Prognostic alternative mRNA splicing signature in non-small cell lung cancer

Alternative Splicing in Lung Adenocarcinoma: From Bench to Bedside

Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor and the most prevalent pathological type of lung cancer. The alternative splicing (AS) of mRNA enables the generation of multiple protein products from a single gene. This is a tightly regulated process that significantly contributes to the proteome diversity in eukaryotes. Recent multi-omics studies have delineated the splicing profiles that underline LUAD tumorigenesis from initiation to metastasis. Such progress holds robust promise to facilitate the development of screening strategies and individualized therapies. Perturbed AS fosters the emergence of novel neoantigen resources and disturbances in the immune microenvironment, which allow new investigations into modulatory targets for LUAD immunotherapy. This review presents an update on the landscape of dysregulated splicing events in LUAD and the associated mechanisms and theranostic perspectives with unique insights into AS-based immunotherapy, such as Chimeric Antigen Receptor T cell therapy. These AS variants can be used in conjunction with current therapeutic modules in LUAD, allowing bench to bedside translation to combat this highly malignant cancer.

Global profiling of alternative splicing in non-small cell lung cancer reveals novel histological and population differences

Lung cancer is one of the most frequently diagnosed cancers in the US. African-American (AA) men are more likely to develop lung cancer with higher incidence and mortality rates than European-American (EA) men. Herein, we report high-confidence alternative splicing (AS) events from high-throughput, high-depth total RNA sequencing of lung tumors and non-tumor adjacent tissues (NATs) in two independent cohorts of patients with adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). We identified novel AS biomarkers with notable differential percent spliced in (PSI) values between lung tumors and NATs enriched in the AA and EA populations, which were associated with oncogenic signaling pathways. We also uncovered tumor subtype- and population-specific AS events associated with cell surface proteins and cancer driver genes. We highlighted significant AS events in SYNE2 specific to LUAD in both populations, as well as those in CD44 from EAs and TMBIM6 from AAs specific to LUAD. Here, we also present the validation of cancer signatures based on direct high-throughput reverse transcription-PCR. Our large survey of lung tumors presents a rich data resource that may help to understand molecular subtypes of lung tumor between AAs and EAs and reveal new therapeutic vulnerabilities that potentially advance health equity.

Prognostic implications of alternative splicing events and key splicing factors in head and neck squamous cell carcinoma

The incidence of head and neck squamous cell carcinoma (HNSCC) remains high, accompanied by low 5-year survival rates. Identifying prognostic factors is essential for advancing personalized treatment approaches. Increasing evidence implicates aberrant alternative splicing (AS) plays a key role in tumor progression. Utilizing data from TCGA and TCGA SpliceSeq, prognosis-associated AS events were identified through Cox regression analysis. A prognostic risk model was developed via multivariate Cox and LASSO regression, with validation conducted using Kaplan-Meier survival analysis and ROC curve analysis. The correlation between splicing factors (SFs) and prognosis-associated AS events was analyzed using Pearson's method, followed by the construction of an SF-AS regulatory network. Key splicing factors (KSFs) were identified using Cytoscape software. Expression of KSFs in HNSCC was confirmed by quantitative PCR and Western blotting. SiRNA-mediated knockdown in HNSCC cell lines (HONE1, HN4, SAS) demonstrated effects on cell proliferation, invasion, and migration, as assessed by CCK8, colony formation, Transwell, and wound healing assays. Tumor growth was further evaluated in a subcutaneous tumor model in vivo. A total of 2347 survival-related AS events were identified, of which eleven were used to construct the prognostic model. Patients in the low-risk group exhibited significantly improved outcomes (P = 0e + 00), underscoring the model's predictive accuracy. Notably, DDX39B and PRPF39 emerged as key splicing factors, exhibiting high expression in HNSCC and correlating with poor prognosis, positioning them as potential biomarkers and therapeutic targets.

Functional proteoform group deconvolution reveals a broader spectrum of ibrutinib off-targets

Proteome-wide profiling has revealed that targeted drugs can have complex protein interaction landscapes. However, it's a challenge to profile drug targets while systematically accounting for the dynamic protein variations that produce populations of multiple proteoforms. We address this problem by combining thermal proteome profiling (TPP) with functional proteoform group detection to refine the target landscape of ibrutinib. In addition to known targets, we implicate additional specific functional proteoform groups linking ibrutinib to mechanisms in immunomodulation and cellular processes like Golgi trafficking, endosomal trafficking, and glycosylation. Further, we identify variability in functional proteoform group profiles in a CLL cohort, linked to treatment status and ex vivo response and resistance. This offers deeper insights into the impacts of functional proteoform groups in a clinical treatment setting and suggests complex biological effects linked to off-target engagement. These results provide a framework for interpreting clinically observed off-target processes and adverse events, highlighting the importance of functional proteoform group-level deconvolution in understanding drug interactions and their functional impacts with potential applications in precision medicine.

One-Size-Fits-Many: Antisense oligonucleotides for rescuing splicing mutations in hotspot exons

Mutations that impact splicing play a significant role in disease etiology but are not fully understood. To characterize the impact of exonic variants on splicing in 71 clinically-actionable disease genes in asymptomatic people, we analyzed 32,112 exonic mutations from ClinVar and Geisinger MyCode using a minigene reporter assay. We identify 1,733 splice-disrupting mutations, of which the most extreme 1-2% of variants are likely to be deleterious. We report that these variants are not distributed evenly across exons but are mostly concentrated in the ∼8% of exons that are most susceptible to splicing mutations (i.e. hotspot exons). We demonstrate that splicing defects in these exons can be reverted by ASOs targeting the splice sites of either their upstream or downstream flanking exons. This finding supports the feasibility of developing single therapeutic ASOs that could revert all splice-altering variants localized to a particular exon.

Integrating single cell and bulk RNA sequencing data identifies RBM17 as a novel response biomarker for immunotherapy in bladder cancer

Checkpoint inhibitors (CPIs) have been widely applied in the treatment of patients with bladder cancer (BLCA). However, there is still unmet need to dissect response predict biomarkers. To uncover CPI response-related marker genes in cancer cells, we utilized SCISSOR, integrating single-cell RNA and bulk RNA sequencing data. Transcriptomic and clinical data from IMvigor210, UNC-108, and BCAN/HCRN datasets were collected to evaluate and validate the identified biomarkers and signatures. Additionally, we analyzed TCGA-BLCA and local-BLCA RNA-seq data to investigate alternative splicing events (ASEs). Cell viability was assessed in T24 and UMUC3 cells with RBM17 upregulation or downregulation. Through SCISSOR analysis, we discovered that the expression levels of RBM17, TAP1, and PSMB8 were significantly associated with CPI response. Since PSMB8 displayed a highly positive correlation with TAP1, we developed a CPI response score (CRS) signature based on the expression profiles of RBM17 and TAP1. The CRS demonstrated robust predictive capacity in IMvigor210, UNC-108, and BCAN/HCRN datasets and was associated with higher tumor mutational burden (TMB), PD-L1 expression, and unique genomic features. Notably, RBM17 was not linked to the clinical outcomes of BLCA patients but positively correlated with BLCA cell proliferation in vitro. In the meantime, RBM17 was correlated with higher activity in core biological pathways, including antigen processing machinery, CD8 + T effector cells, cell cycle, DNA damage repair, epithelial-mesenchymal transition, histone regulation, and immune checkpoints. Moreover, the high-RBM17 group showed enrichment of LumU/Ba/sq subtypes but fewer FGFR3 alterations. Lastly, RBM17 significantly upregulated ASEs in BLCA samples, leading to higher neoantigen levels, a more inflamed tumor microenvironment, and improved CPI response. RBM17 is associated with higher ASEs and neoantigen levels, thereby potentiating the efficacy of CPI in BLCA. The established predictive signature, utilizing only two genes, has the potential to streamline clinical applications, providing a cost-effective alternative to expensive genomic, transcriptomic, and biological feature tests.

Steering research on mRNA splicing in cancer towards clinical translation

Splicing factors are affected by recurrent somatic mutations and copy number variations in several types of haematologic and solid malignancies, which is often seen as prima facie evidence that splicing aberrations can drive cancer initiation and progression. However, numerous spliceosome components also 'moonlight' in DNA repair and other cellular processes, making their precise role in cancer difficult to pinpoint. Still, few would deny that dysregulated mRNA splicing is a pervasive feature of most cancers. Correctly interpreting these molecular fingerprints can reveal novel tumour vulnerabilities and untapped therapeutic opportunities. Yet multiple technological challenges, lingering misconceptions, and outstanding questions hinder clinical translation. To start with, the general landscape of splicing aberrations in cancer is not well defined, due to limitations of short-read RNA sequencing not adept at resolving complete mRNA isoforms, as well as the shallow read depth inherent in long-read RNA-sequencing, especially at single-cell level. Although individual cancer-associated isoforms are known to contribute to cancer progression, widespread splicing alterations could be an equally important and, perhaps, more readily actionable feature of human cancers. This is to say that in addition to 'repairing' mis-spliced transcripts, possible therapeutic avenues include exacerbating splicing aberration with small-molecule spliceosome inhibitors, targeting recurrent splicing aberrations with synthetic lethal approaches, and training the immune system to recognize splicing-derived neoantigens.

Machine-learning and scRNA-Seq-based diagnostic and prognostic models illustrating survival and therapy response of lung adenocarcinoma

Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to avoid overdiagnosis and overtreatment. Taking full advantage of scRNA-Seq data to resolve the tumor heterogeneities, we explored the overall landscape of LUAD microenvironment. Utilizing the stage-specific tumor cell markers, we have developed highly accurate diagnostic and prognostic models with elevated sensitivity and specificity. The diagnostic model, developed through random forest algorithms with a thirteen-gene signature, achieved an accuracy of 96.4% and an AUC of 0.993. These metrics were further demonstrated by benchmarking with available models and scoring systems in independent cohorts. Concurrently, the prognostic model, formulated via Cox regression with a six-gene signature, effectively predicted overall survival, with elevated risk scores associated with increased fractions of cancer-associated fibroblasts, and higher likelihood of immune escape and T-cell exclusion. Subsequently, two nomograms were developed to predict survival and drug responses, facilitating their integration into clinical practice. Overall, this study underscores the potential of our models for efficient, rapid, and cost-effective diagnosis and prognosis of LUAD, adaptable to multiple expression profiling platforms and quantification methods.

Genome-wide identification of pan-cancer common and cancer-specific alternative splicing events in 9 types of cancer

Alternative splicing (AS) has significant clinical relevance with cancers and is a potential source of neoepitopes. In this study, RNA-seq data of 94 solid tumor and matched adjacent normal tissues from 47 clinical patients covering nine cancer types were comprehensively analyzed using SUVA developed by ourselves. The results identified highly conserved pan-cancer differential alternative splicing (DAS) events and cancer-specific DAS events in a series of tumor samples, which in turn revealed the heterogeneity of AS post-transcriptional regulation across different cancers. The co-disturbed network between spliceosome factors (SFs) and common cancer-associated DAS was further constructed, suggesting the potential possibility of the regulation of differentially expressed SFs on DAS. Finally, the common cancer-associated DAS events were fully validated using the TCGA dataset, confirming the significant correlation between cancer-associated DAS and prognosis. Briefly, our study elucidates new insights into conservatived and specific DAS in cancer, providing valuable resources for cancer therapeutic targets.

Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance

RNA splicing shapes the gene regulatory programs that underlie various physiological and disease processes. Here, we present the SCASL (single-cell clustering based on alternative splicing landscapes) method for interrogating the heterogeneity of RNA splicing with single-cell RNA-seq data. SCASL resolves the issue of biased and sparse data coverage on single-cell RNA splicing and provides a new scheme for classifications of cell identities. With previously published datasets as examples, SCASL identifies new cell clusters indicating potentially precancerous and early-tumor stages in triple-negative breast cancer, illustrates cell lineages of embryonic liver development, and provides fine clusters of highly heterogeneous tumor-associated CD4 and CD8 T cells with functional and physiological relevance. Most of these findings are not readily available via conventional cell clustering based on single-cell gene expression data. Our study shows the potential of SCASL in revealing the intrinsic RNA splicing heterogeneity and generating biological insights into the dynamic and functional cell landscapes in complex tissues.

Identification and validation of seed dormancy loci and candidate genes and construction of regulatory networks by WGCNA in maize introgression lines

KEY MESSAGE

Seventeen PHS-QTLs and candidate genes were obtained, including eleven major loci, three under multiple environments and two with co-localization by the other mapping methods; The functions of three candidate genes were validated using mutants; nine target proteins and five networks were filtered by joint analysis of GWAS and WGCNA. Seed dormancy (SD) and pre-harvest sprouting (PHS) affect yield, as well as grain and hybrid quality in seed production. Therefore, identification of genetic and regulatory pathways underlying PHS and SD is key to gene function analysis, allelic variation mining and genetic improvement. In this study, 78,360 SNPs by SLAF-seq of 230 maize chromosome segment introgression lines (ILs), PHS under five environments were used to conduct GWAS (genome wide association study) (a threshold of 1/n), and seventeen unreported PHS QTLs were obtained, including eleven QTLs with PVE > 10% and three QTLs under multiple environments. Two QTL loci were co-located between the other two genetic mapping methods. Using differential gene expression analyses at two stages of grain development, gene functional analysis of Arabidopsis mutants, and gene functional analysis in the QTL region, seventeen PHS QTL-linked candidate genes were identified, and their five molecular regulatory networks constructed. Based on the Arabidopsis T-DNA mutations, three candidate genes were shown to regulate for SD and PHS. Meanwhile, using RNA-seq of grain development, the weighted correlation network analysis (WGCNA) was performed, deducing five regulatory pathways and target genes that regulate PHS and SD. Based on the conjoint analysis of GWAS and WGCNA, four pathways, nine target proteins and target genes were revealed, most of which regulate cell wall metabolism, cell proliferation and seed dehydration tolerance. This has important theoretical and practical significance for elucidating the genetic basis of maize PHS and SD, as well as mining of genetic resources and genetic improvement of traits.

Synthesis and Conformational Analysis of FR901464-Based RNA Splicing Modulators and Their Synergism in Drug-Resistant Cancers

FR901464 is a cytotoxic natural product that binds splicing factor 3B subunit 1 (SF3B1) and PHD finger protein 5A (PHF5A), the components of the human spliceosome. The amide-containing tetrahydropyran ring binds SF3B1, and it remains unclear how the substituents on the ring contribute to the binding. Here, we synthesized meayamycin D, an analogue of FR901464, and three additional analogues to probe the conformation through methyl scanning. We discovered that the amide-containing tetrahydropyran ring assumes only one of the two possible chair conformations and that methylation of the nitrogen distorts the chair form, dramatically reducing cytotoxicity. Meayamycin D induced alternative splicing of MCL-1, showed strong synergism with venetoclax in drug-resistant lung cancer cells, and was cancer-specific over normal cells. Meayamycin D incorporates an alkyl ether and shows a long half-life in mouse plasma. The characteristics of meayamycin D may provide an approach to designing other bioactive L-shaped molecules.

RNA splicing alterations in lung cancer pathogenesis and therapy

RNA splicing alterations are widespread and play critical roles in cancer pathogenesis and therapy. Lung cancer is highly heterogeneous and causes the most cancer-related deaths worldwide. Large-scale multi-omics studies have not only characterized the mutational landscapes but also discovered a plethora of transcriptional and post-transcriptional changes in lung cancer. Such resources have greatly facilitated the development of new diagnostic markers and therapeutic options over the past two decades. Intriguingly, altered RNA splicing has emerged as an important molecular feature and therapeutic target of lung cancer. In this review, we provide a brief overview of splicing dysregulation in lung cancer and summarize the recent progress on key splicing events and splicing factors that contribute to lung cancer pathogenesis. Moreover, we describe the general strategies targeting splicing alterations in lung cancer and highlight the potential of combining splicing modulation with currently approved therapies to combat this deadly disease. This review provides new mechanistic and therapeutic insights into splicing dysregulation in cancer.

Identification of CHMP7 as a promising immunobiomarker for immunotherapy and chemotherapy and impact on prognosis of colorectal cancer patients

Introduction: ESCRT is a molecular machine involved in various important physiological processes, such as the formation of multivesicular bodies, cellular autophagy, and cellular membrane repair. CHMP7 is a regulatory subunit of ESCRT-III and is necessary for the proper functioning of ESCRT. In this study, public databases were exploited to explore the role of CHMP7 in tumors. Methods: The research on CHMP7 in oncology is rather limited. In this study, the differential expression of CHMP7 in multiple tumor tissues was analyzed with information from public databases and clinically collected colorectal cancer tissue samples. Subsequently, the mutational landscape of CHMP7, methylation levels, and the relationship between its expression levels and genomic instability were resolved. The immune microenvironment is a compelling emerging star in tumor research. The correlation of CHMP7 with various infiltrating immune cell types in TME was analyzed by online datasets and single-cell sequencing. In terms of clinical treatment, the impact of CHMP7 expression levels on chemotherapy and immunotherapy and the evaluation of small molecule drugs related to CHMP7 were assessed. Results: CHMP7 has a predictive value for the prognosis of patients with tumors and is highly involved in tumor immunity. The downregulation of CHMP7 may lead to genomic instability. A strong correlation between CHMP7 and TME immune cell infiltration has been observed, participating in the formation of suppressive TME and promoting tumor progression. The expression level of CHMP7 is significantly lower in the non-responder group of multiple chemotherapeutic agents. CHMP7 can potentially serve as a new biomarker for predicting the efficacy of tumor chemotherapy and immunotherapy. Conclusion: As a gene of interest, CHMP7 is expected to provide novel and promising targets for further treatment of patients with tumor.

MEN1 is a regulator of alternative splicing and prevents R-loop-induced genome instability through suppression of RNA polymerase II elongation

The fidelity of alternative splicing (AS) patterns is essential for growth development and cell fate determination. However, the scope of the molecular switches that regulate AS remains largely unexplored. Here we show that MEN1 is a previously unknown splicing regulatory factor. MEN1 deletion resulted in reprogramming of AS patterns in mouse lung tissue and human lung cancer cells, suggesting that MEN1 has a general function in regulating alternative precursor mRNA splicing. MEN1 altered exon skipping and the abundance of mRNA splicing isoforms of certain genes with suboptimal splice sites. Chromatin immunoprecipitation and chromosome walking assays revealed that MEN1 favored the accumulation of RNA polymerase II (Pol II) in regions encoding variant exons. Our data suggest that MEN1 regulates AS by slowing the Pol II elongation rate and that defects in these processes trigger R-loop formation, DNA damage accumulation and genome instability. Furthermore, we identified 28 MEN1-regulated exon-skipping events in lung cancer cells that were closely correlated with survival in patients with lung adenocarcinoma, and MEN1 deficiency sensitized lung cancer cells to splicing inhibitors. Collectively, these findings led to the identification of a novel biological role for menin in maintaining AS homeostasis and link this role to the regulation of cancer cell behavior.

Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma

Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next introduce LPRI (Lung Cancer Prognostic Regulon Index), a precision oncology framework to identify new biomarkers associated with prognosis by leveraging the single cell regulon atlas and bulk RNA sequencing or microarray datasets. We confirmed that LPRI could be a robust biomarker to guide prognosis stratification across lung adenocarcinoma cohorts. Finally, a multi-omics data analysis to characterize molecular alterations associated with LPRI was performed from The Cancer Genome Atlas (TCGA) dataset. Our study provides a comprehensive chart of regulons in LUAD. Additionally, LPRI will be used to help prognostic prediction and developing personalized treatment for future studies.

PD-L1 Exon 3 Is a Hidden Switch of Its Expression and Function in Oral Cancer Cells

The interaction between programmed cell death 1 ligand 1 (PD-L1) and programmed cell death protein 1 (PD-1) protects tumor cells from immune surveillance. PD-L1 exon 3 is a potential alternative exon and encodes an Ig variable (IgV) domain. Here, we found that a lack of exon 3 leads to the significant loss of cellular membrane locations and the dramatically reduced protein expression of PD-L1, indicating that PD-L1 exon 3 is essential for its protein expression and translocation to the cell membrane. Notably, oral cancer cells show almost no exon 3 skipping to ensure the expression of the full-length, functional PD-L1 protein. We discovered two key exonic splicing enhancers (ESEs) for exon 3 inclusion. Two efficient antisense oligonucleotides (ASOs) were identified to block these two ESEs, which can significantly trigger exon 3 skipping and decrease the production of full-length, functional PD-L1 on the surface of cancer cells. Treatment of oral cancer cells with these ASOs significantly enhanced immune cells' suppression of cancer cell proliferation. Surprisingly, these two ASOs also significantly inhibited cell growth and induced cell pyroptosis in oral cancer cells. Altogether, the results of our study demonstrate the pivotal roles of exon 3 in PD-L1 expression and provide a novel anti-PD-L1 method.

Macrofungal Diversity and Distribution Patterns in the Primary Forests of the Shaluli Mountains

The Shaluli Mountains are located in the southeastern part of the Tibetan Plateau at an elevation of 2500-5000 m. They are characterized by a typical vertical distribution of climate and vegetation and are considered a global biodiversity hotspot. We selected ten vegetation types at different elevation gradients representing distinct forests in the Shaluli Mountains to assess the macrofungal diversity, including subalpine shrub, Pinus spp., Populus spp., Pinus spp. and Quercus spp., Quercus spp., Abies spp., Picea spp. and Abies spp., Picea spp., Juniperus spp., and alpine meadow. In total, 1654 macrofungal specimens were collected. All specimens were distinguished by morphology and DNA barcoding, resulting in the identification of 766 species belonging to 177 genera in two phyla, eight classes, 22 orders, and 72 families. Macrofungal species composition varied widely among vegetation types, but ectomycorrhizal fungi were predominant. In this study, the analysis of observed species richness, the Chao1 diversity index, the invsimpson diversity index, and the Shannon diversity index revealed that the vegetation types with higher macrofungal alpha diversity in the Shaluli Mountains were composed of Abies, Picea, and Quercus. The vegetation types with lower macrofungal alpha diversity were subalpine shrub, Pinus spp., Juniperus spp., and alpine meadow. The results of curve-fitting regression analysis showed that macrofungal diversity in the Shaluli Mountains was closely related to elevation, with a trend of increasing and then decreasing with rising elevation. This distribution of diversity is consistent with the hump-shaped pattern. Constrained principal coordinate analysis based on Bray-Curtis distances indicated that macrofungal community composition was similar among vegetation types at similar elevations, while vegetation types with large differences in elevation differed significantly in macrofungal community composition. This suggests that large changes in elevation increase macrofungal community turnover. This study is the first investigation of the distribution pattern of macrofungal diversity under different vegetation types in high-altitude areas, providing a scientific basis for the conservation of macrofungal resources.

Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide

The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available.

LncRNA BC promotes lung adenocarcinoma progression by modulating IMPAD1 alternative splicing

BACKGROUND

The therapeutic value of targeted therapies in patients with lung cancer is reduced when tumours acquire secondary resistance after an initial period of successful treatment. However, the molecular events behind the resistance to targeted therapies in lung cancer remain largely unknown.

AIMS

To discover the important role and mechanism of lncRNA BC in promoting tumor metastasis and influencing clinical prognosis of LUAD.

MATERIALS & METHODS

Microarrays were used to screen a comprehensive set of lncRNAs with differential expression profiles in lung cancer cells. The functional role and mechanism of lncRNA were further investigated by gain- and loss-of-function assays. RNA pull-down, protein assays, and mass spectrometry were used to identify proteins that interacted with lncRNA. TaqMan PCR was used to measure lncRNA in lung adenocarcinoma and adjacent nontumor tissues from 428 patients. The clinical significance of lncRNA identified was statistically confirmed in this cohort of patients.

RESULTS

In this study, we show that the long non-coding RNA BC009639 (BC) is involved in acquired resistance to EGFR-targeted therapies. Among the 235 long non-coding RNAs that were differentially expressed in lung cancer cell lines, with different metastatic potentials, BC promoted growth, invasion, metastasis, and resistance to EGFR-tyrosine kinase inhibitors (EGFR-TKIs), both in vitro and in vivo. BC was highly expressed in 428 patients with lung adenocarcinoma (LUAD) and high BC expression correlated with reduced efficacy of EGFR-TKI therapy. To uncover the molecular mechanism of BC-mediated EGFR-TKI resistance in lung cancer, we screened and identified nucleolin and hnRNPK that interact with BC. BC formed the splicing complex with nucleolin and hnRNPK to facilitate the production of a non-protein-coding inositol monophosphatase domain containing 1 (IMPAD1) splice variant, instead of the protein-coding variant. The BC-mediated alternative splicing (AS) of IMPAD1 resulted in the induction of the epithelial-mesenchymal transition and resistance to EGFR-TKI in lung cancer. High BC expression correlated with clinical progress and poor survival among 402 patients with LUAD.

DISSCUSSION

Through alternative splicing, BC boosted the non-coding IMPAD1-203 transcript variant while suppressing the IMPAD1-201 variant. In order to control the processing of pre-mRNA, BC not only attracted RNA binding proteins (NCL, IGF2BP1) or splicing factors (hnRNPK), but also controlled the formation of the splicing-regulator complex by creating RNA-RNA-duplexes.

CONCLUSION

Our results reveal an important role for BC in mediating resistance to EGFR-targeted therapy in LUAD through IMPAD1 AS and in implication for the targeted therapy resistance.

Oncogenic KRAS alters splicing factor phosphorylation and alternative splicing in lung cancer

BACKGROUND

Alternative RNA splicing is widely dysregulated in cancers including lung adenocarcinoma, where aberrant splicing events are frequently caused by somatic splice site mutations or somatic mutations of splicing factor genes. However, the majority of mis-splicing in cancers is unexplained by these known mechanisms. We hypothesize that the aberrant Ras signaling characteristic of lung cancers plays a role in promoting the alternative splicing observed in tumors.

METHODS

We recently performed transcriptome and proteome profiling of human lung epithelial cells ectopically expressing oncogenic KRAS and another cancer-associated Ras GTPase, RIT1. Unbiased analysis of phosphoproteome data identified altered splicing factor phosphorylation in KRAS-mutant cells, so we performed differential alternative splicing analysis using rMATS to identify significantly altered isoforms in lung epithelial cells. To determine whether these isoforms were uniquely regulated by KRAS, we performed a large-scale splicing screen in which we generated over 300 unique RNA sequencing profiles of isogenic A549 lung adenocarcinoma cells ectopically expressing 75 different wild-type or variant alleles across 28 genes implicated in lung cancer.

RESULTS

Mass spectrometry data showed widespread downregulation of splicing factor phosphorylation in lung epithelial cells expressing mutant KRAS compared to cells expressing wild-type KRAS. We observed alternative splicing in the same cells, with 2196 and 2416 skipped exon events in KRASG12V and KRASQ61H cells, respectively, 997 of which were shared (p < 0.001 by hypergeometric test). In the high-throughput splicing screen, mutant KRAS induced the greatest number of differential alternative splicing events, second only to the RNA binding protein RBM45 and its variant RBM45M126I. We identified ten high confidence cassette exon events across multiple KRAS variants and cell lines. These included differential splicing of the Myc Associated Zinc Finger (MAZ). As MAZ regulates expression of KRAS, this splice variant may be a mechanism for the cell to modulate wild-type KRAS levels in the presence of oncogenic KRAS.

CONCLUSION

Proteomic and transcriptomic profiling of lung epithelial cells uncovered splicing factor phosphorylation and mRNA splicing events regulated by oncogenic KRAS. These data suggest that in addition to widespread transcriptional changes, the Ras signaling pathway can promote post-transcriptional splicing changes that may contribute to oncogenic processes.

Knowledge mapping of alternative splicing of cancer from 2012 to 2021: A bibliometric analysis

Background

As a processing method of RNA precursors, alternative splicing (AS) is critical to normal cellular activities. Aberrant AS events are associated with cancer development and can be promising targets to treat cancer. However, no detailed and unbiased study describes the current state of AS of cancer research. We aim to measure and recognize the current state and trends of AS cancer research in this study.

Methods

The Web of Science Core Collection was used to acquire the articles. Utilizing three bibliometric tools (CiteSpace, VOSviewer, R-bibliometrix), we were able to measure and recognize the influence and collaboration data of individual articles, journals, and co-citations. Analysis of co-occurrence and burst information helped us identify the trending research areas related to AS of cancer.

Results

From 2012 to 2021, the total number of papers on AS of cancer published in 766 academic journals was 3,507, authored by 20,406 researchers in 405 institutions from 80 countries/regions. Research involving AS of cancer genes was primarily conducted in the United States and China; simultaneously, the Chinese Academy of Sciences, Fudan University, and National Cancer Institute were the institutions with strong research capabilities. Scorilas Andreas is the scholar with the most publications, while the most co-citations were generated by Wang, Eric T. Plos One published the most papers on AS of cancer, while J Biol Chem was the most co-cited academic journal in this field. The results of keyword co-occurrence analysis can be divided into three types: molecular (P53, CD44, androgen receptor, srsf3, esrp1), pathological process (apoptosis, EMT, metastasis, angiogenesis, proliferation), and disease (breast cancer, colorectal cancer, prostate cancer, hepatocellular carcinoma, gastric cancer).

Conclusion

Research on AS of cancer has been increasing in intensity over the past decade. Current AS of cancer studies focused on the hallmarks of AS in cancer and AS signatures including diagnostic and therapeutic targets. Among them, the current trends are splicing factors regulating epithelial-mesenchymal transition and other hallmarks, aberrant splicing events in tumors, and further mechanisms. These might give researchers interested in this field a forward-looking perspective and inform further research.

Comprehensive analysis of prognostic value, relationship to cell cycle, immune infiltration and m6A modification of ZSCAN20 in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common tumor across the globe with a high mortality rate. ZSCAN20 is a ZNF transcription factor, a key determinant of gene expression. Nonetheless, the mechanism of ZSCAN20 as a potential clinical biomarker and therapeutic target for HCC is not understood. Here, TIMER, TCGA, ICGC databases and immunohistochemical (IHC) and Western Blot found ZSCAN20 mRNA and protein levels were upregulated. Additionally, Kaplan-Meier Plotter, GEPIA and TCGA databases showed high ZSCAN20 expression was related to the short survival time of HCC patients. Multivariate Cox analysis exposed that ZSCAN20 can act as an independent prognostic factor. We observed methylation level of ZSCAN20 was associated with the clinicopathological characteristics and prognosis of HCC patients through UALCAN. Furthermore, enrichment examination exposed functional association between ZSCAN20 and cell cycle, immune infiltration. Functional experiments showed that interference with ZSCAN20 significantly reduced the invasion, migration and proliferation abilities of HCC cells. An immune infiltration analysis showed that ZSCAN20 was associated with immune cells, particularly T cells. The expression of ZSCAN20 was correlated with poor prognosis in the Regulatory T-cell. And Real-Time RT-PCR analysis found interference with ZSCAN20 significantly reduced the expression of some chemokines. Finally, the TCGA and ICGC data analysis suggested that the ZSCAN20 expression was greatly related to m6A modifier related genes. In conclusion, ZSCAN20 can serve as a prognostic biomarker for HCC and provide clues about cell cycle, immune infiltration, and m6A modification.

A prognostic signature model for unveiling tumor progression in lung adenocarcinoma

A more accurate prognosis is important for clinical treatment of lung adenocarcinoma. However, due to the limitation of sample and technical bias, most prognostic signatures lacked reproducibility, and few were applied to clinical practice. In addition, understanding the molecular driving mechanism is indispensable for developing more promising therapies for lung adenocarcinoma. Here, we built an unbiased prognostic significance model to perform an integrative analysis, including differentially expressed genes and clinical data with lung adenocarcinoma patients from TCGA. Multivariable Cox proportional hazards model with the Lasso penalty and 10-fold cross-validate were used to identify the best gene signature. We generated a 17-gene signature for prognostic risk prediction based on the overall survival time of lung adenocarcinoma patients. To further test the model's predictive ability, we have applied an independent GEO database to verify the predictive ability of prognostic signature. The model can more objectively describe several biological processes related to tumors and reveal important molecular mechanisms in tumor development by GO and KEGG analysis. Furthermore, differential expression analysis by GSEA revealed that tumor microenvironments such as ER stress, exosome, and immune microenvironment were enriched. Using single-cell RNA sequence technology, we found that risk score was positively correlated with lung adenocarcinoma marker genes and copy number variation but negatively correlated with lung epithelial marker genes. High-risk cell populations with the model had stronger cancer stemness and tumor-related pathway activation. As we expected, the risk score was in accordance with the malignancy of each cluster from tumor progression. In conclusion, the risking model established in this study is more reliable than others in evaluating the prognosis of LUAD patients.

Identification and Validation of Three-Gene Signature in Lung Squamous Cell Carcinoma by Integrated Transcriptome and Methylation Analysis

Since DNA methylation (DNAm) is associated with the carcinogenesis of various cancers, this study aimed to explore potential DNAm prognostic signatures of lung squamous cell carcinoma (LUSC). First, transcriptomic and methylation profiles of LUSC were obtained from The Cancer Genome Atlas database (TCGA). DNAm-related genes were screened by integrating DNAm and transcriptome profiles via MethylMix package. Subsequently, a prognostic signature was conducted with the least absolute shrinkage and selector operation (LASSO) Cox analysis. This signature combined with the clinicopathological parameters was then utilized to construct a prognostic nomogram via the rms package. A signature based on three DNAm-related genes claudin 1 (CLDN1), ATP-binding cassette subfamily C member 5 (ABCC5), and cystatin A (CSTA) that were hypomethylated and upregulated in LUSC was constructed. Univariate and multivariate Cox regression analysis suggested that this signature, combined with age and TNM.N stage, was significantly correlated with survival rate. Time-dependent receiver operating characteristics and calibration curves suggested the nomogram constructed with age and TNM.N stage variables could accurately evaluate the 3- and 5-year outcome of LUSC. Finally, the average mRNA and protein expression levels of CLDN1, ABCC5, and CSTA in LUSC were verified to be significantly higher than those in paracancerous tissues. Moreover, silencing CLDN1, ABCC5, and CSTA expressions could significantly reduce the carcinogenesis of the A549 cell line. The DNAm-driven prognostic signature consists of CLDN1, ABCC5, and CSTA incorporated with age and TNM. N stage could facilitate the prediction outcome of LUSC.

An integrative analysis of DNA methylation and gene expression to predict lung adenocarcinoma prognosis

Background: Abnormal DNA methylation of gene promoters is an important feature in lung adenocarcinoma (LUAD). However, the prognostic value of DNA methylation remains to be further explored. Objectives. We sought to explore DNA methylation characteristics and develop a quantifiable criterion related to DNA methylation to improve survival prediction for LUAD patients.

Methods: Illumina Human Methylation450K array data, level 3 RNA-seq data and corresponding clinical information were obtained from TCGA. Cox regression analysis and the Akaike information criterion were used to construct the best-prognosis methylation signature. Receiver operating characteristic curve analysis was used to validate the prognostic ability of the DNA methylation-related feature score. qPCR was used to measure the transcription levels of the identified genes upon methylation.

Results: We identified a set of DNA methylation features composed of 11 genes (MYEOV, KCNU1, SLC27A6, NEUROD4, HMGB4, TACR3, GABRA5, TRPM8, NLRP13, EDN3 and SLC34A1). The feature score, calculated based on DNA methylation features, was independent of tumor recurrence and TNM stage in predicting overall survival. Of note, the combination of this feature score and TNM stage provided a better overall survival prediction than either of them individually. The transcription levels of all the hypermethylated genes were significantly increased after demethylation, and the expression levels of 3 hypomethylated proteins were significantly higher in tumor tissues than in normal tissues, as indicated by immunohistochemistry data from the Human Protein Atlas. Our results suggested that these identified genes with prognostic features were regulated by DNA methylation of their promoters.

Conclusion: Our studies demonstrated the potential application of DNA methylation markers in the prognosis of LUAD.

Multi-omics analysis reveals RNA splicing alterations and their biological and clinical implications in lung adenocarcinoma

Alternative RNA splicing is one of the most important mechanisms of posttranscriptional gene regulation, which contributes to protein diversity in eukaryotes. It is well known that RNA splicing dysregulation is a critical mechanism in tumor pathogenesis and the rationale for the promising splice-switching therapeutics for cancer treatment. Although we have a comprehensive understanding of DNA mutations, abnormal gene expression profiles, epigenomics, and proteomics in lung adenocarcinoma (LUAD), little is known about its aberrant alternative splicing profiles. Here, based on the multi-omics data generated from over 1000 samples, we systematically studied the RNA splicing alterations in LUAD and revealed their biological and clinical implications. We identified 3688 aberrant alternative splicing events (AASEs) in LUAD, most of which were alternative promoter and exon skip. The specific regulatory roles of RNA binding proteins, somatic mutations, and DNA methylations on AASEs were comprehensively interrogated. We dissected the functional implications of AASEs and concluded that AASEs mainly affected biological processes related to tumor proliferation and metastasis. We also found that one subtype of LUAD with a particular AASEs pattern was immunogenic and had a better prognosis and response rate to immunotherapy. These findings revealed novel events related to tumorigenesis and tumor immune microenvironment and laid the foundation for the development of splice-switching therapies for LUAD.

Landscape of Alternative Splicing Events Related to Prognosis and Immune Infiltration in Glioma: A Data Analysis and Basic Verification

Background

Glioma is a prevalent primary brain cancer with high invasiveness and typical local diffuse infiltration. Alternative splicing (AS), as a pervasive transcriptional regulatory mechanism, amplifies the coding capacity of the genome and promotes the progression of malignancies. This study was aimed at identifying AS events and novel biomarkers associated with survival for glioma.

Methods

RNA splicing patterns were collected from The Cancer Genome Atlas SpliceSeq database, followed by calculating the percentage of splicing index. Expression profiles and related clinical information of glioma were integrated based on the UCSC Xena database. The AS events in glioma were further analyzed, and glioma prognosis-related splicing factors were identified with the use of bioinformatics analysis and laboratory techniques. Further immune infiltration analysis was performed.

Results

Altogether, 9028 AS events were discovered. Upon univariate Cox analysis, 425 AS events were found to be related to the survival of patients with glioma, and 42 AS events were further screened to construct the final prognostic model (area under the curve = 0.974). Additionally, decreased expression of the splicing factors including Neuro-Oncological Ventral Antigen 1 (NOVA1), heterogeneous nuclear ribonucleoprotein C (HNRNPC), heterogeneous nuclear ribonucleoprotein L-like protein (HNRNPLL), and RNA-Binding Motif Protein 4 (RBM4) contributed to the poor survival in glioma. The immune infiltration analysis demonstrated that AS events were related to the proportion of immune cells infiltrating in glioma.

Conclusions

It is of great value for comprehensive consideration of AS events, splicing networks, and related molecular subtype clusters in revealing the underlying mechanism and immune microenvironment remodeling for glioma, which provides clues for the further verification of related therapeutic targets.

Deciphering associations between three RNA splicing-related genetic variants and lung cancer risk

Limited efforts have been made in assessing the effect of genome-wide profiling of RNA splicing-related variation on lung cancer risk. In the present study, we first identified RNA splicing-related genetic variants linked to lung cancer in a genome-wide profiling analysis and then conducted a two-stage (discovery and replication) association study in populations of European ancestry. Discovery and validation were conducted sequentially with a total of 29,266 cases and 56,450 controls from both the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium as well as the OncoArray database. For those variants identified as significant in the two datasets, we further performed stratified analyses by smoking status and histological type and investigated their effects on gene expression and potential regulatory mechanisms. We identified three genetic variants significantly associated with lung cancer risk: rs329118 in JADE2 (P = 8.80E-09), rs2285521 in GGA2 (P = 4.43E-08), and rs198459 in MYRF (P = 1.60E-06). The combined effects of all three SNPs were more evident in lung squamous cell carcinomas (P = 1.81E-08, P = 6.21E-08, and P = 7.93E-04, respectively) than in lung adenocarcinomas and in ever smokers (P = 9.80E-05, P = 2.70E-04, and P = 2.90E-05, respectively) than in never smokers. Gene expression quantitative trait analysis suggested a role for the SNPs in regulating transcriptional expression of the corresponding target genes. In conclusion, we report that three RNA splicing-related genetic variants contribute to lung cancer susceptibility in European populations. However, additional validation is needed, and specific splicing mechanisms of the target genes underlying the observed associations also warrants further exploration.

Identification of an inflammatory response signature associated with prognostic stratification and drug sensitivity in lung adenocarcinoma

Increasing evidence has confirmed the close connection between inflammatory response and tumorigenesis. However, the relationship between inflammatory response genes (IRGs) and the prognosis of lung adenocarcinoma (LUAD) as well as the response to drug therapy remains poorly investigated. Here, we comprehensively analyzed IRGs RNA expression profiling and clinical features of over 2000 LUAD patients from 12 public datasets. The Cox regression method and LASSO analysis were combined to develop a novel IRG signature for risk stratification and drug efficacy prediction in LUAD patients. Enriched pathways, tumor microenvironment (TME), genomic and somatic mutation landscape in different subgroups were evaluated and compared with each other. This established IRG signature including 11 IRGs (ADM, GPC3, IL7R, NMI, NMURI, PSEN1, PTPRE, PVR, SEMA4D, SERPINE1, SPHK1), could well categorize patients into significantly different prognostic subgroups, and have better predictive in independently assessing survival as compared to a single clinical factor. High IRG scores (IRGS) patients might benefit more from immunotherapy and chemotherapy. Comprehensive analysis uncovered significant differences in enriched pathways, TME, genomic and somatic mutation landscape between the two subgroups. Additionally, integrating the IRGS and TNM stage, a reliable prognostic nomogram was developed to optimize survival prediction, and validated in an independent external dataset for clinical application. Take together, the proposed IRG signature in this study is a promising biomarker for risk stratification and drug efficacy prediction in LUAD patients. This study may be meaningful for explaining the responses of clinical therapeutic drugs and providing new strategies for administrating sufferer of LUAD.

Immunological Characteristics of Alternative Splicing Profiles Related to Prognosis in Bladder Cancer

Several studies have found that pathological imbalance of alterative splicing (AS) events is associated with cancer susceptibility. carcinogenicity. Nevertheless, the relationship between heritable variation in AS events and carcinogenicity has not been extensively explored. Here, we downloaded AS event signatures, transcriptome profiles, and matched clinical information from The Cancer Genome Atlas (TCGA) database, identified the prognostic AS-related events via conducting the univariate Cox regression algorism. Subsequently, the prognostic AS-related events were further reduced by the least absolute shrinkage and selection operator (LASSO) logistic regression model, and employed for constructing the risk model. Single-sample (ssGSEA), ESTIMATE, and the CIBERSORT algorithms were conducted to evaluate tumor microenvironment status. CCK8, cell culture scratch, transwell invasion assays and flow cytometry were conducted to confirm the reliability of the model. We found 2751 prognostic-related AS events, and constructed a risk model with seven prognostic-related AS events. Compared with high-risk score patients, the overall survival rate of the patients with low-risk score was remarkably longer. Besides, we further found that risk score was also closely related to alterations in immune cell infiltration and immunotherapeutic molecules, indicating its potential as an observation of immune infiltration and clinical response to immunotherapy. In addition, the downstream target gene (DYM) could be a promising prognostic factor for bladder cancer. Our investigation provided an indispensable reference for ulteriorly exploring the role of AS events in the tumor microenvironment and immunotherapy efficiency, and rendered personalized prognosis monitoring for bladder cancer.

Identification of prognostic alternative splicing signature in gastric cancer

Background

Aberrant alternative splicing (AS) events could be viewed as prognostic indicators in a large number of malignancies. This study aims to identify prognostic AS events, illuminate the function of the splicing variants biomarkers and provide reliable evidence for formulating public health strategies for gastric cancer (GC) surveillance.

Methods

RNA-Seq data, clinical information and percent spliced in (PSI) values were available in The cancer genome atlas (TCGA) and TCGA SpliceSeq data portal. A three-step regression method was conducted to identify prognostic AS events and construct multi-AS-based signatures. The associations between prognostic AS events and splicing factors were also investigated.

Results

We identified a total of 1,318 survival-related AS events in GC, parent genes of which were implicated in numerous oncogenic pathways. The final prognostic signatures stratified by seven types of AS events or not stratified performed well in risk prediction for GC patients. Moreover, five signatures based on AA, AD, AT, ES and RI events function as independent prognostic indicators after multivariate adjustment of other clinical variables. Splicing network also showed marked correlation between the expression of splicing factors and PSI value of AS events in GC patients.

Conclusion

Our findings provide a landscape of AS events and regulatory network in GC, indicating that AS events might serve as prognostic biomarkers and therapeutic targets for GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13690-022-00894-3.

Immunoprognostic model of lung adenocarcinoma and screening of sensitive drugs

Screening of mRNAs and lncRNAs associated with prognosis and immunity of lung adenocarcinoma (LUAD) and used to construct a prognostic risk scoring model (PRS-model) for LUAD. To analyze the differences in tumor immune microenvironment between distinct risk groups of LUAD based on the model classification. The CMap database was also used to screen potential therapeutic compounds for LUAD based on the differential genes between distinct risk groups. he data from the Cancer Genome Atlas (TCGA) database. We divided the transcriptome data into a mRNA subset and a lncRNA subset, and use multiple methods to extract mRNAs and lncRNAs associated with immunity and prognosis. We further integrated the mRNA and lncRNA subsets and the corresponding clinical information, randomly divided them into training and test set according to the ratio of 5:5. Then, we performed the Cox risk proportional analysis and cross-validation on the training set to construct a LUAD risk scoring model. Based on the risk scoring model, patients were divided into distinct risk group. Moreover, we evaluate the prognostic performance of the model from the aspects of Area Under Curve (AUC) analysis, survival difference analysis, and independent prognostic analysis. We analyzed the differences in the expression of immune cells between the distinct risk groups, and also discuss the connection between immune cells and patient survival. Finally, we screened the potential therapeutic compounds of LUAD in the Connectivity Map (CMap) database based on differential gene expression profiles, and verified the compound activity by cytostatic assays. We extracted 26 mRNAs and 74 lncRNAs related to prognosis and immunity by using different screening methods. Two mRNAs (i.e., KLRC3 and RAET1E) and two lncRNAs (i.e., AL590226.1 and LINC00941) and their risk coefficients were finally used to construct the PRS-model. The risk score positions of the training and test set were 1.01056590 and 1.00925190, respectively. The expression of mRNAs involved in model construction differed significantly between the distinct risk population. The one-year ROC areas on the training and test sets were 0.735 and 0.681. There was a significant difference in the survival rate of the two groups of patients. The PRS-model had independent predictive capabilities in both training and test sets. Among them, in the group with low expression of M1 macrophages and resting NK cells, LUAD patients survived longer. In contrast, the monocyte expression up-regulated group survived longer. In the CMap drug screening, three LUAD therapeutic compounds, such as resveratrol, methotrexate, and phenoxybenzamine, scored the highest. In addition, these compounds had significant inhibitory effects on the LUAD A549 cell lines. The LUAD risk score model constructed using the expression of KLRC3, RAET1E, AL590226.1, LINC00941 and their risk coefficients had a good independent prognostic power. The optimal LUAD therapeutic compounds screened in the CMap database: resveratrol, methotrexate and phenoxybenzamine, all showed significant inhibitory effects on LUAD A549 cell lines.

Identification and Characterization of Immunogene-Related Alternative Splicing Patterns and Tumor Microenvironment Infiltration Patterns in Breast Cancer

Alternative splicing (AS) plays a crucial role in tumor development and tumor microenvironment (TME) formation. However, our current knowledge about AS, especially immunogene-related alternative splicing (IGAS) patterns in cancers, remains limited. Herein, we identified and characterized post-transcriptional mechanisms of breast cancer based on IGAS, TME, prognosis, and immuno/chemotherapy. We screened the differentially spliced IGAS events and constructed the IGAS prognostic model (p-values < 0.001, AUC = 0.939), which could be used as an independent prognostic factor. Besides, the AS regulatory network suggested a complex cooperative or competitive relationship between splicing factors and IGAS events, which explained the diversity of splice isoforms. In addition, more than half of the immune cells displayed varying degrees of infiltration in the IGAS risk groups, and the prognostic characteristics of IGAS demonstrated a remarkable and consistent trend correlation with the infiltration levels of immune cell types. The IGAS risk groups showed substantial differences in the sensitivity of immunotherapy and chemotherapy. Finally, IGAS clusters defined by unsupervised cluster analysis had distinct prognostic patterns, suggesting an essential heterogeneity of IGAS events. Significant differences in immune infiltration and unique prognostic capacity of immune cells were also detected in each IGAS cluster. In conclusion, our comprehensive analysis remarkably enhanced the understanding of IGAS patterns and TME in breast cancer, which may help clarify the underlying mechanisms of IGAS in neoplasia and provide clues to molecular mechanisms of oncogenesis and progression.

Switched alternative splicing events as attractive features in lung squamous cell carcinoma

Background

Alternative splicing (AS) plays important roles in transcriptome and proteome diversity. Its dysregulation has a close affiliation with oncogenic processes. This study aimed to evaluate AS-based biomarkers by machine learning algorithms for lung squamous cell carcinoma (LUSC) patients.

Method

The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database were utilized. After data composition balancing, Boruta feature selection and Spearman correlation analysis were used for differentially expressed AS events. Random forests and a nested fivefold cross-validation were applied for lymph node metastasis (LNM) classifier building. Random survival forest combined with Cox regression model was performed for a prognostic model, based on which a nomogram was developed. Functional enrichment analysis and Spearman correlation analysis were also conducted to explore underlying mechanisms. The expression of some switch-involved AS events along with parent genes was verified by qRT-PCR with 20 pairs of normal and LUSC tissues.

Results

We found 16 pairs of splicing events from same parent genes which were strongly related to the splicing switch (intrapair correlation coefficient = − 1). Next, we built a reliable LNM classifier based on 13 AS events as well as a nice prognostic model, in which switched AS events behaved prominently. The qRT-PCR presented consistent results with previous bioinformatics analysis, and some AS events like ITIH5-10715-AT and QKI-78404-AT showed remarkable detection efficiency for LUSC.

Conclusion

AS events, especially switched ones from the same parent genes, could provide new insights into the molecular diagnosis and therapeutic drug design of LUSC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02429-2.

A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma

Background

Alternative splicing (AS) is a gene regulatory mechanism that drives protein diversity. Dysregulation of AS is thought to play an essential role in cancer initiation and development. This study aimed to construct a prognostic signature based on AS and explore the role in the tumor immune microenvironment (TIME) in lung adenocarcinoma.

Methods

We analyzed transcriptome profiling and clinical lung adenocarcinoma data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq. Prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan–Meier survival analyses, and proportional hazards model. The context of TIME in lung adenocarcinoma was also analyzed. Gene and protein expression data of Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A) were obtained from ONCOMINE and Human Protein Atlas. Splicing factor (SF) regulatory networks were visualized.

Results

A total of 19,054 survival-related AS events in lung adenocarcinoma were screened in 1,323 genes. Exon skip (ES) and mutually exclusive exons (ME) exhibited the most and fewest AS events, respectively. Based on AS subtypes, eight AS prognostic signatures were constructed. Patients with high-risk scores were associated with poor overall survival. A nomogram with good validity in prognostic prediction was generated. AUCs of risk scores at 1, 2, and 3 years were 0.775, 0.736, and 0.759, respectively. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. Low-risk patients had a higher StromalScore, ImmuneScore, and ESTIMATEScore. AS-based risk score signature was positively associated with CD8+ T cells. CDKN2A was also found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks.

Conclusion

Taken together, our findings show a clear association between AS and immune cell infiltration events and patient outcome, which could provide a basis for the identification of novel markers and therapeutic targets for lung adenocarcinoma. SF networks provide information of regulatory mechanisms.

The pattern of alternative splicing in lung adenocarcinoma shows novel events correlated with tumorigenesis and immune microenvironment

Lung adenocarcinoma (LUAD) is the leading cause of cancer deaths worldwide due to the lack of early diagnostic markers and specific drugs. Previous studies have shown the association of LUAD growth with aberrant alternative splicing (AS). Herein, clinical data of 535 tumor tissues and 59 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. Each sample was analyzed using the ESTIMATE algorithm; a comparison between higher and lower score groups (stromal or immune) was made to determine the overall- and progression-free survival-related differentially expressed AS (DEAS) events. We then performed unsupervised clustering of these DEASs, followed by determining their relationship with survival rate, immune cells, and the tumor microenvironment (TME). Next, two prognostic signatures were developed using bioinformatics tools to explore the prognosis of cases with LUAD. Five OS- and six PFS-associated DEAS events were implemented to establish a prognostic risk score model. When compared to the high-risk group (HRG), the PFS and OS of the low-risk group (LRG) were found to be considerable. Additionally, a better prognosis was found considerably associated with the ESTIMATE score of the patients as well as immune cells infiltration. Our analysis of AS events in LUAD not only helps to clarify the tumorigenesis mechanism of AS but also provides ideas for revealing potential prognostic biomarkers and therapeutic targets.

Alternative Splicing Events in Immune Infiltration of Lung Adenocarcinoma

BACKGROUND The exact mechanisms of lung adenocarcinoma (LUAD) etiology and pathogenesis remain unclear. MATERIAL AND METHODS In this study, we evaluated alternative splicing (AS) events in LUAD. We separately analyzed LUAD data from The Cancer Genome Atlas (TCGA) database and AS-related feature lists from SpliceSeq to develop risk models for AS events and further validated risk models for AS events. The association between immune-related features and the risk model of AS events was evaluated. RESULTS We found that exon skip (ES) and mutually exclusive exons (ME) exhibited the most and least AS events, respectively. The risk score and stage of LUAD patients were strongly associated with prognosis and were independent influences on the prognosis of LUAD. The pathological stage (stage, T, N) and risk model were incorporated to construct a column line graph with better predictive ability. Risk models were associated with tumor microenvironment, and higher risk score values were associated with higher M2 macrophage content and lower M0 macrophage and helper T lymphocyte content. The correlation between core genes and immunity was further assessed by analyzing differentially-expressed genes between risk models. These results suggest an association between the level of risk for AS events and the density of immune cell infiltration. CONCLUSIONS Our findings suggest a clear association between AS risk model and patient prognosis, and was performed to confirm the biological relationship between AS and immunity.

Comprehensive analysis of an immune infiltrate-related competitive endogenous RNA network reveals potential prognostic biomarkers for non-small cell lung cancer

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA-miRNA-mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease

Background

Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST.

Methods

The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction.

Results

A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively.

Conclusions

This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

Prognostic Significance of Alternative Splicing Genes in Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

Background

Alternative splicing (AS) acts on many tumors and its relationship with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) needs to be researched.

Methods

RNA sequencing data and clinical information of CESC cohorts were obtained from the Cancer Genome Atlas (TCGA) and SpliceSeq was used to analyze the splicing profile of mRNA in CESC. UpSetR displayed the intersections among AS events and univariate analysis chose survival-associated AS and splicing factor (SF) genes. Functional analysis was operated on Enrichr, STRING database and MCODE analysis were used to evaluate protein-protein interaction (PPI) information. LASSO and multivariate analysis constructed prognostic model and risk analysis of tumor infiltrating immune cells was also conducted.

Results

A total of 402 AS-generated genes were found to be associated with CESC prognosis. Functional analysis showed that Golgi to lysosome transport was enriched. PPI network suggested that UBA52 was most functional. Dendritic cells activated, dendritic cells resting, macrophages M0, mast cells resting, T cells CD4 memory activated and T cells CD8 were most correlative with the risk score.

Conclusion

SFs and AS events can directly or indirectly affect the prognosis of CESC patients and this study identified SNRPA and CELF2 as two CESC-engaged SFs.

Maximizing the Utility of Transcriptomics Data in Inflammatory Skin Diseases

Inflammatory skin diseases are induced by disorders of the host defense system of the skin, which is composed of a barrier, innate and acquired immunity, as well as the cutaneous microbiome. These disorders are characterized by recurrent cutaneous lesions and intense itch, which seriously affecting life quality of people across all ages and ethnicities. To elucidate molecular factors for typical inflammatory skin diseases (such as psoriasis and atopic dermatitis), transcriptomic profiling assays have been largely performed. Additionally, single-cell RNA sequencing (scRNA-seq) as well as spatial transcriptomic profiling have revealed multiple potential translational targets and offered guides to improve diagnosis and treatment strategies for inflammatory skin diseases. High-throughput transcriptomics data has shown unprecedented power to disclose the complex pathophysiology of inflammatory skin diseases. Here, we will summarize discoveries from transcriptomics data and discuss how to maximize the transcriptomics data to propel the development of diagnostic biomarkers and therapeutic targets in inflammatory skin diseases.

NOVA1-Mediated SORBS2 Isoform Promotes Colorectal Cancer Migration by Activating the Notch Pathway

Background: Gene expression and alternative splicing (AS) can promote cancer development via complex mechanisms. We aimed to identify and verify the hub AS events and splicing factors associated with the progression of colorectal cancer (CRC).

Methods: RNA-Seq data, clinical data, and AS events of 590 CRC samples were obtained from the TCGA and TCGASpliceSeq databases. Cox univariable and multivariable analyses, KEGG, and GO pathway analyses were performed to identify hub AS events and splicing factor/spliceosome genes, which were further validated in five CRCs.

Results: In this study, we first compared differentially expressed genes and gene AS events between normal and tumor tissues. Differentially expressed genes were different from genes with differentially expressed AS events. Prognostic analysis and co-expression network analysis of gene expression and gene AS events were conducted to screen five hub gene AS events involved in CRC progression: EPB41L2, CELF2, TMEM130, VCL, and SORBS2. Using qRT-PCR, we also verified that the gene AS events SORBS2 were downregulated in tumor tissue, and gene AS events EPB41L2, CELF2, TMEM130, and VCL were upregulated in tumor tissue. The genes whose mRNA levels were significantly related to the five hub gene AS events were significantly enriched in the GO term of cell division and Notch signaling pathway. Further coexpression of gene AS events and alternative splicing factor genes revealed NOVA1 as a crucial factor regulating the hub gene AS event expression in CRC. Through in vitro experiments, we found that NOVA1 inhibited gene AS event SORBS2, which induced the migration of CRC cells via the Notch pathway.

Conclusion: Integrated analysis of gene expression and gene AS events and further experiments revealed that NOVA1-mediated SORBS2 promoted the migration of CRC, indicating its potential as a therapeutic target.

Comprehensive Combined Proteomics and Genomics Analysis Identifies Prognostic Related Transcription Factors in Breast Cancer and Explores the Role of DMAP1 in Breast Cancer

Transcription factors (TFs) are important for regulating gene transcription and are the hallmark of many cancers. The identification of breast cancer TFs will help in developing new diagnostic and individualized cancer treatment tools. In this study, we used quantitative proteomic analyses of nuclear proteins and massive transcriptome data to identify enriched potential TFs and explore the possible role of the transcription factor DMAP1 in breast cancer. We identified 13 prognostic-related TFs and constructed their regulated genes, alternative splicing (AS) events, and splicing factor (SF) regulation networks. DMAP1 was reported less in breast cancer. The expression of DMAP1 decreased in breast cancer tumors compared with normal tissues. The poor prognosis of patients with low DMAP1 expression may relate to the activated PI3K/Akt signaling pathway, as well as other cancer-relevant pathways. This may be due to the low methylation and high expression of these pathway genes and the fact that such patients show more sensitivity to some PI3K/Akt signaling pathway inhibitors. The high expression of DMAP1 was correlated with low immune cell infiltration, and the response to immune checkpoint inhibitor treatment in patients with high DMAP1 expression was low. Our study identifies some transcription factors that are significant for breast cancer progression, which can be used as potential personalized prognostic markers in the future.

hnRNPA2B1 Promotes Colon Cancer Progression via the MAPK Pathway

HNRNPA2B1, an RNA-binding protein, plays a key role in primary microRNA processing, alternative splicing, mRNA metabolism and transport. Interestingly, hnRNPA2B1 also works as an N6-methyladenosine (m6A) reader and is critical during tumorigenesis of various tissue types. However, its role in colon cancer is still unclear. In this study, we aimed to elucidate the biological functions of hnRNPA2B1 and to explore its underlying mechanisms in colon cancer. We examined the expression of hnRNPA2B1 in Oncomine and TCGA databases. Then verified the findings in colon cancer cells and clinical samples with western blotting and immunohistochemistry (IHC). We used CRISPR/Cas9 directed gene editing to knockout hnRNPA2B1 expression in human colon cancer cell line SW480 and HCT-116 and carried out both in vivo and in vitro experiments. The results were further confirmed by RNA-seq analyses. We found that hnRNPA2B1 significantly promoted colon cancer cell proliferation both in vitro and in vivo, while knockout of hnRNPA2B1 induced apoptosis and cell cycle arrest in SW480. RNA-seq analyses revealed that the ERK/MAPK pathway was activated by hnRNPA2B1 upregulation. In addition, both hnRNPA2B1 and MAPK pathway were activated in clinical colon cancer specimens and positively correlated. Mechanistically, hnRNPA2B1 appeared to be an upstream regulator of the ERK/MAPK pathway and inhibition of MAPK signaling blocked the effects of hnRNPA2B1. Taken together, our data demonstrated that the RNA-binding protein hnRNPA2B1 promotes cell proliferation and regulates cell cycle and apoptosis of human colon cancer by activating the ERK/MAPK signaling, which may provide a new insight into the development of hnRNPA2B1 as a potential therapeutic target for treatment of colon cancer.

Development and Validation of a Prognostic Autophagy-Related Gene Pair Index Related to Tumor-Infiltrating Lymphocytes in Early-Stage Lung Adenocarcinoma

The role of autophagy in lung cancer is context-dependent and complex. Recent studies have reported the important role of autophagy in tumor immune escape. However, the association between autophagy and tumor-infiltrating lymphocytes (TILs) in early-stage lung adenocarcinoma (LUAD) remains unclear. In this study, we aimed to develop and validate the autophagy-related gene pair index (ATGPI) and autophagy clinical prognostic index (ACPI) in multiple LUAD cohorts, including The Cancer Genome Atlas (TCGA) cohort, Gene Expression Omnibus cohorts, and one cohort from Union Hospital, Wuhan (UH cohort), using a Cox proportional hazards regression model with the least absolute shrinkage and selection operator. Multivariate Cox regression analysis demonstrated that there was a significant difference in overall survival (OS) between patients with high and low ATGPI in the testing [hazard ratio (HR) = 1.97; P < 0.001] and TCGA validation (HR = 2.25; P < 0.001) cohorts. Time-dependent receiver operating characteristic curve analysis was also performed. We found that high ATGPI could accurately identify patients with early-stage LUAD with shorter OS, with the areas under the curve of 0.703 and 0.676 in the testing and TCGA validation cohorts, respectively. Concordance index (C-index) was used to evaluate the efficiency of ATGPI and ACPI. The C-index of ACPI was higher than that of ATGPI in the testing (0.71 vs. 0.66; P < 0.001), TCGA validation (0.69 vs. 0.65; P = 0.028), and UH (0.80 vs. 0.70; P = 0.015) cohorts. TIL analysis demonstrated that the proportions of tumor-infiltrating CD4⁺ T cells were lower in the high-ATGPI group than in the low-ATGPI group in both the TCGA validation and UH cohorts. These results indicate the potential clinical use of ATG signatures which are associated with TILs, in identifying patients with early-stage LUAD with different OS.

Comprehensive analysis of aberrant alternative splicing related to carcinogenesis and prognosis of papillary thyroid cancer

As a key mechanism, alternative splicing (AS) plays a role in the cancer initiation and development. However, in papillary thyroid cancer (PTC), data for the comprehensive AS event profile and its clinical implications are lacking. Herein, a genome-wide AS event profiling using RNA-Seq data and its correlation with matched clinical information was performed using a 389 PTC patient cohort from the project of The Cancer Genome Atlas (TCGA). We identified 1,925 cancer-associated AS events (CASEs) by comparing paired tumors and neighboring healthy tissues. Parent genes with CASEs remarkably enriched in the pathways were linked with carcinogenesis, such as P53, KRAS, IL6-JAK-STAT3, apoptosis, and MYC signaling. The regulatory networks of AS implied an obvious correlation between the expression of splicing factor and CASE. We identified eight CASEs as predictors for overall survival (OS) and disease-free survival (DFS). The established risk score model based on DFS-associated CASEs successfully predicted the prognosis of PTC patients. From the unsupervised clustering analysis results, it is found that different clusters based on AS correlated with prognosis, molecular features, and immune characteristics. Taken together, the comprehensive genome-wide AS landscape analysis in PTC showed new AS events linked with tumorigenesis and prognosis, which provide new insights for clinical monitoring and therapy for PTC.

Genome-Wide Profiling of Alternative Splicing Signatures Associated with Prognosis and Immune Microenvironment of Hepatocellular Carcinoma

Background

The potential roles of alternative splicing (AS) in HCC remain unknown. This study aimed to identify AS signatures associated with the prognosis that influence the immune microenvironment of HCC.

Material/Methods

The SpliceSeq tool was employed for genome-wide profiling of 7 AS events in 361 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature was built by integrating Cox regression and the least absolute shrinkage and selection operator (LASSO). The support vector machine (SVM) and receiver operating characteristic curve (ROC) were employed to analyze the AS events in the signatures to discriminate the immune microenvironment.

Results

There were 3546 AS events highly linked to the survival of patients with HCC. The AS signature could effectively stratify HCC patients. Clustering analysis revealed 3 different immune clusters characterized with significantly different prognoses and were significantly correlated with AS signatures. The AS events in the final prognostic signature classified the immune cluster with an average AUC of the ROC (0.88). Moreover, a potential regulatory network of splicing events in HCC is presented.

Conclusions

We established the prognostic signature based on AS, which can effectively stratify HCC patients and predict the immune subtypes. Moreover, novel RNA splicing patterns and splicing-regulatory networks involved in HCC were discovered.

Alternative RNA Splicing-The Trojan Horse of Cancer Cells in Chemotherapy

Almost all transcribed human genes undergo alternative RNA splicing, which increases the diversity of the coding and non-coding cellular landscape. The resultant gene products might have distinctly different and, in some cases, even opposite functions. Therefore, the abnormal regulation of alternative splicing plays a crucial role in malignant transformation, development, and progression, a fact supported by the distinct splicing profiles identified in both healthy and tumor cells. Drug resistance, resulting in treatment failure, still remains a major challenge for current cancer therapy. Furthermore, tumor cells often take advantage of aberrant RNA splicing to overcome the toxicity of the administered chemotherapeutic agents. Thus, deciphering the alternative RNA splicing variants in tumor cells would provide opportunities for designing novel therapeutics combating cancer more efficiently. In the present review, we provide a comprehensive outline of the recent findings in alternative splicing in the most common neoplasms, including lung, breast, prostate, head and neck, glioma, colon, and blood malignancies. Molecular mechanisms developed by cancer cells to promote oncogenesis as well as to evade anticancer drug treatment and the subsequent chemotherapy failure are also discussed. Taken together, these findings offer novel opportunities for future studies and the development of targeted therapy for cancer-specific splicing variants.

Identification of survival-related alternative splicing signatures in acute myeloid leukemia

Aberrant RNA alternative splicing (AS) variants play critical roles in tumorigenesis and prognosis in human cancers. Here, we conducted a comprehensive profiling of aberrant AS events in acute myeloid leukemia (AML). RNA AS profile, including seven AS types, and the percent spliced in (PSI) value for each patient were generated by SpliceSeq using RNA-seq data from TCGA. Univariate followed by multivariate Cox regression analysis were used to identify survival-related AS events and develop the AS signatures. A nomogram was developed, and its predictive efficacy was assessed. About 27,892 AS events and 3,178 events were associated with overall survival (OS) after strict filtering. Parent genes of survival-associated AS events were mainly enriched in leukemia-associated processes including chromatin modification, autophagy, and T-cell receptor signaling pathway. The 10 AS signature based on seven types of AS events showed better efficacy in predicting OS of patients than those built on a single AS event type. The area under curve (AUC) value of the 10 AS signature for 3-year OS was 0.91. Gene set enrichment analysis (GSEA) confirmed that these survival-related AS events contribute to AML progression. Moreover, the nomogram showed good predictive performance for patient's prognosis. Finally, the correlation network of AS variants with splicing factor genes found potential important regulatory genes in AML. The present study presented a systematic analysis of survival-related AS events and developed AS signatures for predicting the patient’s survival. Further studies are needed to validate the signatures in independent AML cohorts and might provide a promising perspective for developing therapeutic targets.