Identifying important microbial biomarkers for the diagnosis of colon cancer using a random forest approach.

Colon cancer is one of the most common cancers, with 30-50 % of patients returning or metastasizing within 5 years of treatment. Increasingly, researchers have highlighted the influence of microbes on cancer malignant activity, while no studies have explored the relationship between colon cancer and the microbes in tumors. Here, we used tissue and blood samples from 67 colon cancer patients to identify pathogenic microorganisms associated with the diagnosis and prediction of colon cancer and evaluate the predictive performance of each pathogenic marker and its combination based on the next-generation sequencing data by using random forest algorithms. The results showed that we constructed a database of 13,187 pathogenic microorganisms associated with human disease and identified 2 pathogenic microorganisms (Synthetic.construct_32630 and Dicrocoelium.dendriticum_57078) associated with colon cancer diagnosis, and the constructed diagnostic prediction model performed well for tumor tissue samples and blood samples. In summary, for the first time, we provide new molecular markers for the diagnosis of colon cancer based on the expression of pathogenic microorganisms in order to provide a reference for improving the effective screening rate of colon cancer in clinical practice and ameliorating the personalized treatment of colon cancer patients.

PEX26 Functions as a Metastasis Suppressor in Colorectal Cancer.

BACKGROUND/AIMS: Aberrant Peroxisomal Biogenesis Factor 26 (PEX26) occurs in multiple cell process. However, the role of PEX26 in colorectal cancer (CRC) development remains unknown. We aimed to study PEX26 expression, regulation, and function in CRC cells. METHODS: Using the bioinformatic analysis, real-time quantitative PCR, and immunohistochemistry staining, we detected the expression of PEX26 in CRC and normal tissues. We performed functional experiments in vitro to elucidate the effect of PEX26 on CRC cells. We analyzed the RNA-seq data to reveal the downstream regulating network of PEX26. RESULTS: PEX26 is significantly down-regulated in CRC and its low expression correlates with the poor overall survival of CRC patients. We further demonstrated that PEX26 over-expression inhibits the ability of CRC cell migration, invasion, and epithelial-mesenchymal transition (EMT), while PEX26 knockdown promotes the malignant phenotypes of migration, invasion, and EMT via activating the Wnt pathway. CONCLUSION: Overall, our results showed that the loss of PEX26 contributes to the malignant phenotype of CRC. PEX26 may serve as a novel metastasis repressor for CRC.

Kaempferol prevents aseptic loosening via enhance the Wnt/ß-catenin signaling pathway in vitro and in vivo.

Kaempferol has demonstrated notable positive effects on the osteogenic differentiation of mesenchymal stem cells (MSC) and osteoblasts. A substantial body of research has emphasized the role of dislodged titanium particles in aseptic loosening following joint replacement surgery. This study predominantly investigates the suppressive influence of Kaempferol on osteolysis induced by titanium (Ti) alloy particles. In vitro investigations disclosed that Kaempferol effectively enhanced mineralization and alkaline phosphatase (ALP) activity in bone-marrow mesenchymal stem cells exposed to Ti particles. In addition, we conducted a comprehensive analysis of osteogenic differentiation microarray data_sets (GSE37676, GSE79814, and GSE114474) to identify differentially expressed genes. Significantly, Kaempferol upregulated the expression of critical osteogenic markers, including Runt-related transcription factor 2 (Runx2), osteocalcin (OCN), osterix/Sp-7, and ß-catenin. In vivo experiments, including H&E staining and Immunohistochemistry, provided compelling evidence that Kaempferol exerted a robust inhibitory effect on periprosthetic osteolysis in mice, with particularly pronounced results at higher doses. Moreover, it elevated the expression levels of osteogenic factors and Wnt/ß-catenin signaling components. These findings collectively indicate that Kaempferol mitigates the hindrance to osteogenesis posed by titanium particles by activating the Runx2 and Wnt/ß-catenin signaling pathways. This research lays a solid foundation for the prospective utilization of Kaempferol in the management of aseptic loosening following arthroplasty, offering promising therapeutic potential.

Comparative analyses of the prognosis, tumor immune microenvironment, and drug treatment response between left-sided and right-sided colon cancer by integrating scRNA-seq and bulk RNA-seq data.

BACKGROUND: In this study, we compared the prognosis, tumor immune microenvironment (TIM), and drug treatment response between left-sided (LCC) and right-sided (RCC) colon cancer to predict outcomes in patients with LCC and RCC. METHODS: Based on identified differentially expressed genes and using single-cell RNA sequencing data, we constructed and validated a prognostic model for LCC and RCC patients in the TCGA-COAD cohort and GSE103479 cohort. Moreover, we compared the differences of TIM characteristics and drug treatment response between LCC and RCC patients. RESULTS: We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance. The RCC patients with higher risk scores were significantly associated with greater metastasis (P = 2.6×10-5), N stage (P = 0.012), advanced pathological stage (P = 1.4×10-4), and more stable microsatellite status (P = 0.007) but not T stage (P = 0.200). For LCC patients, the risk scores were not significantly associated with tumor stage and microsatellite status (P > 0.05). Additionally, immune infiltration by CD8 and regulatory T cells and M0, M1, and M2 macrophages differed significantly between LCC and RCC patients (P < 0.05). APC and TP53 mutations were significantly more common in LCC patients (P < 0.05). In contrast, KRAS, SYNE1, and MUC16 mutations were significantly more common in RCC patients (P < 0.05). In addition, tumor mutation burden values were significantly higher in RCC patients than in LCC patients (P = 5.9×10-8). Moreover, the expression of immune checkpoint targets was significantly higher in RCC patients than in LCC patients (P < 0.05), indicating that RCC patients maybe more sensitive to immunotherapy. However, LCC and RCC patients did not differ significantly in their sensitivity to eight selected chemicals or target drugs (P > 0.05). The average half-maximal inhibitory concentrations for camptothecin, teniposide, vinorelbine, and mitoxantrone were significantly lower in low-risk than in high-risk RCC patients (P < 0.05), indicating that the lower risk score of RCC patients, the more sensitive they were to these four drugs. CONCLUSIONS: We investigated the differences in prognosis, TIM, and drug treatment response between LCC and RCC patients, which may contribute to accurate colon cancer prognosis and treatment of colon cancer.

Identification of m6A-related lncRNAs as prognostic signature within colon tumor immune microenvironment.

BACKGROUND: The current study probed prognosis-related potential for m6A-related lncRNAs signatures within colon tumor immune microenvironment (TIM). METHODS: After downloading transcriptomic datasets for colon cancer (CC) patients from The Cancer Genome Atlas (TCGA), they were divided, in a 1:1 ratio, within training or test datasets. m6A-related lncRNAs were then scrutinized across such dataset using Pearson correlation assessment before generating a m6A-related lncRNAs prognosis-related model using the training dataset. The latter was then validated with the test and the whole dataset. In addition, we compared the differences of TIM and the estimated IC50 of drug response between the high- and low-risk groups. RESULTS: Overall survival (OS) resulted as linked with 11 m6A-related lncRNAs, while within the developed prognosis-related model, areas-under-curves were as follows: within training dataset, values at 3-, 4-, and 5-years were 0.777, 0.819, and 0.805, accordingly, and for test one, they were 0.697, 0.682, and 0.706, respectively. Finally, the values for the whole dataset were 0.675 (3-year), 0.682 (4-years), and 0.679 (5-years), accordingly. Moreover, CC cases categorized within low-risk cohort demonstrated enhanced OS (p < .0001), lower metastasis (p = 2e-06) and lower T stage (p = .0067), more instability for microsatellite status (p = .012), and downregulation for PD-L1, PD-1, CTLA-4, LAG3, and HAVCR2 (p < .05). In addition, risk scorings were significantly linked to the degree of infiltrative intensity for CD8 and CD4 (memory resting) T-cells, T-regulatory (Tregs), and Mast cells triggering (p < .05). Patients with low infiltrative propensity for CD4 T-cells also had better OS (p = .016). Moreover, six representative drugs were found to be sensitive for treating CC patients. CONCLUSION: A robust m6A-related prognostic model with great performances was developed before exploring the TIM characteristics and its potential therapeutic drugs, which might improve the prognosis and therapeutic efficacy.

Loss of Tumor Suppressor C9orf9 Promotes Metastasis in Colorectal Cancer.

The whole genome sequencing of tumor samples identifies thousands of somatic mutations. However, the function of these genes or mutations in regulating cancer progression remains unclear. We previously performed exome sequencing in patients with colorectal cancer, and identified one splicing mutation in C9orf9. The subsequent target sequencing of C9orf9 gene based on a validation cohort of 50 samples also found two function mutations, indicating that the loss of wild-type C9orf9 may participate in the tumorigenesis of colorectal cancer. In this research, we aimed to further confirm the function of C9orf9 in the CRC phenotype. Our Q-PCR analysis of the tumor and matched normal samples found that C9orf9 was downregulated in the CRC samples. Function assays revealed that C9orf9 exerts its tumor suppressor role mainly on cancer cell migration and invasion, and its loss was essential for certain tumor-microenvironment signals to induce EMT and metastasis in vivo. RNA-sequencing showed that stable-expressing C9orf9 can inhibit the expression of several metastasis-related genes and pathways, including vascular endothelial growth factor A (VEGFA), one of the essential endothelial cell mitogens which plays a critical role in normal physiological and tumor angiogenesis. Overall, our results showed that the loss of C9orf9 contributes to the malignant phenotype of CRC. C9orf9 may serve as a novel metastasis repressor for CRC.

High-dose X-ray radiation induced MgO degradation and breakdown in spin transfer torque magnetic tunnel junctions.

Magnetic tunnel junction (MTJ) with magnesium oxide (MgO) tunnel barrier is the core element of spin transfer torque-based magnetic random access memory. For the application in the space environment, the total ionizing dose radiation effects on MTJs need to be evaluated. In this work, the MTJs were exposed to X-ray radiation with different doses of up to 10 Mrad(Si). Measurements of current induced magnetization switching (CIMS) behavior of these MTJs were performed before and after radiation. The results show negligible changes in the tunneling magnetoresistance and current switching properties after 8 Mrad(Si) X-ray radiation. However, with a total dose of 9 Mrad(Si), a significant reduction in junction resistance of a fairly large number of MTJs was observed, which showed characteristics of MTJ breakdown. Moreover, in this study, all experimental MTJs became functionally disabled due to MgO breakdown under 10 Mrad(Si) X-ray radiation. The CoFeB/MgO/CoFeB interface microstructure was observed using X-ray photoelectron spectroscopy and high-resolution transmission electron microscopy (HRTEM). Interfacial structural results indicate that the MgO degradation and breakdown behavior caused by X-ray ionizing radiation can give rise to radiation-induced oxygen vacancies across the tunnel barrier oxide layer.

Exploring immune-related signatures for predicting immunotherapeutic responsiveness, prognosis, and diagnosis of patients with colon cancer.

The present study focused on identifying the immune-related signatures and exploring their performance in predicting the prognosis, immunotherapeutic responsiveness, and diagnosis of patients with colon cancer. Firstly, the immunotherapeutic response-related differential expressed genes (DEGs) were identified by comparing responders and non-responders from an anti-PD-L1 cohort using the edgeR R package. Then, the immunotherapeutic response related DEGs was intersected with immune-related genes (IRGs) to obtain the immunotherapeutic response and immune-related genes (IRIGs). Then, an immunotherapeutic response and immune-related risk score (IRIRScore) model consisting of 6 IRIGs was constructed using the univariable Cox regression analysis and multivariate Cox regression analysis based on the COAD cohort from the cancer genome atlas (TCGA) database, which was further validated in two independent gene expression omnibus database (GEO) datasets (GSE39582 and GSE17536) and anti-PD-L1 cohort. A nomogram with good accuracy was established based on the immune-related signatures and clinical factors (C-index = 0.75). In the training dataset and GSE39582, higher IRIRScore was significantly associated with higher TMN and advanced pathological stages. Based on the anti-PD-L1 cohort, patients who were sensitive to immunotherapy had significantly lower risk score than non-responders. Furthermore, we explored the immunotherapy-related signatures based on the training dataset. Kaplan-Meier curve revealed a high level of T cells regulatory (Tregs) was significantly related to poor overall survival (OS), while a high level of T cells CD4 memory resting was significantly related to better OS. Besides, the TMB value of patients in the high-risk group was significantly higher than those in a low-risk group. Moreover, patients in the high-risk group had significantly higher expression levels of immune checkpoint inhibitors. In addition, the immune-related signatures were applied to establish prediction models using the random forest algorithm. Among them, TDGF1 and NRG1 revealed excellent diagnostic predictive performance (AUC >0.8). In conclusion, the current findings provide new insights into immune-related immunotherapeutic responsiveness, prognosis, and diagnosis of colon cancer.

MicroRNAs Are Key Molecules Involved in the Gene Regulation Network of Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common types of cancer and one of the leading causes of mortality worldwide. MicroRNAs (miRNAs) play central roles in normal cell maintenance, development, and other physiological processes. Growing evidence has illustrated that dysregulated miRNAs can participate in the initiation, progression, metastasis, and therapeutic resistance that confer miRNAs to serve as clinical biomarkers and therapeutic targets for CRC. Through binding to the 3'-untranslated region (3'-UTR) of target genes, miRNAs can lead to target mRNA degradation or inhibition at a post-transcriptional level. During the last decade, studies have found numerous miRNAs and their potential targets, but the complex network of miRNA/Targets in CRC remains unclear. In this review, we sought to summarize the complicated roles of the miRNA-target regulation network (Wnt, TGF-ß, PI3K-AKT, MAPK, and EMT related pathways) in CRC with up-to-date, high-quality published data. In particular, we aimed to discuss the downstream miRNAs of specific pathways. We hope these data can be a potent supplement for the canonical miRNA-target regulation network.

Exploring Immune-Related Prognostic Signatures in the Tumor Microenvironment of Colon Cancer.

Background: Colon cancer is a common malignant tumor with poor prognosis. The aim of this study is to explore the immune-related prognostic signatures and the tumor immune microenvironment of colon cancer. Methods: The mRNA expression data of TCGA-COAD from the UCSC Xena platform and the list of immune-related genes (IRGs) from the ImmPort database were used to identify immune-related differentially expressed genes (DEGs). Then, we constructed an immune-related risk score prognostic model and validated its predictive performance in the test dataset, the whole dataset, and two independent GEO datasets. In addition, we explored the differences in tumor-infiltrating immune cell types, tumor mutation burden (TMB), microsatellite status, and expression levels of immune checkpoints and their ligands between the high-risk and low-risk score groups. Moreover, the potential value of the identified immune-related signature with respect to immunotherapy was investigated based on an immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent. Results: Seven immune-related DEGs were identified as prognostic signatures. The areas under the curves (AUCs) of the constructed risk score model for overall survival (OS) were calculated (training dataset: 0.780 at 3 years, 0.801 at 4 years, and 0.766 at 5 years; test dataset: 0.642 at 3 years, 0.647 at 4 years, and 0.629 at 5 years; and the whole dataset: 0.642 at 3 years, 0.647 at 4 years, and 0.629 at 5 years). In the high-risk score group of the whole dataset, patients had worse OS, higher TMN stages, advanced pathological stages, and a higher TP53 mutation rate (p < 0.05). In addition, a high level of resting NK cells or M0 macrophages, and high TMB were significantly related to poor OS (p < 0.05). Also, we observed that high-risk score patients had a high expression level of PD-L1, PD-1, and CTLA-4 (p < 0.05). The patients with high-risk scores demonstrated worse prognosis than those with low-risk scores in multiple datasets (GSE39582: p = 0.0023; GSE17536: p = 0.0008; immunotherapeutic cohort without platinum treatment: p = 0.0014; immunotherapeutic cohort with platinum treatment: p = 0.0027). Conclusion: We developed a robust immune-related prognostic signature that performed great in multiple cohorts and explored the characteristics of the tumor immune microenvironment of colon cancer patients, which may give suggestions for the prognosis and immunotherapy in the future.

Construction of a novel methylation-related prognostic model for colorectal cancer based on microsatellite status.

The present study aimed to construct a novel methylation-related prognostic model based on microsatellite status that may enhance the prognosis of colorectal cancer (CRC) from methylation and microsatellite status perspective. DNA methylation and mRNA expression data with clinical information were downloaded from The Cancer Genome Atlas (TCGA) data set. The samples were divided into microsatellite stability and microsatellite instability group, and CIBERSORT was used to assess the immune cell infiltration characteristics. After identifying the differentially methylated genes and differentially expression genes using R packages, the methylation-driven genes were further identified. Prognostic genes that were used to establish the methylation-related risk score model were generated by the univariate and multivariate Cox regression model. Finally, we established and evaluated the methylation-related prognostic model for CRC patients. A total of 69 MDGs were obtained and three of these genes (MIOX, TH, DKFZP434K028) were selected to construct the prognostic model. Patients in the low-risk score group had a conspicuously better overall survival than those in the high-risk score group (p < .0001). The area under the receiver operating characteristic curve for this model was 0.689 at 3 years, 0.674 at 4 years, and 0.658 at 5 years. The Wilcoxon test showed that higher risk score was associated with higher T stage (p = .01), N stages (p = .0028), metastasis (p = .013), and advanced pathological stage (p = .0013). However, the more instability of microsatellite status, the lower risk score of CRC patients (p = .0048). Our constructed methylation-related prognostic model based on microsatellite status presents potential significance in assessing recurrence risk stratification, tumor staging, and immunotherapy for CRC patients.

Discrimination of sepsis stage metabolic profiles with an LC/MS-MS-based metabolomics approach.

BACKGROUND: To identify metabolic biomarkers that can be used to differentiate sepsis from systemic inflammatory response syndrome (SIRS), assess severity and predict outcomes. METHODS: 65 patients were involved in this study, including 35 patients with sepsis, 15 patients with SIRS and 15 normal patients. Small metabolites that were present in patient serum samples were measured by liquid chromatography mass spectrometry techniques and analysed using multivariate statistical methods. RESULTS: The metabolic profiling of normal patients and patients with SIRS or sepsis was markedly different. A significant decrease in the levels of lactitol dehydrate and S-phenyl-d-cysteine and an increase in the levels of S-(3-methylbutanoyl)-dihydrolipoamide-E and N-nonanoyl glycine were observed in patients with sepsis in comparison to patients with SIRS (p<0.05). Patients with severe sepsis and septic shock displayed lower levels of glyceryl-phosphoryl-ethanolamine, Ne, Ne dimethyllysine, phenylacetamide and d-cysteine (p<0.05) in their sera. The profiles of patients with sepsis 48 h before death illustrated an obvious state of metabolic disorder, such that S-(3-methylbutanoyl)-dihydrolipoamide-E, phosphatidylglycerol (22:2 (13Z, 16Z)/0:0), glycerophosphocholine and S-succinyl glutathione were significantly decreased (p<0.05). The receiver operating characteristic curve of the differential expression of these metabolites was also performed. CONCLUSIONS: The body produces significant evidence of metabolic disorder during SIRS or sepsis. Seven metabolites may potentially be used to diagnose sepsis. TRIAL REGISTRATION NUMBER: ClinicalTrial.gov identifier NCT01649440.

Urinary proteomics analysis for sepsis biomarkers with iTRAQ labeling and two-dimensional liquid chromatography-tandem mass spectrometry.

BACKGROUND: Proteomics has only recently been applied to the field of critical care research. Sepsis is a major factor contributing to intensive care unit admissions and deaths. The purpose of this study was to screen potential urinary biomarkers for sepsis using A proteomics approach. METHODS: Fifteen sepsis and 15 systemic inflammatory response syndrome patients were involved in this study. Urinary proteins were identified by isobaric tag for relative and absolute quantitation coupled with liquid chromatography-tandem mass spectrometry. Mass spectroscopy analysis was performed with the Mascot software and the International Protein Index. Bioinformatics analyses were performed using the hierarchy cluster analysis, the STRING software, the Gene Ontology, and the Kyoto Encyclopedia of Genes and Genome database. RESULTS: One hundred thirty proteins were identified, and 34 differentially expressed proteins were selected (fold change, >1.5). On the basis of the Gene Ontology and the Kyoto Encyclopedia of Genes and Genome database, these 34 proteins were identified to be involved in inflammation, immunity, and structural or cytoskeletal processes. Five proteins were selected by a protein-protein interaction network for sepsis differentiation: cadherin 1, haptoglobin, complement 3, alpha-1-antitrypsin, and ceruloplasmin. CONCLUSION: Urinary proteomics may represent a suitable approach for sepsis-related research. The detection of urinary biomarkers is expected to become a noninvasive and acceptable method, which facilitates the close surveillance of diseases and reduces medical costs. LEVELS OF EVIDENCE: Diagnostic study, level IV.