Single-Cell Sequencing Analysis Identified ASTN2 as a Migration Biomarker in Adult Glioblastoma

Integrative multi-omic profiling of the neoantigen landscape of glioblastoma for the development of therapeutic vaccines reveals vast heterogeneity in immunogenic signatures

Introduction

Glioblastoma (GBM) is the most common primary brain malignancy. Few neoantigens have been tested in trials as the cancer vaccine against GBM.

Methods

To better understand the neoantigen landscape and its associated tumor microenvironment (TME) for the optimized vaccine design of our initiated GBM trial, we apply the integrative multi-omics approach to comprehensively profile the mutation, HLA typing, TCR/BCR repertoire, immune cell components on the tumor tissue and peripheral blood mononuclear cell (PMBC) specimen of 24 GBM patients.

Results

On average, 148 mutated genes and 200 mutated sites per patient were identified, with no predominant mutated sites and genes in this cohort. Diversified HLA genotypes and expression rate across A, B, and C alleles, with A30:01&A11:01, B13:02, and C06:02, as the most frequent genotypes at respective alleles. Clustered CDR3 of TCR/BCR existed in tumor tissue with decreased richness compared with PMBC. NK and Th1 cells were revealed as the predominant immune cells within the tumor microenvironment (TME). Neoantigens were feasible predicted and designed for each patient, with an average number of 107. Very few neoantigens were shared by more than two patients and no dominant neoantigen could be identified. A minimum of 11-peptide bulk was required to cover this 24-patient cohort, guaranteeing each patient could have at least one neoantigen.

Discussion

In summary, our data reveals a heterogeneous landscape of the neoantigen and its associated immune TME of GBM, based on which a peptide bulk is feasibly developed to cover these patients as a cohort.

Identification of Molecular Targets and Anti-Cancer Agents in GBM: New Perspectives for Cancer Therapy

The authors of the present literature piece were invited to participate in the present Special Issue at the beginning of 2022, and we were all very enthusiastic at the prospect of assembling a series of articles on new molecular targets and anti-cancer agents in glioblastoma multiforme (GBM) [...].

Identification and validation of key genes associated with atrial fibrillation in the elderly

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia and significantly increases the risk of stroke and heart failure (HF), contributing to a higher mortality rate. Increasing age is a major risk factor for AF; however, the mechanisms of how aging contributes to the occurrence and progression of AF remain unclear. This study conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes and determine their potential associations with aging-related AF.

Materials and methods

WGCNA was performed using the AF dataset GSE2240 obtained from the Gene Expression Omnibus, which contained data from atrial myocardium in cardiac patients with permanent AF or sinus rhythm (SR). Hub genes were identified in clinical samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were also performed.

Results

Green and pink were the most critical modules associated with AF, from which nine hub genes, PTGDS, COLQ, ASTN2, VASH1, RCAN1, AMIGO2, RBP1, MFAP4, and ALDH1A1, were hypothesized to play key roles in the AF pathophysiology in elderly and seven of them have high diagnostic value. Functional enrichment analysis demonstrated that the green module was associated with the calcium, cyclic adenosine monophosphate (cAMP), and peroxisome proliferator-activated receptors (PPAR) signaling pathways, and the pink module may be associated with the transforming growth factor beta (TGF-β) signaling pathway in myocardial fibrosis.

Conclusion

We identified nine genes that may play crucial roles in the pathophysiological mechanism of aging-related AF, among which six genes were associated with AF for the first time. This study provided novel insights into the impact of aging on the occurrence and progression of AF, and identified biomarkers and potential therapeutic targets for AF.