Molecular subtypes based on immune-related genes predict the prognosis for hepatocellular carcinoma patients

Background: Hepatocellular carcinoma (HCC) is a malignancy exhibiting the highest lethality. The present study aimed to identify different immune-related clusters in HCC and a robust tumor gene signature to facilitate the prognosis prediction for HCC patients. Methods: For the 375 HCC cases collected from the dataset of Cancer Genome Atlas (TCGA), their overall survival (OS) and immune-related genes (IRGs) expression patterns were collected. Thereafter, consensus clustering was employed for grouping and functional enrichment, whereas the ESTIMATE algorithm and the CIBERSORT algorithm were used in subsequent assessment. Immunohistochemistry (IHC) was conducted to verify the protein expression of model genes in HCC and adjacent tissues. Results: According to consensus clustering with 93-survival related IRGs, a total of five subgroups were found. These five clusters had different prognoses, immune statuses, and expression of immune checkpoints. Afterwards, 11 genes were enrolled for constructing the OS-related prediction model for TCGA HCC cases, which was then validated using the database of International Cancer Genome Consortium (ICGC). The protein expression of LCN2, S100A10, FABP6, PLXNA1, KITLG and OXTR were enhanced in HCC tissues relative to that in normal hepatic tissues, while the protein expression of S100A1, CCL26, CMTM4, IL1RN and RARG were reduced in HCC compared with normal tissues. In addition, different immunocyte infiltration levels between low- and high groups were further examined. Conclusions: According to our results, the IRGs-based classifications assist in explaining the HCC heterogeneity, which may help to develop the more efficient individualized treatments.

Automated summary

The study identified different immune-related clusters in hepatocellular carcinoma (HCC) and developed a robust tumor gene signature. By utilizing immune-related genes (IRGs), the research aimed to assist in explaining the heterogeneity of HCC and potentially develop more efficient individualized treatments.