scores predicted higher risk. The area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. The prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. These results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma.

176O - Machine learning-assisted prognostication based on genomic expression in the tumour microenvironment of estrogen receptor positive and HER2 negative breast cancer

BackgroundStroma in the tumor microenvironment (TME) is known to impact prognosis and responses to therapy. Few mathematical models exist to prognosticate patients (pts), based on mRNA expressivity in the TME. MethodsClinical outcomes data and mRNA-seq of 246 pts with stage 2 estrogen receptor (ER) positive (+) and HER2 negative (-) breast cancer were obtained from TCGA. 26 gene groups composed of 191 genes* enriched in cellular and non-cellular elements of TME, mutational burden (MB), and clinical data were analyzed by Kaplan-Meier (KM) analysis and multivariate nonlinear regression assisted by machine learning to achieve confined optimization with model-data minimization among multiple distribution functions. *Due to character limit, more details about these genes will be shown at actual presentation. ResultsPrognostication was modeled with higher risk score (RS) representing worse prognosis in stage 2 ER+HER2- breast cancer. Six genes (C15orf53, PDGFB, IL10, HS3ST2, GPNMB, PADI4) and seven genes (FCRL3, IFNGR2, ICAM2, CXCR4, HLA-DMB, LGMN, ICOSLG) out of 191 genes associated with poor prognosis were identified (p<0.05 and 0.05<p<0.1, respectively). Genomic expression of the six and seven gene groups were labeled as P1 and P2, respectively. RS = -0.173 + 0.151×(Age at diagnosis0.334) + 0.080×(P10.528) + 0.156×(P2-0.116). Based on RS, pts were clustered into 2 groups; high and low RS groups, showing two KM curves with P<0.001, HR=3.762 (95% CI 2.914 – 4.939), confirming the validity of RS modeling. Analysis of immune profiles in high and low RS groups shows that expression of genes associated with immunosuppressive factors, T-helper 2 cells, macrophages, neutrophils, co-inhibitory factors of T-cells, and antigen presenting cells are higher in high RS group (p<0.05). MB did not contribute to survival. ConclusionsMachine learning-assisted mathematical modeling of RS and gene analysis identified TME-related genes and gene groups that are strongly associated with worse prognosis in stage 2 ER+HER2- breast cancer. RS could potentially prognosticate pts in the clinic with available genomic profiles. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Screening of key genes associated with R‑CHOP immunochemotherapy and construction of a prognostic risk model in diffuse large B‑cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a common subtype of non-Hodgkin lymphoma, which is curable in the majority of patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) immunochemotherapy. However, the therapeutic mechanism of R-CHOP has not been elucidated. The GSE32918 and GSE57611 datasets were retrieved from The Gene Expression Omnibus database. The differentially expressed genes (DEGs) associated with R-CHOP therapy were identified using limma. Combined with prognostic information in GSE32918, DEGs found to be significantly associated with prognosis were selected using univariate Cox regression analysis and a risk prediction model was constructed. Based on this model, the samples in the training set (GSE32918) were divided into high and low risk score groups according to the median risk score. A total of 801 DEGs were identified between the R-CHOP treated DLBCL and primary DLBCL samples, from this 116 prognosis-associated genes were selected. Using Cox proportional hazards model, an optimal combination of 12 genes [including calcium/calmodulin dependent protein kinase I (CAMK1), hippocalcin like 4 (HPCAL4) and ephrin A5 (EFNA5)] was selected, and the sample risk score prediction model was constructed and validated. The DEGs between high risk score and low risk score groups were significantly enriched in functions associated with ‘response to DNA damage stimulus’, and pathways including ‘cytokine-cytokine receptor interaction’ and ‘cell cycle’. The optimal combination of the 12 genes, including CAMK1, HPCAL4 and EFNA5, was found to be useful in predicting the prognosis of patients with DLBCL after R-CHOP treatment. Therefore, these genes may be affected by R-CHOP in DLBCL.