The trimeric transcription factor (TF) NF-Y regulates the CCAAT box, a DNA element enriched in promoters of genes overexpressed in many types of cancer. The regulatory NF-YA is present in two major isoforms, NF-YAl (“long”) and NF-YAs (“short”). There is growing indication that NF-YA levels are increased in tumors. Here, we report interrogation of RNA-Seq TCGA (The Cancer Genome Atlas)—all 576 samples—and GEO (Gene Expression Ominibus) datasets of lung adenocarcinoma (LUAD). NF-YAs is overexpressed in the three subtypes, proliferative, inflammatory, and TRU (terminal respiratory unit). CCAAT is enriched in promoters of tumor differently expressed genes (DEG) and in the proliferative/inflammatory intersection, matching with KEGG (Kyoto Encyclopedia of Genes and Genomes) terms cell-cycle and signaling. Increasing levels of NF-YAs are observed from low to high CpG island methylator phenotypes (CIMP). We identified 166 genes overexpressed in LUAD cell lines with low NF-YAs/NF-YAl ratios: applying this centroid to TCGA samples faithfully predicted tumors’ isoform ratio. This signature lacks CCAAT in promoters. Finally, progression-free intervals and hazard ratios concurred with the worst prognosis of patients with either a low or high NF-YAs/NF-YAl ratio. In conclusion, global overexpression of NF-YAs is documented in LUAD and is associated with aggressive tumor behavior; however, a similar prognosis is recorded in tumors with high levels of NF-YAl and overexpressed CCAAT-less genes.
The CCAAT box is recognized by the trimeric transcription factor NF-Y, whose NF-YA subunit is present in two major splicing isoforms, NF-YAl (“long”) and NF-YAs (“short”). Little is known about the expression levels of NF-Y subunits in tumors, and nothing in lung cancer. By interrogating RNA-seq TCGA and GEO datasets, we found that, unlike NF-YB/NF-YC, NF-YAs is overexpressed in lung squamous cell carcinomas (LUSC). The ratio of the two isoforms changes from normal to cancer cells, with NF-YAs becoming predominant in the latter. NF-YA increased expression correlates with common proliferation markers. We partitioned all 501 TCGA LUSC tumors in the four molecular cohorts and verified that NF-YAs is similarly overexpressed. We analyzed global and subtype-specific RNA-seq data and found that CCAAT is the most abundant DNA matrix in promoters of genes overexpressed in all subtypes. Enriched Gene Ontology terms are cell-cycle and signaling. Survival curves indicate a worse clinical outcome for patients with increasing global amounts of NF-YA; same with hazard ratios with very high and, surprisingly, very low NF-YAs/NF-YAl ratios. We then analyzed gene expression in this latter cohort and identified a different, pro-migration signature devoid of CCAAT. We conclude that overexpression of the NF-Y regulatory subunit in LUSC has the scope of increasing CCAAT-dependent, proliferative (NF-YAshigh) or CCAAT-less, pro-migration (NF-YAlhigh) genes. The data further reinstate the importance of analysis of single isoforms of TFs involved in tumor development.
NF-Y is a pioneer trimeric transcription factor formed by the Histone Fold Domain (HFD) NF-YB/NF-YC subunits and NF-YA. Three subunits are required for DNA binding. CCAAT-specificity resides in NF-YA and transactivation resides in Q-rich domains of NF-YA and NF-YC. They are involved in alternative splicing (AS). We recently showed that NF-YA is overexpressed in breast and lung carcinomas. We report here on the overexpression of all subunits in the liver hepatocellular carcinoma (HCC) TCGA database, specifically the short NF-YAs and NF-YC2 (37 kDa) isoforms. This is observed at all tumor stages, in viral-infected samples and independently from the inflammatory status. Up-regulation of NF-YAs and NF-YC, but not NF-YB, is associated to tumors with mutant p53. We used a deep-learning-based method (DeepCC) to extend the partitioning of the three molecular clusters to all HCC TCGA tumors. In iCluster3, CCAAT is a primary matrix found in promoters of up-regulated genes, and cell-cycle pathways are enriched. Finally, clinical data indicate that, globally, only NF-YAs, but not HFD subunits, correlate with the worst prognosis; in iCluster1 patients, however, all subunits correlate. The data show a difference with other epithelial cancers, in that global overexpression of the three subunits is reported and clinically relevant in a subset of patients; yet, they further reinstate the regulatory role of the sequence-specific subunit.
Background Approaches based on expression signatures of prostate cancer (PCa) have been proposed to predict patient outcomes and response to treatments. The transcription factor NF-Y participates to the progression from benign epithelium to both localized and metastatic PCa and is associated with aggressive transcriptional profile. The gene encoding for NF-YA, the DNA-binding subunit of NF-Y, produces two alternatively spliced transcripts, NF-YAs and NF-YAl. Bioinformatic analyses pointed at NF-YA splicing as a key transcriptional signature to discriminate between different tumor molecular subtypes. In this study, we aimed to determine the pathophysiological role of NF-YA splice variants in PCa and their association with aggressive subtypes. Methods Data on the expression of NF-YA isoforms were extracted from the TCGA (The Cancer Genome Atlas) database of tumor prostate tissues and validated in prostate cell lines. Lentiviral transduction and CRISPR-Cas9 technology allowed the modulation of the expression of NF-YA splice variants in PCa cells. We characterized 3D cell cultures through in vitro assays and RNA-seq profilings. We used the rank-rank hypergeometric overlap approach to identify concordant/discordant gene expression signatures of NF-YAs/NF-YAl-overexpressing cells and human PCa patients. We performed in vivo studies in SHO-SCID mice to determine pathological and molecular phenotypes of NF-YAs/NF-YAl xenograft tumors. Results NF-YA depletion affects the tumorigenic potential of PCa cells in vitro and in vivo. Elevated NF-YAs levels are associated to aggressive PCa specimens, defined by Gleason Score and TNM classification. NF-YAl overexpression increases cell motility, while NF-YAs enhances cell proliferation in PCa 3D spheroids and xenograft tumors. The transcriptome of NF-YAs-spheroids has an extensive overlap with localized and metastatic human PCa signatures. According to PCa PAM50 classification, NF-YAs transcript levels are higher in LumB, characterized by poor prognosis compared to LumA and basal subtypes. A significant decrease in NF-YAs/NF-YAl ratio distinguishes PCa circulating tumor cells from cancer cells in metastatic sites, consistently with pro-migratory function of NF-YAl. Stratification of patients based on NF-YAs expression is predictive of clinical outcome. Conclusions Altogether, our results indicate that the modulation of NF-YA isoforms affects prostate pathophysiological processes and contributes to cancer-relevant phenotype, in vitro and in vivo. Evaluation of NF-YA splicing may represent a new molecular strategy for risk assessment of PCa patients.
Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq) has opened new avenues of research in the genome-wide characterization of regulatory DNAprotein interactions at the genetic and epigenetic level. As a consequence, it has become the de facto standard for studies on the regulation of transcription, and literally thousands of data sets for transcription factors and cofactors in different conditions and species are now available to the scientific community. However, while pipelines and best practices have been established for the analysis of a single experiment, there is still no consensus on the best way to perform an integrated analysis of multiple datasets in the same condition, in order to identify the most relevant and widespread regulatory modules composed by different transcription factors and cofactors. We present here a computational pipeline for this task, that integrates peak summit colocalization, a novel statistical framework for the evaluation of its significance, and motif enrichment analysis. We show examples of its application to ENCODE data, that led to the identification of relevant regulatory modules composed of different factors, as well as the organization on DNA of the binding motifs responsible for their recruitment.
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