HMGCS2 in metabolic pathways was associated with overall survival in hepatocellular carcinoma: A LASSO-derived study

Peptidomic analysis reveals novel peptide PDLC promotes cell proliferation in hepatocellular carcinoma via Ras/Raf/MEK/ERK pathway

Hepatocellular carcinoma (HCC) still presents poor prognosis with low overall survival rates and limited therapeutic options available. Recently, attention has been drawn to peptidomic analysis, an emerging field of proteomics for the exploration of new potential peptide drugs for the treatment of various diseases. However, research on the potential function of HCC peptides is lacking. Here, we analyzed the peptide spectrum in HCC tissues using peptidomic techniques and explored the potentially beneficial peptides involved in HCC. Changes in peptide profiles in HCC were examined using liquid chromatography-mass spectrometry (LC-MS/MS). Analyze the physicochemical properties and function of differently expressed peptides using bioinformatics. The effect of candidate functional peptides on HCC cell growth and migration was evaluated using the CCK-8, colony formation, and transwell assays. Transcriptome sequencing analysis and western blot were employed to delve into the mode of action of potential peptide on HCC. Peptidomic analysis of HCC tissue yielded a total of 8683 peptides, of which 452 exhibited up-regulation and 362 showed down-regulation. The peptides that were differentially expressed, according to bioinformatic analysis, were closely linked to carbon metabolism and the mitochondrial inner membrane. The peptide functional validation identified a novel peptide, PDLC (peptide derived from liver cancer), which was found to dramatically boost HCC cell proliferation through the Ras/Raf/MEK/ERK signaling cascade. Our research defined the peptide's properties and pattern of expression in HCC and identified a novel peptide, PDLC, with a function in encouraging HCC progression, offering an entirely new potential therapeutic target the disease.

Induction of resistance to neurotrophic tropomyosin-receptor kinase inhibitors by HMGCS2 via a mevalonate pathway

INTRODUCTION

A neurotrophic tropomyosin receptor kinase (NTRK)-tyrosine kinase inhibitor (TKI) has shown dramatic efficacy against malignant tumors harboring an NTRK fusion gene. However, almost all tumors eventually acquire resistance to NTRK-TKIs.

METHOD

To investigate the mechanism of resistance to NTRK-TKIs, we established cells resistant to three types of NTRK-TKIs (larotrectinib, entrectinib, and selitrectinib) using KM12 colon cancer cells with a TPM3-NTRK1 rearrangement.

RESULT

Overexpression of 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) was observed in three resistant cells (KM12-LR, KM12-ER, and KM12-SR) by microarray analysis. Lower expression of sterol regulatory element-binding protein 2 (SREBP2) and peroxisome proliferator activated receptor α (PPARα) was found in two cells (KM12-ER and KM12-SR) in which HMGCS2 was overexpressed compared to the parental KM12 and KM12-LR cells. In resistant cells, knockdown of HMGCS2 using small interfering RNA improved the sensitivity to NTRK-TKI. Further treatment with mevalonolactone after HMGCS2 knockdown reintroduced the NTRK-TKI resistance. In addition, simvastatin and silibinin had a synergistic effect with NTRK-TKIs in resistant cells, and delayed tolerance was observed after sustained exposure to clinical concentrations of NTRK-TKI and simvastatin in KM12 cells. In xenograft mouse models, combination treatment with entrectinib and simvastatin reduced resistant tumor growth compared with entrectinib alone.

CONCLUSION

These results suggest that HMGCS2 overexpression induces resistance to NTRK-TKIs via the mevalonate pathway in colon cancer cells. Statin inhibition of the mevalonate pathway may be useful for overcoming this mechanistic resistance.

Identification of cuproptosis and ferroptosis-related subgroups and development of a signature for predicting prognosis and tumor microenvironment landscape in hepatocellular carcinoma

Background

Ferroptosis and cuproptosis play a crucial role in the progression and dissemination of hepatocellular carcinoma (HCC). The primary objective of this study was to develop a unique scoring system for predicting the prognosis and immunological landscape of HCC based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs).

Methods

As the training cohort, we assembled a novel HCC cohort by merging gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database, and Gene Expression Omnibus (GEO) database. The validation cohort consisted of 230 HCC cases taken from the International Cancer Genome Consortium (ICGC) database. Multiple genomic characteristics, such as tumor mutation burden (TMB), and copy number variations were analyzed concurrently. On the basis of the expression of CRGs and FRGs, patients were classified into cuproptosis and ferroptosis subtypes. Then, we constructed a risk model using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression analysis based on ferroptosis and cuproptosis-related differentially expressed genes (DEGs). Patients were separated into two groups according to median risk score. We compared the immunophenotype, tumor microenvironment (TME), cancer stem cell index, and treatment sensitivity of two groups.

Results

Three subtypes of ferroptosis and two subtypes of cuproptosis were identified among the patients. A greater likelihood of survival (P<0.05) was expected for patients in FRGcluster B and CRGcluster B. After that, a confirmed risk signature for ferroptosis and cuproptosis was developed and tested. Patients in the low-risk group had significantly higher survival rates than those in the high-risk group, according to our study (P<0.001). There was also a strong correlation between the signature and other variables including immunophenoscore, TMB, cancer stem cell index, immunological checkpoint genes, and sensitivity to chemotherapeutics.

Conclusions

Through this comprehensive research, we identified a unique risk signature associated with HCC patients' treatment status and prognosis. Our findings highlight FRGs' and CRGs' significance in clinical practice and imply ferroptosis and cuproptosis may be therapeutic targets for HCC patients.

HMGCS2 serves as a potential biomarker for inhibition of renal clear cell carcinoma growth

3-Hydroxymethylglutaryl-CoA synthase 2 (HMGCS2) is the rate-limiting enzyme for ketone body synthesis, and most current studies focus on mitochondrial maturation and metabolic reprogramming. The role of HMGCS2 was evaluated in a pan-cancer multi-database using R language, and HMGCS2 was lowly expressed or not differentially expressed in all tumor tissues compared with normal tissues. Correlation analysis of clinical case characteristics, genomic heterogeneity, tumor stemness, and overall survival revealed that HMGCS2 is closely related to clear cell renal cell carcinoma (KIRC). Single-cell sequencing data from normal human kidneys revealed that HMGCS2 is specifically expressed in proximal tubular cells of normal adults. In addition, HMGCS2 is associated with tumor immune infiltration and microenvironment, and KIRC patients with low expression of HMGCS2 have worse prognosis. Finally, the results of cell counting kit 8 assays, colony formation assays, flow cytometry, and Western blot analysis suggested that upregulation of HMGCS2 increased the expression of key tumor suppressor proteins, inhibited the proliferation of clear cell renal cell carcinoma cells and promoted cell apoptosis. In conclusion, HMGCS2 is abnormally expressed in pan-cancer, may play an important role in anti-tumor immunity, and is expected to be a potential tumor prognostic marker, especially in clear cell renal cell carcinoma.

Dictionary learning LASSO for feature selection with application to hepatocellular carcinoma grading using contrast enhanced magnetic resonance imaging

Introduction

The successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature.

Methods

This study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary.

Results and discussion

The experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.

Targeting mitochondrial metabolism for precision medicine in cancer

During decades, the research field of cancer metabolism was based on the Warburg effect, described almost one century ago. Lately, the key role of mitochondria in cancer development has been demonstrated. Many mitochondrial pathways including oxidative phosphorylation, fatty acid, glutamine, and one carbon metabolism are altered in tumors, due to mutations in oncogenes and tumor suppressor genes, as well as in metabolic enzymes. This results in metabolic reprogramming that sustains rapid cell proliferation and can lead to an increase in reactive oxygen species used by cancer cells to maintain pro-tumorigenic signaling pathways while avoiding cellular death. The knowledge acquired on the importance of mitochondrial cancer metabolism is now being translated into clinical practice. Detailed genomic, transcriptomic, and metabolomic analysis of tumors are necessary to develop more precise treatments. The successful use of drugs targeting metabolic mitochondrial enzymes has highlighted the potential for their use in precision medicine and many therapeutic candidates are in clinical trials. However, development of efficient personalized drugs has proved challenging and the combination with other strategies such as chemocytotoxic drugs, immunotherapy, and ketogenic or calorie restriction diets is likely necessary to boost their potential. In this review, we summarize the main mitochondrial features, metabolic pathways, and their alterations in different cancer types. We also present an overview of current inhibitors, highlight enzymes that are attractive targets, and discuss challenges with translation of these approaches into clinical practice. The role of mitochondria in cancer is indisputable and presents several attractive targets for both tailored and personalized cancer therapy.

A Minimal Subset of Seven Genes Associated with Tumor Hepatocyte Differentiation Predicts a Poor Prognosis in Human Hepatocellular Carcinoma

Simple Summary

Liver cancer is one of the most commonly diagnosed cancers worldwide and the fourth leading cause of cancer-related deaths. Hepatocellular carcinoma (HCC) accounts for at least 80% of all malignant liver primary tumors. A better characterization of molecular mechanisms underlying HCC onset and progression may lead to discover new therapeutic targets and biomarkers. In this study, we performed an integrative transcriptomics analysis to evaluate the clinical relevance of genes associated with hepatocyte differentiation in human HCC. The HepaRG cell line model was used to define a gene expression signature reflecting the status of tumor hepatocyte differentiation. This signature was able to stratify HCC patients into clinically relevant molecular subtypes. Then, a minimal subset of seven differentiation-associated genes was identified to predict a poor prognosis in several cancer datasets.

Abstract

Hepatocellular carcinoma (HCC) is a deadly cancer worldwide as a result of a frequent late diagnosis which limits the therapeutic options. Tumor progression in HCC is closely correlated with the dedifferentiation of hepatocytes, the main parenchymal cells in the liver. Here, we hypothesized that the expression level of genes reflecting the differentiation status of tumor hepatocytes could be clinically relevant in defining subsets of patients with different clinical outcomes. To test this hypothesis, an integrative transcriptomics approach was used to stratify a cohort of 139 HCC patients based on a gene expression signature established in vitro in the HepaRG cell line using well-controlled culture conditions recapitulating tumor hepatocyte differentiation. The HepaRG model was first validated by identifying a robust gene expression signature associated with hepatocyte differentiation and liver metabolism. In addition, the signature was able to distinguish specific developmental stages in mice. More importantly, the signature identified a subset of human HCC associated with a poor prognosis and cancer stem cell features. By using an independent HCC dataset (TCGA consortium), a minimal subset of seven differentiation-related genes was shown to predict a reduced overall survival, not only in patients with HCC but also in other types of cancers (e.g., kidney, pancreas, skin). In conclusion, the study identified a minimal subset of seven genes reflecting the differentiation status of tumor hepatocytes and clinically relevant for predicting the prognosis of HCC patients.