Computational advances of tumor marker selection and sample classification in cancer proteomics

Consensus statement on extracellular vesicles in liquid biopsy for advancing laboratory medicine

Extracellular vesicles (EVs) represent a diverse class of nanoscale membrane vesicles actively released by cells. These EVs can be further subdivided into categories like exosomes and microvesicles, based on their origins, sizes, and physical attributes. Significantly, disease-derived EVs have been detected in virtually all types of body fluids, providing a comprehensive molecular profile of their cellular origins. As a result, EVs are emerging as a valuable addition to liquid biopsy techniques. In this collective statement, the authors share their current perspectives on EV-related research and product development, with a shared commitment to translating this newfound knowledge into clinical applications for cancer and other diseases, particularly as disease biomarkers. The consensus within this document revolves around the overarching recognition of the merits, unresolved questions, and existing challenges surrounding EVs. This consensus manuscript is a collaborative effort led by the Committee of Exosomes, Society of Tumor Markers, Chinese anti-Cancer Association, aimed at expediting the cultivation of robust scientific and clinically applicable breakthroughs and propelling the field forward with greater swiftness and efficacy.

Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model

Background

Assessing cognitive function in patients with chronic obstructive pulmonary disease (COPD) is crucial for ensuring treatment efficacy and avoiding moderate cognitive impairment (MCI) or dementia. We aimed to build better machine learning models and provide useful tools to provide better guidance and assistance for COPD patients' treatment and care.

Methods

A total of 863 COPD patients from a local general hospital were collected and screened, and they were separated into two groups: cognitive impairment (356 patients) and cognitively normal (507 patients). The Montreal Cognitive Assessment (MoCA) was used to test cognitive function. The swarm intelligence optimization algorithm (SIOA) was used to direct feature weighting and hyperparameter optimization, which were considered simultaneous activities. A self-assigning feature weights and residual evolution (SAFWRE) algorithm was built on the concept of linear and nonlinear information fusion.

Results

The best method in SIOA was the circle search algorithm. On the training set, SAFWRE's ROC-AUC was 0.9727, and its PR-AUC was 0.9663; on the test set, SAFWRE's receiver operating characteristic-area under curve (ROC-AUC) was 0.9243, and its precision recall-area under curve (PR-AUC) was 0.9059, and its performance was much superior than that of the control technique. In terms of external data, the classification and prediction performance of various models are comprehensively evaluated. SAFWRE has the most excellent classification performance, with ROC-AUC of 0.8865 and pr-auc of 0.8299.

Conclusion

This work develops a practical visualization system based on these weight attributes which has strong application importance and promotion value.

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

Antioxidant activity of the main phenolics found in red fruits: An in vitro and in silico study

The current study analysed the antioxidant capacity of the main phenolics found in red fruits. In total, there were analysed the antioxidant activity against 1,1-diphenyl-2-picrylhydrazyl radical, nitric oxide and superoxide radicals (DPPH, NO and O2-, respectively) of 23 phenolics. Regarding DPPH, anthocyanins, (-)-epicatechin and kaempferol 3-O-rutinoside were the most active, while isorhamnetin 3-O-glucoside was the least active. Anthocyanins, (-)-epicatechin, quercetin 3-O-glucoside and caffeic acid showed the strongest potential against NO, while ρ-hydroxybenzoic acid was the less efficient. Regarding the O2- assay, quercetin aglycone and their derivatives were the best ones, while cyanidin aglycone did not show any potential to quench this radical. To deeper explore the biological potential of the most promising compounds, docking molecular and ADME studies were also done. The obtained data is another support regarding the biological potential of phenolics and might be useful in encouraging their use and incorporation in new products.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Bioinformatic prediction of miR-320a as a potential negative regulator of CDGSH iron-sulfur domain 2 (CISD2), involved in lung adenocarcinoma bone metastasis via MYC activation, and associated with tumor immune infiltration

Background

Ferroptosis, a form of regulated cell death associated with iron-dependent lipid peroxidation, plays a role in cancer progression. However, the specific mechanisms of ferroptosis in lung adenocarcinoma (LUAD) bone metastasis (BM) remain unclear. Using bioinformatics analysis, this study sought to identify the ferroptosis-associated genes involved in BM in LUAD, thus providing potential novel targets for the treatment of BM in LUAD.

Methods

The RNA expression dataset GSE10799 was acquired from the Gene Expression Omnibus (GEO) database, and intersected with the ferroptosis dataset to identify ferroptosis-related differentially expressed genes (DEGs). The expression of candidate genes and their correlation with the prognosis of LUAD patients were validated in The Cancer Genome Atlas (TCGA) database. A protein gene interaction network was constructed using GeneMania and Retrieval of Interacting Genes/Proteins (STRING) databases. The association between the candidate genes and immune cells was assessed via TCGA and Tumor IMmune Estimation Resource (TIMER) databases. The potential mechanisms were elucidated by a gene set enrichment analysis (GSEA). The relevant microRNAs (miRNAs or miRs) that bind to the 3'untranslated region (3'UTR) end of candidate genes' mRNA was explored using the TargetScan database. The expression of these candidate miRNAs in LUAD was validated and the correlation between candidate miRNAs and candidate mRNAs was tested using the TCGA database. Finally, the clinical data of 40 LUAD patients were retrospectively analyzed to evaluate the clinical value of candidate gene expression for LUAD BM patients.

Results

In this research, 15 ferroptosis-related DEGs in LUAD BM were identified. TCGA database analysis indicated that patients with low levels of CDGSH iron-sulfur domain 2 (CISD2) in LUAD had better disease-specific survival (DSS), overall survival (OS), and a better progression-free interval (PFI) than those with high levels of CISD2. The TIMER database results show that the expression of CISD2 is correlated with the infiltration levels of various immune cells. The GSEA indicated that CISD2 might influence biological activity in LUAD by participating in cell-cycle regulation, mitochondrial translation, DNA damage repair, c-Myc (MYC) activation, and the P53 signaling pathway. Through the combined analysis of the TargetScan and TCGA databases, hsa-miR-320a was identified as the optimal upstream regulatory miRNA. The immunohistochemistry data indicated that the positive CISD2 expression rates and immunohistochemistry scores of the patients with BM were significantly higher than those of the patients without BM (P<0.05). The high expression of CISD2 is a significant risk factor for BM in LUAD.

Conclusions

The downregulation of CISD2 expression may extend DSS, OS, and the PFI of LUAD patients. Thus, CISD2 could serve as a novel predictive biomarker for LUAD patients. Further, miR-320a might negatively regulate CISD2 and participate in LUAD BM by activating MYC. These data provide a potential perspective for developing anticancer therapies for LUAD-BM patients.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning

Background

Osteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples.

Methods

We exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers.

Results

In total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set.

Conclusion

We identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.

Proteomics and metabolomics analyses of Streptococcus agalactiae isolates from human and animal sources

Streptococcus agalactiae (S. agalactiae), group B Streptococcus (GBS), a major cause of infection in a wide variety of diseases, have been compared in different human and animal sources. We aimed to compare the bacterial proteome and metabolome profiles of human and animal S. agalactiae strains to delineate biological interactions relevant to infection. With the innovative advancement in mass spectrometry, a comparative result between both strains provided a solid impression of different responses to the host. For instance, stress-related proteins (Asp23/Gls24 family envelope stress response protein and heat shock protein 70), which play a role in the survival of GBS under extreme environmental conditions or during treatment, are highly expressed in human and animal strains. One human strain contains ꞵ-lactamase (serine hydrolase) and biofilm regulatory protein (lytR), which are important virulence regulators and potential targets for the design of novel antimicrobials. Another human strain contains the aminoglycosides-resistance bifunctional AAC/APH (A0A0U2QMQ5) protein, which confers resistance to almost all clinically used aminoglycosides. Fifteen different metabolites were annotated between the two groups. L-aspartic acid, ureidopropionic acid, adenosine monophosphate, L-tryptophan, and guanosine monophosphate were annotated at higher levels in human strains. Butyric acid, fumaric acid, isoleucine, leucine, and hippuric acid have been found in both human and animal strains. Certain metabolites were uniquely expressed in animal strains, with fold changes greater than 2. For example, putrescine modulates biofilm formation. Overall, this study provides biological insights into the substantial possible bacterial response reflected in its macromolecular production, either at the proteomic or metabolomic level.

Proteomic and computational analyses followed by functional validation of protective effects of trigonelline against calcium oxalate-induced renal cell deteriorations

Trigonelline is a phytoalkaloid commonly found in green and roasted coffee beans. It is also found in decaffeinated coffee. Previous report has shown that extract from trigonelline-rich plant exhibits anti-lithiatic effects in a nephrolithiatic rat model. Nevertheless, cellular mechanisms underlying the anti-lithiatic properties of trigonelline remain hazy. Herein, we used nanoLC-ESI-Qq-TOF MS/MS and MaxQuant-based quantitative proteomics to identify trigonelline-induced changes in protein expression in MDCK renal cells. From a total of 1006 and 1011 proteins identified from control and trigonelline-treated cells, respectively, levels of 62 (23 upregulated and 39 downregulated) proteins were significantly changed by trigonelline. Functional enrichment and reactome pathway analyses suggested that these 62 altered proteins were related to stress response, cell cycle and cell polarity. Functional validation by corresponding experimental assays revealed that trigonelline prevented calcium oxalate monohydrate crystal-induced renal cell deteriorations by inhibiting crystal-induced overproduction of intracellular reactive oxygen species, G0/G1 to G2/M cell cycle shift, tight junction disruption, and epithelial-mesenchymal transition. These findings provide cellular mechanisms and convincing evidence for the renoprotective effects of trigonelline, particularly in kidney stone prevention.

Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis

Idiopathic pulmonary fibrosis (IPF) is the most common and serious type of idiopathic interstitial pneumonia, characterized by chronic, progressive, and low survival rates, while unknown disease etiology. Until recently, patients with idiopathic pulmonary fibrosis have a poor prognosis, high mortality, and limited treatment options, due to the lack of effective early diagnostic and prognostic tools. Therefore, we aimed to identify biomarkers for idiopathic pulmonary fibrosis based on multiple machine-learning approaches and to evaluate the role of immune infiltration in the disease. The gene expression profile and its corresponding clinical data of idiopathic pulmonary fibrosis patients were downloaded from Gene Expression Omnibus (GEO) database. Next, the differentially expressed genes (DEGs) with the threshold of FDR < 0.05 and |log2 foldchange (FC)| > 0.585 were analyzed via R package "DESeq2" and GO enrichment and KEGG pathways were run in R software. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms were combined to screen the key potential biomarkers of idiopathic pulmonary fibrosis. The diagnostic performance of these biomarkers was evaluated through receiver operating characteristic (ROC) curves. Moreover, the CIBERSORT algorithm was employed to assess the infiltration of immune cells and the relationship between the infiltrating immune cells and the biomarkers. Finally, we sought to understand the potential pathogenic role of the biomarker (SLAIN1) in idiopathic pulmonary fibrosis using a mouse model and cellular model. A total of 3658 differentially expressed genes of idiopathic pulmonary fibrosis were identified, including 2359 upregulated genes and 1299 downregulated genes. FHL2, HPCAL1, RNF182, and SLAIN1 were identified as biomarkers of idiopathic pulmonary fibrosis using LASSO logistic regression, RF, and SVM-RFE algorithms. The ROC curves confirmed the predictive accuracy of these biomarkers both in the training set and test set. Immune cell infiltration analysis suggested that patients with idiopathic pulmonary fibrosis had a higher level of B cells memory, Plasma cells, T cells CD8, T cells follicular helper, T cells regulatory (Tregs), Macrophages M0, and Mast cells resting compared with the control group. Correlation analysis demonstrated that FHL2 was significantly associated with the infiltrating immune cells. qPCR and western blotting analysis suggested that SLAIN1 might be a signature for the diagnosis of idiopathic pulmonary fibrosis. In this study, we identified four potential biomarkers (FHL2, HPCAL1, RNF182, and SLAIN1) and evaluated the potential pathogenic role of SLAIN1 in idiopathic pulmonary fibrosis. These findings may have great significance in guiding the understanding of disease mechanisms and potential therapeutic targets in idiopathic pulmonary fibrosis.

Comprehensive analysis of basement membrane and immune checkpoint related lncRNA and its prognostic value in hepatocellular carcinoma via machine learning

Background

Hepatocellular carcinoma (HCC), which is characterized by its high malignancy, generally exhibits poor response to immunotherapy. As part of the tumor microenvironment, basement membranes (BMs) are involved in tumor development and immune activities. Presently, there is no integrated analysis linking the basement membrane with immune checkpoints, especially from the perspective of lncRNA.

Methods

Based on transcriptome data from The Cancer Genome Atlas, BMs-related and immune checkpoint-related lncRNAs were identified. By applying univariable Cox regression and Machine learning (LASSO and SVM-RFE algorithm), a 10-lncRNA prognosis signature was constructed. The prognostic significance of this signature was assessed by survival analysis. GSEA, ssGSEA, and drug sensitivity analysis were conducted to investigate potential functional pathways, immune status, and clinical implications of guiding individual treatments in HCC. Finally, the promoting migration effect of LINC01224 was validated via in vitro experiments.

Results

The multiple Cox regression, receiver operating characteristic curves, and stratified survival analysis of clinical subgroups exhibited the robust prognostic ability of the lncRNA signature. Results of the GSEA and drug sensitivity analysis revealed significant differences in potential functional pathways and response to drugs between the two risk groups. In addition, the risk level of HCC patients was distinctly correlated with immune cell infiltration status. More importantly, LINC01224 was independently associated with the OS of HCC patients (P < 0.05), suppressing the expression of LINC01224 inhibited the migration of HCC cells.

Conclusion

This study developed a reliable signature for the prognosis of HCC based on BM and immune checkpoint related lncRNA, revealing that LINC01224 might be a prognostic biomarker for HCC associated with the progression of HCC.

Functional Outcomes of Patients with Primary Brain Tumors Undergoing Inpatient Rehabilitation at a Tertiary Care Rehabilitation Facility in Saudi Arabia

Rehabilitation services play a crucial role in improving the functionality and quality of life of individuals with a brain tumor; however, outcomes of inpatient rehabilitation based on tumor characteristics are not well known in the literature. This study was carried out to evaluate the effects of tumor characteristics on functional outcomes. A retrospective chart review was conducted for all adults with a diagnosis of primary brain tumor admitted for IPR between January 2014 and December 2019. Information was collected regarding demographics, characteristics of primary brain tumors, length of stay (LOS) and Functional Independence Measurement (FIM) scores. There were 46 patients, with the majority being male. The most common brain tumors were glioblastoma multiforme and meningioma. The mean LOS was 47.93 ± 26.40 days and the mean FIM gain was 78 ± 14. The type, grade and location of primary brain tumors did not show a significant correlation with the length of stay and functional gains during inpatient rehabilitation. There was a positive correlation between the FIM at admission and discharge, and a significant inverse correlation between the FIM score at admission and LOS. In-patient rehabilitation improved the functional outcomes in adult patients with primary brain tumors. Strategies to incorporate IPR in the care continuum of patients with brain tumors need to be adapted to improve regional services.

Rapid Identification of Benign Gallbladder Diseases Using Serum Surface-Enhanced Raman Spectroscopy Combined with Multivariate Statistical Analysis

In this study, we looked at the viability of utilizing serum to differentiate between gallbladder (GB) stones and GB polyps using Surface-enhanced Raman spectroscopy (SERS), which has the potential to be a quick and accurate means of diagnosing benign GB diseases. Rapid and label-free SERS was used to conduct the tests on 148 serum samples, which included those from 51 patients with GB stones, 25 patients with GB polyps and 72 healthy persons. We used an Ag colloid as a Raman spectrum enhancement substrate. In addition, we employed orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component linear discriminant analysis (PCA-LDA) to compare and diagnose the serum SERS spectra of GB stones and GB polyps. The diagnostic results showed that the sensitivity, specificity, and area under curve (AUC) values of the GB stones and GB polyps based on OPLS-DA algorithm reached 90.2%, 97.2%, 0.995 and 92.0%, 100%, 0.995, respectively. This study demonstrated an accurate and rapid means of combining serum SERS spectra with OPLS-DA to identify GB stones and GB polyps.

Identification of the diagnostic genes and immune cell infiltration characteristics of gastric cancer using bioinformatics analysis and machine learning

Background: Finding reliable diagnostic markers for gastric cancer (GC) is important. This work uses machine learning (ML) to identify GC diagnostic genes and investigate their connection with immune cell infiltration. Methods: We downloaded eight GC-related datasets from GEO, TCGA, and GTEx. GSE13911, GSE15459, GSE19826, GSE54129, and GSE79973 were used as the training set, GSE66229 as the validation set A, and TCGA & GTEx as the validation set B. First, the training set screened differentially expressed genes (DEGs), and gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG), disease Ontology (DO), and gene set enrichment analysis (GSEA) analyses were performed. Then, the candidate diagnostic genes were screened by LASSO and SVM-RFE algorithms, and receiver operating characteristic (ROC) curves evaluated the diagnostic efficacy. Then, the infiltration characteristics of immune cells in GC samples were analyzed by CIBERSORT, and correlation analysis was performed. Finally, mutation and survival analyses were performed for diagnostic genes. Results: We found 207 up-regulated genes and 349 down-regulated genes among 556 DEGs. gene ontology analysis significantly enriched 413 functional annotations, including 310 biological processes, 23 cellular components, and 80 molecular functions. Six of these biological processes are closely related to immunity. KEGG analysis significantly enriched 11 signaling pathways. 244 diseases were closely related to Ontology analysis. Multiple entries of the gene set enrichment analysis analysis were closely related to immunity. Machine learning screened eight candidate diagnostic genes and further validated them to identify ABCA8, COL4A1, FAP, LY6E, MAMDC2, and TMEM100 as diagnostic genes. Six diagnostic genes were mutated to some extent in GC. ABCA8, COL4A1, LY6E, MAMDC2, TMEM100 had prognostic value. Conclusion: We screened six diagnostic genes for gastric cancer through bioinformatic analysis and machine learning, which are intimately related to immune cell infiltration and have a definite prognostic value.

Development and evaluation of a radiomics model of resting 13N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease

BACKGROUND

Coronary angiography (CAG) is usually performed in patients with coronary heart disease (CHD) to evaluate the coronary artery stenosis. However, patients with iodine allergy and renal dysfunction are not suitable for CAG. We try to develop a radiomics machine learning model based on rest 13N-ammonia (13N-NH3) positron emission tomography (PET) myocardial perfusion imaging (MPI) to predict coronary stenosis.

METHODS

Eighty-four patients were included with the inclusion criteria: adult patients; suspected CHD; resting MPI and CAG were performed; and complete data. Coronary artery stenosis >75% were considered to be significant stenosis. Patients were randomly divided into a training group and a testing group with a ratio of 1:1. Myocardial blood flow (MBF), perfusion defect extent (EXT), total perfusion deficit (TPD), and summed rest score (SRS) were obtained. Myocardial static images of the left ventricular (LV) coronary segments were segmented, and radiomics features were extracted. In the training set, the conventional parameter (MPI model) and radiomics (Rad model) models were constructed using the machine learning method and were combined to construct a nomogram. The models' performance was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, decision analysis curve (DCA), and calibration curves. Testing and subgroup analysis were performed.

RESULTS

MPI model was composed of MBF and EXT, and Rad model was composed of 12 radiomics features. In the training set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.795/0.778/0.937/0.511, 0.912/0.825/0.760/0.936 and 0.911/0.865/0.924/0.766 respectively. In the testing set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.798/0.722/0.659/0.841, 0.887/0.810/0.744/0.932 and 0.900/0.849/0.854/0.841 respectively. The AUC of Rad model and nomogram were significantly higher than that of MPI model. The DCA curve also showed that the clinical net benefit of the Rad model and nomogram was similar but greater than that of MPI model. The calibration curve showed good agreement between the observed and predicted values of the Rad model. In the subgroup analysis of Rad model, there was no significant difference in AUC between subgroups.

CONCLUSIONS

The Rad model is more accurate than the MPI model in predicting coronary stenosis. This noninvasive technique could help improve risk stratification and had good generalization ability.

Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is a life-threatening condition for premature infants in neonatal intensive care units. Finding indicators that can predict NEC development before symptoms appear would provide more time to apply targeted interventions. In this study, stools from 132 very-low-birth-weight (VLBW) infants were collected daily in the context of a multi-center prospective study aimed at investigating the potential of fecal biomarkers for NEC prediction using proteomics technology. Eight of the VLBW infants received a stage-3 NEC diagnosis. Stools collected from the NEC infants up to 10 days before their diagnosis were available for seven of them. Their samples were matched with those from seven pairs of non-NEC controls. The samples were processed for liquid chromatography-tandem mass spectrometry analysis using SWATH/DIA acquisition and cross-compatible proteomic software to perform label-free quantification. ROC curve and principal component analyses were used to explore discriminating information and to evaluate candidate protein markers. A series of 36 proteins showed the most efficient capacity with a signature that predicted all seven NEC infants at least a week in advance. Overall, our study demonstrates that multiplexed proteomic signature detection constitutes a promising approach for the early detection of NEC development in premature infants.

A novel hypoxia-driven gene signature that can predict the prognosis of hepatocellular carcinoma

Hypoxia environment exists in already started hepatocellular carcinoma (HCC) and promotes its progression by driving changes in the gene expression profiles of cells. However, the status of hypoxia-driven genes in HCC is largely unknown. In the present study, 368 HCC tissues from The Cancer Genome Atlas were divided into high and low hypoxia groups according to their hypoxia signatures. A total of 1,142 differentially expressed genes (DEGs) were identified between the two groups, and 34 of these DEGs were highly expressed in HCC tissues compared with adjacent tissues, especially in HCC tissues from patients with stage III-IV HCC. After constructing a protein-protein interaction network and applying the least absolute shrinkage and selection operator Cox regression method for 34 DEGs, a three-gene signature (complement factor H related 3 [CFHR3], egl-9 family hypoxia inducible factor 3 [EGLN3], and chromogranin A [CHGA]) was constructed and had prognostic value to predicted outcome of patients with HCC. This three-gene signature was suitable for classifying patients with HCC in the International Cancer Genome Consortium. CFHR3 shows remarkable diagnostic value in HCC. Hypoxia decreased CFHR3 expression, but increased HCC cell proliferation and motility. Overexpression of CFHR3 in HCC cells under hypoxia reversed the stimulatory effects of hypoxia and suppressed cell proliferation and metastasis in vivo. In conclusion, we identified a novel hypoxia-driven gene signature (CFHR3, EGLN3, and CHGA) for reliable prognostic prediction of HCC, and demonstrated that overexpression of CFHR3 may be a potential strategy to overcome hypoxia and treat HCC.

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

Objective

We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail.

Methods

Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using “limma” package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells.

Results

129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells.

Conclusions

In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

Predictive Biomarkers for Postmyocardial Infarction Heart Failure Using Machine Learning: A Secondary Analysis of a Cohort Study

Background

There are few biomarkers with an excellent predictive value for postacute myocardial infarction (MI) patients who developed heart failure (HF). This study aimed to screen candidate biomarkers to predict post-MI HF.

Methods

This is a secondary analysis of a single-center cohort study including nine post-MI HF patients and eight post-MI patients who remained HF-free over a 6-month follow-up. Transcriptional profiling was analyzed using the whole blood samples collected at admission, discharge, and 1-month follow-up. We screened differentially expressed genes and identified key modules using weighted gene coexpression network analysis. We confirmed the candidate biomarkers using the developed external datasets on post-MI HF. The receiver operating characteristic curves were created to evaluate the predictive value of these candidate biomarkers.

Results

A total of 6,778, 1,136, and 1,974 genes (dataset 1) were differently expressed at admission, discharge, and 1-month follow-up, respectively. The white and royal blue modules were most significantly correlated with post-MI HF (dataset 2). After overlapping dataset 1, dataset 2, and external datasets (dataset 3), we identified five candidate biomarkers, including FCGR2A, GSDMB, MIR330, MED1, and SQSTM1. When GSDMB and SQSTM1 were combined, the area under the curve achieved 1.00, 0.85, and 0.89 in admission, discharge, and 1-month follow-up, respectively.

Conclusions

This study demonstrates that FCGR2A, GSDMB, MIR330, MED1, and SQSTM1 are the candidate predictive biomarker genes for post-MI HF, and the combination of GSDMB and SQSTM1 has a high predictive value.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.

Mechanism of zinc ejection by disulfiram in nonstructural protein 5A

Hepatitis C virus (HCV) is a notorious member of the Flaviviridae family of enveloped, positive-strand RNA viruses. Non-structural protein 5A (NS5A) plays a key role in HCV replication and assembly. NS5A is a multi-domain protein which includes an N-terminal amphipathic membrane anchoring alpha helix, a highly structured domain-1, and two intrinsically disordered domains 2-3. The highly structured domain-1 contains a zinc finger (Zf)-site, and binding of zinc stabilizes the overall structure, while ejection of this zinc from the Zf-site destabilizes the overall structure. Therefore, NS5A is an attractive target for anti-HCV therapy by disulfiram, through ejection of zinc from the Zf-site. However, the zinc ejection mechanism is poorly understood. To disclose this mechanism based on three different states, A-state (NS5A protein), B-state (NS5A + Zn), and C-state (NS5A + Zn + disulfiram), we have performed molecular dynamics (MD) simulation in tandem with DFT calculations in the current study. The MD results indicate that disulfiram triggers Zn ejection from the Zf-site predominantly through altering the overall conformation ensemble. On the other hand, the DFT assessment demonstrates that the Zn adopts a tetrahedral configuration at the Zf-site with four Cys residues, which indicates a stable protein structure morphology. Disulfiram binding induces major conformational changes at the Zf-site, introduces new interactions of Cys39 with disulfiram, and further weakens the interaction of this residue with Zn, causing ejection of zinc from the Zf-site. The proposed mechanism elucidates the therapeutic potential of disulfiram and offers theoretical guidance for the advancement of drug candidates.

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Immunoassay-aptasensor for the determination of tumor-derived exosomes based on the combination of magnetic nanoparticles and hybridization chain reaction

The detection of tumor-related exosomes is of great significance. In this work, a fluorescence aptasensor was designed for the determination of tumor-related exosomes based on the capture of magnetic nanoparticles (MNPs) and specific recognition of an aptamer. MNPs were used as substrates to capture the exosomes by modifying the CD63 antibody on the MNP surface. Probe 1 consists of PDL-1 aptamer sequence and a section of other sequences. PDL-1 expression was observed on the surface of exosomes; the aptamer of PDL-1 could combine with PDL-1 with high affinity. Thus, the immunoassay-type compounds of "MNPs-exosomes-probe 1" were formed. The other section of probe 1 triggered the HCR with probe 2 and probe 3 and formed the super-long dsDNA. The addition of GelRed resulted in the generation of an amplified fluorescence signal. The proposed design demonstrated a good linearity with the exosome concentration ranging from 300 to 107 particles per mL and with a low detection limit of 100 particles per mL. This aptasensor also exhibited high specificity for tumor-related exosomes, and was successfully applied in biological samples.

Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R ²) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

CXCL5 expression in tumor tissues is associated with poor prognosis in patients with pancreatic cancer

Immunotherapy based on the tumor microenvironment is a feasible method for treating cancer; therefore, it is necessary to investigate the immune microenvironment of pancreatic cancer and the influencing factors of the immune microenvironment. Chemokines are an important factor affecting the tumor immune microenvironment. In the present study, chemokines or chemokine receptors were screened to identify those differentially expressed in pancreatic cancer compared with normal controls and associated with patient prognosis. Chemokines or chemokine receptors that are differentially expressed in pancreatic cancer tumor tissues were initially screened using the Gene Expression Omnibus database. Next, survival analysis was performed using GEPIA, a website based on The Cancer Genome Atlas (TCGA) database. Immunohistochemical staining of CXCL5 was performed in tissue microarrays (TMAs) containing 119 cases of pancreatic cancer. Histochemistry score (H-SCORE) was used to evaluate the expression of CXCL5. Next, association analysis of the H-SCORE of CXCL5 and the clinical characteristics of patients was performed, as well as Kaplan-Meier survival and Cox multivariate regression analyses. The results of the bioinformatics analysis demonstrated that CXCL5 was highly expressed in pancreatic cancer tissues. High expression of CXCL5 in pancreatic cancer tissues was associated with a poor prognosis in patients in TCGA cohort. The expression level of CXCL5 in tumor tissues was significantly higher compared with that in adjacent peritumoral normal tissues in the immunohistochemical analysis. There was no significant association between CXCL5 expression in pancreatic cancer tumor tissues and clinicopathological factors. Patients with pancreatic cancer with high CXCL5 expression had a poor prognosis, as determined by Kaplan-Meier survival analysis based on the TMA dataset. The results of Cox multivariate regression analysis showed that CXCL5 was an independent factor for a poor prognosis in patients with pancreatic cancer. In conclusion, the results of the present study revealed that the chemokine CXCL5 was highly expressed in pancreatic cancer tissues; high CXCL5 expression was associated with a poor prognosis in patients with pancreatic cancer.