In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer

Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.

Predictive Model Evaluation for PHM

In the past decades, machine learning techniques or algorithms, particularly, classifiers have been widely applied to various real-world applications such as PHM. In developing high-performance classifiers, or machine learning-based models, i.e. predictive model for PHM, the predictive model evaluation remains a challenge. Generic methods such as accuracy may not fully meet the needs of models evaluation for prognostic applications. This paper addresses this issue from the point of view of PHM systems. Generic methods are first reviewed while outlining their limitations or deficiencies with respect to PHM. Then, two approaches developed for evaluating predictive models are presented with emphasis on specificities and requirements of PHM. A case of real prognostic application is studies to demonstrate the usefulness of two proposed methods for predictive model evaluation. We argue that predictive models for PHM must be evaluated not only using generic methods, but also domain-oriented approaches in order to deploy the models in real-world applications.

Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t -test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081 ∗ log 2 CD 36 + 0.043 ∗ log 2 COL 5 A 2 + 0.001 ∗ log 2 ITGB 5 + 0.039 ∗ log 2 SDC 2 + 0.135 ∗ log 2 SV 2 B + 0.012 ∗ log 2 THBS 1 + 0.068 ∗ log 2 VTN + 0.023 ∗ log 2 VWF . The risk score model could well predict the outcome of patients with gastric cancer in both training ( n = 351 , HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046 ) and validation ( n = 300 , HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014 ) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures ( P < 0.0001 ). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001 ) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001 ) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

A Novel Mathematical Equation for a Noninvasive Determination of the Number of Alveoli of the Human Lung

Background: There had always been a spirited effort in understanding the transport of air or molecular oxygen plus other gases from alveolar air space into the pulmonary capillaries and from the latter back into the former using mathematical models; the determination of the number of alveoli using cadaver and invasive and partially noninvasive methods have been made. There is a need for a noninvasive method of mathematical nature, with evaluative, diagnostic, and prognostic application. Objectives: The objectives of this research were to derive a mathematical equation for the noninvasive determination of the number of alveoli during rest and physical activity and elucidate the usefulness and advantage of the model over known methods. Methods: Theoretical and computational (calculational) methods; data in the literature were substituted into the model mathematical equation for the computation of the number of alveoli in the human lungs. Results and Discussion: The computed number (Nalv) of alveoli differed from one country or subcontinental region to another. The Nalv for the male were expectedly larger than for the female subjects. Conclusion: The mathematical equation for totally noninvasive determination by computation is derivable and was derived. The total number (Nalv) of alveoli mobilised for function is a function of the width (d) of the nares (d 22/15), rate (Rv) of gas flow , and radius (ralv) of a functional alveolus . The equation has the potential to be of diagnostic, evaluative and prognostic value in medical practice. This new computational approach could be faster than other known approaches for the determination of the Nalv. A noninvasive approach by computation, relying on other noninvasively determined respiratory parameters, can eliminate the possibility of tissue damage.

Extracellular vesicles as biomarkers for diagnostic and prognostic application in cardiovascular disease

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. Early diagnosis and the development of a prognosis is important for management or secondary prevention of the disease. In the past few decades, various biomarkers have been identified for improved risk assessment, more accurate diagnosis and prognosis, and a better understanding of the underlying pathophysiology in CVD. Extracellular vesicles (EVs) are thought to be important to cell-to-cell communication in the heart, and EV counts, cellular origin, and EV content have been related to CVD. This review examines current evidence for the potential application of EVs as a new class of biomarkers in CVD. Keywords: extracellular vesicles, biomarker, liquid biopsy, cardiovascular disease, myocardial infarction, heart failure, pulmonary arterial hypertension

BIRC7 and STC2 expression are associated with tumorigenesis and poor outcome in extrahepatic cholangiocarcinoma

Background: Extrahepatic cholangiocarcinoma (EHCC) is a highly aggressive epithelial malignancy and has a poor prognosis for the insensitivity to therapies and difficulty in detection. Novel targets and biomarkers are urgently needed to develop for functional, diagnostic and prognostic application on EHCC.Methods: Immunohistochemical staining technique using the EnVision antibody complex was performed on the samples obtained from 100 EHCC，30 peritumoral extrahepatic biliary tract (EHBT), 10 EHBT adenomas and 15 normal EHBT tissues.Results: The positive rates of BIRC7 and STC2 expression in tissues obtained from peritumoral EHBT, EHBT adenomas and normal EHBT were significantly lower than those in EHCC tissues. BIRC7 and STC2 proteins were expressed at significantly higher levels in patients with lymph node metastasis, invasion of adjacent tissues, and higher TNM stage (III and/or IV) and unable to undergo resection (biopsy only). Kaplan-Meier survival curves indicated that significantly decreased overall survival rate in patients with positive-BIRC7 or positive-STC2 expression compared with patients of negative-BIRC7 or negative-STC2 expression, respectively. Cox-proportional regression analysis demonstrated that positive-BIRC7 and positive-STC2 expression, along with poor differentiation of EHCC, tumor size >3cm, lymph node metastasis, invasion of adjacent tissues and unable to undergo resection are independent prognostic factors of EHCC patients.Conclusions：The levels of BIRC7 and STC2 expression were correlated with clinicopathological characteristics of EHCC, and positive expression of BIRC7 and STC2 are associated with progression and poor clinical outcomes of EHCC. BIRC7 and STC2 might be a potential biomarker for EHCC diagnosis and prognosis.

BIRC7 and STC2 Expression Are Associated With Tumorigenesis and Poor Outcome in Extrahepatic Cholangiocarcinoma

Background: Extrahepatic cholangiocarcinoma (EHCC) is a highly aggressive epithelial malignancy and has a poor prognosis for the insensitivity to therapies and difficulty in detection. Novel targets and biomarkers are urgently needed to develop for functional, diagnostic and prognostic application on EHCC. Methods: Immunohistochemical staining technique using the EnVision antibody complex was performed on the samples obtained from 100 EHCC, 30 peritumoral extrahepatic biliary tract (EHBT), 10 EHBT adenomas and 15 normal EHBT tissues. Results: The positive rates of BIRC7 and STC2 expression in tissues obtained from peritumoral EHBT, EHBT adenomas and normal EHBT were significantly lower than those in EHCC tissues. BIRC7 and STC2 proteins were expressed at significantly higher levels in patients with lymph node metastasis, invasion of adjacent tissues, and higher TNM stage (III and/or IV) and unable to undergo resection (biopsy only). Kaplan-Meier survival curves indicated that significantly decreased overall survival rate in patients with positive-BIRC7 or positive-STC2 expression compared with patients of negative-BIRC7 or negative-STC2 expression, respectively. Cox-proportional regression analysis demonstrated that positive-BIRC7 and positive-STC2 expression, along with poor differentiation of EHCC, tumor size >3 cm, lymph node metastasis, invasion of adjacent tissues and unable to undergo resection are independent prognostic factors of EHCC patients. Conclusions: The levels of BIRC7 and STC2 expression were correlated with clinicopathological characteristics of EHCC, and positive expression of BIRC7 and STC2 are associated with progression and poor clinical outcomes of EHCC. BIRC7 and STC2 might be a potential biomarker for EHCC in clinic.