A 10-Gene Signature Identified by Machine Learning for Predicting the Response to Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma

Abstract

Background. Transarterial chemoembolization (TACE) is recommended for intermediate-stage HCC patients. Owing to substantial variation in its efficacy, indicators of patient responses to TACE need to be determined. Methods. A Gene Expression Omnibus (GEO) dataset consisting of patients of different TACE-response status was retrieved. Differentially expressed genes (DEGs) were calculated and variable gene ontology analyses were conducted. Potential drugs and response to immunotherapy were predicted using multiple bioinformatic algorithms. We built and compared 5 machine-learning models with finite genes to predict patients’ response to TACE. The model was also externally validated to discern different survival outcomes after TACE. Tumor-infiltrating lymphocytes (TILs) and tumor stemness index were evaluated to explore potential mechanism of our model. Results. The gene set variation analysis revealed enhanced pathways related to G2/M checkpoint, E2F, mTORC1, and myc in TACE nonresponders. TACE responders had better immunotherapy response too. 373 DEGs were detected and the upregulated DEGs in nonresponders were enriched in IL-17 signal pathway. 5 machine-learning models were constructed and evaluated, and a linear support vector machine (SVM)-based model with 10 genes was selected (AQP1, FABP4, HERC6, LOX, PEG10, S100A8, SPARCL1, TIAM1, TSPAN8, and TYRO3). The model achieved an AUC and accuracy of 0.944 and 0.844, respectively, in the development cohort. In the external validation cohort comprised of patients receiving adjuvant TACE and postrecurrence TACE treatment, the predicted response group significantly outlived the predicted nonresponse counterparts. TACE nonresponders tend to have more macrophage M0 cells and lower resting mast cells in the tumor tissue and the stemness index is also higher than responders. Those characteristics were successfully captured by our model. Conclusion. The model based on expression data of 10 genes could potentially predict HCC patients’ response and prognosis after TACE treatment. The discriminating power was TACE-specific.

Differential expression of hepatic cancer stemness and hypoxia markers in residual cancer after locoregional therapies for hepatocellular carcinoma

“…Recently, two papers have described gene signatures to predict the response to TACE in patients with HCC. [ 46 , 47 ] Fako et al reported TACE‐specific 14‐gene signature as TACE Navigator, which was associated with improved survival in patients who received either adjuvant or postrelapse TACE. It was suggested that hypoxia response may be linked to TACE treatment resistance based on up‐regulation of hypoxia inducible factor 1 alpha subunit and vascular endothelial growth factor in nonresponders compared with responders.…”

Development and Validation of a Vascular Endothelial Growth Factor A-associated Prognostic Model for Unresectable Hepatocellular Carcinoma

“…In addition, our model was mainly constructed based on laboratory and imaging indicators, so our model was relatively simple and had more clinical applicability than many models or signatures constructed by various gene expressions or modifications. 67 , 68 …”

“…Notably, an ideal prognosis predictive model requires not only appropriate development methods but also conciseness for clinical application. 68 Thus, we stratified the cases into three prognostic strata according to scores drawn by the nomogram to meet this demand. Significant differences in survival were demonstrated among patients divided into three categories, with the median OS of NR, 8.9 months, and 5.3 months, respectively.…”

“…In addition, our model was mainly constructed based on laboratory and imaging indicators, so our model was relatively simple and had more clinical applicability than many models or signatures constructed by various gene expressions or modifications. 67,68 To further evaluated the prediction performance of the VEGF-associated model, we compared it with CNLC stage, AJCC stage, BCLC stage, and the model without "VEGF". The results showed that the performance of the final model was superior to the other models at each time point, and we confirmed that VEGF is indispensable to this model.…”

Serum alanine aminotransferase to hemoglobin ratio and radiological features predict the prognosis of postoperative adjuvant TACE in patients with hepatocellular carcinoma

“…PA-TACE has been shown to improve the overall survival (OS) of patients with HCC and portal vein tumor thrombus after surgical resection or patients diagnosed with B stage tumors according to the Barcelona Clinic Liver Cancer evaluation system (6,7). However, whether patients can benefit from PA-TACE remains controversial (8), As the responses to TACE in patients with HCC is variable (9), it is necessary to identify the patients who can benefit from PA-TACE and implement individualized treatments.…”