Impact of host characteristics on liver fibrosis progression in patients with chronic hepatitis C

Smatra se da oko 200 milijuna ljudi u svijetu boluje od kroničnog hepatitisa C što predstavlja velik javnozdravstveni problem. Kronični hepatitis C je bolest vrlo heterogenog kliničkog tijeka pri čemu je osnovni čimbenik dugoročne prognoze brzina napredovanja jetrene fibroze koja konačno rezultira cirozom i njenim komplikacijama uključivo i nastankom hepatocelularnog karcinoma. Na prirodni tijek bolesti značajno utječu karakteristike domaćina. S lošijom prognozom povezuju se muški spol, starija dob kod stjecanja infekcije, bijela rasa, određeni aleli gena sustava HLA, interleukina i kemokina, koagulacijskih faktora, faktora rasta, tkivnih metaloproteinaza, opterećenje željezom te steatoza jetre u sklopu metaboličkog sindroma. Prepoznavanje i analiza ovih čimbenika iznimno je značajna za brzu identifikaciju bolesnika s potencijalnom brzom progresijom bolesti i optimizaciju njihovog liječenja.Hepatitis C virus infection is an important global public health problem with 200 million people suffering from chronic disease worldwide. The clinical course of chronic hepatitis C is heterogeneous. The most important predictor of long-term diseases outcome is the dynamics of liver fibrosis progression which leads to cirrhosis and hepatocellular carcinoma. Natural course of chronic infection is significantly determined by the characteristics of the host. Male sex, older age at the disease onset, Caucasian race, host\u27s genetic polymorphism for HLA, interleukins, chemokines, coagulation factors, growth factors, tissue metalloproteinases as well as iron overload and liver steatosis represent host factors associated with unfavorable disease outcome. Recognition and analysis of these factors is important for the early identification of rapid progressors and treatment optimization of these patients

The neglected liaison: Targeting cancer cell metabolic reprogramming modifies the composition of non‐malignant populations of the tumor microenvironment

Metabolic reprogramming is a well‐known hallmark of cancer, whereby the development of drugs that target cancer cell metabolism is gaining momentum. However, when establishing preclinical studies and clinical trials, it is often neglected that a tumor mass is a complex system in which cancer cells coexist and interact with several types of microenvironment populations, including endothelial cells, fibroblasts and immune cells. We are just starting to understand how such populations are affected by the metabolic changes occurring in a transformed cell and little is known about the impact of metabolism‐targeting drugs on the non‐malignant tumor components. Here we provide a general overview of the links between cancer cell metabolism and tumor microenvironment (TME), particularly focusing on the emerging literature reporting TME‐specific effects of metabolic therapies

A mutation screening of oncogenes, tumor suppressor gene TP53 and nuclear encoded mitochondrial complex I genes in oncocytic thyroid tumors.

Background: Thyroid neoplasias with oncocytic features represent a specific phenotype in non-medullary thyroid cancer, reflecting the unique biological phenomenon of mitochondrial hyperplasia in the cytoplasm. Oncocytic thyroid cells are characterized by a prominent eosinophilia (or oxyphilia) caused by mitochondrial abundance. Although disruptive mutations in the mitochondrial DNA (mtDNA) are the most significant hallmark of such tumors, oncocytomas may be envisioned as heterogeneous neoplasms, characterized by multiple nuclear and mitochondrial gene lesions. We investigated the nuclear mutational profile of oncocytic tumors to pinpoint the mutations that may trigger the early oncogenic hit. Methods: Total DNA was extracted from paraffin-embedded tissues from 45 biopsies of oncocytic tumors. High-resolution melting was used for mutation screening of mitochondrial complex I subunits genes. Specific nuclear rearrangements were investigated by RT-PCR (RET/PTC) or on isolated nuclei by interphase FISH (PAX8/PPARγ). Recurrent point mutations were analyzed by direct sequencing. Results: In our oncocytic tumor samples, we identified rare TP53 mutations. The series of analyzed cases did not include poorly- or undifferentiated thyroid carcinomas, and none of the TP53 mutated cases had significant mitotic activity or high-grade features. Thus, the presence of disruptive TP53 mutations was completely unexpected. In addition, novel mutations in nuclear-encoded complex I genes were identified. Conclusions: These findings suggest that nuclear genetic lesions altering the bioenergetics competence of thyroid cells may give rise to an aberrant mitochondria-centered compensatory mechanism and ultimately to the oncocytic phenotype. Keywords: Oncocytic carcinoma, Nuclear mitochondrial complex I subunits, Oncogene mutation analysi

AI-aided holographic flow cytometry for label-free identification of ovarian cancer cells in the presence of unbalanced datasets

Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification

Mitochondrial Metabolism and Cancer

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The association between Helicobacter pylori and gastric cancer: the role of infection's eradication. Recent advances

The association between Helicobacter pylori and gastric cancer has been firmly established. Eradication of H. pylori seems to modestly reduce gastric cancer risk as well as to induce regression of preneoplastic lesions. However, gastric cancer can still occur despite eradication and endoscopic follow-up might still be needed

Complex I impairment in mitochondrial diseases and cancer: parallel roads leading to different outcomes.

Respiratory chain complex I (CI) dysfunctions have been recognized as one of the most frequent causes of mitochondrial neuro-muscular disorders. Moreover, latest reports reveal that CI impairment is a major contributing factor in many other pathological processes, including cancer. In fact, energy depletion, oxidative stress and metabolites unbalance are frequently associated with CI functional and structural alterations. The occurrence of mitochondrial DNA (mtDNA) mutations is a shared feature in neuro-muscular diseases and cancer; however, the two diverging phenotypes arise depending on the mutation type (disassembling versus non-disassembling mutations), the mutant load and the cytotype. In this review, we unify our knowledge on CI impairment caused by mutations in structural CI genes and assembly chaperones, both in mitochondrial disorders and cancer, stratifying such mutations based on their functional versus structural effects. We summarize shared and specific metabolic consequences of CI dysfunction in these pathologies, which allow us to draw two parallel roads that lead to different clinical outcomes. This article is part of a Directed Issue entitled: Bioenergetic dysfunction, adaptation and therapy