Patiyal Sumeet, Agrawal Piyush
Cancer Data Science Laboratory, National Cancer Institute, NIH, Bethesda, MD, 20814, USA.
Division of Medical Research, Research Centre, SRM Medical College Hospital, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Sci Rep. 2025 Aug 7;15(1):28864. doi: 10.1038/s41598-025-13604-0.
Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most prevalent cancer worldwide and is classified as human papillomavirus (HPV) positive or negative. Substantial heterogeneity has been observed in the two groups, posing a significant clinical challenge. In the disease context, global transcriptional changes are likely driven by a few key genes that reflect the disease etiology more accurately compared to differentially expressed genes (DEGs). We implemented our network-based tool PathExt on 501 TCGA-HNSCC samples (64 HPV positive & 437 HPV negative) to characterize central genes in two subtypes, where in subtype 1, HPV-positive samples were considered as cases and negative as controls, and vice versa in subtype 2. We also identified DEGs and performed several analyses on multiple benchmarking datasets to compare the biology of central genes with DEGs. PathExt key genes performed better with respect to DEGs in both subtypes in recapitulating disease etiology. Gene ontology analysis using central genes revealed shared biological processes such as "epithelial cell proliferation" as well as subtype-specific processes (immune- and metabolic-related processes in subtype 1 and peptide-related processes in subtype 2). However, in the case of DEGs, no subtype-specific processes were seen. Additionally, PathExt central genes did better than DEGs on external validation datasets that were specific to HNSCC and included HNSCC-specific cancer driver genes, FDA-approved therapeutic targets, and pan-cancer tumor suppressor genes. Unlike DEGs, central genes exhibit significant expression in various cell types, enrichment for cancer hallmarks, and mutated protein systems. Central gene expression-based machine learning model shows better performance than DEGs in classifying responders/non-responders with 0.74 AUROC. Lastly, the top 10 potential therapeutic targets and drugs were proposed. Overall, we observed PathExt as a complementary approach to DEGs, characterizing common and HNSCC subtype-specific key genes associated with distinct HNSCC molecular subtypes.
头颈部鳞状细胞癌（HNSCC）是全球第七大常见癌症，分为人乳头瘤病毒（HPV）阳性或阴性两类。两组中均观察到显著的异质性，这带来了重大的临床挑战。在疾病背景下，与差异表达基因（DEGs）相比，全球转录变化可能由少数几个能更准确反映疾病病因的关键基因驱动。我们在501个TCGA - HNSCC样本（64个HPV阳性和437个HPV阴性）上应用了基于网络的工具PathExt，以表征两种亚型中的核心基因，其中在亚型1中，HPV阳性样本被视为病例，阴性样本被视为对照，而在亚型2中则相反。我们还鉴定了DEGs，并在多个基准数据集上进行了多项分析，以比较核心基因与DEGs的生物学特性。在概括疾病病因方面，PathExt关键基因在两种亚型中相对于DEGs表现更好。使用核心基因进行的基因本体分析揭示了共同的生物学过程，如“上皮细胞增殖”，以及亚型特异性过程（亚型1中的免疫和代谢相关过程以及亚型2中的肽相关过程）。然而，对于DEGs，未观察到亚型特异性过程。此外，在特定于HNSCC的外部验证数据集上，PathExt核心基因的表现优于DEGs，这些数据集包括HNSCC特异性癌症驱动基因、FDA批准的治疗靶点和泛癌肿瘤抑制基因。与DEGs不同，核心基因在各种细胞类型中表现出显著表达，富集癌症特征，以及突变蛋白系统。基于核心基因表达的机器学习模型在分类反应者/无反应者方面表现优于DEGs，AUROC为0.74。最后，提出了前10个潜在的治疗靶点和药物。总体而言，我们观察到PathExt是一种补充DEGs的方法，可表征与不同HNSCC分子亚型相关的常见和HNSCC亚型特异性关键基因。
