A novel algorithm for lung adenocarcinoma based on N6 methyladenosine-related immune long noncoding RNAs as a reliable biomarker for predicting survival outcomes and selecting sensitive anti-tumor therapies.

BACKGROUND: Lung cancer is a highly heterogeneous malignant tumor with high incidence and mortality. Recently, increasing evidence has demonstrated that N6-methyladenosine (m6A) methylation and the tumor microenvironment (TME) play important roles in the occurrence and development of lung adenocarcinoma (LUAD). METHODS: In this study, we constructed a novel and reliable algorithm based on m6A-related immune lncRNAs (mrilncRNAs), consisting of molecular subtypes and a prognostic signature. RESULTS: According to the analyses of molecular subtypes, patients in cluster 1 were in a more advanced stage, showed poor prognosis, were sensitive to immunotherapy (anti-programmed cell death 1 Ligand 1 (PD-L1) and anti-lymphocyte activating 3 (LAG-3)), and had a highest tumor mutational burden (TMB), while anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) therapy seemed to be a good choice for patients in cluster 3. Subsequently, the results of the risk assessment model indicated that the low-risk patients exhibited a survival advantage, had an earlier stage, and showed a higher response to common anti-cancer drugs, including chemotherapy (Docetaxel, Paclitaxel), molecular targeted therapy (Erlotinib), and immunotherapy (anti-CTLA-4 therapy), while Gefitinib could be a good choice for patients with high-risk scores. CONCLUSION: In conclusion, the constructed algorithm exhibits promising practical prospects, and allows the selection of suitable and sensitive anti-cancer drugs, which could provide theoretical support to predict the survival outcomes of patients with LUAD.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma.

PURPOSE: The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics. METHODS: We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes. RESULTS: Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM. CONCLUSIONS: MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

A novel mitochondria-related algorithm for predicting the survival outcomes and drug sensitivity of patients with lung adenocarcinoma.

Background: Mitochondria have always been considered too be closely related to the occurrence and development of malignant tumors. However, the bioinformatic analysis of mitochondria in lung adenocarcinoma (LUAD) has not been reported yet. Methods: In the present study, we constructed a novel and reliable algorithm, comprising a consensus cluster analysis and risk assessment model, to predict the survival outcomes and tumor immunity for patients with terminal LUAD. Results: Patients with LUAD were classified into three clusters, and patients in cluster 1 exhibited the best survival outcomes. The patients in cluster 3 had the highest expression of PDL1 (encoding programmed cell death 1 ligand 11) and HAVCR2 (encoding Hepatitis A virus cellular receptor 2), and the highest tumor mutation burden (TMB). In the risk assessment model, patients in the low-risk group tended to have a significantly better survival outcome. Furthermore, the risk score combined with stage could act as a reliable independent prognostic indicator for patients with LUAD. The prognostic signature is a novel and effective biomarker to select anti-tumor drugs. Low-risk patients tended to have a higher expression of CTLA4 (encoding cytotoxic T-lymphocyte associated protein 4) and HAVCR2. Moreover, patients in the high-risk group were more sensitive to Cisplatin, Docetaxel, Erlotinib, Gemcitabine, and Paclitaxel, while low-risk patients would probably benefit more from Gefitinib. Conclusion: We constructed a novel and reliable algorithm comprising a consensus cluster analysis and risk assessment model to predict survival outcomes, which functions as a reliable guideline for anti-tumor drug treatment for patients with terminal LUAD.

Metabolism-associated molecular classification of uterine corpus endometrial carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies. Currently, for UCEC cancer, molecular classification based on metabolic gene characteristics is rarely established. Here, we describe the molecular subtype features of UCEC by classifying metabolism-related gene profiles. Therefore, integrative analysis was performed on UCEC patients from the TCGA public database. Consensus clustering of RNA expression data on 2,752 previously reported metabolic genes identified two metabolic subtypes, namely, C1 and C2 subtypes. Two metabolic subtypes for prognostic characteristics, immune infiltration, genetic alteration, and responses to immunotherapy existed with distinct differences. Then, differentially expressed genes (DEGs) among the two metabolic subtypes were also clustered into two subclusters, and the aforementioned features were similar to the metabolic subtypes, supporting that the metabolism-relevant molecular classification is reliable. The results showed that the C1 subtype has high metabolic activity, high immunogenicity, high gene mutation, and a good prognosis. The C2 subtype has some features with low metabolic activity, low immunogenicity, high copy number variation (CNV) alteration, and poor prognosis. Finally, a model was identified, with three gene metabolism-related signatures, which can predict the prognosis. These findings of this study demonstrate a new classification in UCEC based on the metabolic pattern, thereby providing valuable information for understanding UCEC's molecular characteristics.

Comprehensive analysis reveals hub genes associated with immune cell infiltration in allergic rhinitis.

Objectives: Allergic rhinitis (AR) refers to a form of respiratory inflammation that mainly affects the sinonasal mucosa. The purpose of this study was to explore the level of immune cell infiltration and the pathogenesis of AR. Methods: We performed a comprehensive analysis of two gene expression profiles (GSE50223 and GSE50101, a total of 30 patients with AR and 31 healthy controls). CIBERSORT was used to evaluate the immune cell infiltration levels. Weighted gene coexpression network analysis was applied to explore potential genes or gene modules related to immune status, and enrichment analyses including gene ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and gene set variation analysis, were performed to analyze the potential mechanisms in AR. A protein-protein interaction network was constructed to investigate the hub genes, and consensus clustering was conducted to identify the molecular subtypes of AR. Results: Compared to the healthy controls, patients with AR had high abundance levels and proportions of CD4+ memory-activated T cells. One hundred and eight immune-related differentially expressed genes were identified. Enrichment analysis suggested that AR was mainly related to leukocyte cell-cell adhesion, cytokine-cytokine receptor interaction, T-cell activation, and T-cell receptor signaling pathway. Ten hub genes, including TYROBP, CSF1R, TLR8, FCER1G, SPI1, ITGAM, CYBB, FCGR2A, CCR1, and HCK, which were related to immune response, might be crucial to the pathogenesis of AR. Three molecular subtypes with significantly different immune statuses were identified. Conclusion: This study improves our understanding of the molecular mechanisms in AR via comprehensive strategies and provides potential diagnostic biomarkers and therapeutic targets of AR.

Neutrophil-Related Gene Expression Signatures in Idiopathic Pulmonary Fibrosis: Implications for Disease Characteristic and Identification of Diagnostic Hub Genes.

Background: Idiopathic pulmonary fibrosis (IPF) is a disease with unclear etiology and a poor prognosis. Although the involvement of neutrophils in IPF pathogenesis has been suggested, the exact nature of this relationship remains unclear. Methods: We analyzed data from the Gene Expression Omnibus (GEO) using immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), and consensus cluster analysis. Neutrophil-related genes and hub genes related to neutrophils were identified and differentially expressed between IPF patients and healthy controls. We also validated the expression differences of hub genes in a bleomycin-induced mice model. Results: Immune infiltration analysis revealed a significantly decreased percentage of neutrophils in the lung tissue of IPF patients compared with healthy controls (P<0.001) in both the train and validation sets. Neutrophil-related genes in IPF were identified by WGCNA, and functional enrichment analysis showed that these genes were mainly involved in the cytokine-cytokine receptor interaction pathway and correlated with lung disease, consistent with DEGs between IPF and healthy controls. Eight hub genes related to neutrophils were identified, including MMP16, ARG1, IL1R2, PROK2, MS4A2, PIR, and ZNF436. Consensus cluster analysis revealed a low neutrophil-infiltrating cluster that was correlated with IPF (P<0.001), and a principal component analysis-generated score could distinguish IPF patients from healthy controls, with an area under the curve of 0.930 in the train set and 0.768 in the validation set. We also constructed a diagnostic model using hub genes related to neutrophils, which showed a reliable diagnostic value with an area under the curve of 0.955 in the train set and 0.995 in the validation set. Conclusion: Our findings provide evidence of a low neutrophil-infiltrating characteristic in the IPF microenvironment and identify hub genes related to neutrophils that may serve as diagnostic biomarkers for the disease.

Identification of Candidate Genes Associated With Prognosis in Glioblastoma.

Background: Glioblastoma (GBM) is the most common malignant primary brain tumor, which associated with extremely poor prognosis. Methods: Data from datasets GSE16011, GSE7696, GSE50161, GSE90598 and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) between patients and controls. DEGs common to all five datasets were analyzed for functional enrichment and for association with overall survival using Cox regression. Candidate genes were further screened using least absolute shrinkage and selection operator (LASSO) and random forest algorithms, and the effects of candidate genes on prognosis were explored using a Gaussian mixed model, a risk model, and concordance cluster analysis. We also characterized the GBM landscape of immune cell infiltration, methylation, and somatic mutations. Results: We identified 3,139 common DEGs, which were associated mainly with PI3K-Akt signaling, focal adhesion, and Hippo signaling. Cox regression identified 106 common DEGs that were significantly associated with overall survival. LASSO and random forest algorithms identified six candidate genes (AEBP1, ANXA2R, MAP1LC3A, TMEM60, PRRG3 and RPS4X) that predicted overall survival and GBM recurrence. AEBP1 showed the best prognostic performance. We found that GBM tissues were heavily infiltrated by T helper cells and macrophages, which correlated with higher AEBP1 expression. Stratifying patients based on the six candidate genes led to two groups with significantly different overall survival. Somatic mutations in AEBP1 and modified methylation of MAP1LC3A were associated with GBM. Conclusion: We have identified candidate genes, particularly AEBP1, strongly associated with GBM prognosis, which may help in efforts to understand and treat the disease.

Low hemoglobin is associated with worse outcomes via larger hematoma volume in intracerebral hemorrhage due to systemic disease.

Whether hemoglobin is associated with outcomes of a specific subtype of intracerebral hemorrhage (ICH) is unknown. A total of 4643 patients with ICH from a multicenter cohort were included in the analysis (64.0% male; mean age [SD], 58.3 [15.2] year), of whom 1319 (28.4%) had anemia on admission. The unsupervised consensus cluster method was employed to classify the patients into three clusters. The patients of cluster 3 were characterized by a high frequency of anemia (85.3%) and mainly composed of patients of systemic disease ICH subtype (SD-ICH; 90.0%) according to the SMASH-U etiologies. In SD-ICH, a strong interaction effect was observed between anemia and 3-month death (adjusted odds ratio [aOR] 4.33, 95% confidence interval [CI] 1.60-11.9, p = 0.004), and the hemoglobin levels were linearly associated with 3-month death (aOR 0.75, 95% CI 0.60-0.92; p = 0.009), which was partially mediated by larger baseline hematoma volume (p = 0.008). This study demonstrated a strong linear association between low hemoglobin levels and worse outcomes in SD-ICH, suggesting that hemoglobin-elevating therapy might be extensively needed in a specific subtype of ICH.

Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction-a bioinformatics analysis.

Background: Accurate myocardial infarction (AMI) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) modification plays an important role in the development of cardiac remodeling and the cardiomyocyte contractile function. The aim of this study is to analyze the m6A-related molecular biological mechanisms of AMI in terms of accurate diagnosis and prognosis. Methods: The platform data and probe data of the GSE66360 data set were downloaded. The differential analysis was conducted by combining the m6A-related gene expression. Thereafter, a diagnostic model was established using the random-forest method. The diagnostic accuracy of the diagnostic models was assessed by using the area under the receiver operating characteristic (ROC) curve (AUC). Next, the patients with AMI were clustered by unsupervised machine learning using the R software. Finally, an immune cell clustering analysis for each cluster was conducted to determine the correlations between m6A-related gene expression and the infiltration amount of the immune cells. The case and control groups were not matched in terms of demographics. Results: The GSE6636 data set comprised 99 participants (49 patients with AMI and 50 without in control group). The differential analysis identified 10 m6A-related genes: 5 writers [Methyltransferase-like 3 (METTL3), Methyltransferase-like 14 (METTL14), Wilms tumor 1-associated protein (WTAP), Zinc Finger CCCH-Type Containing 13 (ZC3H13), and Casitas B-lineage proto-oncogene like 1 (CBLL1)], 4 readers [YT521-B homology domain-containing family 3 (YTHDF3), Fragile X mental retardation type 1 (FMR1), YT521-B homology-domain-containing protein 1 (YTHDC1), and insulin-like growth factor binding protein 3 (IGFBP3)] and 1 eraser [fat mass and obesity associated (FTO) gene]. The Mean Decrease Gini (MDG) values of these 10 genes were greater than 2. The FTO, WTAP, YTHDC1, IGFBP3, and CBLL1 were included in the model with a C index of 0.842. METTL3, ZC3H13, WTAP, and CBLL1 were highly expressed in Type A, and YTHDF3 was highly expressed in Type B. Conclusions: A diagnostic model of AMI was established based on the genes of FTO, WTAP, YTHDC1, IGFBP3, and CBLL1. Additionally, 2 molecular subtypes were successfully identified from the above-mentioned gene. Our findings could provide a novel method for the accurate diagnosis of AMI.

Screening and Prognostic Analysis of Immune-Related Genes in Pancreatic Cancer.

Pancreatic cancer remains to have a high mortality, which is partly due to the lack of effective treatment strategies. In this study, genes with potential associations with immunophenotyping of pancreatic cancer were screened through bioinformatics analysis and the correlation between immune-related genes and the prognosis of pancreatic cancer patients was assessed. Firstly, differentially expressed immune genes were extracted from the pancreatic cancer-related datasets obtained for purposes of this study. The samples were processed by the "Consensus Cluster Plus" R package to determine the number of immune subtypes. Then, the pancreatic cancer immunophenotyping-related gene modules were determined. Differential analysis of immune gene modules was performed, and the function of genes related to pancreatic cancer immune subtypes was identified. The number of immune cells in the samples was calculated, followed by the differential expression analysis of immune cell numbers in each immune subtype of pancreatic cancer. The immune infiltration score was also estimated, and the correlation between the immune infiltration score and the patient prognosis with different immune subtypes was determined. Gene differences between each immune subtype were identified by differential expression analysis, and key immune genes affecting immunophenotyping were obtained. Following the analysis, 426 immune-related genes were identified to have potential involvement in the occurrence and development of pancreatic cancer, of which CD19 may be the most critical gene affecting the immunophenotyping of pancreatic cancer. CD19 played a significant role in the occurrence and development of IS2 and IS3 immune subtypes of pancreatic cancer through its action on B cells and T cells. Moreover, the expression of CD19 was increased in the collected pancreatic cancer tissues. Overall, our findings uncovered the critical role of CD19 in the prognosis of pancreatic cancer patients.

Alternative splicing events are prognostic in hepatocellular carcinoma.

Alternative splicing events (ASEs) play a role in cancer development and progression. We investigated whether ASEs are prognostic for overall survival (OS) in hepatocellular carcinoma (HCC). RNA sequencing data was obtained for 343 patients included in The Cancer Genome Atlas. Matched splicing event data for these patients was then obtained from the TCGASpliceSeq database, which includes data for seven types of ASEs. Univariate and multivariate Cox regression analysis demonstrated that 3,814 OS-associated splicing events (OS-SEs) were correlated with OS. Prognostic indices were developed based on the most significant OS-SEs. The prognostic index based on all seven types of ASEs (PI-ALL) demonstrated superior efficacy in predicting OS of HCC patients at 2,000 days compared to those based on single ASE types. Patients were stratified into two risk groups (high and low) based on the median prognostic index. Kaplan-Meier survival analysis demonstrated that PI-ALL had the greatest capacity to distinguish between patients with favorable vs. poor outcomes. Finally, univariate Cox regression analysis demonstrated that the expression of 23 splicing factors was correlated with OS-SEs in the HCC cohort. Our data indicate that a prognostic index based on ASEs is prognostic for OS in HCC.