The Absence of Intra-Tumoral Tertiary Lymphoid Structures is Associated with a Worse Prognosis and mTOR Signaling Activation in Hepatocellular Carcinoma with Liver Transplantation: A Multicenter Retrospective Study.
Jian-Hua LiLi ZhangHao XingYan GengShaocheng LvXiao LuoWeiqiao HeZhi FuGuangming LiBin HuShengran JiangZhe YangNingqi ZhuQuanbao ZhangJing ZhaoYifeng TaoConghuan ShenRuidong LiFeng TangShusen ZhengYun BaoQiang HeDaoying GengZhengxin WangPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2024)
Tertiary lymphoid structure (TLS) can predict the prognosis and sensitivity of tumors to immune checkpoint inhibitors (ICIs) therapy, whether it can be noninvasively predicted by radiomics in hepatocellular carcinoma with liver transplantation (HCC-LT) has not been explored. In this study, it is found that intra-tumoral TLS abundance is significantly correlated with recurrence-free survival (RFS) and overall survival (OS). Tumor tissues with TLS are characterized by inflammatory signatures and high infiltration of antitumor immune cells, while those without TLS exhibit uncontrolled cell cycle progression and activated mTOR signaling by bulk and single-cell RNA-seq analyses. The regulators involved in mTOR signaling (RHEB and LAMTOR4) and S-phase (RFC2, PSMC2, and ORC5) are highly expressed in HCC with low TLS. In addition, the largest cohort of HCC patients is studied with available radiomics data, and a classifier is built to detect the presence of TLS in a non-invasive manner. The classifier demonstrates remarkable performance in predicting intra-tumoral TLS abundance in both training and test sets, achieving areas under receiver operating characteristic curve (AUCs) of 92.9% and 90.2% respectively. In summary, the absence of intra-tumoral TLS abundance is associated with mTOR signaling activation and uncontrolled cell cycle progression in tumor cells, indicating unfavorable prognosis in HCC-LT.
Keyphrases
- cell cycle
- cell proliferation
- rna seq
- single cell
- free survival
- end stage renal disease
- newly diagnosed
- ejection fraction
- antibiotic resistance genes
- clinical trial
- high throughput
- lymph node metastasis
- gene expression
- prognostic factors
- transcription factor
- magnetic resonance imaging
- machine learning
- oxidative stress
- genome wide
- magnetic resonance
- contrast enhanced
- mesenchymal stem cells
- big data
- chronic kidney disease
- dna methylation
- deep learning