Analysis and Characterization of Mitochondrial DNA Mutations in The Cancer Genome Atlas Hepatocellular Carcinoma Cohort

Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy and represents the second cause of cancer related death worldwide, characterized by high recurrence rates and poor survival, even when detected and treated at its early stages. Mitochondrial mutations have been known to play a role in carcinogenesis, but to date, few studies correctly prioritize and interpret the variants discovered. Thus, we aimed to identify and analyze the occurrence and clinical impact of mtDNA mutations in the HCC dataset from The Cancer Genome Atlas (TCGA) consortium - National Cancer Institute. Whole exome sequencing fastq files from 377 TCGA-HCC patients (paired tumor, non- tumor tissues) were processed to reconstruct the mtDNA genomes using the MToolBox automated pipeline. Pairwise comparison between blood/normal solid tissue and tumor was performed in order to identify the potentially germline and tumor-specific somatic mtDNA variants. Information regarding the variability and pathogenicity of the variants were obtained from HmtVar database. The assembly of the mitochondrial reads showed an adequate coverage and quality for 104 patients. Variants were classified as pathogenic based on the allele frequency and disease score using the HmtVar criteria. After discarding the germline variants used in haplogroup classification, fixing the heteroplasmic fraction (HF) at 0.4 and prioritizing the variants we found 13 pathogenic/likely-pathogenic missense mutations and three tRNA pathogenic mutations in tRNA genes. HCC tumors presented a total of 302 somatic variants. After applying the same criteria, we found 24 pathogenic mutations in 22 patients. The burden of pathogenic mtDNA mutations resulted associated with a poorer survival of these patients. We found 21% of HCC patients to harbor somatic pathogenic mtDNA mutations in their tumors. We found that these patients had a poorer survival than those harbouring non-pathogenic variants. mtDNA mutations could cause mitochondrial dysfunction and impact the prognosis and survival of HCC patients

MiR-494 induces metabolic changes through G6pc targeting and modulates sorafenib response in hepatocellular carcinoma

BackgroundMetabolic reprogramming is a well-known marker of cancer, and it represents an early event during hepatocellular carcinoma (HCC) development. The recent approval of several molecular targeted agents has revolutionized the management of advanced HCC patients. Nevertheless, the lack of circulating biomarkers still affects patient stratification to tailored treatments. In this context, there is an urgent need for biomarkers to aid treatment choice and for novel and more effective therapeutic combinations to avoid the development of drug-resistant phenotypes. This study aims to prove the involvement of miR-494 in metabolic reprogramming of HCC, to identify novel miRNA-based therapeutic combinations and to evaluate miR-494 potential as a circulating biomarker.MethodsBioinformatics analysis identified miR-494 metabolic targets. QPCR analysis of glucose 6-phosphatase catalytic subunit (G6pc) was performed in HCC patients and preclinical models. Functional analysis and metabolic assays assessed G6pc targeting and miR-494 involvement in metabolic changes, mitochondrial dysfunction, and ROS production in HCC cells. Live-imaging analysis evaluated the effects of miR-494/G6pc axis in cell growth of HCC cells under stressful conditions. Circulating miR-494 levels were assayed in sorafenib-treated HCC patients and DEN-HCC rats.ResultsMiR-494 induced the metabolic shift of HCC cells toward a glycolytic phenotype through G6pc targeting and HIF-1A pathway activation. MiR-494/G6pc axis played an active role in metabolic plasticity of cancer cells, leading to glycogen and lipid droplets accumulation that favored cell survival under harsh environmental conditions. High miR-494 serum levels associated with sorafenib resistance in preclinical models and in a preliminary cohort of HCC patients. An enhanced anticancer effect was observed for treatment combinations between antagomiR-494 and sorafenib or 2-deoxy-glucose in HCC cells.ConclusionsMiR-494/G6pc axis is critical for the metabolic rewiring of cancer cells and associates with poor prognosis. MiR-494 deserves attention as a candidate biomarker of likelihood of response to sorafenib to be tested in future validation studies. MiR-494 represents a promising therapeutic target for combination strategies with sorafenib or metabolic interference molecules for the treatment of HCC patients who are ineligible for immunotherapy

miRNA levels are associated with body mass index in endometrial cancer and may have implications for therapy

Endometrial cancer (EC) is the most prevalent gynecological cancer in high-income countries. Its incidence is skyrocketing due to the increase in risk factors such as obesity, which represents a true pandemic. This study aimed to evaluate microRNA (miRNA) expression in obesity-related EC to identify potential associations between this specific cancer type and obesity. miRNA levels were analyzed in 84 EC patients stratified based on body mass index (BMI; >= 30 or <30) and nine noncancer women with obesity. The data were further tested in The Cancer Genome Atlas (TCGA) cohort, including 384 EC patients, 235 with BMI >= 30 and 149 with BMI <30. Prediction of miRNA targets and analysis of their expression were also performed to identify the potential epigenetic networks involved in obesity modulation. In the EC cohort, BMI >= 30 was significantly associated with 11 deregulated miRNAs. The topmost deregulated miRNAs were first analyzed in 84 EC samples by single miRNA assay and then tested in the TCGA dataset. This independent validation provided further confirmation about the significant difference of three miRNAs (miR-199a-5p, miR-449a, miR-449b-5p) in normal-weight EC patients versus EC patients with obesity, resulting significantly higher expressed in the latter. Moreover, the three miRNAs were significantly correlated with grade, histological type, and overall survival. Analysis of their target genes revealed that these miRNAs may regulate obesity-related pathways. In conclusion, we identified specific miRNAs associated with BMI that are potentially involved in modulating obesity-related pathways and that may provide novel implications for the clinical management of obese EC patients

Post-operative residual disease and number of cycles of neoadjuvant chemotherapy in advanced epithelial ovarian carcinoma

BackgroundThe optimal number of neoadjuvant chemotherapy cycles in patients with advanced ovarian cancer is still disputed. ObjectiveTo evaluate the impact of the number of neoadjuvant chemotherapy cycles and role of optimal cytoreduction on the prognosis of patients with advanced ovarian cancer. MethodsClinical and pathological details were examined. Patients were evaluated combining the number of cycles of neoadjuvant chemotherapy-namely, 'interval debulking surgery' after up to four neoadjuvant chemotherapy cycles, and 'delayed debulking surgery' after more than four cycles of therapy. ResultsA total of 286 patients were included in the study. Complete cytoreduction with no residual peritoneal disease (CC0) was achieved in 74 (74%) patients with interval debulking surgery and 124 (66.7%) patients with delayed interval debulking. Of those with residual disease, there were 26/88 (29.5%) patients in the interval debulking surgery group and 62/88 (70.5%) patients in the delayed debulking surgery group. Comparison of patients with delayed debulking-CC0 and interval debulking-CC0 showed no difference in progression-free survival (p=0.3) or overall survival (p=0.4), while significantly worse outcomes were observed in patients with interval debulking-CC1 (p=0.02 and p=0.04, respectively). Specifically, patients with interval debulking-CC1 had an approximately 67% increased risk of disease progression (p=0.04; HR=2.01 (95% CI 1.04 to 4.18)) and a 69% higher risk of death than patients with delayed debulking-CC0 (p=0.03; HR=2.34 (95% CI 1.11 to 4.67)). ConclusionIncreasing the number of neoadjuvant chemotherapy cycles does not worsen patient outcomes if complete resection is achieved. Nevertheless, additional prospective trials are necessary to establish the optimum number of neoadjuvant chemotherapy cycles

LncRNAs as novel players in hepatocellular carcinoma recurrence

Long non-coding RNAs (lncRNAs) are ncRNAs more than 200 nucleotides long that participate to a wide range of biological functions. However, their role in cancer is poorly known. By using an NGS-based approach we analyzed the intragenic and poliA-lncRNAs in hepatocellular carcinoma (HCC) and we assayed the relationships between their deregulated expression and clinical-pathological characteristics. The expression profile of lncRNAs was studied in a discovery series of 28 HCC and matched cirrhosis and was validated in an independent cohort of 32 HCC patients both in tissue and serum. The correlation between lncRNA expression and clinical-pathological variables, EMT markers and putative sponged microRNAs level were investigated. Functional experiments were performed in HCC-derived cell lines to clarify the role of selected lncRNAs in HCC. A panel of deregulated lncRNAs differentiated HCC from cirrhotic tissue. CASC9 and LUCAT1 were up-regulated in a subset of HCC-derived cell lines and in half of HCCs which displayed a lower recurrence after surgery. LUCAT1 and CASC9 silencing increased cell motility and invasion capability in HCC cells and influenced the EMT phenotype. LUCAT1 was demonstrated to directly sponge the onco-miR-181d-5p. Both LUCAT1 and CASC9 were secreted in exosomes, and higher circulating CASC9 levels were associated with tumor size and HCC recurrence after surgery, suggesting its potential usage as putative non-invasive prognostic biomarker of recurrence

Classification Systems of Endometrial Cancer: A Comparative Study about Old and New

Endometrial cancer is the most common gynecological malignancy of the female reproductive organs. Historically it was divided into type I and type II, until 2013 when the Cancer Genome Atlas molecular classification was proposed. Here, we applied the different classification types on our endometrial cancer patient cohort in order to identify the most predictive one. We enrolled 117 endometrial cancer patients available for the study and collected the following parameters: age, body mass index, stage, menopause, Lynch syndrome status, parity, hypertension, type of localization of the lesion at hysteroscopy, type of surgery and complications, and presence of metachronous or synchronous tumors. The tumors were classified according to the European Society for Medical Oncology, Proactive Molecular Risk Classifier for Endometrial Cancer, Post-Operative Radiation Therapy in Endometrial Carcinoma, and Cancer Genome Atlas classification schemes. Our data confirmed that European Society for Medical Oncology risk was the strongest predictor of prognosis in our cohort. The parameters correlated with poor prognosis were the histotype, FIGO stage, and grade. Our study cohort shows that risk stratification should be based on the integration of histologic, clinical, and molecular parameters

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

Background: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. Methods: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). Results: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. Conclusions: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes