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.

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.

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.

Bioinformatics algorithm for lung adenocarcinoma based on macropinocytosis-related long noncoding RNAs as a reliable indicator for predicting survival outcomes and selecting suitable anti-tumor drugs

As a highly conserved endocytic mechanism during evolution, macropinocytosis is enhanced in several malignant tumors, which promotes tumor growth by ingesting extracellular nutrients. Recent research has emphasized the crucial role of macropinocytosis in tumor immunity. In the present study, we established a new macropinocytosis-related algorithm comprising molecular subtypes and a prognostic signature, in which patients with lung adenocarcinoma (LUAD) were classified into different clusters and risk groups based on the expression of 16 macropinocytosis-related long noncoding RNAs. According to the molecular subtypes, we discovered that patients with LUAD in cluster1 had a higher content of stromal cells and immune cells, stronger intensity of immune activities, higher expression of PD1, PDL1, and HAVCR2, and a higher tumor mutational burden, while patients in cluster2 exhibited better survival advantages. Furthermore, the constructed prognostic signature revealed that low-risk patients showed better survival outcomes, earlier tumor stage, higher abundance of stromal cells and immune cells, higher immune activities, higher expression of PD1, PDL1, CTLA4, and HAVCR2, and more sensitivity to Paclitaxel and Erlotinib. By contrast, patients with high scores were more suitable for Gefitinib treatment. In conclusion, the novel algorithm that divided patients with LUAD into different groups according to their clusters and risk groups, which could provide theoretical support for predicting their survival outcomes and selecting drugs for chemotherapy, targeted therapy, and immunotherapy.

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.

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.

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.