Serine Protease Inhibitor Kazal Type 1, A Potential Biomarker for the Early Detection, Targeting, and Prediction of Response to Immune Checkpoint Blockade Therapies in Hepatocellular Carcinoma.

Background: We aimed to characterize serine protease inhibitor Kazal type 1 () as a gene signature for the early diagnosis, molecular targeting, and prediction of immune checkpoint blockade (ICB) treatment response of hepatocellular carcinoma (HCC).

Methods: The transcriptomics, proteomics, and phenotypic analyses were performed separately or in combination.

Results: We obtained the following findings on . Firstly, in the transcriptomic training dataset, which included 279 stage I and II tumor samples (out of 1,884 stage I-IV HCC specimens) and 259 normal samples, significantly higher area under curve (AUC) values and increased integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were demonstrated for HCC discrimination in -associated models compared with those of alpha-fetoprotein (AFP). The calibration of both -related curves fitted significantly better than that of AFP. In the two independent transcriptomic validation datasets, which included 201, 103 stage I-II tumor and 192, 169 paired non-tumor specimens, respectively, the obtained results were consistent with the above-described findings. In the proteomic training dataset, which included 98 stage I and II tumor and 165 normal tissue samples, the analyses also revealed better AUCs and increased IDI and NRI in the aforementioned -associated settings. A moderate calibration was shown for both -associated models relative to the poor results of AFP. Secondly, in the and/or murine models, the wet-lab experiments demonstrated that promoted the proliferation, clonal formation, migration, chemoresistance, anti-apoptosis, tumorigenesis, and metastasis of HCC cells, while the anti- antibody inhibited the growth of the cells, suggesting that has "tumor marker" and "targetable" characteristics in the management of HCC. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that was engaged in immunity-related pathways, including T-cell activation. Thirdly, in the transcriptomic analyses of the 368 HCC specimens from The Cancer Genome Atlas (TCGA) cohort, the high abundance of was positively correlated with the high levels of activated tumor-infiltrating CD4 and CD8 T lymphocytes and dendritic and natural killer cells, while there were also positive correlations between and immune checkpoints, including PD-1, LAG-3, TIM-3, TIGIT, HAVCR2, and CTLA-4. The ESTIMATE algorithm calculated positive correlations between and the immune and ESTIMATE scores, suggesting a close correlation between and the immunogenic microenvironment within HCC tissues, which may possibly help in predicting the response of patients to ICB therapy.

Conclusions: could be a potential biomarker for the early detection, targeted therapy, and prediction of ICB treatment response in the management of HCC.