IL-17 predicts the effect of TACE combined with apatinib in hepatocellular carcinoma

Useful genes for predicting the efficacy of transarterial chemoembolization in hepatocellular carcinoma

Transarterial chemoembolization (TACE) is generally used to treat patients with hepatocellular carcinoma (HCC), a common and deadly cancer; however, its efficacy varies according to factors such as tumor volume, stage, serum alpha-fetoprotein level, and chosen feeding artery. In addition, gene-related factors have been recently suggested to be involved in the regulation and prediction of TACE outcomes. Accordingly, genes could serve as effective biomarkers to select patients who can benefit from TACE. These gene-related factors can activate signaling pathways affecting cancer cell survival while regulating the epithelial-mesenchymal transition, angiogenesis, and the tumor microenvironment, all directly associated with tumor progression, thereby affecting TACE efficacy. Moreover, this disordered gene expression is associated with poor prognosis in patients with HCC, including TACE resistance, postoperative recurrence, and metastasis. To identify the exact relationship between various genes and TACE efficacy, this review summarizes the involvement of protein-coding and non-coding genes and single nucleotide polymorphisms in TACE efficacy for predicting the efficacy of TACE; the present findings may help improve the efficacy of TACE in clinical settings.

Variation of NK, NKT, CD4+ T, CD8+ T cells, and IL-17A by CalliSpheres® microspheres-transarterial chemoembolization in refractory liver metastases patients

Immune environment plays an important role in the management of liver cancer. The current study aimed to explore the change of NK and NKT cells, IL-17A, CD4⁺ T and CD8⁺ T cells in refractory liver metastases patients before and after CalliSpheres^® microspheres transarterial chemoembolization (CSM-TACE). Peripheral blood (PB) samples from 35 refractory liver metastases patients were collected before CSM-TACE (baseline), 2 days (D2) and 5 days (D5) after CSM-TACE. Then, NK and NKT cells, IL-17A, CD4⁺ T and CD8⁺ T cells from PB samples were detected. All enrolled patients successfully completed CSM-TACE procedure and achieved disease control rate of 100% after 1 month. NKT cells were increased from baseline to D2 and D5 [median (range): 5.88% (1.53%-12.05%) vs. 9.54% (5.19%-15.71%) vs. 7.12% (2.77%-13.29%)], NK cells were also enhanced from baseline to D2 and D5 [median (range): 14.35% (5.85%-20.52%) vs. 20.36% (15.88%-27.30%) vs. 30.82% (22.18%-37.72%)], while IL-17A was declined from baseline to D2 and D5 [median (range): 22.11 (9.46-39.18) pg/ml vs. 12.41 (3.24-26.84) pg/ml vs. 6.55 (1.11-20.98) pg/ml]. Furthermore, IL-17A was negatively correlated with the NK and NKT cells at baseline, D2 and D5 (all p < .05), respectively. Additionally, CD4⁺ T cells and CD4⁺ T/CD8⁺ T ratio were increased while CD8⁺ T cells were declined from baseline to D2 and D5 (all p < .05). NK cells, NKT cells, and CD4⁺ T cells are increased but IL-17A and CD8⁺ T cells are declined after CSM-TACE in refractory liver metastases.

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

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.