Yin Lu, Zhang Zhanshuo, Yan Zhu, Yan Qiuyue
School of Mathmatic and Information, Nanjing Normal University of Special Education, Nanjing, China.
Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Discov Oncol. 2024 Sep 19;15(1):462. doi: 10.1007/s12672-024-01293-6.
Anoikis and epithelial-mesenchymal transition (EMT) are pivotal in the distant metastasis of lung adenocarcinoma (LUAD). A detailed understanding of their interplay and the identification of key genes is vital for effective therapeutic strategies against LUAD metastasis.
Key prognostic genes related to anoikis and EMT were identified through univariate Cox regression analysis. We utilized ten machine learning algorithms to develop the Anoikis and EMT-Related Optimal Model (AEOM). The TCGA-LUAD dataset served as the training cohort, while six additional international multicenter LUAD datasets were employed as validation cohorts. The average concordance index (c-index) was used to evaluate model performance and identify the most effective model. Subsequent multi-omics analyses were conducted to explore differences in pathway enrichment, immune infiltration, and mutation landscapes between high and low AEOM groups. Experimental validation demonstrated that RHPN2, a key biomarker within the model, acts as an oncogene facilitating LUAD progression.
The AEOM displayed superior prognostic predictive performance for LUAD patients, outperforming numerous previously published LUAD signatures. Biologically, the AEOM was notably associated with immune features; the high AEOM group exhibited decreased immune activity and a tendency towards immune-cold tumors, as well as a higher tumor mutational burden (TMB). Subgroup analysis revealed that the low AEOM + high TMB group had the most favorable prognosis. The high AEOM group was primarily enriched in cell cycle-related pathways, promoting cancer cell proliferation. RHPN2, a crucial gene within the AEOM (correlation = 0.85, P < 0.05), was linked to poorer prognosis in LUAD patients with elevated RHPN2 expression. Further in vitro experiments showed that RHPN2 modulates LUAD cell proliferation and invasion.
The AEOM provides a robust prognostic model for LUAD, uncovering critical immune and biological pathways, with RHPN2 identified as a key oncogenic driver. These findings offer valuable insights for targeted therapies and enhanced patient outcomes.
失巢凋亡和上皮-间质转化（EMT）在肺腺癌（LUAD）的远处转移中起关键作用。深入了解它们之间的相互作用并鉴定关键基因对于制定有效的抗LUAD转移治疗策略至关重要。
通过单变量Cox回归分析确定与失巢凋亡和EMT相关的关键预后基因。我们利用十种机器学习算法开发了失巢凋亡和EMT相关最优模型（AEOM）。TCGA-LUAD数据集用作训练队列，另外六个国际多中心LUAD数据集用作验证队列。使用平均一致性指数（c指数）评估模型性能并确定最有效的模型。随后进行多组学分析，以探讨高AEOM组和低AEOM组之间在通路富集、免疫浸润和突变图谱方面的差异。实验验证表明，模型中的关键生物标志物RHPN2作为一种癌基因促进LUAD进展。
AEOM对LUAD患者显示出卓越的预后预测性能，优于许多先前发表的LUAD特征。从生物学角度来看，AEOM与免疫特征显著相关；高AEOM组表现出免疫活性降低和免疫冷肿瘤倾向，以及更高的肿瘤突变负荷（TMB）。亚组分析显示，低AEOM+高TMB组预后最有利。高AEOM组主要富集在细胞周期相关通路，促进癌细胞增殖。RHPN2是AEOM中的一个关键基因（相关性=0.85，P<0.05），与RHPN2表达升高的LUAD患者预后较差有关。进一步的体外实验表明，RHPN2调节LUAD细胞的增殖和侵袭。
AEOM为LUAD提供了一个强大的预后模型，揭示了关键的免疫和生物学通路，其中RHPN2被确定为关键的致癌驱动因素。这些发现为靶向治疗和改善患者预后提供了有价值的见解。
