Yu, Dan

Abstract Enhancer is a critical epigenetic feature in head and neck squamous cell carcinoma (HNSCC), yet the functional roles of individual enhancers remain poorly understood. Here, we conducted an integrative multi-omics analysis based on publicly available ATAC-seq, H3K27ac ChIP-seq, transcriptomic profiling, and genetic association datasets to systematically map HNSCC-associated enhancers and their target genes. Integrating ATAC-seq and H3K27ac ChIP-seq, we identified 20,362 enhancers and 18,040 enhancer-associated genes, highlighting widespread regulatory complexity. Functional characterization of the TERT -associated enhancer GH05J001312 (GRCh38/hg38: chr5:1312099-1317743) revealed strong transcriptional activity in HNSCC. CRISPR-mediated deletion of a core sequence significantly reduced TERT expression, impaired cellular proliferation in vitro , and suppressed tumor growth in vivo , confirming its role as a key cis-regulatory element. RNA-seq analysis of enhancer-edited cells uncovered 742 differentially expressed genes enriched in cancer-related pathways, including MAPK and IL-17 signaling, indicating a broad transcriptional impact. Collectively, our findings establish GH05J001312 as a functional enhancer driving oncogenic programs in HNSCC and suggest enhancer-targeted strategies as a potential therapeutic avenue.

Abstract Approximately two-thirds of hypopharyngeal squamous cell carcinoma (HPSCC) are diagnosed in advanced stage, with the worst prognosis among head and neck squamous cell carcinomas (HNSCCs). Identifying biomarkers for high-risk patients who need aggressive treatment is crucial. We present mutational, transcriptomic and proteomic studies of 103 Chinese HPSCC patients, observing a higher prevalence and poorer prognosis in males comparing to females. Genes in estrogen response pathways are up-regulated and proteins phosphorylated by protein kinase C (PKC) and Cyclin-dependent kinases (CDKs) are aberrantly regulated. We identify aberrant copy number regions including SOX2 (3q26.33), FGFR (8p11.23), CCND1 (11q13.3), CDKN2A/2B (9p21.3), and MYC (8q24.21). HPV status combined with highly mutated genes as predictive markers was assessed, including SYNE1 in HPV(-), MUC4 in HPV(+) patients. We confirm the presence of four known gene expression (GE) subtypes of HNSCC in our HPSCC cohort and present the genomic alterations, patient characteristics and survival status of each subtype. A predictive model involving clinical factors and transcriptomic expression of six genes was established and cross-site validated. The findings open new opportunities for stratifying high-risk patients and molecular targets for novel therapeutic strategies. Citation Format: Yanxin Ren, Wei Xiong, Chun Feng, Dan Yu, Xiaoyan Wang, Qing Yang, Siting Yu, Hongjiang Zhang, Bangyun Huo, Honglu Jiang, Zuli Li, Junlin Wang, Yu-Xiong Su, Ping Yang, Yong Liao, Qi Zhong, Junwen Wang. Multi-omics insights into the molecular signature and prognosis of hypopharyngeal squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4999.

Abstract The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particularly single-cell transcriptomic data containing rich cell-to-cell variations, it is highly desirable to infer TF–gene interactions (TGIs) using deep learning technologies. Numerous models or software including deep learning–based algorithms have been designed to identify transcriptional regulatory relationships between TFs and the downstream genes. However, these methods do not significantly improve predictions of TGIs due to some limitations regarding constructing underlying interactive structures linking regulatory components. In this study, we introduce a deep learning framework, DeepTGI, that encodes gene expression profiles from single-cell and/or bulk transcriptomic data and predicts TGIs with high accuracy. Our approach could fuse the features extracted from Auto-encoder with self-attention mechanism and other networks and could transform multihead attention modules to define representative features. By comparing it with other models or methods, DeepTGI exhibits its superiority to identify more potential TGIs and to reconstruct the GRNs and, therefore, could provide broader perspectives for discovery of more biological meaningful TGIs and for understanding transcriptional gene regulatory mechanisms.