TENET 2.0: Identification of key transcriptional regulators and enhancers in lung adenocarcinoma

Integration of gene expression and DNA methylation data across different experiments

Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to solve it. Here, we present INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples. To enable integration, INTEND learns a predictive model between the two omics by training on multi-omic data measured on the same set of samples. In comprehensive testing on 11 TCGA (The Cancer Genome Atlas) cancer datasets spanning 4329 patients, INTEND achieves significantly superior results compared with four state-of-the-art integration algorithms. We also demonstrate INTEND's ability to uncover connections between DNA methylation and the regulation of gene expression in the joint analysis of two lung adenocarcinoma single-omic datasets from different sources. INTEND's data-driven approach makes it a valuable multi-omic data integration tool. The code for INTEND is available at https://github.com/Shamir-Lab/INTEND.

Measuring biological age using a functionally interpretable multi-tissue RNA clock

The quantification of the biological age of cells yields great promises for accelerating the discovery of novel rejuvenation strategies. Here, we present MultiTIMER, the first multi-tissue aging clock that measures the biological, rather than chronological, age of cells from their transcriptional profiles by evaluating key cellular processes. We applied MultiTIMER to more than 70,000 transcriptional profiles and demonstrate that it accurately responds to cellular stressors and known interventions while informing about dysregulated cellular functions.

Genomic, epigenomic, and transcriptomic signatures of prostate cancer between African American and European American patients

Prostate cancer is the second most common cancer in men in the United States, and racial disparities are greatly observed in the disease. Specifically, African American (AA) patients have 60% higher incidence and mortality rates, in addition to higher grade and stage prostate tumors, than European American (EA) patients. In order to narrow the gap between clinical outcomes for these two populations, genetic and molecular signatures contributing to this disparity have been characterized. Over the past decade, profiles of prostate tumor samples from different ethnic groups have been developed using molecular and functional assays coupled with next generation sequencing or microarrays. Comparative genome-wide analyses of genomic, epigenomic, and transcriptomic profiles from prostate tumor samples have uncovered potential race-specific mutations, copy number alterations, DNA methylation, and gene expression patterns. In this study, we reviewed over 20 published studies that examined the aforementioned molecular contributions to racial disparities in AA and EA prostate cancer patients. The reviewed genomic studies revealed mutations, deletions, amplifications, duplications, or fusion genes differentially enriched in AA patients relative to EA patients. Commonly reported genomic alterations included mutations or copy number alterations of FOXA1, KMT2D, SPOP, MYC, PTEN, TP53, ZFHX3, and the TMPRSS2-ERG fusion. The reviewed epigenomic studies identified that CpG sites near the promoters of PMEPA1, RARB, SNRPN, and TIMP3 genes were differentially methylated between AA and EA patients. Lastly, the reviewed transcriptomic studies identified genes (e.g. CCL4, CHRM3, CRYBB2, CXCR4, GALR1, GSTM3, SPINK1) and signaling pathways dysregulated between AA and EA patients. The most frequently found dysregulated pathways were involved in immune and inflammatory responses and neuroactive ligand signaling. Overall, we observed that the genomic, epigenomic, and transcriptomic alterations evaluated between AA and EA prostate cancer patients varied between studies, highlighting the impact of using different methods and sample sizes. The reported genomic, epigenomic, and transcriptomic alterations do not only uncover molecular mechanisms of tumorigenesis but also provide researchers and clinicians valuable resources to identify novel biomarkers and treatment modalities to improve the disparity of clinical outcomes between AA and EA patients.

Lung Cancer Gene Regulatory Network of Transcription Factors Related to the Hallmarks of Cancer

The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan-Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients' survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer.

Characterizing chromatin interactions of regulatory elements and nucleosome positions, using Hi-C, Micro-C, and promoter capture Micro-C

BACKGROUND

Regulatory elements such as promoters, enhancers, and insulators interact each other to mediate molecular processes. To capture chromatin interactions of regulatory elements, 3C-derived methods such as Hi-C and Micro-C are developed. Here, we generated and analyzed Hi-C, Micro-C, and promoter capture Micro-C datasets with different sequencing depths to study chromatin interactions of regulatory elements and nucleosome positions in human prostate cancer cells.

RESULTS

Compared to Hi-C, Micro-C identifies more high-resolution loops, including ones around structural variants. By evaluating the effect of sequencing depth, we revealed that more than 2 billion reads of Micro-C are needed to detect chromatin interactions at 1 kb resolution. Moreover, we found that deep-sequencing identifies additional long-range loops that are longer than 1 Mb in distance. Furthermore, we found that more than 50% of the loops are involved in insulators while less than 10% of the loops are promoter-enhancer loops. To comprehensively capture chromatin interactions that promoters are involved in, we performed promoter capture Micro-C. Promoter capture Micro-C identifies loops near promoters with a lower amount of sequencing reads. Sequencing of 160 million reads of promoter capture Micro-C resulted in reaching a plateau of identifying loops. However, there was still a subset of promoters that are not involved in loops even after deep-sequencing. By integrating Micro-C with NOMe-seq and ChIP-seq, we found that active promoters involved in loops have a more accessible region with lower levels of DNA methylation and more highly phased nucleosomes, compared to active promoters that are not involved in loops.

CONCLUSION

We determined the required sequencing depth for Micro-C and promoter capture Micro-C to generate high-resolution chromatin interaction maps and loops. We also investigated the effect of sequencing coverage of Hi-C, Micro-C, and promoter capture Micro-C on detecting chromatin loops. Our analyses suggest the presence of distinct regulatory element groups, which are differently involved in nucleosome positions and chromatin interactions. This study does not only provide valuable insights on understanding chromatin interactions of regulatory elements, but also present guidelines for designing research projects on chromatin interactions among regulatory elements.

Identification of the Transcriptional Regulatory Role of RUNX2 by Network Analysis in Lung Cancer Cells

The use of a new bioinformatics pipeline allowed the identification of deregulated transcription factors (TFs) coexpressed in lung cancer that could become biomarkers of tumor establishment and progression. A gene regulatory network (GRN) of lung cancer was created with the normalized gene expression levels of differentially expressed genes (DEGs) from the microarray dataset GSE19804. Moreover, coregulatory and transcriptional regulatory network (TRN) analyses were performed for the main regulators identified in the GRN analysis. The gene targets and binding motifs of all potentially implicated regulators were identified in the TRN and with multiple alignments of the TFs' target gene sequences. Six transcription factors (E2F3, FHL2, ETS1, KAT6B, TWIST1, and RUNX2) were identified in the GRN as essential regulators of gene expression in non-small-cell lung cancer (NSCLC) and related to the lung tumoral process. Our findings indicate that RUNX2 could be an important regulator of the lung cancer GRN through the formation of coregulatory complexes with other TFs related to the establishment and progression of lung cancer. Therefore, RUNX2 could become an essential biomarker for developing diagnostic tools and specific treatments against tumoral diseases in the lung after the experimental validation of its regulatory function.

Characterizing and Targeting Genes Regulated by Transcription Factor MYBL2 in Lung Adenocarcinoma Cells

Overexpression of MYBL2 is associated with poor survival of lung adenocarcinoma patients, but the molecular mechanism by which it regulates transcription and carcinogenesis has not yet been elucidated. In this study, we performed ChIP-seq using an MYBL2-targeted antibody and discovered that MYBL2 primarily binds to the promoters of highly expressed genes in lung adenocarcinoma cells. Using a knockdown experiment of MYBL2 and global transcriptome profiling, we identified that over a thousand genes are dysregulated by MYBL2, and MYBL2 acts as a transcriptional activator in lung adenocarcinoma cells. Moreover, we revealed that the binding sites of FOXM1 are largely shared with MYBL2 binding sites, and genes involved in cell cycle phase transitions are regulated by these transcription factors. We furthermore investigated the effect of a previously reported FOXM1 inhibitor, FDI-6, in lung adenocarcinoma cells. We demonstrated that FDI-6 decreases the proliferation of lung adenocarcinoma cells and inhibits the activities of FOXM1 as well as MYBL2. Moreover, we found that genes involved in cell death and cell cycle are inhibited by FDI-6. Overall, our findings suggest that MYBL2 and FOXM1 activate cell cycle genes together, acting as oncogenic transcription factors in lung adenocarcinoma cells, and they are potential treatment targets for the disease.

The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing

A thorough interrogation of the immune landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in non-small-cell lung cancer (NSCLC) patients. Single-cell RNA sequencing (scRNA-seq) techniques have prompted the opportunity to dissect the distinct immune signatures between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of NSCLC. Here, we performed scRNA-seq on 72,475 immune cells from 40 samples of tumor and matched adjacent normal tissues spanning 19 NSCLC patients, and drew a systematic immune cell transcriptome atlas. Joint analyses of the distinct cellular compositions, differentially expressed genes (DEGs), cell–cell interactions, pseudotime trajectory, transcriptomic factors and prognostic factors based on The Cancer Genome Atlas (TCGA), revealed the central roles of cytotoxic and effector T and NK cells and the distinct functional macrophages (Mφ) subtypes in the immune microenvironment heterogeneity between LUAD and LUSC. The dominant subtype of Mφ was FABP4-Mφ in LUAD and SPP1-Mφ in LUSC. Importantly, we identified a novel lymphocyte-related Mφ cluster, which we named SELENOP-Mφ, and further established its antitumor role in both types, especially in LUAD. Our comprehensive depiction of the immune heterogeneity and definition of Mφ clusters could help design personalized treatment for lung cancer patients in clinical practice.

Comprehensive Analysis of ABCA Family Members in Lung Adenocarcinoma with Prognostic Values

ATP-binding cassette subfamily A (ABCA) has received wide recognition because it possesses the capacity to translocate its derivatives, xenobiotics, vitamins, and cholesterol across biological membranes. Some ABCA members have causative relevance to inborn diseases, and a number of studies have explored their functions in cancer progression and metastasis. Here, we explored the interrelation between ABCA genes and lung adenocarcinoma (LUAD). We specified the expression and functions of ABCA members in LUAD using the GEPIA, GEO, Human Protein Atlas, UALCAN, TIMER, and Kaplan-Meier Plotter databases. ABCA5, ABCA6, ABCA8, ABCA9, and ABCA10 were found to be significantly less expressed in LUAD and correlated with TP53 mutation in patients with LUAD. Furthermore, ABCA5, ABCA6, and ABCA8 were relevant to overall survival of patients with LUAD. In conclusion, this study showed that ABCA members may be related to the TP53 mutation of LUAD. Moreover, it may serve as a potential marker for the prognosis of LUAD.

Bioinformatics and System Biology Approach to Reveal the Interaction Network and the Therapeutic Implications for Non-Small Cell Lung Cancer Patients With COVID-19

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the leading cause of coronavirus disease-2019 (COVID-19), is an emerging global health crisis. Lung cancer patients are at a higher risk of COVID-19 infection. With the increasing number of non-small-cell lung cancer (NSCLC) patients with COVID-19, there is an urgent need of efficacious drugs for the treatment of COVID-19/NSCLC.

Methods: Based on a comprehensive bioinformatic and systemic biological analysis, this study investigated COVID-19/NSCLC interactional hub genes, detected common pathways and molecular biomarkers, and predicted potential agents for COVID-19 and NSCLC.

Results: A total of 122 COVID-19/NSCLC interactional genes and 21 interactional hub genes were identified. The enrichment analysis indicated that COVID-19 and NSCLC shared common signaling pathways, including cell cycle, viral carcinogenesis, and p53 signaling pathway. In total, 10 important transcription factors (TFs) and 44 microRNAs (miRNAs) participated in regulations of 21 interactional hub genes. In addition, 23 potential candidates were predicted for the treatment of COVID-19 and NSCLC.

Conclusion: This study increased our understanding of pathophysiology and screened potential drugs for COVID-19 and NSCLC.

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

Methylation of cytosines on DNA is a prominent modification associated with gene expression regulation. Aberrant DNA methylation patterns have recurrently been linked to dysregulation of the regulatory program in cancer cells. To shed light on the underlying molecular mechanism driving this process, we hypothesised that aberrant methylation patterns could be controlled by the binding of specific transcription factors (TFs) across cancer types. By combining DNA methylation arrays and gene expression data with TF binding sites (TFBSs), we explored the interplay between TF binding and DNA methylation in 19 cancer types. We performed emQTL (expression–methylation quantitative trait loci) analyses independently in each cancer type and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding sites in at least 2 cancer types. The 13 TFs are mainly associated with local demethylation and are enriched for pioneer function, suggesting a specific role for these TFs in modulating chromatin structure and transcription in cancer patients. Furthermore, we confirmed that de novo methylation is precluded across cancers at CpGs lying in genomic regions enriched for TF binding signatures associated with SP1, CTCF, NRF1, GABPA, KLF9, and/or YY1. The modulation of DNA methylation associated with TF binding was observed at cis-regulatory regions controlling immune- and cancer-associated pathways, corroborating that the emQTL signals were derived from both cancer and tumor-infiltrating cells. As a case example, we experimentally confirmed that FOXA1 knock-down is associated with higher methylation in regions bound by FOXA1 in breast cancer MCF-7 cells. Finally, we reported physical interactions between FOXA1 with TET1 and TET2 both in an in vitro setup and in vivo at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer cells.

Pan-cancer predictions of transcription factors mediating aberrant DNA methylation

BACKGROUND

Aberrant DNA methylation is a hallmark of cancer cells. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors initially thought to be repressed from binding by DNA methylation, have recently emerged as being able to shape DNA methylation patterns.

RESULTS

Here, we integrated the massive amount of data available from The Cancer Genome Atlas to predict transcription factors driving aberrant DNA methylation in 13 cancer types. We identified differentially methylated regions between cancer and matching healthy samples, searched for transcription factor motifs enriched in those regions and selected transcription factors with corresponding changes in gene expression. We predict transcription factors known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer. We also predict transcription factors that lead to hyper-methylated regions upon transcription factor loss such as EGR1 in several cancer types. Finally, we validate that FOXA1 and GATA3 mediate hypo-methylated regions in breast cancer cells.

CONCLUSION

Our work highlights the importance of some transcription factors as upstream regulators shaping DNA methylation patterns in cancer.

LncRNA MAFG-AS1 Promotes Lung Adenocarcinoma Cell Migration and Invasion by Targeting miR-3196 and Regulating SOX12 Expression

Lung adenocarcinoma (LUAD) patients exhibit poor prognosis, primarily due to metastasis. Emerging studies have demonstrated that long noncoding RNAs (lncRNAs) play critical roles in cancer progression and metastasis besides their physiological function. Here, we investigated the potential role of lncRNA MAF BZIP Transcription Factor G Antisense RNA 1 (MAFG-AS1) in LUAD metastasis by analyzing its expression in The Cancer Genome Atlas (TCGA) LUAD database, and its function in LUAD using in vitro and in vivo experiments. We performed bioinformatics analysis, western blotting, dual-luciferase reporter gene assay, RNA immunoprecipitation (RIP), and rescue assays to reveal the molecular mechanisms underlying MAFG-AS1 function. We observed augmented expression of MAFG-AS1 in LUAD tissues compared with normal adjacent tissues, and its association with poor prognosis. Furthermore, MAFG-AS1 overexpression promoted LUAD cell migration, proliferation, invasion, and epithelial mesenchymal transition (EMT). Besides, MAFG-AS1 also targeted miR-3196 directly by acting as an endogenous sponge, thereby rescuing the inhibition of SOX12, a target of miR-3196. Thus, the rescue assays demonstrated that MAFG-AS1 promotes cell migration, invasion, and EMT by modulating the miR-3196/SOX12 pathway. In conclusion, our findings suggest that MAFG-AS1/miR-3196/SOX12 axis regulates LUAD progression and is a potential therapeutic target for LUAD.

Lung Cancer Computational Biology and Resources

Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.

Comprehensive epigenomic profiling of human alveolar epithelial differentiation identifies key epigenetic states and transcription factor co-regulatory networks for maintenance of distal lung identity

BACKGROUND

Disruption of alveolar epithelial cell (AEC) differentiation is implicated in distal lung diseases such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and lung adenocarcinoma that impact morbidity and mortality worldwide. Elucidating underlying disease pathogenesis requires a mechanistic molecular understanding of AEC differentiation. Previous studies have focused on changes of individual transcription factors, and to date no study has comprehensively characterized the dynamic, global epigenomic alterations that facilitate this critical differentiation process in humans.

RESULTS

We comprehensively profiled the epigenomic states of human AECs during type 2 to type 1-like cell differentiation, including the methylome and chromatin functional domains, and integrated this with transcriptome-wide RNA expression data. Enhancer regions were drastically altered during AEC differentiation. Transcription factor binding analysis within enhancer regions revealed diverse interactive networks with enrichment for many transcription factors, including NKX2-1 and FOXA family members, as well as transcription factors with less well characterized roles in AEC differentiation, such as members of the MEF2, TEAD, and AP1 families. Additionally, associations among transcription factors changed during differentiation, implicating a complex network of heterotrimeric complex switching in driving differentiation. Integration of AEC enhancer states with the catalog of enhancer elements in the Roadmap Epigenomics Mapping Consortium and Encyclopedia of DNA Elements (ENCODE) revealed that AECs have similar epigenomic structures to other profiled epithelial cell types, including human mammary epithelial cells (HMECs), with NKX2-1 serving as a distinguishing feature of distal lung differentiation.

CONCLUSIONS

Enhancer regions are hotspots of epigenomic alteration that regulate AEC differentiation. Furthermore, the differentiation process is regulated by dynamic networks of transcription factors acting in concert, rather than individually. These findings provide a roadmap for understanding the relationship between disruption of the epigenetic state during AEC differentiation and development of lung diseases that may be therapeutically amenable.

Transcription start site-level expression of thyroid transcription factor 1 isoforms in lung adenocarcinoma and its clinicopathological significance

There are multiple transcription start sites (TSSs) in agreement with multiple transcript variants encoding different isoforms of NKX2-1/TTF-1 (thyroid transcription factor 1); however, the clinicopathological significance of each transcript isoform of NKX2-1/TTF-1 in lung adenocarcinoma (LAD) is unknown. Herein, TSS-level expression of NKX2-1/TTF-1 isoforms was evaluated in 71 LADs using bioinformatic analysis of cap analysis of gene expression (CAGE)-sequencing data, which provides genome-wide expression levels of the 5'-untranslated regions and the TSSs of different isoforms. Results of CAGE were further validated in 664 LADs using in situ hybridisation. Fourteen of 17 TSSs in NKX2-1/TTF-1 (80% of known TSSs in FANTOM5, an atlas of mammalian promoters) were identified in LADs, including TSSs 1-13 and 15; four isoforms of NKX2-1/TTF-1 transcripts (NKX2-1_001, NKX2-1_002, NKX2-1_004, and NKX2-1_005) were expressed in LADs, although NKX2-1_005 did not contain a homeodomain. Among those, six TSSs regulated NKX2-1_004 and NKX2-1_005, both of which contain exon 1. LADs with low expression of isoforms from TSS region 11 regulating exon 1 were significantly associated with poor prognosis in the CAGE data set. In the validation set, 62 tumours (9.3%) showed no expression of NKX2-1/TTF-1 exon 1; such tumours were significantly associated with older age, EGFR wild-type tumours, and poor prognosis. In contrast, 94 tumours, including 22 of 30 pulmonary invasive mucinous adenocarcinomas (IMAs) exhibited exon 1 expression without immunohistochemical TTF-1 protein expression. Furthermore, IMAs commonly exhibited higher exon 1 expression relative to that of exon 4/5, which contained a homeodomain in comparison with EGFR-mutated LADs. These transcriptome and clinicopathological results reveal that LAD use at least 80% of NKX2-1 TSSs and expression of the NKX2-1/TTF-1 transcript isoform without exon 1 (NKX2-1_004 and NKX2-1_005) defines a distinct subset of LAD characterised by aggressive behaviour in elder patients. Moreover, usage of alternative TSSs regions regulating NKX2-1_005 may occur in subsets of LADs.

FOXM1: A Multifunctional Oncoprotein and Emerging Therapeutic Target in Ovarian Cancer

Forkhead box M1 (FOXM1) is a member of the conserved forkhead box (FOX) transcription factor family. Over the last two decades, FOXM1 has emerged as a multifunctional oncoprotein and a robust biomarker of poor prognosis in many human malignancies. In this review article, we address the current knowledge regarding the mechanisms of regulation and oncogenic functions of FOXM1, particularly in the context of ovarian cancer. FOXM1 and its associated oncogenic transcriptional signature are enriched in >85% of ovarian cancer cases and FOXM1 expression and activity can be enhanced by a plethora of genomic, transcriptional, post-transcriptional, and post-translational mechanisms. As a master transcriptional regulator, FOXM1 promotes critical oncogenic phenotypes in ovarian cancer, including: (1) cell proliferation, (2) invasion and metastasis, (3) chemotherapy resistance, (4) cancer stem cell (CSC) properties, (5) genomic instability, and (6) altered cellular metabolism. We additionally discuss the evidence for FOXM1 as a cancer biomarker, describe the rationale for FOXM1 as a cancer therapeutic target, and provide an overview of therapeutic strategies used to target FOXM1 for cancer treatment.