Differentiated super-enhancers in lung cancer cells

Super-enhancer MYCNOS-SE promotes chemoresistance in small cell lung cancer by recruiting transcription factors CTCF and KLF15

Small cell lung cancer (SCLC) is an aggressive form of lung cancer that often becomes resistant to chemotherapy. Understanding the molecular mechanisms of chemoresistance is crucial for identifying effective therapeutic targets. In this study, we used RNA-Seq to identify highly expressed molecules associated with chemoresistance. We also performed H3K27Ac and ATAC-Seq binding analyses to identify super-enhancers (SE) and their corresponding transcription factors. Both in vitro and in vivo experiments were conducted to examine the impact of these molecules and clinical samples were collected to establish their prognostic value. Our findings revealed elevated expression of MYCNOS, which exhibited chemoresistant properties in both in vitro and in vivo models of SCLC. We identified MYCNOS-SE as a significant SE in SCLC that regulates the distal target gene MYCNOS. This SE recruits transcription factors CTCF and KLF15 to regulate MYCNOS expression. Additionally, MYCNOS, an antisense of MYCN, was found to modulate chemotherapy sensitivity through the NOTCH pathway. This study highlights the significance of SE -regulated target genes as markers for chemoresistance in SCLC. Furthermore, it suggests that MYCNOS could serve as a predictor to identify patients who may benefit from NOTCH inhibitors. These findings provide valuable insights for future studies aimed at developing therapeutic strategies targeting these identified pathways.

Pathogenic role of super-enhancers as potential therapeutic targets in lung cancer

Lung cancer is still one of the deadliest malignancies today, and most patients with advanced lung cancer pass away from disease progression that is uncontrollable by medications. Super-enhancers (SEs) are large clusters of enhancers in the genome's non-coding sequences that actively trigger transcription. Although SEs have just been identified over the past 10 years, their intricate structure and crucial role in determining cell identity and promoting tumorigenesis and progression are increasingly coming to light. Here, we review the structural composition of SEs, the auto-regulatory circuits, the control mechanisms of downstream genes and pathways, and the characterization of subgroups classified according to SEs in lung cancer. Additionally, we discuss the therapeutic targets, several small-molecule inhibitors, and available treatment options for SEs in lung cancer. Combination therapies have demonstrated considerable advantages in preclinical models, and we anticipate that these drugs will soon enter clinical studies and benefit patients.

A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. To classify patients into different groups and facilitate the suitable therapeutic strategy, we first selected eight microRNA (miRNA) signatures in The Cancer Genome Atlas (TCGA)-LUAD cohort based on multi-strategy combination, including differential expression analysis, regulatory relationship, univariate survival analysis, importance clustering, and multivariate combinations analysis. Using the eight miRNA signatures, we further built novel risk scores based on the predefined cutoff and beta coefficients and divided the patients into high-risk and low-risk groups with significantly different overall survival time (p-value < 2 e-16). The risk-score model was confirmed with an independent dataset (p-value = 4.71 e-4). We also observed that the risk scores of early-stage patients were significantly lower than those of late-stage patients. Moreover, our model can also provide new insights into the current clinical staging system and can be regarded as an alternative system for patient stratification. This model unified the variable value as the beta coefficient facilitating the integration of biomarkers obtained from different omics data.