Target prediction and potential application of dihydroartemisinin on hepatocarcinoma treatment.

With high incidence of hepatocarcinoma and limited effective treatments, most patients suffer in pain. Antitumor drugs are single-targeted, toxicity, causing adverse side effects and resistance. Dihydroartemisinin (DHA) inhibits tumor through multiple mechanisms effectively. This study explores and evaluates safety and potential mechanism of DHA towards human hepatocarcinoma based on network pharmacology in a comprehensive way. Adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of DHA were evaluated with pkCSM, SwissADME, and ADMETlab. Potential targets of DHA were obtained from SwissTargetPrediction, Drugbank, TargetNET, and PharmMapper. Target gene of hepatocarcinoma was obtained from OMIM, GeneCards, and DisGeNET. Overlapping targets and hub genes were identified and analyzed for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway. Molecular docking was utilized to investigate the interactions sites and hydrogen bonds. Cell counting kit-8 (CCK8), wound healing, invasion, and migration assays on HepG2 and SNU387 cell proved DHA inhibits malignant biological features of hepatocarcinoma cell. DHA is safe and desirable for clinical application. A total of 131 overlapping targets were identified. Biofunction analysis showed targets were involved in kinase activity, protein phosphorylation, intracellular reception, signal transduction, transcriptome dysregulation, PPAR pathway, and JAK-STAT signaling axis. Top 9 hub genes were obtained using MCC (Maximal Clique Centrality) algorithm, namely CDK1, CCNA2, CCNB1, CCNB2, KIF11, CHEK1, TYMS, AURKA, and TOP2A. Molecular docking suggests that all hub genes form a stable interaction with DHA for optimal binding energy were all less than - 5 kcal/mol. Dihydroartemisinin might be a potent and safe anticarcinogen based on its biological safety and effective therapeutic effect.

PMFFNet: A hybrid network based on feature pyramid for ovarian tumor segmentation.

Ovarian cancer is a highly lethal malignancy in the field of oncology. Generally speaking, the segmentation of ovarian medical images is a necessary prerequisite for the diagnosis and treatment planning. Therefore, accurately segmenting ovarian tumors is of utmost importance. In this work, we propose a hybrid network called PMFFNet to improve the segmentation accuracy of ovarian tumors. The PMFFNet utilizes an encoder-decoder architecture. Specifically, the encoder incorporates the ViTAEv2 model to extract inter-layer multi-scale features from the feature pyramid. To address the limitation of fixed window size that hinders sufficient interaction of information, we introduce Varied-Size Window Attention (VSA) to the ViTAEv2 model to capture rich contextual information. Additionally, recognizing the significance of multi-scale features, we introduce the Multi-scale Feature Fusion Block (MFB) module. The MFB module enhances the network's capacity to learn intricate features by capturing both local and multi-scale information, thereby enabling more precise segmentation of ovarian tumors. Finally, in conjunction with our designed decoder, our model achieves outstanding performance on the MMOTU dataset. The results are highly promising, with the model achieving scores of 97.24%, 91.15%, and 87.25% in mACC, mIoU, and mDice metrics, respectively. When compared to several Unet-based and advanced models, our approach demonstrates the best segmentation performance.

External Validation of the Risk Assessment Model of Venous Thromboembolism in Multicenter Internal Medicine Inpatients.

To external validate the risk assessment model (RAM) of venous thromboembolism (VTE) in multicenter internal medicine inpatients. We prospectively collected 595 internal medical patients (310 with VTE patients, 285 non-VTE patients) were from Beijing Shijitan Hospital, Beijing Chaoyang Hospital, and the respiratory department of Beijing Tsinghua Changgeng Hospital from January 2022 to December 2022 for multicenter external validation. The prediction ability of Caprini RAM, Padua RAM, The International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) RAM, and Shijitan (SJT) RAM were compared. This study included a total of 595 internal medicine inpatients, including 242 (40.67%) in the respiratory department, 17 (2.86%) in the respiratory intensive care unit, 49 (8.24%) in the neurology department, 34 (5.71%) in the intensive care unit, 26 (4.37%) in the geriatric department, 22 (3.70%) in the emergency department, 71 (11.93%) in the nephrology department, 63 (10.59%) in the cardiology department, 24 (4.03%) in the hematology department, 6 (1.01%) in the traditional Chinese medicine department, 9 (1.51%) cases in the rheumatology department, 7 (1.18%) in the endocrinology department, 14 (2.35%) in the oncology department, and 11 (1.85%) in the gastroenterology department. Multivariate logistic regression analysis showed that among internal medicine inpatients, age > 60 years old, heart failure, nephrotic syndrome, tumors, history of VTE, and elevated D-dimer were significantly correlated with the occurrence of VTE (P < .05). The incidence of VTE increases with the increase of D-dimer. It was found that the effectiveness of SJT RAM (AUC = 0.80 ± 0.03) was better than Caprini RAM (AUC = 0.74 ± 0.03), Padua RAM (AUC = 0.72 ± 0.03) and IMPROVE RAM (AUC = 0.52 ± 0.03) (P < .05). The sensitivity and Yoden index of SJT RAM were higher than those of Caprini RAM, Pauda RAM, and IMPROVE RAM (P < .05), but specificity was not significantly different between the 4 models (P > .05). The SJT RAM derived from general hospitalized Chinese patients has effective and better predictive ability for internal medicine inpatients at risk of VTE.

Pulmonary histoplasmosis on the Chinese mainland: two case reports and literature review.

INTRODUCTION: Pulmonary histoplasmosis is a fungal disease that is endemic in North and Central America. It is relatively rare in China and commonly misdiagnosed as tuberculosis or cancer due to nonspecific clinical and radiographic manifestations. Rapid and accurate pathogen tests are critical for the diagnosis of pulmonary histoplasmosis. METHODOLOGY: We report two cases of pulmonary histoplasmosis. We collected all the relevant case reports on the Chinese mainland (from 1990 to 2022) to analyze features of this disease among Chinese patients. RESULTS: A total of 42 articles reporting 101 cases were identified, and the two cases reported in this article were also included for analysis. Sixty-three (61.2%) patients had respiratory symptoms and 35 (34.0%) patients were asymptomatic. The most common radiographic findings were pulmonary nodules or masses (81.6%). Twenty-two (21.4%) patients were misdiagnosed as tuberculosis, and 37 (35.9%) were misdiagnosed as lung tumors before pathological findings. Metagenomic next­generation sequencing (mNGS) testing provided a rapid diagnostic and therapeutic basis for three patients. CONCLUSIONS: Clinical features and imaging findings of pulmonary histoplasmosis are not specific. Relevant epidemiological history and timely pathogen detection are important for diagnosis. mNGS can shorten the time required for diagnosis and allow earlier initiation of targeted antibiotic therapy.

Expression of circular RNA has-circ-0009158 and identification of associated miRNA-mRNA network in hepatocellular carcinoma.

Primary hepatocellular carcinoma (HCC) affects people all over the world. Circular RNAs are involved in the growth and development of several malignancies and regulate a number of biological processes. However, the roles of has-circ-0009158 in HCC remain unknown. This study explored the expression and associated miRNA-mRNA network of has-circ-0009158 in HCC. Quantitative real-time polymerase chain reaction was used to measure the expression of hsa-circ-0009158 in the HCC tissues of 143 patients and four human HCC cell lines. Then, the potential relationship of hsa-circ-0009158 expression with clinical characteristics and prognosis of patients was analyzed using the GO and KEGG databases. Correlated miRNA-mRNA networks were forecasted using the TCGA database and Cytoscape software. The hsa-circ-0009158 expression was significantly upregulated in HCC tissues and cell lines (P<0.001). The multivariate Cox analysis revealed that HCC patients were associated with high hsa-circ-0009158 expression. The bioinformatics analysis screened 1 miRNA, and 248 mRNAs associated with the circRNA in HCC. A pathway analysis suggested that the differentially expressed genes (DEGs) may be linked to the development and growth of HCC tumors. Ten hub genes (MELK, NCAPG, BUB1B, BIRC5, CDCA8, CENPF, BUB1, CDK1, TTK, TPX2) were identified from the PPI network based on the 248 genes. Additionally, the 10 hub genes that were verified had an association between high expression levels and low overall survival rates. As a result, the high expression of hsa-circ-0009158 was found to be a separate risk factor for recurrence and a poor prognosis in HCC patients.

Single-cell transcriptome sequencing reveals aberrantly activated inter-tumor cell signaling pathways in the development of clear cell renal cell carcinoma.

BACKGROUND: Aberrant intracellular or intercellular signaling pathways are important mechanisms that contribute to the development and progression of cancer. However, the intercellular communication associated with the development of ccRCC is currently unknown. The purpose of this study was to examine the aberrant tumor cell-to-cell communication signals during the development of ccRCC. METHODS: We conducted an analysis on the scRNA-seq data of 6 ccRCC and 6 normal kidney tissues. This analysis included sub clustering, CNV analysis, single-cell trajectory analysis, cell-cell communication analysis, and transcription factor analysis. Moreover, we performed validation tests on clinical samples using multiplex immunofluorescence. RESULTS: This study identified eleven aberrantly activated intercellular signaling pathways in tumor clusters from ccRCC samples. Among these, two of the majors signaling molecules, MIF and SPP1, were mainly secreted by a subpopulation of cancer stem cells. This subpopulation demonstrated high expression levels of the cancer stem cell markers POU5F1 and CD44 (POU5F1hiCD44hiE.T), with the transcription factor POU5F1 regulating the expression of SPP1. Further research demonstrated that SPP1 binds to integrin receptors on the surface of target cells and promotes ccRCC development and progression by activating potential signaling mechanisms such as ILK and JAK/STAT. CONCLUSION: Aberrantly activated tumor intercellular signaling pathways promote the development and progression of ccRCC. The cancer stem cell subpopulation (POU5F1hiCD44hiE.T) promotes malignant transformation and the development of a malignant phenotype by releasing aberrant signaling molecules and interacting with other tumor cells.

Erratum to "BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells".

[This corrects the article DOI: 10.1515/biol-2020-0054.].

A Prognostic Model of Genetic Markers for Triple-Negative Breast Cancer Based on Machine Learning and Bioinformatics Analysis.

Triple negative breast cancer (TNBC) that has low survival rate and prognosis due to its heterogeneity and lack of reliable molecular targets for effective targeted therapy. Therefore, finding new biomarkers is crucial for the targeted treatment of TNBC. The experimental data from the Cancer Genome Atlas database (TCGA).First, key genes associated with TNBC prognosis were screened and used for survival analysis using a single-factor COX regression analysis combined with three algorithms: LASSO, RF and SVM-RFE. Multi-factor COX regression analysis was then used to construct a TNBC risk prognostic model. Four key genes associated with TNBC prognosis were screened as TENM2, OTOG, LEPR and HLF. Among them, OTOG is a new biomarker. Survival analysis showed a significant effect of four key genes on OS in TNBC patients (P<0.05). The experiment showed that four key genes could provide new ideas for targeting therapy for TNBC patients and improved prognosis and survival.

Comprehensive analysis of CPSF4-related alternative splice genes in hepatocellular carcinoma.

BACKGROUND: An important stage in controlling gene expression is RNA alternative splicing (AS), and aberrant AS can trigger the development and spread of malignancies, including hepatocellular carcinoma (HCC). A crucial component of AS is cleavage and polyadenylation-specific factor 4 (CPSF4), a component of the CPSF complex, but it is unclear how CPSF4-related AS molecules describe immune cell infiltration in the total tumor microenvironment (TME). METHODS: Using RNA-sequencing data and clinical data from TCGA-LIHC from the Cancer Genome Atlas (TCGA) database, the AS genes with differential expression were found. The univariate Cox analysis, KM analysis, and Spearman analysis were used to identify the AS genes related to prognosis. Screening of key AS genes that are highly correlated with CPSF4. Key genes were screened using Cox regression analysis and stepwise regression analysis, and prognosis prediction models and the topography of TME cell infiltration were thoroughly analyzed. RESULTS: A model consisting of seven AS genes (STMN1, CLSPN, MDK, RNFT2, PRR11, RNF157, GHR) was constructed that was aimed to predict prognostic condition. The outcomes of the HCC samples in the high-risk group were considerably worse than those in the lower risk group (p < 0.0001), and different risk patient groups were formed. According to the calibration curves and the area under the ROC curve (AUC) values for survival at 1, 2, and 3 years, the clinical nomogram performs well in predicting survival in HCC patients. These values were 0.76, 0.70, and 0.69, respectively. Moreover, prognostic signature was markedly related to immune infiltration and immune checkpoint genes expression. CONCLUSION: By shedding light on the function of CPSF4 and the seven AS genes in the formation and progression of HCC, this research analysis contributes to the development of more useful prognostic, diagnostic, and possibly therapeutic biomarkers.

Recruitment of myeloid­derived suppressor cells and regulatory T­cells is associated with the occurrence of acute myocardial infarction.

The roles of myeloid-derived suppressor cells (MDSCs) and regulatory T-cells (Tregs) in acute myocardial infarction (AMI) remain elusive. The present study aimed to analyze the proportions of the granulocytic and monocytic populations of MDSCs (G-MDSCs and M-MDSCs, respectively), and Tregs in the peripheral blood mononuclear cells (PBMCs) of patients with AMI. The present study recruited 34 patients with AMI and 37 healthy controls without clinical signs of myocardial ischemia. PBMCs were isolated from the peripheral blood samples of patients with AMI within 24 h following admission to the hospital and from those of the healthy controls during a physical examination. Two subsets of MDSCs, G-MDSCs (CD15+CD33+CD11b+CD14-HLA-DRlow) and M-MDSCs (CD14+CD15-CD11b+HLA-DRlow), and Tregs (CD3+CD4+CD25highCD127low T-cells) in the PBMCs derived from the patients with AMI and healthy controls were analyzed using flow cytometry. The effects of MDSCs derived from patients with AMI on naïve CD4+ T-cells were examined in the co-culture system. The results revealed that the proportions of G-MDSCs and M-MDSCs were higher in the peripheral blood of patients with AMI than in that of the healthy controls. The patients with AMI had significantly higher numbers of programmed death-ligand (PD-L)1- and PD-L2-positive G-MDSCs and M-MDSCs compared with the healthy controls (P<0.05). The MDSCs could acquire a granulocytic phenotype following AMI, and the G-MDSCs and M-MDSCs would be more likely to express PD-L2 and PD-L1, respectively. The ratios of Tregs to CD4+ T-cells and PD-1+ Tregs in the peripheral blood of patients with AMI were significantly higher than those in the healthy controls (P<0.05). The results of flow cytometry demonstrated an increase in the numbers of inducible Tregs in the co-culture system with the G-MDSCs derived from patients with AMI compared with the G-MDSCs derived from the healthy controls (P<0.01). On the whole, the findings presented herein demonstrate the accumulation of MDSCs, and the upregulation of PD-L1 and PD-L2 expression on the surface of MDSCs in patients with AMI. MDSCs can induce the expansion of Tregs by binding PD-1 on the surface of Tregs, thus playing a crucial role in AMI.

Machine learning-based immune prognostic model and ceRNA network construction for lung adenocarcinoma.

PURPOSE: Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients. METHODS: In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA. RESULTS: Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001). CONCLUSION: In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD.

Donepezil accelerates the release of PLGA microparticles via catalyzing the polymer degradation regardless of the end groups and molecular weights.

Poly (lactic-co-glycolic acid) (PLGA) is one of the most successful polymers for sustained parenteral drug products in the market. However, rational selection of PLGA in the formulations is still challenging due to the lack of fundamental studies. The present study aimed to investigate the influence of donepezil (DP) on the in-vitro and in-vivo performance of PLGA sustained microspheres. Three kinds of PLGAs with different end groups and molecular weights were selected. Then DP-loaded PLGA microspheres (DP-MSs) with similar particle size, drug loading, and encapsulation efficiency were prepared using an o/w emulsion-solvent evaporation method. Laser diffraction and scanning electron microscopy showed that the prepared DP-MSs were about 35 µm and spherical in shape. Differential scanning calorimetry and X-ray diffraction indicated that DP was in an amorphous state inside the microspheres. Unexpectedly, the molecular weight and end group of PLGAs did not significantly influence the in-vitro and in-vivo performance of the DP-MSs. The gel permeation chromatography indicated that the degradation rates of PLGAs were accelerated with the incorporation of DP into the microspheres, and the molecular weight of all three kinds of PLGAs sharply dropped to about 11,000 Da within the initial three days. The basic catalysis effect induced by DP might be responsible for the accelerated degradation of PLGAs, which led to similar in-vitro release profiles of DP from different PLGA matrices. A point-to-point level A correlation between the in-vitro release and the in-vivo absorption was observed, which confirmed the accelerated release of DP from the DP-MSs in-vivo. The results indicated that the influence of DP on the degradation of PLGA should be considered when developing DP-sustained microspheres.

Multi-Omics Analysis of GNL3L Expression, Prognosis, and Immune Value in Pan-Cancer.

Guanine nucleotide-binding protein-like 3-like protein (GNL3L) is a novel, evolutionarily conserved, GTP-binding nucleolar protein. This study aimed to investigate the expression, prognosis, and immune value of GNL3L in pan-cancer from multiple omics analyses. Firstly, the expression and prognostic value of GNL3L in pan-cancer were discussed using the TIMER2 database, the GEPIA database, the cBioportal database, COX regression analysis, and enrichment analysis. The association of GNL3L with tumor mutational burden (TMB), tumor microsatellite instability (MSI), mismatch repair (MMR) genes, and immune cells was then analyzed. Finally, an esophageal cancer (ESCA) prediction model was established, and GNL3L clone formation assays were performed. The final results showed that GNL3L is differentially expressed in the vast majority of cancers, is associated with the prognosis of various cancers, and may affect cancer occurrence through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, it was found that the correlation between GNL3L and TMB, MSI, MMR, and various immune cells is significant. The established ESCA prediction model had a strong predictive ability, and GNL3L could significantly affect the proliferation of esophageal cancer cells. In conclusion, GNL3L may serve as an important prognostic biomarker and play an immunomodulatory role in tumors.

MiR-702-5p ameliorates diabetic encephalopathy in db/db mice by regulating 12/15-LOX.

The purpose of this study was to investigate the effect of miR-702-5p on diabetic encephalopathy (DE) and the interaction of miR-702-5p/12/15-LOX in the central nervous system (CNS). In this study, db/db mice were used as DE animal model and HT22 cells were treated with high-glucose (HG). Based on the bioinformatics prediction of possible binding sites between miR-702-5p and 12/15-LOX, we found that the expression of miR-702-5p was significantly down-regulated while 12/15-LOX up-regulated in vivo and in vitro, and the expression changes were inversely correlated. In vivo, diabetic mice with cognitive dysfunction and hippocampal neuronal damage had a concomitant increase in amyloid precursor protein (APP), amyloid beta(Aß), tau, BAX protein expressions; by contrast, Bcl-2 protein expression was significantly decreased. Overexpression of miR-702-5p significantly reduced the histopathological damage of the hippocampus, improved the learning and memory function of db/db mice, down-regulated 12/15-LOX, APP, Aß, tau, BAX protein expressions significantly and up-regulated the expression of Bcl-2. In vitro, miR-702-5p mimic reversed the decline in cell viability and the increase in cell apoptosis induced by HG. Simultaneously, reduced 12/15-LOX, APP, Aß, BAX protein expressions, and increased Bcl-2 protein expression were detected in the miR-702-5p mimic group. Moreover, combined administration of miR-702-5p mimic and 12/15-LOX overexpression lentivirus significantly reversed the protective effect of up-regulation of miR-702-5p. In conclusion, miR-702-5p has a neuroprotective effect on DE, and this effect was achieved by inhibiting 12/15-LOX. However, miR-702-5p had an endogenous regulatory effect on 12/15-LOX rather than a direct targeting relationship.

Burden of schizophrenia among Japanese patients: a cross-sectional National Health and Wellness Survey.

BACKGROUND: Schizophrenia places a great humanistic and financial burden to patients, families, and societies, and the burden is substantially impacted by comorbid conditions. This study aimed to estimate the lifetime prevalence of schizophrenia and to assess the health-related quality of life (HRQoL), work productivity, and indirect cost among schizophrenia patients with and without comorbidities (depressive symptoms, sleep disturbances, and anxiety problems). METHODS: This is a secondary analysis of existing data collected in 2019 from the Japan National Health and Wellness Survey. The schizophrenia patients were categorized based on their Patient Health Questionnaire-9 score, self-reported experience of sleep disturbances, and anxiety problems. The lifetime prevalence was estimated using the total number of diagnosed schizophrenia patients as the numerator and the total number of respondents as the denominator. The HRQoL was evaluated through the Short Form 12-Item (version 2) Health Survey and EuroQoL 5-dimensions scale. Work productivity and annual indirect costs were evaluated through the Work Productivity and Activity Impairment instrument and monthly wage rates. Multivariate analyses included the comparison of outcomes using generalized linear models. RESULTS: The study was conducted with 178 schizophrenia patients with an average age of 42.7 years old and an estimated lifetime prevalence of 0.59% (95% CI: 0.51%, 0.68%). Patients who experienced sleep disturbances, more severe depressive symptoms, and anxiety problems had lower HRQoL, higher levels of absenteeism, presenteeism, total work productivity and activity impairment, and almost twice more indirect costs, compared to those without these conditions. CONCLUSION: Comorbid conditions among patients with schizophrenia impact significantly on their quality of life, work productivity as well as indirect costs.

Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images.

Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a challenging task to recognize tens of thousands of histopathological images of liver cancer by naked eye, which poses numerous challenges to inexperienced clinicians. In addition, factors such as long time-consuming, tedious work and huge number of images impose a great burden on clinical diagnosis. Therefore, our study combines convolutional neural networks with histopathology images and adopts a feature fusion approach to help clinicians efficiently discriminate the differentiation types of primary hepatocellular carcinoma histopathology images, thus improving their diagnostic efficiency and relieving their work pressure. In this study, for the first time, 73 patients with different differentiation types of primary liver cancer tumors were classified. We performed an adequate classification evaluation of liver cancer differentiation types using four pre-trained deep convolutional neural networks and nine different machine learning (ML) classifiers on a dataset of liver cancer histopathology images with multiple differentiation types. And the test set accuracy, validation set accuracy, running time with different strategies, precision, recall and F1 value were used for adequate comparative evaluation. Proved by experimental results, fusion networks (FuNet) structure is a good choice, which covers both channel attention and spatial attention, and suppresses channel interference with less information. Meanwhile, it can clarify the importance of each spatial location by learning the weights of different locations in space, then apply it to the study of classification of multi-differentiated types of liver cancer. In addition, in most cases, the Stacking-based integrated learning classifier outperforms other ML classifiers in the classification task of multi-differentiation types of liver cancer with the FuNet fusion strategy after dimensionality reduction of the fused features by principle component analysis (PCA) features, and a satisfactory result of 72.46% is achieved in the test set, which has certain practicality.

miR-204-5p is sponged by TUG1 to aggravate neuron damage induced by focal cerebral ischemia and reperfusion injury through upregulating COX2.

Studies have reported that miR-204-5p is involved in multiple biological processes. However, little is known about the expression and mechanism of miR-204-5p in cerebral ischemia and reperfusion injury. This study found that miR-204-5p expression was significantly downregulated in the blood of patients with ischemic stroke, MCAO/R rat brains, and OGD/R neurons. Overexpression of miR-204-5p markedly reduced infarct volume and neurological impairment and alleviated the inflammatory response in vivo. miR-204-5p promoted neuronal viability and reduced apoptotic cells in vitro. Mechanically, miR-204-5p was negatively regulated by the expression lncRNA TUG1 upstream and down-regulated COX2 expression downstream. Therefore, the TUG1/miR-204-5p/COX2 axis was involved in ischemia and reperfusion-induced neuronal damage. This finding may provide a novel strategy for the treatment of cerebral ischemia and reperfusion injury.

Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis.

Advanced metastasis of colon cancer makes it more difficult to treat colon cancer. Finding the markers of colon cancer (Colon Cancer) can diagnose the stage of cancer in time and improve the prognosis with timely treatment. This paper uses gene expression profiling data from The Cancer Genome Atlas (TCGA) for the diagnosis of colon cancer and its staging. In this study, we first selected the gene modules with the greatest correlation with cancer by Weighted Gene Co-expression Network Analysis (WGCNA), extracted the characteristic genes for differential expression results using the least absolute shrinkage and selection operator algorithm (Lasso) and performed survival analysis, and then combined the genes in the modules with the Lasso-extracted feature genes were combined to diagnose colon cancer versus healthy controls using RF, SVM and decision trees, and colon cancer staging was diagnosed using differentially expressed genes for each stage. Finally, Protein-Protein Interaction Networks (PPI) networks were done for 289 genes to identify clusters of aggregated proteins for survival analysis. Finally, the RF model had the best results in the diagnosis of colon cancer versus control group fold cross-validation with an average accuracy of 99.81%, F1 value reaching 0.9968, accuracy of 99.88%, and recall of 99.5%, and an average accuracy of 91.5%, F1 value reaching 0.7679, accuracy of 86.94%, and recall in the diagnosis of colon cancer stages I, II, III and IV. The recall rate reached 73.04%, and eight genes associated with colon cancer prognosis were identified for GCNT2, GLDN, SULT1B1, UGT2B15, PTGDR2, GPR15, BMP5 and CPT2.

Multi-omic characterization of genome-wide abnormal DNA methylation reveals diagnostic and prognostic markers for esophageal squamous-cell carcinoma.

This study investigates aberrant DNA methylations as potential diagnosis and prognosis markers for esophageal squamous-cell carcinoma (ESCC), which if diagnosed at advanced stages has <30% five-year survival rate. Comparing genome-wide methylation sites of 91 ESCC and matched adjacent normal tissues, we identified 35,577 differentially methylated CpG sites (DMCs) and characterized their distribution patterns. Integrating whole-genome DNA and RNA-sequencing data of the same samples, we found multiple dysregulated transcription factors and ESCC-specific genomic correlates of identified DMCs. Using featured DMCs, we developed a 12-marker diagnostic panel with high accuracy in our dataset and the TCGA ESCC dataset, and a 4-marker prognostic panel distinguishing high-risk patients. In-vitro experiments validated the functions of 4 marker host genes. Together these results provide additional evidence for the important roles of aberrant DNA methylations in ESCC development and progression. Our DMC-based diagnostic and prognostic panels have potential values for clinical care of ESCC, laying foundations for developing targeted methylation assays for future non-invasive cancer detection methods.

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.