Transcriptomic profiling of peroxisome-related genes reveals a novel prognostic signature in hepatocellular carcinoma

Pathologically successful conversion hepatectomy for advanced giant hepatocellular carcinoma after multidisciplinary therapy: A case report and review of literature

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of death due to its complexity, heterogeneity, rapid metastasis and easy recurrence after surgical resection. We demonstrated that combination therapy with transcatheter arterial chemoembolization (TACE), hepatic arterial infusion chemotherapy (HAIC), Epclusa, Lenvatinib and Sintilimab is useful for patients with advanced HCC.

CASE SUMMARY

A 69-year-old man who was infected with hepatitis C virus (HCV) 30 years previously was admitted to the hospital with abdominal pain. Enhanced computed tomography (CT) revealed a low-density mass in the right lobe of the liver, with a volume of 12.9 cm × 9.4 cm × 15 cm, and the mass exhibited a "fast-in/fast-out" pattern, with extensive filling defect areas in the right branch of the portal vein and an alpha-fetoprotein level as high as 657 ng/mL. Therefore, he was judged to have advanced HCC. During treatment, the patient received three months of Epclusa, three TACE treatments, two HAIC treatments, three courses of sintilimab, and twenty-one months of lenvatinib. In the third month of treatment, the patient developed severe side effects and had to stop immunotherapy, and the Lenvatinib dose had to be halved. Postoperative pathological diagnosis indicated a complete response. The patient recovered well after the operation, and no tumor recurrence was found.

CONCLUSION

Multidisciplinary conversion therapy for advanced enormous HCC caused by HCV infection has a significant effect. Individualized drug adjustments should be made during any treatment according to the patient's tolerance to treatment.

Adverse clinical outcomes and immunosuppressive microenvironment of RHO-GTPase activation pattern in hepatocellular carcinoma

BACKGROUND

Emerging evidence suggests that Rho GTPases play a crucial role in tumorigenesis and metastasis, but their involvement in the tumor microenvironment (TME) and prognosis of hepatocellular carcinoma (HCC) is not well understood.

METHODS

We aim to develop a tumor prognosis prediction system called the Rho GTPases-related gene score (RGPRG score) using Rho GTPase signaling genes and further bioinformatic analyses.

RESULTS

Our work found that HCC patients with a high RGPRG score had significantly worse survival and increased immunosuppressive cell fractions compared to those with a low RGPRG score. Single-cell cohort analysis revealed an immune-active TME in patients with a low RGPRG score, with strengthened communication from T/NK cells to other cells through MIF signaling networks. Targeting these alterations in TME, the patients with high RGPRG score have worse immunotherapeutic outcomes and decreased survival time in the immunotherapy cohort. Moreover, the RGPRG score was found to be correlated with survival in 27 other cancers. In vitro experiments confirmed that knockdown of the key Rho GTPase-signaling biomarker SFN significantly inhibited HCC cell proliferation, invasion, and migration.

CONCLUSIONS

This study provides new insight into the TME features and clinical use of Rho GTPase gene pattern at the bulk-seq and single-cell level, which may contribute to guiding personalized treatment and improving clinical outcome in HCC.

Establishment and validation of a novel peroxisome-related gene prognostic risk model in kidney clear cell carcinoma

BACKGROUND

Kidney clear cell carcinoma (KIRC) is the most common subtype of renal cell carcinoma. Peroxisomes play a role in the regulation of tumorigenesis and cancer progression, yet the prognostic significance of peroxisome-related genes (PRGs) remains rarely studied. The study aimed to establish a novel prognostic risk model and identify potential biomarkers in KIRC.

METHODS

The significant prognostic PRGs were screened through differential and Cox regression analyses, and LASSO Cox regression analysis was performed to establish a prognostic risk model in the training cohort, which was validated internally in the testing and entire cohorts, and further assessed in the GSE22541 cohort. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function and pathway differences between the high-risk and low-risk groups. The relationship between risk score and immune cell infiltration levels was evaluated in the CIBERSORT, ESTIMATE and TIMER databases. Finally, potential biomarkers were identified and validated from model genes, using immunohistochemistry.

RESULTS

Fourteen significant prognostic PRGs were identified using multiple analyses, and 9 genes (ABCD1, ACAD11, ACAT1, AGXT, DAO, EPHX2, FNDC5, HAO1, and HNGCLL1) were obtained to establish a prognostic model via LASSO Cox regression analysis. Combining the risk score with clinical factors to construct a nomogram, which provided support for personalized treatment protocols for KIRC patients. GO and KEGG analyses highlighted associations with substance metabolism, transport, and the PPAR signaling pathways. Tumor immune infiltration indicated immune suppression in the high-risk group, accompanied by higher tumor purity and the expression of 9 model genes was positively correlated with the level of immune cell infiltration. ACAT1 has superior prognostic capabilities in predicting the outcomes of KIRC patients.

CONCLUSIONS

The peroxisome-related prognostic risk model could better predict prognosis in KIRC patients.

A decision tree model to predict liver cirrhosis in hepatocellular carcinoma patients: a retrospective study

Background

The severity of liver cirrhosis in hepatocellular carcinoma (HCC) patients is essential for determining the scope of surgical resection. It also affects the long-term efficacy of systemic anti-tumor therapy and transcatheter arterial chemoembolization (TACE). Non-invasive tools, including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 (FIB-4), and γ-glutamyl transferase to platelet ratio (GPR), are less accurate in predicting cirrhosis in HCC patients. We aimed to build a novel decision tree model to improve diagnostic accuracy of liver cirrhosis.

Patients and Methods

The Mann-Whitney U test, χ2 test, and multivariate logistic regression analysis were used to identify independent cirrhosis predictors. A decision tree model was developed using machine learning algorithms in a training cohort of 141 HCC patients. Internal validation was conducted in 99 HCC patients. The diagnostic accuracy and calibration of the established model were evaluated using receiver operating characteristic (ROC) and calibration curves, respectively.

Results

Sex and platelet count were identified as independent cirrhosis predictors. A decision tree model integrating imaging-reported cirrhosis, APRI, FIB-4, and GPR was established. The novel model had an excellent diagnostic performance in the training and validation cohorts, with area under the curve (AUC) values of 0.853 and 0.817, respectively. Calibration curves and the Hosmer-Lemeshow test showed good calibration of the novel model. The decision curve analysis (DCA) indicated that the decision tree model could provide a larger net benefit to predict liver cirrhosis.

Conclusion

Our developed decision tree model could successfully predict liver cirrhosis in HCC patients, which may be helpful in clinical decision-making.

Peroxisomal trans-2-enoyl-CoA inhibits proliferation, migration and invasion of hepatocellular carcinoma cells

OBJECTIVES

Peroxisomal trans-2-enoyl-CoA reductase (PECR) encodes proteins related to fatty acid metabolism and synthesis. It has been confirmed that PECR has decreased expression in colon cancer and breast cancer, while the role of PECR in liver cancer is unknown. We aimed to study the role and mechanism of PECR in the genesis and development of liver cancer.

METHODS

In this study, the expression of PECR was queried in the Cancer Genome Atlas Database and Western Blotting and RT-PCR experiments were carried out in paired liver cancer tissues to detect the expression of PECR. Functional tests were evaluated by cell count kit-8 (CCK-8), Flow cytometry, wound healing assay, Transwell, migration. In vivo study, we constructed a nude mouse tumorigenic model to observe the effect of PECR on the proliferation of liver cancer. And the tumor body of the mouse was taken out for histochemistry (IHC). Multiple Cox regression was used to analyze the correlation between PECR and Clinicopathology.

RESULTS

We confirmed that the overexpression of PECR inhibited the proliferation, migration and invasion of hepatocellular carcinoma and promoted the apoptosis of hepatocellular carcinoma. The low expression group of PECR promoted the proliferation and metastasis of liver cancer. In vivo, overexpression of PECR inhibits the proliferation of mouse tumors. In addition, the mechanism study shows that PECR may indirectly affect the proliferation of hepatocellular carcinoma cells through ERK pathway.

CONCLUSION

In general, PECR may be a new diagnostic marker and a potential therapeutic target for hepatocellular carcinoma.

Development of a macrophages-related 4-gene signature and nomogram for the overall survival prediction of hepatocellular carcinoma based on WGCNA and LASSO algorithm

BACKGROUND

Immune system instability and poor prognosis are the two major clinical performance of hepatocellular carcinoma (HCC). Abnormal expression of MiR-424-5p has been reported to accelerate the progression of liver cancer, but it mediated immune cell infiltration imbalance is still unknown. We aim to mine the immune-related genes (IRGs) targeted by miR-424-5p and construct a multi-gene signature to improve the prognostic prediction of HCC.

METHODS

The HCC-related data of the cancer genome atlas (TCGA) database and the GSE14520 dataset of the gene expression omnibus (GEO) database were downloaded as the discovery dataset and the validation dataset, respectively. Weighted gene co-expression network analysis (WGCNA), the deconvolution algorithm of CIBERSORT and LASSO algorithm participated in the identification of IRGs and the development of prognostic signature and nomogram.

RESULTS

Our study found that the abundance of macrophages M0, M1 and M2 are all drastically changed during the cancerous process. A total of 920 macrophages infiltration-related LRGs were identified and a novel 4-gene signature (CDCA8, CBX2, UCK2 and SOCS2) with superior prognostic independence was established. The prognostic signature based risk score has superior capability to identify high-risk patients and predict overall survival (p < 0.001; AUC = 0.798 for 1 year; AUC = 0.748 for 3 years; AUC = 0.721 for 5 years). And it (C-index = 0.726) has a better prognostic potential than the TNM stage (C-index = 0.619), which is widely adopted in clinical practice. Additionally, the nomogram formed by combining the risk score and TNM stage further improved the accuracy of survival prediction (C-index = 0.733).

CONCLUSION

In summary, the immune landscape with abnormal infiltration of macrophages may be one of the prelude to the cancerous process. The novel macrophages-related 4-gene signature is expected to become a potential prognostic marker in liver cancer.