Consensus clustering applied to multi-omics disease subtyping

ANXA5 predicts prognosis and immune response and mediates proliferation and migration in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy that often develops unnoticed. Typically, these tumors are identified at advanced stages, resulting in a relatively low chance of successful treatment. Anoikis serves as a natural defense against the spread of tumor cells, meaning circumventing anoikis can effectively inhibit tumor metastasis. Nonetheless, studies focusing on anoikis in the context of HNSCC remain scarce.

METHODS

Anoikis-related genes (ARGs) were identified by using the GeneCards and Harmonizome databases. Expression data of these genes and relevant clinical features were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A LASSO regression and a prognostic risk score model were developed to determine their prognostic significance. The analysis included the use of the CIBERSORT algorithm to quantify immune and stromal cell presence. Furthermore, in vitro and in vivo, we confirmed the expression and functional roles of proteins and mRNA of genes independently predictive of prognosis.

RESULTS

The study identified eight genes linked to prognosis (ANXA5, BAK1, CDKN2A, PPARG, CCR7, MAPK11, CRYAB, CRYBA1) and developed a prognostic model that effectively forecasts the survival outcomes for patients with HNSCC. A higher survival likelihood is associated with lower risk scores. In addition, a significant relationship was found between immune and risk score, and ANXA5 deletion promoted the killing of HNSCC cells by activated CD8+ T cells. During the screening process, 65 different chemotherapeutic drugs were found to have significant differences in IC50 values when comparing high- and low-risk categories. ANXA5 emerged as a gene with independent prognostic significance, exhibiting notably elevated protein and mRNA levels in HNSCC tissue compared to non-tumorous tissue. The suppression of ANXA5 gene activity resulted in a substantial decrease in both the growth and mobility of HNSCC cells. Animal model experiments demonstrated that inhibiting ANXA5 suppressed HNSCC growth and migration in vivo.

CONCLUSION

Through bioinformatics, a prognostic risk model of high precision was developed, offering valuable insights into the survival rates and immune responses in patients with HNSCC. ANXA5 is highlighted as a significant prognostic factor among the identified genes, indicating its promise as a potential therapeutic target for those with HNSCC.

Significance of pyroptosis-related genes in the diagnosis and classification of diabetic kidney disease

OBJECTIVE

This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD).

METHODS

Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes.

RESULTS

A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes (CASP1, CITED2, HTRA1, PTGS2, S100A12) were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, CASP1, CITED2, HTRA1, PTGS2 and S100A12 are potential DKD biomarkers.

CONCLUSION

Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.

Inhibition of the FOXO1-ROCK1 axis mitigates cardiomyocyte injury under chronic hypoxia in Tetralogy of Fallot by maintaining mitochondrial quality control

INTRODUCTION

Persistent chronic myocardial hypoxia causes disturbances in mitochondrial quality control (MQC), ultimately leading to increased cardiomyocyte injury in patients with Tetralogy of Fallot (TOF). The present study aimed to identify the key effector molecules of cardiomyocyte injury under chronic hypoxia in TOF.

METHODS

Clinical data from TOF patients were collected and whole transcriptome sequencing was performed on myocardial samples. Chronic hypoxia models were established in cardiac-specific knockout mice and cardiomyocytes, and a series of molecular experiments were used to determine the specific mechanisms involved.

RESULTS

Clinical cohort data and whole-transcriptome sequencing analysis of myocardial samples from TOF patients revealed that forkhead box O1 (FOXO1) plays an important role in chronic hypoxic cardiomyocyte injury. In a model of chronic hypoxia established in FOXO1 cardiac-specific knockout mice and FOXO1 gene-deficient cardiomyocytes, the AMPK signaling pathway regulates the expression of FOXO1, which in turn disrupts MQC by regulating the transcriptional activation of Rho-associated protein kinase 1 (ROCK1), and increasing the production of mitochondrial ROS, thereby exacerbating damage to cardiomyocytes. Excessive reactive oxygen species (ROS) production during MQC dysfunction further activates Cox7a2L to increase the assembly of the respiratory chain supercomplex. In addition, we found that miR-27b-3p partially binds to the 3' untranslated region of FOXO1 to exert a protective effect.

CONCLUSIONS

Maintenance of MQC under chronic hypoxia is achieved through a series of injury-protection mechanisms, suggesting that FOXO1 inhibition may be crucial for future mitigation of chronic hypoxic cardiomyocyte injury in TOF.

FANCD2 as a ferroptosis-related target for recurrent implantation failure by integrated bioinformatics and Mendelian randomization analysis

Despite advancements in assisted reproductive technology, recurrent implantation failure (RIF) remains a challenge. Endometrial factors, including ferroptosis and immunity, may contribute to this issue. This study integrated bioinformatics analysis and Mendelian randomization (MR) to investigate the expression and significance of DEFRGs in RIF. We intersected 484 ferroptosis-associated genes with 515 differentially expressed genes (DEGs) to identify key DEFRGs. Subsequent analyses included enrichment analysis, molecular subtype identification, machine learning model development for biomarker discovery, immune cell infiltration assessment, single-cell RNA sequencing, and MR to explore the causal relationships of selected genes with RIF. In this study, we identified 11 differentially expressed ferroptosis-related genes (DEFRGs) between RIF and healthy individuals. Cluster analysis revealed two distinct molecular subtypes with different immune profiles and DEFRG expressions. Machine learning models highlighted MUC1, GJA1 and FANCD2 as potential diagnostic biomarkers, with high accuracy in RIF prediction. Single-cell analysis further revealed the cellular localization and interactions of DEFRGs. MR suggested a protective effect of FANCD2 against RIF. Validation in RIF patients confirmed the differential expression of key DEFRGs, consistent with bioinformatics findings. This comprehensive study emphasize the significant role of DEFRGs in the pathogenesis of RIF, suggesting that modulating these genes could offer new avenues for treatment. The FANCD2 is a potential gene contributing to RIF pathogenesis through a non-classical ferroptosis-dependent pathway, providing a foundation for personalized therapeutic strategies in RIF management.

Prognostic aging gene-based score for colorectal cancer: unveiling links to drug resistance, mutation burden, and personalized treatment strategies

OBJECTIVE

Colorectal cancer (CRC) is characterized by high incidence and mortality rates worldwide. In this study, we present a novel aging-related gene-based risk scoring system (Aging score) as a predictive tool for CRC prognosis.

METHOD

We identified prognostic aging-related genes using univariate Cox regression analysis, revealing key biological processes in CRC progression. We then constructed a robust prognostic model using LASSO and multivariate Cox regression analyses, including four critical genes: CAV1, FOXM1, MAD2L1, and WT1.

RESULT

The Aging score demonstrated high prognostic performance across the training, testing, and entire TCGA-CRC datasets, proving its reliability. High-risk patients identified by the Aging score had significantly shorter overall survival times than low-risk patients, indicating its potential for patient stratification and personalized treatment. The Aging score remained an independent prognostic factor compared to age, gender, and tumor stage. Additionally, the score was linked to tumor mutation burden and microsatellite instability, indicators of immune checkpoint inhibitor response. High-risk patients also showed higher estimated IC50 values for common chemotherapeutic drugs, suggesting possible treatment resistance.

CONCLUSION

Our findings highlight the Aging score's potential to enhance clinical decision-making and pave the way for personalized CRC management.

Artificial intelligence in ovarian cancer drug resistance advanced 3PM approach: subtype classification and prognostic modeling

Background

Ovarian cancer patients' resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel.

Objectives

Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM.

Methods

This study employed "Beyondcell," an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells. Drug resistance features were used to perform 10 multi-omics clustering on the TCGA training set to identify patient subgroups with differential drug responses. Concurrently, a deep learning prognostic model with KAN architecture which had a flexible activation function to better fit the model was constructed for this training set. The constructed patient subtype classifier and prognostic model were evaluated using three external validation sets from GEO: GSE17260, GSE26712, and GSE51088.

Results

This study identified that endothelial cells are resistant to paclitaxel, doxorubicin, and docetaxel, suggesting their potential as targets for cellular therapy in ovarian cancer patients. Based on drug resistance features, 10 multi-omics clustering identified four patient subtypes with differential responses to four chemotherapy drugs, in which subtype CS2 showed the highest drug sensitivity to all four drugs. The other subtypes also showed enrichment in different biological pathways and immune infiltration, allowing for targeted treatment based on their characteristics. Besides, this study applied the latest KAN architecture in artificial intelligence to replace the MLP structure in the DeepSurv prognostic model, finally demonstrating robust performance on patients' prognosis prediction.

Conclusions

This study, by classifying patients and constructing prognostic models based on resistance characteristics to first-line drugs, has effectively applied multi-omics data into the realm of 3PM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00374-4.

Telomere-related gene risk model for prognosis prediction in colorectal cancer

Background

Colorectal cancer (CRC) is the third-most prevalent cancer globally. The biological significance of telomeres in CRC carcinogenesis and progression is underscored by accumulating data. Nevertheless, not much is known about how telomere-related genes (TRGs) affect CRC prognosis. Therefore, the aim of this study was to investigate the role of TRGs in CRC prognosis.

Methods

We retrospectively obtained the expression profiles and clinical data of CRC patients from public databases. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, we created a telomere-related risk model to predict survival outcomes, identifying ten telomere-related differentially expressed genes (TRDEGs). Based on TRDEGs, we stratified patients from The Cancer Genome Atlas (TCGA) into low- and high-risk subsets. Subsequently, we conducted comprehensive analyses, including survival assessment, immune cell infiltration, drug sensitivity, and prediction of molecular interactions using Kaplan-Meier curves, ESTIMATE, CIBERSORT, OncoPredict, and other approaches.

Results

The model showed exceptional predictive accuracy for survival. Significant differences in survival were observed between the two groups of participants grouped according to the model (P<0.001), and this difference was further confirmed in the external validation set (GSE39582) (P=0.004). Additionally, compared to the low-risk group, the high-risk group exhibited significantly advanced tumor node metastasis (TNM) stages, lower proportions of activated CD4+ T cells, effector memory CD4+ T cells, and memory B cells, but increased ratios of M2 macrophages and regulatory T cells (Tregs), elevated tumor immune dysfunction and exclusion (TIDE) scores, and diminished sensitivity to dabrafenib, lapatinib, camptothecin, docetaxel, and telomerase inhibitor IX, reflecting the signature's capacity to distinguish clinical pathological characteristics, immune environment, and drug efficacy. Finally, we validated the expression of the ten TRDEGs (ACACB, TPX2, SRPX, PPARGC1A, CD36, MMP3, NAT2, MMP10, HIGD1A, and MMP1) through quantitative real-time polymerase chain reaction (qRT-PCR) and found that compared to normal cells, the expression levels of ACACB, HIGD1A, NAT2, PPARGC1A, and TPX2 in CRC cells were elevated, whereas those of CD36, SRPX, MMP1, MMP3, and MMP10 were reduced.

Conclusions

Overall, we constructed a telomere-related biomarker capable of predicting prognosis and treatment response in CRC individuals, offering potential guidance for drug therapy selection and prognosis prediction.

An investigation of the molecular characterization of the tripartite motif (TRIM) family and primary validation of TRIM31 in gastric cancer

Most TRIM family members characterized by the E3-ubiquitin ligases, participate in ubiquitination and tumorigenesis. While there is a dearth of a comprehensive investigation for the entire family in gastric cancer (GC). By combining the TCGA and GEO databases, common TRIM family members (TRIMs) were obtained to investigate gene expression, gene mutations, and clinical prognosis. On the basis of TRIMs, a consensus clustering analysis was conducted, and a risk assessment system and prognostic model were developed. Particularly, TRIM31 with clinical prognostic and diagnostic value was chosen for single-gene bioinformatics analysis, in vitro experimental validation, and immunohistochemical analysis of clinical tissue microarrays. The combined dataset consisted of 66 TRIMs, of which 52 were differentially expressed and 43 were differentially prognostic. Significant survival differences existed between the gene clusters obtained by consensus clustering analysis. Using 4 differentially expressed genes identified by multivariate Cox regression and LASSO regression, a risk scoring system was developed. Higher risk scores were associated with a poorer prognosis, suppressive immune cell infiltration, and drug resistance. Transcriptomic data and clinical sample tissue microarrays confirmed that TRIM31 was highly expressed in GC and associated with a poor prognosis. Pathway enrichment analysis, cell migration and colony formation assay, EdU assay, reactive oxygen species (ROS) assay, and mitochondrial membrane potential assay revealed that TRIM31 may be implicated in cell cycle regulation and oxidative stress-related pathways, contribute to gastric carcinogenesis. This study investigated the whole functional and expression profile and a risk score system based on the TRIM family in GC. Further investigation centered around TRIM31 offers insight into the underlying mechanisms of action exhibited by other members of its family in the context of GC.

Identification of novel endoplasmic reticulum-related genes and their association with immune cell infiltration in major depressive disorder

BACKGROUND

Several lines of evidence point to an interaction between genetic predisposition and environmental factors in the onset of major depressive disorder (MDD). This study is aimed to investigate the pathogenesis of MDD by identifying key biomarkers, associated immune infiltration using bioinformatic analysis and human postmortem sample.

METHODS

The Gene Expression Omnibus (GEO) database of GSE98793 was adopted to identify hub genes linked to endoplasmic reticulum (ER) stress-related genes (ERGs) in MDD. Another GEO database of GSE76826 was employed to validate the novel target associated with ERGs and immune infiltration in MDD. Moreover, human postmortem sample from MDD patients was utilized to confirm the differential expression analysis of hub genes.

RESULTS

We discovered 12 ER stress-related differentially expressed genes (ERDEGs). A LASSO Cox regression analysis helped construct a diagnostic model for these ERDEGs, incorporating immune infiltration analysis revealed that three hub genes (ERLIN1, SEC61B, and USP13) show the significant and consistent expression differences between the two groups. Western blot analysis of postmortem brain samples indicated notably higher expression levels of ERLIN1 and SEC61B in the MDD group, with USP13 also tending to increase compared to control group.

LIMITATIONS

The utilization of the MDD gene chip in this analysis was sourced from the GEO database, which possesses a restricted number of pertinent gene chip samples.

CONCLUSIONS

These findings indicate that ERDEGs especially including ERLIN1, SEC61B, and USP13 associated the infiltration of immune cells may be potential diagnostic indicators for MDD.

Consensus Clustering Analysis Based on Enhanced-CT Radiomic Features: Esophageal Squamous Cell Carcinoma patients' 3-Year Progression-Free Survival

RATIONALE AND OBJECTIVES

To assess the efficacy of consensus cluster analysis based on CT radiomics in stratifying risk and predicting postoperative progression-free survival (PFS) in patients diagnosed with esophageal squamous cell carcinoma (ESC).

MATERIALS AND METHODS

We conducted a retrospective study involving 546 patients diagnosed with ESC between January 2016 and March 2021. All patients underwent preoperative enhanced CT examinations. From the enhanced CT images, radiomics features were extracted, and a consensus clustering algorithm was applied to group the patients based on these features. Statistical analysis was performed to examine the relationship between the clustering results and gene protein expression, histopathological features, and patients' 3-year PFS. We applied the Kruskal-Wallis test for continuous data, chi-square or Fisher's exact tests for categorical data, and the log-rank test for PFS.

RESULTS

This study identified four groups: Cluster 1 (n = 100, 18.3%), Cluster 2 (n = 197, 36.1%), Cluster 3 (n = 205, 37.5%), and Cluster 4 (n = 44, 8.1%). The cancer gene Breast Cancer Susceptibility Gene 1 (BRCA1) was most highly expressed in Cluster 4 (75%), showing significant differences between the four subtypes with a P-value of 0.035. The expression of programmed death-1 (PD-1) was highest in Cluster 1 (51%), with a P-value of 0.022. Vascular invasion occurred most frequently in Cluster 2 (28.9%), with a P-value of 0.022. The majority of patients with stage T3-4 were in Cluster 2 (67%), with a P-value of 0.003. Kaplan-Meier survival analysis revealed significant differences in PFS between the four groups (P = 0.013). Among them, patients in Cluster 1 had the best prognosis, while those in Cluster 2 had the worst.

CONCLUSION

This study highlights the effectiveness of consensus clustering analysis based on enhanced CT radiomics features in identifying associations between radiomics features, histopathological characteristics, and prognosis in different clusters. These findings provide valuable insights for clinicians in accurately and effectively evaluating the prognosis of esophageal cancer.

A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan-Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer

Background

Exosomes are promising tools for the development of new diagnostic and therapeutic approaches. Exosomes possess the ability to activate signaling pathways that contribute to the remodeling of the tumor microenvironment, angiogenesis, and the regulation of immune responses. We aimed to develop a prognostic score based on exosomes derived from breast cancer.

Materials and methods

Training was conducted on the TCGA-BRCA dataset, while validation was conducted on GSE20685, GSE5764, GSE7904, and GSE29431. A total of 121 genes related to exosomes were retrieved from the ExoBCD database. The Cox proportional hazards model is used to develop risk score model. The GSVA package was utilized to analyze single-sample gene sets and identify exosome signatures, while the WGCNA package was utilized to identify gene modules associated with clinical outcomes. The clusterProfiler and GSVA R packages facilitated gene set enrichment and variation analyses. Furthermore, CIBERSORT quantified immune infiltration, and a correlation between gene expression and drug sensitivity was assessed using the TIDE algorithm.

Results

An exosome-related prognostic score was established using the following selected genes: ABCC9, PIGR, CXCL13, DOK7, CD24, and IVL. Various immune cells that promote cancer immune evasion were associated with a high-risk prognostic score, which was an independent predictor of outcome. High-risk and low-risk groups exhibited significantly different infiltration abundances (p < 0.05). By conducting a sensitivity comparison, we found that patients with high-risk scores exhibited more favorable responses to immunotherapy than those with low-risk scores.

Conclusion

The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy.

Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies

OBJECTIVE

Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades.

DESIGN

We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease.

RESULTS

Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues.

CONCLUSIONS

Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.

A machine learning-based diagnostic model for myocardial infarction patients: Analysis of neutrophil extracellular traps-related genes and eQTL Mendelian randomization

To identify neutrophil extracellular trap (NET)-associated gene features in the blood of patients with myocardial infarction (MI) using bioinformatics and machine learning, with the aim of exploring potential diagnostic utility in atherosclerosis. The datasets GSE66360 and GSE48060 were downloaded from the Gene Expression Omnibus (GEO) public database. GSE66360 was used as the training set, and GSE48060 was used as an independent validation set. Differential genes related to NETs were screened using R software. Machine learning was performed based on the differential expression of NET-related genes across different samples. The advantages and disadvantages of 4 machine learning algorithms (Random Forest [RF], Extreme Gradient Boosting [XGBoost, XGB], Generalized Linear Models [GLM], and Support Vector Machine-Recursive Feature Elimination [SVM-RFE]) were compared, and the optimal method was used to screen feature genes and construct diagnostic models, which were then validated in the external validation dataset. Correlations between feature genes and immune cells were analyzed, and samples were reclustered based on the expression of feature genes. Differences in downstream molecular mechanisms and immune responses were explored for different clusters. Weighted Gene Co-expression Network Analysis was performed on different clusters, and disease-related NET genes were extracted, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Finally, Mendelian randomization was employed to further investigate the causal relationship between the expression of model genes and the occurrence of MI. Forty-seven NET-related differential genes were obtained, and after comparing the 4 machine learning methods, support vector machine was used to screen ATG7, MMP9, interleukin 6 (IL6), DNASE1, and PDE4B as key genes for the construction of diagnostic models. The diagnostic value of the model was validated in an independent external validation dataset. These five genes showed strong correlations with neutrophils. Different sample clusters also demonstrated differential enrichment in pathways such as nitrogen metabolism, complement and coagulation cascades, cytokine-cytokine receptor interaction, renin-angiotensin system, and steroid biosynthesis. The Mendelian randomization results demonstrate a causal relationship between the expression of ATG7 and the incidence of myocardial infarction. The feature genes ATG7, MMP9, IL6, DNASE1, and PDE4B, identified using bioinformatics, may serve as potential diagnostic biomarkers and therapeutic targets for Myocardial infarction. Specifically, the expression of ATG7 could potentially be a significant factor in the occurrence of MI.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.

Multi-omics analysis revealed the regulation mode of intratumor microorganisms and microbial signatures in gastrointestinal cancer

OBJECTIVE

Gastrointestinal cancer is one of the most common malignant tumors in the world, and its incidence rate is always high. In recent years, research has shown that microorganisms may play a broad role in the diagnosis, pathogenesis, and treatment of cancer.

METHODS

In this study, samples were first classified according to the microbial expression data of Gastrointestinal cancer, followed by functional enrichment and Immunoassay. In order to better understand the role of intratumor microorganisms in the prognosis, we screened gene signatures and constructed risk model through univariate cox and lasso regression and multivariable cox, then screened microbial signatures using zero-inflated model regression model and constructed risk index (RI), and finally predicted the immunotherapeutic effect of the risk model.

RESULTS

The results indicate that the composition of tumor microorganisms in the C3 subtype is closely related to tumor angiogenesis, and there is a significant difference in the proportion of innate and acquired immune cells between the C2 and C1 subtypes, as well as differences in the physiological functions of immune cells. There are significant differences in the expression of microbial signatures between high and low risk subtypes, with 9 microbial signatures upregulated in high risk subtypes and 15 microbial signatures upregulated in low risk subtypes. These microbial signatures were significantly correlated with the prognosis of patients. The results of immunotherapy indicate that immunotherapy for high-risk subtypes is more effective.

CONCLUSION

Overall, we analyze from the perspective of microorganisms within tumors, pointing out new directions for the diagnosis and treatment of cancer.

Characteristics of n6-methyladenosine (m6A) regulators and role of FTO/TNC in scleroderma

BACKGROUND

m6A regulators have important roles in a variety of autoimmune diseases, but their potential function in scleroderma, a refractory connective tissue disease, remains unclear. Tenascin C (TNC) is known to be a factor promoting collagen deposition in the development of scleroderma, but the regulatory relationship between TNC and m6A regulators is unknown.

METHODS

We extracted GSE33463 data consisting of forty-one healthy controls and sixty-one patients with scleroderma, and we analyzed the expression levels of twenty-one m6A regulators as well as the associations between them. In addition, we obtained random forest (RF) and nomogram models to predict the likehood of scleroderma. Next, we categorized the m6Aclusters and geneclusters by consensus clustering, and we performed an immune cell infiltration analysis for each cluster. Finally, we injected adenoviruses into a bleomycin (BLM)-induced mouse model of scleroderma, which was used to overexpress FTO and TNC. We assess the extent of skin fibrosis in the mice samples using pathology stains and measuring their hydroxyproline content and collagen mRNA.

RESULTS

We initially identified fourteen differentially expressed m6A regulators (WTAP, RBM15, CBLL1, FTO, ALKBH5, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, RBMX, HNRNPC, IGFBP1 and IGFBP2). We found ALKBH5 to be positively associated with CBLL1 and RBM15, and FTO to be negatively associated with WTAP. In addition, we identified four m6A regulators (CBLL1, IGFBP1, YTHDF2 and IGFBP2) using a RF model, and we designed a nomogram model with those variables that proved reliable according to the calibration curve and clinical impact curve. We found that the m6Acluster A was correlated with Type 1 T helper cell infiltration and the genecluster A was correlated with regulatory T cell infiltration. Finally, we showed that FTO overexpression downregulated the m6A and mRNA levels of TNC, and alleviated skin fibrosis in the mouse model of scleroderma. Thus, our overexpression experiments provide preliminary evidence suggesting that TNC is an adverse factor in scleroderma.

CONCLUSION

Our approach might be useful as a new and accurate scleroderma diagnosis method. Moreover, our results suggested that FTO/TNC might be a novel scleroderma therapeutic target.

Integrated analysis reveals crosstalk between pyroptosis and immune regulation in renal fibrosis

Purpose

The pathogenesis of renal fibrosis (RF) involves intricate interactions between profibrotic processes and immune responses. This study aimed to explore the potential involvement of the pyroptosis signaling pathway in immune microenvironment regulation within the context of RF. Through comprehensive bioinformatics analysis and experimental validation, we investigated the influence of pyroptosis on the immune landscape in RF.

Methods

We obtained RNA-seq datasets from Gene Expression Omnibus (GEO) databases and identified Pyroptosis-Associated Regulators (PARs) through literature reviews. Systematic evaluation of alterations in 27 PARs was performed in RF and normal kidney samples, followed by relevant functional analyses. Unsupervised cluster analysis revealed distinct pyroptosis modification patterns. Using single-sample gene set enrichment analysis (ssGSEA), we examined the correlation between pyroptosis and immune infiltration. Hub regulators were identified via weighted gene coexpression network analysis (WGCNA) and further validated in a single-cell RNA-seq dataset. We also established a unilateral ureteral obstruction-induced RF mouse model to verify the expression of key regulators at the mRNA and protein levels.

Results

Our comprehensive analysis revealed altered expression of 19 PARs in RF samples compared to normal samples. Five hub regulators, namely PYCARD, CASP1, AIM2, NOD2, and CASP9, exhibited potential as biomarkers for RF. Based on these regulators, a classifier capable of distinguishing normal samples from RF samples was developed. Furthermore, we identified correlations between immune features and PARs expression, with PYCARD positively associated with regulatory T cells abundance in fibrotic tissues. Unsupervised clustering of RF samples yielded two distinct subtypes (Subtype A and Subtype B), with Subtype B characterized by active immune responses against RF. Subsequent WGCNA analysis identified PYCARD, CASP1, and NOD2 as hub PARs in the pyroptosis modification patterns. Single-cell level validation confirmed PYCARD expression in myofibroblasts, implicating its significance in the stress response of myofibroblasts to injury. In vivo experimental validation further demonstrated elevated PYCARD expression in RF, accompanied by infiltration of Foxp3+ regulatory T cells.

Conclusions

Our findings suggest that pyroptosis plays a pivotal role in orchestrating the immune microenvironment of RF. This study provides valuable insights into the pathogenesis of RF and highlights potential targets for future therapeutic interventions.

A model for predicting clinical prognosis based on brain metastasis-related genes in patients with breast cancer

Background

Brain metastasis (BM) is a clinically relevant cause of death in patients with breast cancer (BRCA). This study was designed to develop a clinical model capable of predicting BRCA patients' prognostic outcomes according to the expression of BM-related genes (BMRGs).

Methods

The public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases served as data sources. BMRGs of BRCA were selected from previous literature. Differences among BRCA molecular subtypes were compared using R 'limma' package. The impact of BM-related differentially expressed genes (BM_DEGs) on BRCA patients' outcomes was explored with a risk score model, after which the relationship between these risk scores and immune cell infiltration was examined. Risk scores were also used to judge the predicted efficacy of immunotherapeutic interventions. The utility of risk scores in combination with clinicopathological characteristics was evaluated as a predictor of patient's survival through univariate and multivariate analyses.

Results

The R limma package was used to explore differential gene expression, after which 12 BM_DEGs were incorporated into a risk scoring model. The resultant risk scores were able to predict immunotherapeutic treatment efficacy. In addition, a nomogram incorporating risk scores, stage, and age was established. The nomogram was able to reliably predict the overall survival (OS) of BRCA patients, yielding predictive outcomes that aligned well with actual observations.

Conclusions

In summary, a predictive clinical model for BRCA patients was successfully established in this study, providing a valuable tool that may be particularly helpful for the assessment of patients facing a risk of BM development.

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods

SUMMARY

A variety of computational methods have been developed to identify functionally related gene modules from genome-wide gene expression profiles. Integrating the results of these methods to identify consensus modules is a promising approach to produce more accurate and robust results. In this application note, we introduce COMMO, the first web server to identify and analyze consensus gene functionally related gene modules from different module detection methods. First, COMMO implements eight state-of-the-art module detection methods and two consensus clustering algorithms. Second, COMMO provides users with mRNA and protein expression data for 33 cancer types from three public databases. Users can also upload their own data for module detection. Third, users can perform functional enrichment and two types of survival analyses on the observed gene modules. Finally, COMMO provides interactive, customizable visualizations and exportable results. With its extensive analysis and interactive capabilities, COMMO offers a user-friendly solution for conducting module-based precision medicine research.

AVAILABILITY AND IMPLEMENTATION

COMMO web is available at https://commo.ncpsb.org.cn/, with the source code available on GitHub: https://github.com/Song-xinyu/COMMO/tree/master.

Autoencoder-assisted latent representation learning for survival prediction and multi-view clustering on multi-omics cancer subtyping

Cancer subtyping (or cancer subtypes identification) based on multi-omics data has played an important role in advancing diagnosis, prognosis and treatment, which triggers the development of advanced multi-view clustering algorithms. However, the high-dimension and heterogeneity of multi-omics data make great effects on the performance of these methods. In this paper, we propose to learn the informative latent representation based on autoencoder (AE) to naturally capture nonlinear omic features in lower dimensions, which is helpful for identifying the similarity of patients. Moreover, to take advantage of survival information or clinical information, a multi-omic survival analysis approach is embedded when integrating the similarity graph of heterogeneous data at the multi-omics level. Then, the clustering method is performed on the integrated similarity to generate subtype groups. In the experimental part, the effectiveness of the proposed framework is confirmed by evaluating five different multi-omics datasets, taken from The Cancer Genome Atlas. The results show that AE-assisted multi-omics clustering method can identify clinically significant cancer subtypes.

Integrating multiple microarray datasets to explore the significance of ferroptosis regulators in the diagnosis and subtype classification of osteoarthritis

Osteoarthritis (OA) is a chronic joint disease that reduces quality of life for patients. Ferroptosis plays a significant role in OA. However, its underlying mechanism remains unclear. In this study, we integrated 7 OA synovial datasets from the GEO database to screen for significant ferroptosis-related genes. The top 5 ferroptosis regulators were used to construct nomogram models to predict OA prevalence. Consensus clustering was applied to classify OA patients into different ferroptosis patterns based on significant ferroptosis-related genes. Subsequently, an immune cell infiltration study was performed to investigate the relationship between the significant ferroptosis regulators and immune cells. As a result, we screened 11 ferroptosis-related genes in OA patients. Five candidate ferroptosis regulators (SLC7A11, ALOX5, SLC1A5, GOT1, and GSS) were used to predict OA risk. The nomogram model based on these 5 genes is important for assessing the occurrence of OA. Consensus clustering analysis showed that OA patients could be classified into 2 ferroptosis patterns (Clusters A and B). Immune cell infiltration levels were higher in Cluster B than in Cluster A. Two subtypes, gene Clusters A and B, were classified according to the expression of ferroptosis-related DEGs among the ferroptosis patterns. Cluster A and gene Cluster A had higher ferroptosis scores than Cluster B or gene Cluster B, whereas the expression levels of the proinflammatory cytokines interleukin (IL)-1β, tumor necrosis factor, IL-6, IL-18, and IL-10 were higher in Cluster B or gene Cluster B than those in Cluster A or gene Cluster A. Different subtypes of ferroptosis play critical roles in OA. Furthermore, immunotherapy strategies for OA treatment may be guided by our study on ferroptosis patterns.

[Omics to serve myology]

Despite efforts in biomedical research, pathophysiological mechanisms and therapeutic targets of diseases remain difficult to identify. The development of high-throughput techniques led to the advent of innovatve technologies called omics. They aim at characterizing as exhaustively as possible a set of molecules: genes, RNAs, proteins, metabolites, etc. These a priori methods allow a precise molecular characterization of diseases and a better understanding of complex pathophysiological mechanisms. In this paper, we will review most omics approaches, their integration and their applications in the context of myology.

Development of genomic phenotype and immunophenotype of acute respiratory distress syndrome using autophagy and metabolism-related genes

Background

Distinguishing ARDS phenotypes is of great importance for its precise treatment. In the study, we attempted to ascertain its phenotypes based on metabolic and autophagy-related genes and infiltrated immune cells.

Methods

Transcription datasets of ARDS patients were obtained from Gene expression omnibus (GEO), autophagy and metabolic-related genes were from the Human Autophagy Database and the GeneCards Database, respectively. Autophagy and metabolism-related differentially expressed genes (AMRDEGs) were further identified by machine learning and processed for constructing the nomogram and the risk prediction model. Functional enrichment analyses of differentially expressed genes were performed between high- and low-risk groups. According to the protein-protein interaction network, these hub genes closely linked to increased risk of ARDS were identified with CytoHubba. ssGSEA and CIBERSORT was applied to analyze the infiltration pattern of immune cells in ARDS. Afterwards, immunologically characterized and molecular phenotypes were constructed according to infiltrated immune cells and hub genes.

Results

A total of 26 AMRDEGs were obtained, and CTSB and EEF2 were identified as crucial AMRDEGs. The predictive capability of the risk score, calculated based on the expression levels of CTSB and EEF2, was robust for ARDS in both the discovery cohort (AUC = 1) and the validation cohort (AUC = 0.826). The mean risk score was determined to be 2.231332, and based on this score, patients were classified into high-risk and low-risk groups. 371 differential genes in high- and low-risk groups were analyzed. ITGAM, TYROBP, ITGB2, SPI1, PLEK, FGR, MPO, S100A12, HCK, and MYC were identified as hub genes. A total of 12 infiltrated immune cells were differentially expressed and have correlations with hub genes. According to hub genes and implanted immune cells, ARDS patients were divided into two different molecular phenotypes (Group 1: n = 38; Group 2: n = 19) and two immune phenotypes (Cluster1: n = 22; Cluster2: n = 35), respectively.

Conclusion

This study picked up hub genes of ARDS related to autophagy and metabolism and clustered ARDS patients into different molecular phenotypes and immunophenotypes, providing insights into the precision medicine of treating patients with ARDS.

Use of a ferroptosis-related gene signature to construct diagnostic and prognostic models for assessing immune infiltration in metabolic dysfunction-associated fatty liver disease

Introduction: Metabolic dysfunction-associated fatty liver disease (MAFLD), a serious health problem worldwide, can involve ferroptosis. This study aimed to comprehensively analyze the ferroptosis-related genes associated with MAFLD. Methods: Ferroptosis-related differentially expressed genes (FRDEGs) were identified in patients with MAFLD and healthy individuals. Gene ontology functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and gene set enrichment analysis (GSEA) were used to analyze the relevant action pathways of the FRDEGs. The Encyclopedia of RNA Interactomes, CHIPBase, and comparative toxicogenomics databases were used to build mRNA-miRNA, mRNA-transcription factor (TF), and mRNA-drug interaction networks, respectively. A diagnostic model was constructed and bioinformatics analysis methods, such as least absolute shrinkage and selection operator regression analysis, Cox regression analysis, nomogram-based analysis, consensus clustering analysis, and single-sample GSEA, were used to systematically investigate the prognostic values and immunologic characteristics. Results: A total of 13 FRDEGs were obtained and eight were used to construct a diagnostic model and perform a prognostic analysis. Hub genes were also used to construct mRNA-miRNA and mRNA-TF interaction networks and potential drug or molecular compounds. Two MAFLD subtypes were identified: cluster2, which represents an "immunoactive" type, and cluster1, which represents an "immunosuppressive" type; a significant correlation was observed between the immune cell contents and the expression of three FRDEGs (NR4A1, FADS2, and SCD). Conclusion: A ferroptosis-related gene signature was constructed to diagnose MAFLD-associated steatohepatitis, predict the prognosis of MAFLD patients, and analyze the immunologic characteristics of MAFLD. Our findings may provide insights into developing innovative MAFLD treatment techniques.

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

Identification and validation of a prognostic signature of cuproptosis-related genes for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a highly lethal form of cancer. Cuproptosis is a recently discovered form of regulated cell death. However, its significance in ESCC remains largely unknown. In this study, we observed significant expression differences in most of the 12 cuproptosis-related genes (CRGs) in the TCGA-ESCC dataset, which was validated using GSE20347, GSE38129, and individual ESCC datasets. We were able to divide patients in the TCGA-ESCC cohort into two subgroups based on disease, and found significant differences in survivor outcomes and biological functions between these subgroups. Additionally, we identified 11 prognosis-related genes from the 12 CRGs using LASSO COX regression analysis and constructed a CRGs signature for ESCC. Patients were categorized into high- and low-risk subgroups based on their median risk score, with those in the high-risk subgroup having significantly worse overall survival than those in the low-risk subgroup. The CRGs signature was also highly accurate in predicting prognosis and survival outcomes. Univariate and multivariate Cox regression analyses revealed that 8 of the 11 CRGs were independent prognostic factors for predicting survival in ESCC patients. Furthermore, our nomogram performed well and could serve as a useful tool for predicting prognosis. Finally, our risk model was found to be relevant to the sensitivity of targeted agents and immune infiltration. Functional enrichment analysis demonstrated that the risk model was associated with biological pathways of tumor migration and invasion. In summary, our study may provide a promising prognostic signature based on CRGs and offers potential targets for personalized therapy.

Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach

BACKGROUND

Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach.

METHODS

The current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguously clustered pairs (PAC) analysis. We calculated the standardized mean difference (SMD) of each variable and used the cutoff of ± 0.3 to identify each cluster's key features. We examined the relationship between the phenotypes and several clinical endpoints utilizing logistic regression (LR) analysis.

RESULTS

The consensus cluster analysis identified two clusters (Cluster 1: n = 9,848; Cluster 2: n = 12,077). The key features of patients in Cluster 1, compared with Cluster 2, included: lower blood pressure, lower eGFR (estimated glomerular filtration rate), higher BUN (blood urea nitrogen), higher creatinine, lower albumin, higher potassium, lower bicarbonate, lower red blood cell (RBC), higher red blood cell distribution width (RDW), higher SOFA score, higher APS III score, and higher APACHE IV score on admission. The results of LR analysis showed that the Cluster 2 was associated with lower in-hospital mortality (odds ratio [OR]: 0.374; 95% confidence interval [CI]: 0.347-0.402; P < 0.001), ICU mortality (OR: 0.349; 95% CI: 0.318-0.382; P < 0.001), and the incidence of acute kidney injury (AKI) after admission (OR: 0.478; 95% CI: 0.452-0.505; P < 0.001).

CONCLUSIONS

ML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes.

Molecular characteristics, clinical significance, and immune landscape of extracellular matrix remodeling-associated genes in colorectal cancer

Background

Extracellular matrix (ECM) remodeling is one of the hallmark events in cancer and has been shown to be closely related to tumor immunity. Immunotherapy has evolved as an important tool to treat various cancers and improve patient prognosis. The positive response to immunotherapy relies on the unique interaction between cancer and the tumor microenvironment (TME). However, the relationship between ECM remodeling and clinical outcomes, immune cell infiltration, and immunotherapy in colorectal cancer (CRC) remains unknown.

Methods

We systematically evaluated 69 ECM remodeling-associated genes (EAGs) and comprehensively identified interactions between ECM remodeling and prognosis and the immune microenvironment in CRC patients. The EAG_score was used to quantify the subtype of ECM remodeling in patients. We then assessed their value in predicting prognosis and responding to treatment in CRC.

Results

After elaborating the molecular characteristics of ECM remodeling-related genes in CRC patients, a model consisting of two ECM remodeling-related genes (MEIS2, SLC2A3) was developed for predicting the prognosis of CRC patients, Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) analysis verified its reliable predictive ability. Furthermore, we created a highly reliable nomogram to enhance the clinical feasibility of the EAG_score. Significantly differences in TME and immune function, such as macrophages and CD8+ T cells, were observed between high- and low-risk CRC patients. In addition, drug sensitivity is also strongly related to EAG_score.

Conclusion

Overall, we developed a prognostic model associated with ECM remodeling, provided meaningful clinical implications for immunotherapy, and facilitated individualized treatment for CRC patients. Further studies are needed to reveal the underlying mechanisms of ECM remodeling in CRC.

Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer

Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy.

Analysis of the influence of pyroptosis-related genes on molecular characteristics in patients with acute myocardial infarction

Pyroptosis is a newly identified mode of programmed cell death, but the potential role in patients with acute myocardial infarction (AMI) remains unclear. In this study, bioinformatics methods were used to identify differentially expressed genes from peripheral blood transcriptome data between normal subjects and patients with AMI which were downloaded by the Gene Expression Omnibus database. Comparing Random Forest (RF) and Support Vector Machine (SVM) training algorithms were used to identify pyroptosis-related genes, predicting patients with AMI by nomogram based on informative genes. Moreover, clustering was used to amplify the feature of pyroptosis, in order to facilitate analysis distinct biological differences. Diversity analysis indicated that a majority of pyroptosis-related genes are expressed at higher levels in patients with AMI. The receiver operating characteristic curves show that the RF model is more responsive than the SVM machine learning model to the pyroptosis characteristics of these patients in vivo. We obtained a column line graph diagnostic model which was developed based on 19 genes established by the RF model. After the consensus clustering algorithm of single sample Gene Set Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis, the results for them found that pyroptosis-related genes mediate the activation of multiple immune cells and many inflammatory pathways in the body. We used RF and SVM algorithms to determine 19 pyroptosis-related genes and evaluate their immunological effects in patients with AMI. We also constructed a series of by nomogram related to pyroptosis-related genes to predict the risk of developing AMI.

The Biological Processes of Ferroptosis Involved in Pathogenesis of COVID-19 and Core Ferroptoic Genes Related With the Occurrence and Severity of This Disease

Background

A worldwide outbreak of coronavirus disease 2019 (COVID-19) has resulted in millions of deaths. Ferroptosis is a form of iron-dependent cell death which is characterized by accumulation of lipid peroxides on cellular membranes, and is related with many physiological and pathophysiological processes of diseases such as cancer, inflammation and infection. However, the role of ferroptosis in COVID-19 has few been studied.

Material and Method

Based on the RNA-seq data of 100 COVID-19 cases and 26 Non-COVID-19 cases from GSE157103, we identified ferroptosis related differentially expressed genes (FRDEGs, adj.P-value < .05) using the "Deseq2" R package. By using the "clusterProfiler" R package, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Next, a protein-protein interaction (PPI) network of FRDEGs was constructed and top 30 hub genes were selected by cytoHubba in Cytoscape. Subsequently, we established a prediction model for COVID-19 by utilizing univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) regression. Based on core FRDEGs, COVID-19 patients were identified as two clusters using the "ConsenesusClusterPlus" R package. Finally, the miRNA-mRNA network was built by Targetscan online database and visualized by Cytoscape software.

Results

A total of 119 FRDEGs were identified and the GO and KEGG enrichment analyses showed the most important biologic processes are oxidative stress response, MAPK and PI3K-AKT signaling pathway. The top 30 hub genes were selected, and finally, 7 core FRDEGs (JUN, MAPK8, VEGFA, CAV1, XBP1, HMOX1, and HSPB1) were found to be associated with the occurrence of COVID-19. Next, the two patterns of COVID-19 patients had constructed and the cluster A patients were likely to be more severe.

Conclusion

Our study suggested that ferroptosis was involved in the pathogenesis of COVID-19 disease and the functions of core FRDEGs may become a new research aspect of this disease.

Development of prognostic indicator based on NAD+ metabolism related genes in glioma

Background

Studies have shown that Nicotinamide adenine dinucleotide (NAD+) metabolism can promote the occurrence and development of glioma. However, the specific effects and mechanisms of NAD+ metabolism in glioma are unclear and there were no systematic researches about NAD+ metabolism related genes to predict the survival of patients with glioma.

Methods

The research was performed based on expression data of glioma cases in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Firstly, TCGA-glioma cases were classified into different subtypes based on 49 NAD+ metabolism-related genes (NMRGs) by consensus clustering. NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were gotten by intersecting the 49 NMRGs and differentially expressed genes (DEGs) between normal and glioma samples. Then a risk model was built by Cox analysis and the least shrinkage and selection operator (LASSO) regression analysis. The validity of the model was verified by survival curves and receiver operating characteristic (ROC) curves. In addition, independent prognostic analysis of the risk model was performed by Cox analysis. Then, we also identified different immune cells, HLA family genes and immune checkpoints between high and low risk groups. Finally, the functions of model genes at single-cell level were also explored.

Results

Consensus clustering classified glioma patients into two subtypes, and the overall survival (OS) of the two subtypes differed. A total of 11 NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were screened by overlapping 5,995 differentially expressed genes (DEGs) and 49 NAD+ metabolism-related genes (NMRGs). Next, four model genes, PARP9, BST1, NMNAT2, and CD38, were obtained by Cox regression and least absolute shrinkage and selection operator (Lasso) regression analyses and to construct a risk model. The OS of high-risk group was lower. And the area under curves (AUCs) of Receiver operating characteristic (ROC) curves were >0.7 at 1, 3, and 5 years. Cox analysis showed that age, grade G3, grade G4, IDH status, ATRX status, BCR status, and risk Scores were reliable independent prognostic factors. In addition, three different immune cells, Mast cells activated, NK cells activated and B cells naive, 24 different HLA family genes, such as HLA-DPA1 and HLA-H, and 8 different immune checkpoints, such as ICOS, LAG3, and CD274, were found between the high and low risk groups. The model genes were significantly relevant with proliferation, cell differentiation, and apoptosis.

Conclusion

The four genes, PARP9, BST1, NMNAT2, and CD38, might be important molecular biomarkers and therapeutic targets for glioma patients.

Integrated bioinformatic analysis of mitochondrial metabolism-related genes in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a common hematologic malignancy characterized by poor prognoses and high recurrence rates. Mitochondrial metabolism has been increasingly recognized to be crucial in tumor progression and treatment resistance. The purpose of this study was to examined the role of mitochondrial metabolism in the immune regulation and prognosis of AML.

Methods

In this study, mutation status of 31 mitochondrial metabolism-related genes (MMRGs) in AML were analyzed. Based on the expression of 31 MMRGs, mitochondrial metabolism scores (MMs) were calculated by single sample gene set enrichment analysis. Differential analysis and weighted co-expression network analysis were performed to identify module MMRGs. Next, univariate Cox regression and the least absolute and selection operator regression were used to select prognosis-associated MMRGs. A prognosis model was then constructed using multivariate Cox regression to calculate risk score. We validated the expression of key MMRGs in clinical specimens using immunohistochemistry (IHC). Then differential analysis was performed to identify differentially expressed genes (DEGs) between high- and low-risk groups. Functional enrichment, interaction networks, drug sensitivity, immune microenvironment, and immunotherapy analyses were also performed to explore the characteristic of DEGs.

Results

Given the association of MMs with prognosis of AML patients, a prognosis model was constructed based on 5 MMRGs, which could accurately distinguish high-risk patients from low-risk patients in both training and validation datasets. IHC results showed that MMRGs were highly expressed in AML samples compared to normal samples. Additionally, the 38 DEGs were mainly related to mitochondrial metabolism, immune signaling, and multiple drug resistance pathways. In addition, high-risk patients with more immune-cell infiltration had higher Tumor Immune Dysfunction and Exclusion scores, indicating poor immunotherapy response. mRNA-drug interactions and drug sensitivity analyses were performed to explore potential druggable hub genes. Furthermore, we combined risk score with age and gender to construct a prognosis model, which could predict the prognosis of AML patients.

Conclusion

Our study provided a prognostic predictor for AML patients and revealed that mitochondrial metabolism is associated with immune regulation and drug resistant in AML, providing vital clues for immunotherapies.

Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis

Objective

Staphylococcus aureus (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-related genes (FRGs) in SA-induced OM is still unclear. The purpose of this study was to determine the role of ferroptosis-related genes in the diagnosis, molecular classification and immune infiltration of SA-induced OM by bioinformatics.

Methods

Datasets related to SA-induced OM and ferroptosis were collected from the Gene Expression Omnibus (GEO) and ferroptosis databases, respectively. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were combined to screen out differentially expressed-FRGs (DE-FRGs) with diagnostic characteristics, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore specific biological functions and pathways. Based on these key DE-FRGs, a diagnostic model was established, and molecular subtypes were divided to explore the changes in the immune microenvironment between molecular subtypes.

Results

A total of 41 DE-FRGs were identified. After screening and intersecting with LASSO and SVM-RFE algorithms, 8 key DE-FRGs with diagnostic characteristics were obtained, which may regulate the pathogenesis of OM through the immune response and amino acid metabolism. The ROC curve indicated that the 8 DE-FRGs had excellent diagnostic ability for SA-induced OM (AUC=0.993). Two different molecular subtypes (subtype 1 and subtype 2) were identified by unsupervised cluster analysis. The CIBERSORT analysis revealed that the subtype 1 OM had higher immune cell infiltration rates, mainly in T cells CD4 memory resting, macrophages M0, macrophages M2, dendritic cells resting, and dendritic cells activated.

Conclusion

We developed a diagnostic model related to ferroptosis and molecular subtypes significantly related to immune infiltration, which may provide a novel insight for exploring the pathogenesis and immunotherapy of SA-induced OM.

Diagnostic signature, subtype classification, and immune infiltration of key m6A regulators in osteomyelitis patients

Background: As a recurrent inflammatory bone disease, the treatment of osteomyelitis is always a tricky problem in orthopaedics. N6-methyladenosine (m6A) regulators play significant roles in immune and inflammatory responses. Nevertheless, the function of m6A modification in osteomyelitis remains unclear. Methods: Based on the key m6A regulators selected by the GSE16129 dataset, a nomogram model was established to predict the incidence of osteomyelitis by using the random forest (RF) method. Through unsupervised clustering, osteomyelitis patients were divided into two m6A subtypes, and the immune infiltration of these subtypes was further evaluated. Validating the accuracy of the diagnostic model for osteomyelitis and the consistency of clustering based on the GSE30119 dataset. Results: 3 writers of Methyltransferase-like 3 (METTL3), RNA-binding motif protein 15B (RBM15B) and Casitas B-lineage proto-oncogene like 1 (CBLL1) and three readers of YT521-B homology domain-containing protein 1 (YTHDC1), YT521-B homology domain-containing family 3 (YTHDF2) and Leucine-rich PPR motif-containing protein (LRPPRC) were identified by difference analysis, and their Mean Decrease Gini (MDG) scores were all greater than 10. Based on these 6 significant m6A regulators, a nomogram model was developed to predict the incidence of osteomyelitis, and the fitting curve indicated a high degree of fit in both the test and validation groups. Two m6A subtypes (cluster A and cluster B) were identified by the unsupervised clustering method, and there were significant differences in m6A scores and the abundance of immune infiltration between the two m6A subtypes. Among them, two m6A regulators (METTL3 and LRPPRC) were closely related to immune infiltration in patients with osteomyelitis. Conclusion: m6A regulators play key roles in the molecular subtypes and immune response of osteomyelitis, which may provide assistance for personalized immunotherapy in patients with osteomyelitis.

Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery

Background

Low cardiac output syndrome (LCOS) is the most serious physiological abnormality with high mortality for patients after cardiac surgery. This study aimed to explore the multidimensional data of clinical features and outcomes to provide individualized care for patients with LCOS.

Methods

The electronic medical information of the intensive care units (ICUs) was extracted from a tertiary hospital in South China. We included patients who were diagnosed with LCOS in the ICU database. We used the consensus clustering approach based on patient characteristics, laboratory data, and vital signs to identify LCOS subgroups. The consensus clustering method involves subsampling from a set of items, such as microarrays, and determines to cluster of specified cluster counts (k). The primary clinical outcome was in-hospital mortality and was compared between the clusters.

Results

A total of 1,205 patients were included and divided into three clusters. Cluster 1 (n = 443) was defined as the low-risk group [in-hospital mortality =10.1%, odds ratio (OR) = 1]. Cluster 2 (n = 396) was defined as the medium-risk group [in-hospital mortality =25.0%, OR = 2.96 (95% CI = 1.97–4.46)]. Cluster 3 (n = 366) was defined as the high-risk group [in-hospital mortality =39.2%, OR = 5.75 (95% CI = 3.9–8.5)].

Conclusion

Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.

The molecular pathophysiology of mood disorders: From the analysis of single molecular layers to multi-omic integration

Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as "omics". To maximize the potential for discovery, computational biologists have created and adapted integrative multi-omic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers. In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently. We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in other medical fields, and discuss their potential for molecular psychiatry studies.

m6A Regulator-Mediated Methylation Modification Patterns and Characteristics in COVID-19 Patients

Background

RNA N6-methyladenosine (m6A) regulators may be necessary for diverse viral infectious diseases, and serve pivotal roles in various physiological functions. However, the potential roles of m6A regulators in coronavirus disease 2019 (COVID-19) remain unclear.

Methods

The gene expression profile of patients with or without COVID-19 was acquired from Gene Expression Omnibus (GEO) database, and bioinformatics analysis of differentially expressed genes was conducted. Random forest modal and nomogram were established to predict the occurrence of COVID-19. Afterward, the consensus clustering method was utilized to establish two different m6A subtypes, and associations between subtypes and immunity were explored.

Results

Based on the transcriptional data from GSE157103, we observed that the m6A modification level was markedly enriched in the COVID-19 patients than those in the non-COVID-19 patients. And 18 essential m6A regulators were identified with differential analysis between patients with or without COVID-19. The random forest model was utilized to determine 8 optimal m6A regulators for predicting the emergence of COVID-19. We then established a nomogram based on these regulators, and its predictive reliability was validated by decision curve analysis. The consensus clustering algorithm was conducted to categorize COVID-19 patients into two m6A subtypes from the identified m6A regulators. The patients in cluster A were correlated with activated T-cell functions and may have a superior prognosis.

Conclusions

Collectively, m6A regulators may be involved in the prevalence of COVID-19 patients. Our exploration of m6A subtypes may benefit the development of subsequent treatment modalities for COVID-19.

Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data

The discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have certain guiding significance for cancer subtyping. It is a challenge to effectively utilize the valuable information of distinct subtypes to produce more accurate and reliable subtypes. A weighted ensemble sparse latent representation (subtype-WESLR) is proposed to detect cancer subtypes on heterogeneous omics data. Using a weighted ensemble strategy to fuse base clustering obtained by distinct methods as prior knowledge, subtype-WESLR projects each sample feature profile from each data type to a common latent subspace while maintaining the local structure of the original sample feature space and consistency with the weighted ensemble and optimizes the common subspace by an iterative method to identify cancer subtypes. We conduct experiments on various synthetic datasets and eight public multi-view datasets from The Cancer Genome Atlas. The results demonstrate that subtype-WESLR is better than competing methods by utilizing the integration of base clustering of exist methods for more precise subtypes.