Hypoxia-associated alternative splicing signature in lung adenocarcinoma

LncRNA H19 promotes tumor angiogenesis in smokers by targeting anti-angiogenic miRNAs

A key concept in drug discovery is the identification of candidate therapeutic targets such as long noncoding RNAs (lncRNAs) because of their extensive involvement in neoplasms, and impressionability by smoking. Induced by exposure to cigarette smoke, lncRNA H19 targets and inactivates miR-29, miR-30a, miR-107, miR-140, miR-148b, miR-199a and miR-200, which control the rate of angiogenesis by inhibiting BiP, DLL4, FGF7, HIF1A, HIF1B, HIF2A, PDGFB, PDGFRA, VEGFA, VEGFB, VEGFC, VEGFR1, VEGFR2 and VEGFR3. Nevertheless, these miRNAs are often dysregulated in bladder cancer, breast cancer, colorectal cancer, glioma, gastric adenocarcinoma, hepatocellular carcinoma, meningioma, non-small-cell lung carcinoma, oral squamous cell carcinoma, ovarian cancer, prostate adenocarcinoma and renal cell carcinoma. As such, the present perspective article seeks to establish an evidence-based hypothetical model of how a smoking-related lncRNA known as H19 might aggravate angiogenesis by interfering with miRNAs that would otherwise regulate angiogenesis in a nonsmoking individual.

Novel, alternative splicing signature to detect lymph node metastasis in prostate adenocarcinoma with machine learning

Background

The presence of lymph node metastasis leads to a poor prognosis for prostate cancer (Pca). Recently, many studies have indicated that gene signatures may be able to predict the status of lymph nodes. The purpose of this study is to probe and validate a new tool to predict lymph node metastasis (LNM) based on alternative splicing (AS).

Methods

Gene expression profiles and clinical information of prostate adenocarcinoma cohort were retrieved from The Cancer Genome Atlas (TCGA) database, and the corresponding RNA-seq splicing events profiles were obtained from the TCGA SpliceSeq. Limma package was used to identify the differentially expressed alternative splicing (DEAS) events between LNM and non-LNM groups. Eight machine learning classifiers were built to train with stratified five-fold cross-validation. SHAP values was used to explain the model.

Results

333 differentially expressed alternative splicing (DEAS) events were identified. Using correlation filter and the least absolute shrinkage and selection operator (LASSO) method, a 96 AS signature was identified that had favorable discrimination in the training set and validated in the validation set. The linear discriminant analysis (LDA) was the best classifier after 100 iterations of training. The LDA classifier was able to distinguish between LNM and non-LNM with an area under the receiver operating curve of 0.962 ± 0.026 in the training set (D1 = 351) and 0.953 in the validation set (D2 = 62). The decision curve analysis plot proved the clinical application of the AS-based model.

Conclusion

Machine learning combined with AS data could robustly distinguish between LNM and non-LNM in Pca.

Alternative splicing signature of alveolar type II epithelial cells of Tibetan pigs under hypoxia-induced

Alternative splicing (AS) allows the generation of multiple transcript variants from a single gene and affects biological processes by generating protein diversity in organisms. In total, 41,642 AS events corresponding to 9,924 genes were identified, and SE is the most abundant alternatively spliced type. The analysis of functional categories demonstrates that alternatively spliced differentially expressed genes (DEGs) were enriched in the MAPK signaling pathway and hypoxia-inducible factor 1 (HIF-1) signaling pathway. Proteoglycans in cancer between the normoxic (21% O2, TN and LN) and hypoxic (2% O2, TL and LL) groups, such as SLC2A1, HK1, HK2, ENO3, and PFKFB3, have the potential to rapidly proliferate alveolar type II epithelial (ATII) cells by increasing the intracellular levels of glucose and quickly divert to anabolic pathways by glycolysis intermediates under hypoxia. ACADL, EHHADH, and CPT1A undergo one or two AS types with different frequencies in ATII cells between TN and TL groups (excluding alternatively spliced DEGs shared between normoxic and hypoxic groups), and a constant supply of lipids might be obtained either from the circulation or de novo synthesis for better growth of ATII cells under hypoxia condition. MCM7 and MCM3 undergo different AS types between LN and LL groups (excluding alternatively spliced DEGs shared between normoxic and hypoxic groups), which may bind to the amino-terminal PER-SIM-ARNT domain and the carboxyl terminus of HIF-1α to maintain their stability. Overall, AS and expression levels of candidate mRNAs between Tibetan pigs and Landrace pigs revealed by RNA-seq suggest their potential involvement in the ATII cells grown under hypoxia conditions.

Post-transcriptional regulation through alternative splicing in the lungs of Tibetan pigs under hypoxia

In multicellular organisms, alternative splicing (AS) is central to the regulation of multiple biological processes. To further elucidate the adaptive strategy of AS in the lungs of Tibetan pigs in response to hypoxia, we identified and analyzed five basic AS types and 59,930 AS events in 18,179 genes. We found that approximately 65.10% of the total expressed genes underwent AS in the lungs of Tibetan pigs at a high altitude (TH). The frequencies of AS events were similar among the different groups (5.06-5.30 events in each gene on average). Skipped exons (SEs) were the predominant type of AS event, followed by mutually exclusive exons (MXEs), alternative 3' splice sites (A3SSs) and alternative 5' splice sites (A5SSs). Retained introns (RIs), the remaining type of AS event, showed lower frequencies. Further comparison analysis of differentially expressed genes (DEGs) and differentially spliced genes (DSGs) identified 2,209 differential splicing events in the above 18,000 expressed genes, including 918 increased and 1,291 decreased splicing events between the TH and Tibetan pigs at a low altitude (TL) groups. We identified 227 hypoxia-related genes involved in lung development that were differentially regulated through AS. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis clearly identified many DEGs and DSGs at high or low altitude. Seven pathways in the top 20 enriched KEGG terms overlapped for the DEGs and DSGs, including the chemokine signaling pathway, B cell receptor signaling pathway, and cytokine-cytokine receptor interaction, which exert many immunoregulatory and inflammatory actions critical to the lung under hypoxia. Twelve pathways overlapped in hypoxic DEGs and DSGs and included antigen processing, presentation and biosynthesis. ​GO analysis of the DEGs and DSGs among the four groups showed that numerous GO terms were enriched in the biological category, and the proportion of genes with downregulated expression was greater among 227 hypoxic genes than that of all genes. The results suggest that AS plays an essential role in the regulation of gene expression during hypoxia and that numerous genes involved in lung development are differentially regulated through AS.

The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes?

Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.

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.

Scouting for common genes in the heterogenous hypoxic tumor microenvironment and their validation in glioblastoma

Investigating the therapeutic and prognostic potential of genes in the heterogeneous hypoxic niche of glioblastoma. We have analyzed RNA expression of U87MG cells cultured in hypoxia compared to normoxia. Common differentially expressed genes (DEGs) from GSE45301 and GSE18494 and their functional enrichment was performed using MetaScape and PANTHER. Hub genes and their ontology were identified using MCode cytoHubba and ClueGO and validated with GlioVis, Oncomine, HPA and PrognoScan. Using the GEO2R analysis of GSE45301 and GSE18494 datasets, we have found a total of 246 common DEGs (180 upregulated and 66 downregulated) and identified 2 significant modules involved in ribosome biogenesis and TNF signaling. Meta-analysis of key genes of each module in cytoHubba identified 17 hub genes (ATF3, BYSL, DUSP1, EGFR, JUN, ETS1, LYAR, NIP7, NOLC1, NOP2, NOP56, PNO1, RRS1, TNFAIP3, TNFRSF1B, UTP15, VEGFA). Of the 17 hub genes, ATF3, BYSL, EGFR, JUN, NIP7, NOLC1, PNO1, RRS1, TNFAIP3 and VEGFA were identified as hypoxia signatures associated with poor prognosis in Glioma. Ribosome biogenesis emerged as a vital contender of possible therapeutic potential with BYSL, NIP7, NOLC1, PNO1 and RRS1 showing prognostic value.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02987-2.