A benchmarking framework for the accurate and cost-effective detection of clinically-relevant structural variants for cancer target identification and diagnosis.

BACKGROUND: Accurate clinical structural variant (SV) calling is essential for cancer target identification and diagnosis but has been historically challenging due to the lack of ground truth for clinical specimens. Meanwhile, reduced clinical-testing cost is the key to the widespread clinical utility. METHODS: We analyzed massive data from tumor samples of 476 patients and developed a computational framework for accurate and cost-effective detection of clinically-relevant SVs. In addition, standard materials and classical experiments including immunohistochemistry and/or fluorescence in situ hybridization were used to validate the developed computational framework. RESULTS: We systematically evaluated the common algorithms for SV detection and established an expert-reviewed SV call set of 1,303 tumor-specific SVs with high-evidence levels. Moreover, we developed a random-forest-based decision model to improve the true positive of SVs. To independently validate the tailored 'two-step' strategy, we utilized standard materials and classical experiments. The accuracy of the model was over 90% (92-99.78%) for all types of data. CONCLUSION: Our study provides a valuable resource and an actionable guide to improve cancer-specific SV detection accuracy and clinical applicability.

An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction.

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum-maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.

Proteomic Profiling and Tumor Microenvironment Characterization Reveal Molecular and Immunological Hallmarks of Left-Sided and Right-Sided Colon Cancer Tumorigenesis.

Left-sided and right-sided colon cancer (LSCC and RSCC) display different biological and clinical characteristics. However, the differences in their tumorigenesis and tumor microenvironment remain unclear. In this study, we profiled the proteomic landscapes of LSCC and RSCC with data-independent acquisition mass spectrometry (DIA-MS) using fresh tumor and adjacent normal tissues from 24 patients. A total of 7403 proteingroups were primarily identified with DIA-MS. After quality control, 7212 proteingroups were used for further analysis. Through comparing the difference in proteomic profiles between LSCC and RSCC samples, 2556 commonly and 1982 region-type-specific regulated proteingroups were characterized. During the development of LSCC and RSCC, metabolic, growth, cell division, cell adhesion, and migration pathways were found to be significantly dysregulated (P < 0.05), which was further confirmed by transcriptome data from TCGA. Compared to RSCC, most parts of the immune-related signatures, immune cell infiltration scores, and overall immune scores of LSCC were higher. The systematic elucidation of proteomic and transcriptomic profiles in this work improves our understanding of tumorigenesis and immune microenvironment characteristics of LSCC and RSCC.

Characterization of the tumor microenvironment and identification of spatially predictive biomarkers associated with beneficial neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). However, 20-40% of patients with LARC show little to no response to nCRT. Thus, comprehensively understanding the tumor microenvironment (TME), which might influence therapeutic efficacy, and identifying robust predictive biomarkers is urgently needed. Pre-treatment tumor biopsy specimens from patients with LARC were evaluated in detail through digital spatial profiling (DSP), public RNA sequencing datasets, and multiplex immunofluorescence (mIF). DSP analysis revealed distinct characteristics of the tumor stroma compared to the normal stroma and tumor compartments. We identified high levels of human leukocyte antigen-DR/major histocompatibility complex class II (HLA-DR/MHC-II) in the tumor compartment and B cells in the stroma as potential spatial predictors of nCRT efficacy in the Discovery cohort. Public datasets validated their predictive capacity for clinical outcomes. Using mIF in an independent nCRT cohort and/or the total cohort, we validated that a high density of HLA-DR/MHC-II+ cells in the tumor and CD20 + B cells in the stroma was associated with nCRT efficacy (all p ≤ 0.021). Spatial profiling successfully characterized the LARC TME and identified robust biomarkers with the potential to accurately predict nCRT response. These findings have important implications for individualized therapy.

KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis.

Gene set enrichment (GSE) analysis plays an essential role in extracting biological insight from genome-scale experiments. ORA (overrepresentation analysis), FCS (functional class scoring), and PT (pathway topology) approaches are three generations of GSE methods along the timeline of development. Previous versions of KOBAS provided services based on just the ORA method. Here we presented version 3.0 of KOBAS, which is named KOBAS-i (short for KOBAS intelligent version). It introduced a novel machine learning-based method we published earlier, CGPS, which incorporates seven FCS tools and two PT tools into a single ensemble score and intelligently prioritizes the relevant biological pathways. In addition, KOBAS has expanded the downstream exploratory visualization for selecting and understanding the enriched results. The tool constructs a novel view of cirFunMap, which presents different enriched terms and their correlations in a landscape. Finally, based on the previous version's framework, KOBAS increased the number of supported species from 1327 to 5944. For an easier local run, it also provides a prebuilt Docker image that requires no installation, as a supplementary to the source code version. KOBAS can be freely accessed at http://kobas.cbi.pku.edu.cn, and a mirror site is available at http://bioinfo.org/kobas.

The Prognostic Role of Glutathione Peroxidase 1 and Immune Infiltrates in Glioma Investigated Using Public Datasets.

BACKGROUND Glutathione peroxidase 1 (GPX1) is an essential component of the intracellular antioxidant enzyme system, but little is known about the role of GPX1 in the progression of malignancy in gliomas. Using public datasets, this study investigated the prognostic role of GPX1 and immune infiltrates in glioma. MATERIAL AND METHODS We investigated GPX1 expression levels in different cancers using the ONCOMINE and Tumor Immune Estimation Resource (TIMER) datasets. We also explored the prognostic landscape of GPX1 in gliomas based on The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Some significant pathways were identified by function enrichment analysis. We then explored the association between GPX1 expression and levels of tumor-infiltrating immune cells based on TIMER and Gene Expression Profiling Interactive Analysis (GEPIA) datasets. RESULTS Expression of GPX1 in brain and central nervous system cancers is at a much high level than in normal tissues, and it is higher in glioblastoma (GBM) than in lower-grade glioma (LGG). We found GPX1 expression to be positively correlated with the malignant clinicopathologic characteristics of gliomas. Univariate analysis and multivariate analysis revealed that overexpression of GPX1 was correlated with a worse prognosis in patients, and a nomogram indicated that GPX1 expression can predict clinical prognosis of glioma. Function enrichment analysis showed that some important pathways are related to glioma malignancy. Expression of GPX1 was positively associated with infiltrating levels of 6 types of immune cells and most of their gene markers in GBM and LGG. CONCLUSIONS These results indicate that GPX1 is an independent prognostic factor and a novel biomarker for predicting the progression of malignancy in gliomas, which is associated with immune infiltration.

Hepatic transcriptome analysis from HFD-fed mice defines a long noncoding RNA regulating cellular cholesterol levels.

To elucidate the transcriptomic changes of long noncoding RNAs (lncRNAs) in high-fat diet (HFD)-fed mice, we defined their hepatic transcriptome by RNA sequencing. Aberrant expression of 37 representative lncRNAs and 254 protein-coding RNAs was observed in the livers of HFD-fed mice with insulin resistance compared with the livers from control mice. Of these, 24 lncRNAs and 179 protein-coding RNAs were upregulated, whereas 13 lncRNAs and 75 protein-coding RNAs were downregulated. Functional analyses showed that the aberrantly expressed protein-coding RNAs were enriched in various lipid metabolic processes and in the insulin signaling pathway. Genomic juxtaposition and coexpression patterns identified six pairs of aberrantly expressed lncRNAs and protein-coding genes, consisting of five lncRNAs and five protein-coding genes. Four of these protein-coding genes are targeted genes upregulated by PPARα. As expected, the corresponding lncRNAs were significantly elevated in AML12 cells treated with palmitic acid or the PPARα agonist, WY14643. In Hepa1-6 cells, knockdown of NONMMUG027912 increased the cellular cholesterol level, the expression of cholesterol biosynthesis genes and proteins, and the HMG-CoA reductase activity. This genome-wide profiling of lncRNAs in HFD-fed mice reveals one lncRNA, NONMMUG027912, which is potentially regulated by PPARα and is implicated in the process of cholesterol biosynthesis.

Identification and function annotation of long intervening noncoding RNAs.

RNA-seq technology offers the promise of rapid comprehensive discovery of long intervening noncoding RNAs (lincRNAs). Basic tools such as Tophat and Cufflinks have been widely used for RNA-seq assembly. However, advanced bioinformatics methodologies that allow in-depth analysis of lincRNAs are lacking. Here, we describe a computational protocol that is especially designed for the identification of novel lincRNAs and the prediction of the function. The protocol mainly includes two open-access tools, CNCI and ncFANs. CNCI allows users to distinguish noncoding from protein-coding transcripts and to retrieve novel lincRNAs. ncFANs integrates expression profiles of protein-coding and lincRNA genes to construct coexpression networks. Such networks are subsequently used to perform function predictions of unknown lincRNAs. This protocol will allow users to apply these procedures without the need of additional training. All the tools in current protocol are available http://www.bioinfo.org/np/.

An Occlusion-Aware Framework for Real-Time 3D Pose Tracking.

Random forest-based methods for 3D temporal tracking over an image sequence have gained increasing prominence in recent years. They do not require object's texture and only use the raw depth images and previous pose as input, which makes them especially suitable for textureless objects. These methods learn a built-in occlusion handling from predetermined occlusion patterns, which are not always able to model the real case. Besides, the input of random forest is mixed with more and more outliers as the occlusion deepens. In this paper, we propose an occlusion-aware framework capable of real-time and robust 3D pose tracking from RGB-D images. To this end, the proposed framework is anchored in the random forest-based learning strategy, referred to as RFtracker. We aim to enhance its performance from two aspects: integrated local refinement of random forest on one side, and online rendering based occlusion handling on the other. In order to eliminate the inconsistency between learning and prediction of RFtracker, a local refinement step is embedded to guide random forest towards the optimal regression. Furthermore, we present an online rendering-based occlusion handling to improve the robustness against dynamic occlusion. Meanwhile, a lightweight convolutional neural network-based motion-compensated (CMC) module is designed to cope with fast motion and inevitable physical delay caused by imaging frequency and data transmission. Finally, experiments show that our proposed framework can cope better with heavily-occluded scenes than RFtracker and preserve the real-time performance.

Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts.

It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. This study developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense-antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan. CNCI software is available at http://www.bioinfo.org/software/cnci.

Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks.

More and more evidences demonstrate that the long non-coding RNAs (lncRNAs) play many key roles in diverse biological processes. There is a critical need to annotate the functions of increasing available lncRNAs. In this article, we try to apply a global network-based strategy to tackle this issue for the first time. We develop a bi-colored network based global function predictor, long non-coding RNA global function predictor ('lnc-GFP'), to predict probable functions for lncRNAs at large scale by integrating gene expression data and protein interaction data. The performance of lnc-GFP is evaluated on protein-coding and lncRNA genes. Cross-validation tests on protein-coding genes with known function annotations indicate that our method can achieve a precision up to 95%, with a suitable parameter setting. Among the 1713 lncRNAs in the bi-colored network, the 1625 (94.9%) lncRNAs in the maximum connected component are all functionally characterized. For the lncRNAs expressed in mouse embryo stem cells and neuronal cells, the inferred putative functions by our method highly match those in the known literature.

Nicotine induces the up-regulation of the α7-nicotinic receptor (α7-nAChR) in human squamous cell lung cancer cells via the Sp1/GATA protein pathway.

Nicotine, the addictive component of cigarettes, promotes lung cancer proliferation via the α7-nicotinic acetylcholine receptor (α7-nAChR) subtype. The present manuscript explores the effect of nicotine exposure on α7-nAChR levels in squamous cell carcinoma of the lung (SCC-L) in vitro and in vivo. Nicotine (at concentrations present in the plasma of average smokers) increased α7-nAChR levels in human SCC-L cell lines. Nicotine-induced up-regulation of α7-nAChR was confirmed in vivo by chicken chorioallantoic membrane models. We also observed that the levels of α7-nAChR in human SCC-L tumors (isolated from patients who are active smokers) correlated with their smoking history. Nicotine increased the levels of α7-nAChR mRNA and α7-nAChR transcription in human SCC-L cell lines and SCC-L tumors. Nicotine-induced up-regulation of α7-nAChR required GATA4 and GATA6. ChIP assays showed that nicotine induced the binding of GATA4 or GATA6 to Sp1 on the α7-nAChR promoter, thereby inducing its transcription and increasing its levels in human SCC-L. Our data are clinically relevant because SCC-L patients smoked for decades before being diagnosed with cancer. It may be envisaged that continuous exposure to nicotine (in such SCC-L patients) causes up-regulation of α7-nAChRs, which facilitates tumor growth and progression. Our results will also be relevant to many SCC-L patients exposed to nicotine via second-hand smoke, electronic cigarettes, and patches or gums to quit smoking.

Identification of prognostic biomarkers in hepatitis B virus-related hepatocellular carcinoma and stratification by integrative multi-omics analysis.

BACKGROUND & AIMS: The differentiation of distinct multifocal hepatocellular carcinoma (HCC): multicentric disease vs. intrahepatic metastases, in which the management and prognosis varies substantively, remains problematic. We aim to stratify multifocal HCC and identify novel diagnostic and prognostic biomarkers by performing whole genome and transcriptome sequencing, as part of a multi-omics strategy. METHODS: A complete collection of tumour and somatic specimens (intrahepatic HCC lesions, matched non-cancerous liver tissue and blood) were obtained from representative patients with multifocal HCC exhibiting two distinct postsurgical courses. Whole-genome and transcriptome sequencing with genotyping were performed for each tissue specimen to contrast genomic alterations, including hepatitis B virus integrations, somatic mutations, copy number variations, and structural variations. We then constructed a phylogenetic tree to visualise individual tumour evolution and performed functional enrichment analyses on select differentially expressed genes to elucidate biological processes involved in multifocal HCC development. Multi-omics data were integrated with detailed clinicopathological information to identify HCC biomarkers, which were further validated using a large cohort of HCC patients (n = 174). RESULTS: The multi-omics profiling and tumour biomarkers could successfully distinguish the two multifocal HCC types, while accurately predicting clonality and aggressiveness. The dual-specificity protein kinase TTK, which is a key mitotic checkpoint regulator with links to p53 signaling, was further shown to be a promising overall prognostic marker for HCC in the large patient cohort. CONCLUSIONS: Comprehensive multi-omics characterisation of multifocal tumour evolution may improve clinical decision-making, facilitate personalised medicine, and expedite identification of novel biomarkers and therapeutic targets in HCC.

NONCODE v3.0: integrative annotation of long noncoding RNAs.

Facilitated by the rapid progress of high-throughput sequencing technology, a large number of long noncoding RNAs (lncRNAs) have been identified in mammalian transcriptomes over the past few years. LncRNAs have been shown to play key roles in various biological processes such as imprinting control, circuitry controlling pluripotency and differentiation, immune responses and chromosome dynamics. Notably, a growing number of lncRNAs have been implicated in disease etiology. With the increasing number of published lncRNA studies, the experimental data on lncRNAs (e.g. expression profiles, molecular features and biological functions) have accumulated rapidly. In order to enable a systematic compilation and integration of this information, we have updated the NONCODE database (http://www.noncode.org) to version 3.0 to include the first integrated collection of expression and functional lncRNA data obtained from re-annotated microarray studies in a single database. NONCODE has a user-friendly interface with a variety of search or browse options, a local Genome Browser for visualization and a BLAST server for sequence-alignment search. In addition, NONCODE provides a platform for the ongoing collation of ncRNAs reported in the literature. All data in NONCODE are open to users, and can be downloaded through the website or obtained through the SOAP API and DAS services.

Correction: Mechanisms of the ethanol extract of Gelidium amansii for slow aging in high-fat male Drosophila by metabolomic analysis.

Correction for 'Mechanisms of the ethanol extract of Gelidium amansii for slow aging in high-fat male Drosophila by metabolomic analysis' by Yushi Chen et al., Food Funct., 2022, 13, 10110-10120, https://doi.org/10.1039/D2FO02116A.

Multi-omics analysis unveils immunosuppressive microenvironment in the occurrence and development of multiple pulmonary lung cancers.

Multiple pulmonary lung cancers (MPLCs) are frequently encountered on computed tomography (CT) scanning of chest, yet their intrinsic characteristics associated with genomic features and radiological or pathological textures that may lead to distinct clinical outcomes remain largely unexplored. A total of 27 pulmonary nodules covering different radiological or pathological textures as well as matched adjacent normal tissues and blood samples were collected from patients diagnosed with MPLCs. Whole-exome sequencing (WES) and whole-transcriptome sequencing were performed. The molecular and immune features of MPLCs associated with distinct radiological or pathological textures were comprehensively investigated. Genomics analysis unveiled the distinct branches of pulmonary nodules originating independently within the same individual. EGFR and KRAS mutations were found to be prevalent in MPLCs, exhibiting mutual exclusivity. The group with KRAS mutations exhibited stronger immune signatures compared to the group with EGFR mutations. Additionally, MPLCs exhibited a pronounced immunosuppressive microenvironment, which was particularly distinct when compared with normal tissues. The expression of the FDSCP gene was specifically observed in MPLCs. When categorizing MPLCs based on radiological or pathological characteristics, a progressive increase in mutation accumulation was observed, accompanied by heightened chromatin-level instability as ground-glass opacity component declined or invasive progression occurred. A close association with the immunosuppressive microenvironment was also observed during the progression of pulmonary nodules. Notably, the upregulation of B cell and regulatory T cell marker genes occurred progressively. Immune cell abundance analysis further demonstrated a marked increase in exhausted cells and regulatory T cells during the progression of pulmonary nodules. These results were further validated by independent datasets including nCounter RNA profiling, single-cell RNA sequencing, and spatial transcriptomic datasets. Our study provided a comprehensive representation of the diverse landscape of MPLCs originating within the same individual and emphasized the significant influence of the immunosuppressive microenvironment in the occurrence and development of pulmonary nodules. These findings hold great potential for enhancing the clinical diagnosis and treatment strategies for MPLCs.

Investigation of cuproptosis regulator-mediated modification patterns and SLC30A7 function in GBM.

BACKGROUND: Copper-dependent controlled cell death (cuproptosis) is a novel cell death modality that is distinct from known cell death mechanisms. Nonetheless, the potential role of the cuproptosis regulator in tumour microenvironment (TME) of GBM remains unknown. METHODS: Based on 13 widely recognised cuproptosis regulators, the cuproptosis regulation patterns and the biological characteristics of each pattern were comprehensively assessed in GBMs. Machine learning strategies were used to construct a CupScore to quantify the cuproptosis regulation patterns of individual tumours. A PPI network was constructed to predict core-associated genes of cuproptosis regulators. The function of the novel cuproptosis regulators SLC30A7 was examined by in vitro and in vivo experiment. RESULTS: We identified three distinct cuproptosis regulation patterns, including immune activation, metabolic activation, and immunometabolic double deletion patterns. The CupScore was shown to predict the abundance of tumour inflammation, molecular subtype, stromal activity, gene variation, signalling pathways, and patient prognosis. The low CupScore subtype was characterised by immune activation, isocitrate dehydrogenase mutations, sensitivity to chemotherapy, and clinical benefits. The high CupScore subtype was characterised by activation of the stroma and metabolism and poor survival. Novel cuproptosis regulator SLC30A7 knockdown inhibited the cuproptosi via JAK2/STAT3/ATP7A pathway in GBM. CONCLUSION: Cuproptosis regulators have been shown to play a vital role in TME complexity. Constructing CupScores were trained to evaluate the regulation patterns of cuproptosis in individual tumours. The novel cuproptosis-related genes SLC30A7 was involved in regulation the tumorigenicity of GBM cell via JAK2/STAT3/ATP7A pathway in vitro and in vivo.

SLC7A11 promotes EMT and metastasis in invasive pituitary neuroendocrine tumors by activating the PI3K/AKT signaling pathway.

Invasive pituitary neuroendocrine tumors (PitNETs) are the most prevalent types of intracranial and neuroendocrine tumors. Their aggressive growth and difficulty in complete resection result in a high recurrence rate. Cystine transporter solute carrier family 7 member 11 (SLC7A11) is overexpressed in various cancers, which contributes to tumor growth, progression, and metastasis by promoting cystine uptake and glutathione biosynthesis. We identified SLC7A11 as an invasive biomarker based on three Gene Expression Omnibus cohorts. This study aimed to investigate the role of SLC7A11 in invasive PitNETs. Cell proliferation was assessed using CCK-8 and colony formation assays, while cell apoptosis was estimated with flow cytometry. Wound healing assays and transwell assays were utilized to evaluate migration and invasion ability. Our findings demonstrated that SLC7A11 was markedly upregulated in invasive PitNETs, and was associated with the invasiveness of PitNETs. Knockdown of SLC7A11 could largely suppress tumor cell proliferation, migration, and invasion, while inducing apoptosis. Furthermore, SLC7A11 depletion was implicated in regulating epithelial-mesenchymal transition and inactivating the PI3K/AKT signaling pathway. These insights suggest SLC7A11 as a potential therapeutic target for invasive PitNETs.

TRIM27 promotes the Warburg effect and glioblastoma progression via inhibiting the LKB1/AMPK/mTOR axis.

Altered protein ubiquitination is associated with cancer. The novel tripartite motif (TRIM) family of E3 ubiquitin ligases have been reported to play crucial roles in the development, growth, and metastasis of various tumors. The TRIM family member TRIM27 acts as a potential promoter of tumor development in a wide range of cancers. However, little is known regarding the biological features and clinical relevance of TRIM27 in glioblastoma (GBM). Here, we report findings of elevated TRIM27 expression in GBM tissues and GBM cell lines. Further functional analysis showed that TRIM27 deletion inhibited GBM cell growth both in vitro and in vivo. Furthermore, we found that TRIM27 promoted the growth of GBM cells by enhancing the Warburg effect. Additionally, the inactivation of the LKB1/AMPK/mTOR pathway was critical for the oncogenic effects of TRIM27 in GBM. Mechanistically, TRIM27 could directly bind to LKB1 and promote the ubiquitination and degradation of LKB1, which in turn enhanced the Warburg effect and GBM progression. Collectively, these data suggest that TRIM27 contributes to GNM pathogenesis by inhibiting the LKB1/AMPK/mTOR axis and may be a promising candidate as a potential diagnostic and therapeutic marker for patients with GBM.

Comprehensive analyses of m1A regulator-mediated modification patterns determining prognosis in lower-grade glioma (running title: m1A in LGG).

N1-methyladenosine (m1A) modification is a crucial post-transcriptional regulatory mechanism of messenger RNA (mRNA) in living organisms. Few studies have focused on analysis of m1A regulators in lower-grade gliomas (LGG). We employed the Nonnegative Matrix Factorization (NMF) technique on The Cancer Genome Atlas (TCGA) dataset to categorize LGG patients into 2 groups. These groups exhibited substantial disparities in terms of both overall survival (OS) and levels of infiltrating immune cells. We collected the significantly differentially expressed immune-related genes between the 2 clusters, and performed LASSO regression analysis to obtain m1AScores, and established an m1A-related immune-related gene signature (m1A-RIGS). Next, we categorized all patients with LGG into high- and low-risk subgroups, predictive significance of m1AScore was confirmed by conducting univariate/multivariate Cox regression analyses. Additionally, we confirmed variations in immune-related cells and ssGSEA and among the high-/low-risk subcategories in the TCGA dataset. Finally, our study characterized the effects of MSR1 and BIRC5 on LGG cells utilizing Edu assay and flow cytometry to explore the effects of modulation of these genes on glioma. The results of this study suggested that m1A-RIGS may be an excellent prognostic indicator for patients with LGG, and could also promote development of novel immune-based treatment strategies for LGG. Additionally, BIRC5 and MSR1 may be potential therapeutic targets for LGG.