High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer

Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases

Biomedical research takes advantage of omic data, such as transcriptomics, to unravel the complexity of diseases. A conventional strategy identifies transcriptomic biomarkers characterized by expression patterns associated with a phenotype by relying on feature selection approaches. Hybrid ensemble feature selection (HEFS) has become increasingly popular as it ensures robustness of the selected features by performing data and functional perturbations. However, it remains difficult to make the best suited choices at each step when designing such approaches. We conducted an extensive analysis of four possible HEFS scenarios for the identification of Stage IV colorectal, Stage I kidney and lung and Stage III endometrial cancer biomarkers from transcriptomic data. These scenarios investigate the use of two types of feature reduction by filters (differentially expressed genes and variance) conjointly with two types of resampling strategies (repeated holdout by distribution-balanced stratified and random stratified) for downstream feature selection through an aggregation of thousands of wrapped machine learning models. Based on our results, we emphasize the advantages of using HEFS approaches to identify complex disease biomarkers, given their ability to produce generalizable and stable results to both data and functional perturbations. Finally, we highlight critical issues that need to be considered in the design of such strategies.

Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN) in World Trade Center exposed firefighters: a case-control observational study protocol

BACKGROUND

Particulate matter exposure (PM) is a cause of aerodigestive disease globally. The destruction of the World Trade Center (WTC) exposed first responders and inhabitants of New York City to WTC-PM and caused obstructive airways disease (OAD), gastroesophageal reflux disease (GERD) and Barrett's Esophagus (BE). GERD not only diminishes health-related quality of life but also gives rise to complications that extend beyond the scope of BE. GERD can incite or exacerbate allergies, sinusitis, bronchitis, and asthma. Disease features of the aerodigestive axis can overlap, often necessitating more invasive diagnostic testing and treatment modalities. This presents a need to develop novel non-invasive biomarkers of GERD, BE, airway hyperreactivity (AHR), treatment efficacy, and severity of symptoms.

METHODS

Our observational case-cohort study will leverage the longitudinally phenotyped Fire Department of New York (FDNY)-WTC exposed cohort to identify Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN). Our study population consists of n = 4,192 individuals from which we have randomly selected a sub-cohort control group (n = 837). We will then recruit subgroups of i. AHR only ii. GERD only iii. BE iv. GERD/BE and AHR overlap or v. No GERD or AHR, from the sub-cohort control group. We will then phenotype and examine non-invasive biomarkers of these subgroups to identify under-diagnosis and/or treatment efficacy. The findings may further contribute to the development of future biologically plausible therapies, ultimately enhance patient care and quality of life.

DISCUSSION

Although many studies have suggested interdependence between airway and digestive diseases, the causative factors and specific mechanisms remain unclear. The detection of the disease is further complicated by the invasiveness of conventional GERD diagnosis procedures and the limited availability of disease-specific biomarkers. The management of reflux is important, as it directly increases risk of cancer and negatively impacts quality of life. Therefore, it is vital to develop novel noninvasive disease markers that can effectively phenotype, facilitate early diagnosis of premalignant disease and identify potential therapeutic targets to improve patient care.

TRIAL REGISTRATION

Name of Primary Registry: "Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BADBURN)". Trial Identifying Number: NCT05216133 . Date of Registration: January 31, 2022.

Gastroesophageal Disease and Environmental Exposure: A Systematic Review

Environmental exposure-associated disease is an active area of study, especially in the context of increasing global air pollution and use of inhalants. Our group is dedicated to the study of exposure-related inflammation and downstream health effects. While many studies have focused on the impact of inhalants on respiratory sequelae, there is growing evidence of the involvement of other systems including autoimmune, endocrine, and gastrointestinal. This systematic review aims to provide a recent update that will underscore the associations between inhalation exposures and upper gastrointestinal disease in the contexts of our evolving environmental exposures. Keywords focused on inhalational exposures and gastrointestinal disease. Primary search identified n = 764 studies, of which n = 64 met eligibility criteria. In particular, there was support for existing evidence that PM increases the risk of upper gastrointestinal diseases. Smoking was also confirmed to be major risk factor. Interestingly, studies in this review have also identified waterpipe use as a significant risk factor for gastroesophageal reflux and gastric cancer. Our systematic review identified inhalational exposures as risk factors for aerodigestive disease, further supporting the association between environmental exposure and digestive disease. However, due to limitations on our review's scope, further studies must be done to better understand this interaction.

Enhancing selection of alcohol consumption-associated genes by random forest

Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer

OBJECTIVE

To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches.

METHODS

Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality.

RESULTS

Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts.

CONCLUSIONS

We showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Artificial intelligence assists precision medicine in cancer treatment

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

CpG Site-Based Signature Predicts Survival of Colorectal Cancer

BACKGROUND

A critical unmet medical need in clinical management of colorectal cancer (CRC) pivots around lack of noninvasive and or minimally invasive techniques for early diagnosis and prognostic prediction of clinical outcomes. Because DNA methylation can capture the regulatory landscape of tumors and can be measured in body fluids, it provides unparalleled opportunities for the discovery of early diagnostic and prognostics markers predictive of clinical outcomes. Here we investigated use of DNA methylation for the discovery of potential clinically actionable diagnostic and prognostic markers for predicting survival in CRC.

METHODS

We analyzed DNA methylation patterns between tumor and control samples to discover signatures of CpG sites and genes associated with CRC and predictive of survival. We conducted functional analysis to identify molecular networks and signaling pathways driving clinical outcomes.

RESULTS

We discovered a signature of aberrantly methylated genes associated with CRC and a signature of thirteen (13) CpG sites predictive of survival. We discovered molecular networks and signaling pathways enriched for CpG sites likely to drive clinical outcomes.

CONCLUSIONS

The investigation revealed that CpG sites can predict survival in CRC and that DNA methylation can capture the regulatory state of tumors through aberrantly methylated molecular networks and signaling pathways.

Influence of S100A2 in Human Diseases

S100 proteins are a family of low-molecular-weight proteins characterized by two calcium-binding sites with a helix-loop-helix (“EF-hand-type”) domain. The S100 family of proteins is distributed across various organs and can interact with diverse molecules. Among the proteins of the S100 family, S100 calcium-binding protein A2 (S100A2) has been identified in mammary epithelial cells, glands, lungs, kidneys, and prostate gland, exhibiting various physiological and pathological actions in human disorders, such as inflammatory diseases and malignant tumors. In this review, we introduce basic knowledge regarding S100A2 regulatory mechanisms. Although S100A2 is a tumor suppressor, we describe the various influences of S100A2 on cancer and inflammatory diseases.

Predictors of rapid eye movement sleep behavior disorder in patients with Parkinson’s disease based on random forest and decision tree

Background and objectives

Sleep disorders related to Parkinson’s disease (PD) have recently attracted increasing attention, but there are few clinical reports on the correlation of Parkinson’s disease patients with rapid eye movement (REM) sleep behavior disorder (RBD). Therefore, this study conducted a cognitive function examination for Parkinson’s disease patients and discussed the application effect of three algorithms in the screening of influencing factors and risk prediction effects.

Methods

Three algorithms (logistic regression, machine learning-based regression trees and random forest) were used to establish a prediction model for PD-RBD patients, and the application effects of the three algorithms in the screening of influencing factors and the risk prediction of PD-RBD were discussed.

Results

The subjects included 169 patients with Parkinson’s disease (Parkinson’s disease with RBD [PD-RBD] = 69 subjects; Parkinson’s disease without RBD [PD-nRBD] = 100 subjects). This study compared the predictive performance of RF, decision tree and logistic regression, selected a final model with the best model performance and proposed the importance of variables in the final model. After the analysis, the accuracy of RF (83.05%) was better than that of the other models (decision tree = 75.10%, logistic regression = 71.62%). PQSI, Scopa-AUT score, MoCA score, MMSE score, AGE, LEDD, PD-course, UPDRS total score, ESS score, NMSQ, disease type, RLSRS, HAMD, UPDRS III and PDOnsetage are the main variables for predicting RBD, along with increased weight. Among them, PQSI is the most important factor. The prediction model of Parkinson’s disease RBD that was established in this study will help in screening out predictive factors and in providing a reference for the prognosis and preventive treatment of PD-RBD patients.

Conclusions

The random forest model had good performance in the prediction and evaluation of PD-RBD influencing factors and was superior to decision tree and traditional logistic regression models in many aspects, which can provide a reference for the prognosis and preventive treatment of PD-RBD patients.

Targeting metabolism: A potential strategy for hematological cancer therapy

Most hematological cancer-related relapses and deaths are caused by metastasis; thus, the importance of this process as a target of therapy should be considered. Hematological cancer is a type of cancer in which metabolism plays an essential role in progression. Therefore, we are required to block fundamental metastatic processes and develop specific preclinical and clinical strategies against those biomarkers involved in the metabolic regulation of hematological cancer cells, which do not rely on primary tumor responses. To understand progress in this field, we provide a summary of recent developments in the understanding of metabolism in hematological cancer and a general understanding of biomarkers currently used and under investigation for clinical and preclinical applications involving drug development. The signaling pathways involved in cancer cell metabolism are highlighted and shed light on how we could identify novel biomarkers involved in cancer development and treatment. This review provides new insights into biomolecular carriers that could be targeted as anticancer biomarkers.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.

Quiescin Sulfhydryl Oxidase 2 Overexpression Predicts Poor Prognosis and Tumor Progression in Patients With Colorectal Cancer: A Study Based on Data Mining and Clinical Verification

Background: As a member of the atypical thiol oxidase family, quiescin sulfhydryl oxidase 2 (QSOX2) has been reported to play an important role in several biological processes, but the expression and function of QSOX2 in colorectal cancer (CRC) remains elusive. Methods: The difference of QSOX2 expression, and its relationship with clinicopathological features and prognosis in CRC, was analyzed by bioinformatic analysis and validated by clinical CRC specimen cohort. The functional characterization of QSOX2 was detected via in vitro and vivo experiments in CRC cell lines, while the potential signaling pathways were predicted by Gene Set Enrichment Analysis (GSEA). Results: Our data based on bioinformatical analysis and clinical validation demonstrated that the expression of QSOX2 in CRC tissues was significantly upregulated. Additionally, the chi-square test, logistic regression analysis, and Fisher's exact test showed that QSOX2 overexpression was significantly correlated with advanced clinicopathological parameters, such as pathological stage and lymph node metastasis. The Kaplan-Meier curves and univariate Cox regression model showed that QSOX2 overexpression predicts poor overall survival (OS) and disease-free survival (DFS) in CRC patients. More importantly, multivariate Cox regression model showed that QSOX2 overexpression could serve as an independent factor for CRC patients. In vitro and vivo data showed that the proliferation and metastasis ability of CRC cells were suppressed on condition of QSOX2 inhibition. In addition, GSEA showed that the QSOX2 high expression phenotype has enriched multiple potential cancer-related signaling pathways. Conclusion: QSOX2 overexpression is strongly associated with malignant progression and poor oncological outcomes in CRC. QSOX2 might act as a novel biomarker for prognosis prediction and a new target for biotherapy in CRC.

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

Background: Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invasive, affordable, portable, and accurate screening variables. Methods: This was a retrospective study that used data from electronic medical records of patients with CRC and healthy individuals between July 2017 and June 2018. Laboratory data, including liver enzymes, lipid profiles, complete blood counts, and tumor biomarkers, were extracted from the electronic medical records. Five machine learning models (logistic regression, random forest, k-nearest neighbors, support vector machine [SVM], and naïve Bayes) were used to identify CRC. The performances were evaluated using the areas under the curve (AUCs), sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). Results: A total of 1164 electronic medical records (CRC patients: 582; healthy controls: 582) were included. The logistic regression model achieved the highest performance in identifying CRC (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%, PPV: 84.4%, NPV: 88.9%). The first four weighted features in the model were carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL). A diagnostic model for CRC was established based on the four indicators, with an AUC of 0.849 (0.840-0.860) for identifying all CRC patients, and it performed best in discriminating patients with late colon cancer from healthy individuals with an AUC of 0.905 (0.889-0.929). Conclusions: The logistic regression model based on CEA, HGB, Lp(a), and HDL might be a powerful, noninvasive, and cost-effective method to identify CRC.

Prediction of long-term survival after gastrectomy using random survival forests

BACKGROUND

No well validated and contemporaneous tools for personalized prognostication of gastric adenocarcinoma exist. This study aimed to derive and validate a prognostic model for overall survival after surgery for gastric adenocarcinoma using a large national dataset.

METHODS

National audit data from England and Wales were used to identify patients who underwent a potentially curative gastrectomy for adenocarcinoma of the stomach. A total of 2931 patients were included and 29 clinical and pathological variables were considered for their impact on survival. A non-linear random survival forest methodology was then trained and validated internally using bootstrapping with calibration and discrimination (time-dependent area under the receiver operator curve (tAUC)) assessed.

RESULTS

The median survival of the cohort was 69 months, with a 5-year survival of 53.2 per cent. Ten variables were found to influence survival significantly and were included in the final model, with the most important being lymph node positivity, pT stage and achieving an R0 resection. Patient characteristics including ASA grade and age were also influential. On validation the model achieved excellent performance with a 5-year tAUC of 0.80 (95 per cent c.i. 0.78 to 0.82) and good agreement between observed and predicted survival probabilities. A wide spread of predictions for 3-year (14.8-98.3 (i.q.r. 43.2-84.4) per cent) and 5-year (9.4-96.1 (i.q.r. 31.7-73.8) per cent) survival were seen.

CONCLUSIONS

A prognostic model for survival after a potentially curative resection for gastric adenocarcinoma was derived and exhibited excellent discrimination and calibration of predictions.

The Importance of Sex in the Discovery of Colorectal Cancer Prognostic Biomarkers

Colorectal cancer (CRC) is the third leading cause of cancer deaths. Advances within bioinformatics, such as machine learning, can improve biomarker discovery and ultimately improve CRC survival rates. There are clear sex differences in CRC characteristics, but the impact of sex has not been considered with regards to CRC biomarkers. Our aim here was to investigate sex differences in the transcriptome of a normal colon and CRC, and between paired normal and tumor tissue. Next, we attempted to identify CRC diagnostic and prognostic biomarkers and investigate if they are sex-specific. We collected paired normal and tumor tissue, performed RNA-seq, and applied feature selection in combination with machine learning to identify the top CRC diagnostic biomarkers. We used The Cancer Genome Atlas (TCGA) data to identify sex-specific CRC diagnostic biomarkers and performed an overall survival analysis to identify sex-specific prognostic biomarkers. We found transcriptomic sex differences in both the normal colon tissue and in CRC. Forty-four of the top-ranked biomarkers were sex-specific and 20 biomarkers showed a sex-specific prognostic value. Our data show the importance of sex in the discovery of CRC biomarkers. We propose 20 sex-specific CRC prognostic biomarkers, including ESM1, GUCA2A, and VWA2 for males and CLDN1 and FUT1 for females.

Dietary phenotype and advanced glycation end-products predict WTC-obstructive airways disease: a longitudinal observational study

BACKGROUND

Diet is a modifier of metabolic syndrome which in turn is associated with World Trade Center obstructive airways disease (WTC-OAD). We have designed this study to (1) assess the dietary phenotype (food types, physical activity, and dietary habits) of the Fire Department of New York (FDNY) WTC-Health Program (WTC-HP) cohort and (2) quantify the association of dietary quality and its advanced glycation end product (AGE) content with the development of WTC-OAD.

METHODS

WTC-OAD, defined as developing WTC-Lung Injury (WTC-LI; FEV1 < LLN) and/or airway hyperreactivity (AHR; positive methacholine and/or positive bronchodilator response). Rapid Eating and Activity Assessment for Participants-Short Version (REAP-S) deployed on 3/1/2018 in the WTC-HP annual monitoring assessment. Clinical and REAP-S data of consented subjects was extracted (7/17/2019). Diet quality [low-(15-19), moderate-(20-29), and high-(30-39)] and AGE content per REAP-S questionnaire were assessed for association with WTC-OAD. Regression models adjusted for smoking, hyperglycemia, hypertension, age on 9/11, WTC-exposure, BMI, and job description.

RESULTS

N = 9508 completed the annual questionnaire, while N = 4015 completed REAP-S and had spirometry. WTC-OAD developed in N = 921, while N = 3094 never developed WTC-OAD. Low- and moderate-dietary quality, eating more (processed meats, fried foods, sugary drinks), fewer (vegetables, whole-grains),and having a diet abundant in AGEs were significantly associated with WTC-OAD. Smoking was not a significant risk factor of WTC-OAD.

CONCLUSIONS

REAP-S was successfully implemented in the FDNY WTC-HP monitoring questionnaire and produced valuable dietary phenotyping. Our observational study has identified low dietary quality and AGE abundant dietary habits as risk factors for pulmonary disease in the context of WTC-exposure. Dietary phenotyping, not only focuses our metabolomic/biomarker profiling but also further informs future dietary interventions that may positively impact particulate matter associated lung disease.

hsa_circRNA_001587 upregulates SLC4A4 expression to inhibit migration, invasion, and angiogenesis of pancreatic cancer cells via binding to microRNA-223

Pancreatic cancer (PC) is a malignant tumor that is difficult to diagnose and treat. Circular RNAs (circRNAs) are biomarkers that may be used to diagnose certain cancers or act as targets for cancer treatment. We aimed to explore the functions of human circular RNA 001587 (hsa_circRNA_001587) on the progression of PC and the underlying mechanism. The expression pattern of hsa_circRNA_001587 and microRNA-223 (miR-223) in PC tissues and cells was determined by RT-qPCR. Dual-luciferase reporter gene assay, RNA-pulldown, Argonaute 2 (AGO2) immunoprecipitation assay, and Northern blot analysis were applied to verify the binding relationships among hsa_circRNA_001587, miR-223 and solute carrier family 4 member 4 (SLC4A4). Further analysis of their roles was performed in PC cell line PANC-1. Moreover, we either downregulated or upregulated the expression of hsa_circRNA_001587, miR-223, and SLC4A4 by transfection in vitro. A mouse xenograft model of PC cells was established to evaluate tumor growth in vivo. hsa_circRNA_001587 was poorly expressed, but miR-223 was highly expressed in PC tissues and cell lines. Upregulation of hsa_circRNA_001587 downregulated the expression of matrix metalloproteinase-2 and-9, minichromosome maintenance 2, and vascular endothelial growth factor, and decreased the proliferation, migration, invasion, angiogenic and tumorigenic abilities of PC cells. MiR-223, which can bind with hsa_circRNA_001587, reversed the effects of hsa_circRNA_001587 on PC cells. In addition, SLC4A4 was identified as a target of miR-223, and its knockdown could counteract the regulatory effects of overexpressed hsa_circRNA_001587 or inhibited miR-223 expression on PC cells. Therefore, hsa_circRNA_001587 inhibits PC cell migration, invasion, angiogenesis and tumorigenesis by impairing miR-223-mediated SLC4A4 inhibition.NEW & NOTEWORTHY Human circular (hsa_circ)RNA_001587 and solute carrier family 4 member 4 (SLC4A4) are poorly expressed but microRNA (miR)R-223 is overexpressed in pancreatic cancer (PC) cells. hsa_circRNA_001587 binds to miR-223. Overexpression of hsa_circRNA_001587 inhibits PC progression. Overexpression of miR-223 downregulates the expression of SLC4A4 and promotes PC cell growth. hsa_circRNA_001587 may be a potential target for PC treatment.

An Integrated Bioinformatic Analysis of the S100 Gene Family for the Prognosis of Colorectal Cancer

Background

S100 family genes exclusively encode at least 20 calcium-binding proteins, which possess a wide spectrum of intracellular and extracellular functions in vertebrates. Multiple lines of evidences suggest that dysregulated S100 proteins are associated with human malignancies including colorectal cancer (CRC). However, the diverse expression patterns and prognostic roles of distinct S100 genes in CRC have not been fully elucidated.

Methods

In the current study, we analyzed the mRNA expression levels of S100 family genes and proteins and their associations with the survival of CRC patients using the Oncomine analysis and GEPIA databases. Expressions and mutations of S100 family genes were analyzed using the cBioPortal, and protein-protein interaction (PPI) networks of S100 proteins and their mutation-related coexpressed genes were analyzed using STRING and Cytoscape.

Results

We observed that the mRNA expression levels of S100A2, S100A3, S100A9, S100A11, and S100P were higher and the level of S100B was lower in CRC tissues than those in normal colon mucosa. A high S100A10 levels was associated with advanced-stage CRC. Results from GEPIA database showed that highly expressed S100A1 was correlated with worse overall survival (OS) and disease-free survival (DFS) and that overexpressions of S100A2 and S100A11 were associated with poor DFS of CRC, indicating that S100A1, S100A2, and S100A11 are potential prognostic markers. Unexpectedly, most of S100 family genes showed no significant prognostic values in CRC.

Conclusions

Our findings, though still need to be ascertained, offer novel insights into the prognostic implications of the S100 family in CRC and will inspire more clinical trials to explore potential S100-targeted inhibitors for the treatment of CRC.

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice

BACKGROUND

Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary.

METHODS

We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients.

RESULTS

A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis.

CONCLUSIONS

Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective.

Prognostic Value of SLC4A4 and its Correlation with Immune Infiltration in Colon Adenocarcinoma

BACKGROUND SLC4A4 is differentially expressed in a variety of tumors, but its significance in colon adenocarcinoma has not been determined. MATERIAL AND METHODS Transcriptomes of two cohorts, GSE41258 and GSE32323, contained in The Cancer Genome Atlas (TCGA) were analysed to determine differences in SLC4A4 expression between tumor and normal tissue and their correlations with overall survival. The relationships between SLC4A4 expression and clinical characteristics were determined by COX regression analysis and logistic regression analysis, and correlations of SLC4A4 levels with tumor infiltrating immune cells (TIICs) and genes with high mutation frequency were evaluated by Pearson correlation analysis. Molecular functions and signaling pathways that might be affected by changes in SLC4A4 expression were determined by gene set enrichment analysis (GSEA). The overall distribution of TIICs was determined by two web servers: tumor immune estimation resource (TIMER) and CIBERSORT. RESULTS SLC4A4 expression was lower in colon adenocarcinoma than in normal colon tissue, suggesting that SLC4A4 was associated with poor prognosis. Reduced SLC4A4 expression was also associated with lymph node invasion and distant metastasis and was moderately correlated with increased expression of MUC4 and SMAD4, two genes with high mutation frequency in colon adenocarcinoma. GSEA indicated that changes in SLC4A4 expression affects several biological processes, including mismatch repair, base excision repair, and DNA replication. Eight TIICs in the tumor microenvironment differed significantly in groups with low and high expression of SLC4A4. CONCLUSIONS SLC4A4 may be a novel biomarker predicting prognosis in patients with colon adenocarcinoma. TIICs differed significantly in samples with higher and lower expression of SLC4A4.

Food Intake REstriction for Health OUtcome Support and Education (FIREHOUSE) Protocol: A Randomized Clinical Trial

Fire Department of New York (FDNY) rescue and recovery workers exposed to World Trade Center (WTC) particulates suffered loss of forced expiratory volume in 1 s (FEV1). Metabolic Syndrome increased the risk of developing WTC-lung injury (WTC-LI). We aim to attenuate the deleterious effects of WTC exposure through a dietary intervention targeting these clinically relevant disease modifiers. We hypothesize that a calorie-restricted Mediterranean dietary intervention will improve metabolic risk, subclinical indicators of cardiopulmonary disease, quality of life, and lung function in firefighters with WTC-LI. To assess our hypothesis, we developed the Food Intake REstriction for Health OUtcome Support and Education (FIREHOUSE), a randomized controlled clinical trial (RCT). Male firefighters with WTC-LI and a BMI > 27 kg/m2 will be included. We will randomize subjects (1:1) to either: (1) Low Calorie Mediterranean (LoCalMed)-an integrative multifactorial, technology-supported approach focused on behavioral modification, nutritional education that will include a self-monitored diet with feedback, physical activity recommendations, and social cognitive theory-based group counseling sessions; or (2) Usual Care. Outcomes include reduction in body mass index (BMI) (primary), improvement in FEV1, fractional exhaled nitric oxide, pulse wave velocity, lipid profiles, targeted metabolic/clinical biomarkers, and quality of life measures (secondary). By implementing a technology-supported LoCalMed diet our FIREHOUSE RCT may help further the treatment of WTC associated pulmonary disease.

Exploring the Predictors of Rapid Eye Movement Sleep Behavior Disorder for Parkinson's Disease Patients Using Classifier Ensemble

The rapid eye movement sleep behavior disorder (RBD) of Parkinson's disease (PD) patients can be improved with medications such as donepezil as long as it is diagnosed with a thorough medical examination, since identifying a high-risk group of RBD is a critical issue to treat PD. This study develops a model for predicting the high-risk groups of RBD using random forest (RF) and provides baseline information for selecting subjects for polysomnography. Subjects consisted of 350 PD patients (Parkinson's disease with normal cognition (PD-NC) = 48; Parkinson's disease with mild cognitive impairment (PD-MCI) = 199; Parkinson's disease dementia (PDD) = 103) aged 60 years and older. This study compares the prediction performance of RF, discriminant analysis, classification and regression tree (CART), radial basis function (RBF) neural network, and logistic regression model to select a final model with the best model performance and presents the variable importance of the final model's variable. As a result of analysis, the sensitivity of RF (79%) was superior to other models (discriminant analysis = 14%, CART = 32%, RBF neural network = 25%, and logistic regression = 51%). It was confirmed that age, the motor score of Untitled Parkinson's Disease Rating (UPDRS), the total score of UPDRS, the age when a subject was diagnosed with PD first time, the Korean Mini Mental State Examination, and Korean Instrumental Activities of Daily Living, were major variables with high weight for predicting RBD. Among them, age was the most important factor. The model for predicting Parkinson's disease RBD developed in this study will contribute to the screening of patients who should receive a video-polysomnography.

High expression of FABP4 and FABP6 in patients with colorectal cancer

Objective

To explore the relationship between FABP4 and FABP6 expression and the pathogenesis of colorectal cancer (CRC) and their potential as biomarkers in the diagnosis of CRC.

Methods

In total, 100 CRC patients and 100 controls were enrolled. The serum levels of FABP4 and FABP6 were detected by enzyme-linked immunosorbent assay (ELISA) before and 2 weeks after radical resection of CRC. The protein expressions of FABP4 and FABP6 were observed in colorectal tumor tissues and adjacent tissues by immunohistochemistry and western blot, respectively. The diagnostic performance of FABP4 and FABP6 in patients with CRC was evaluated by receiver operating characteristic (ROC) curve analysis.

Results

The serum levels of FABP4 and FABP6 in patients with CRC were higher than the levels in the controls before surgery (P < 0.001), and significantly decreased at 2 weeks after operation (P < 0.001). Immunohistochemistry showed that FABP4 and FABP6 were mainly distributed in the cytoplasm of human colorectal tumor tissues, and only a small amount distributed in adjacent tissues. Western blot revealed that the protein expressions of FABP4 and FABP6 were significantly higher in tumor tissues than in adjacent tissues (P < 0.001, P = 0.002, respectively). Tumors with high and low FABP4 and FABP6 expression have no significant correlation in tumor size, tumor site, distant organ and lymph node metastasis, histologic grade, lymphatic permeation, neurological invasion, vascular invasion, and Duke’s and TNM classification. Multivariate logistic regression analysis showed that FABP4 and FABP6 were independent risk factors for CRC (adjusted odds ratio 1.916; 95%CI 1.340–2.492; P < 0.001; adjusted odds ratio 2.162; 95%CI 1.046, 1.078); P < 0.001, respectively). In discriminating CRC from the normal control, the optimal sensitivity of FABP4 and FABP6 were 93.20% (95%CI 87.8–96.7) and 83.70% (95%CI 76.7–89.3), respectively, while the optimal specificity of FABP4 and FABP6 were 48.8% (95%CI 39.8–57.9) and 58.4% (95%CI 49.2–67.1), respectively. When combined detection of serum carcinoembryonic (CEA) and FABP4 and FABP6, the optimal sensitivity and specificity were 61.33% (95%CI 53.0–69.2) and 79.82% (95%CI 71.3–86.8), respectively.

Conclusion

Increased expression of FABP4 and FABP6 not only were strong risk factors for the development of CRC but could also represent a potential biomarker for CRC diagnosis in Chinese patients. Combined detection of CEA with FABP4 and FABP6 could improve the diagnostic efficacy of CRC.

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

Steroidomics, an analytical technique for steroid biomarker mining, has received much attention in recent years. This systematic review and functional analysis, following the PRISMA statement, aims to provide a comprehensive review and an appraisal of the developments and fundamental issues in steroid high-throughput analysis, with a focus on cancer research. We also discuss potential pitfalls and proposed recommendations for steroidomics-based clinical research. Forty-five studies met our inclusion criteria, with a focus on 12 types of cancer. Most studies focused on cancer risk prediction, followed by diagnosis, prognosis, and therapy monitoring. Prostate cancer was the most frequently studied cancer. Estradiol, dehydroepiandrosterone, and cortisol were mostly reported and altered in at least four types of cancer. Estrogen and estrogen metabolites were highly reported to associate with women-related cancers. Pathway enrichment analysis revealed that steroidogenesis; androgen and estrogen metabolism; and androstenedione metabolism were significantly altered in cancers. Our findings indicated that estradiol, dehydroepiandrosterone, cortisol, and estrogen metabolites, among others, could be considered oncosteroids. Despite noble achievements, significant shortcomings among the investigated studies were small sample sizes, cross-sectional designs, potential confounding factors, and problematic statistical approaches. More efforts are required to establish standardized procedures regarding study design, analytical procedures, and statistical inference.

Review: Helicobacter pylori and extragastric diseases

In the last year, many studies have demonstrated a potential role of Helicobacter pylori in the pathogenic mechanisms of different extragastric diseases. While the role of H pylori in idiopathic thrombocytopenic purpura, idiopathic iron deficiency anemia, and vitamin B12 deficiency has already been demonstrated, there is growing evidence of other related conditions, especially cardiovascular, metabolic, and neurologic disorders, including neurodegenerative diseases. A summary of the results of the most relevant studies published over the last year on this attractive topic is presented in this review.

Special Issue on Basic and Translational Research in Colorectal Cancer

The present editorial aims to summarize the 17 scientific papers that have contributed to this Special Issue focusing on different aspects of basic and translational research in colorectal cancer.