Bcl2l10 induces metabolic alterations in ovarian cancer cells by regulating the TCA cycle enzymes SDHD and IDH1

Identification and validation of an immune-derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma

OBJECTIVES

Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not yet been thoroughly investigated.

METHODS

Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed an immune-derived multiple programmed cell death index (MPCDI) based on machine learning methods.

RESULTS

Using the median MPCDI scores, we categorized the LUAD patients into two groups: low-MPCDI and high-MPCDI. Our analysis of the TCGA-LUAD training cohort and three external GEO cohorts (GSE37745, GSE30219, and GSE68465) revealed that patients with high-MPCDI experienced a more unfavorable prognosis, whereas those with low-MPCDI had a better prognosis. Furthermore, the results of both univariate and multivariate Cox regression analyses further confirmed that MPCDI serves as a novel independent risk factor. By combining clinical characteristics with the MPCDI, we constructed a nomogram that provides an accurate and reliable quantitative tool for personalized clinical management of LUAD patients. The findings obtained from the analysis of C-index and the decision curve revealed that the nomogram outperformed various clinical variables in terms of net clinical benefit. Encouragingly, the low-MPCDI patients are more sensitive to commonly used chemotherapy drugs, which suggests that MPCDI scores have a guiding role in chemotherapy for LUAD patients.

CONCLUSION

Therefore, MPCDI can be used as a novel clinical diagnostic classifier, providing valuable insights into the clinical management and clinical decision-making for LUAD patients.

The Role of Bcl-2 Family Proteins and Sorafenib Resistance in Hepatocellular Carcinoma

Liver cancer has been reported to be one of the most malignant diseases in the world. It is late diagnosis consequently leads to a difficult treatment, as the cancer reached an advanced stage. Hepatocellular carcinoma (HCC) is the primary type of cancer diagnosed in the liver, with deadly characteristics and a poor prognosis. The first-in-line treatment for advanced HCC is sorafenib. Sorafenib acts by inhibiting cell proliferation and by inducing apoptosis as well as blocks receptors associated with these mechanisms. Due to its constant use, sorafenib resistance has been described, especially to proteins of the Bcl-2 family, and their overexpression of Bcl-XL and Mcl-1. This review focuses on the role of the Bcl-2 proteins in relation to sorafenib resistance as a consequence of first-in-line treatment in HCC.

Deregulated Metabolic Pathways in Ovarian Cancer: Cause and Consequence

Ovarian cancers are tumors that originate from the different cells of the ovary and account for almost 4% of all the cancers in women globally. More than 30 types of tumors have been identified based on the cellular origins. Epithelial ovarian cancer (EOC) is the most common and lethal type of ovarian cancer which can be further divided into high-grade serous, low-grade serous, endometrioid, clear cell, and mucinous carcinoma. Ovarian carcinogenesis has been long attributed to endometriosis which is a chronic inflammation of the reproductive tract leading to progressive accumulation of mutations. Due to the advent of multi-omics datasets, the consequences of somatic mutations and their role in altered tumor metabolism has been well elucidated. Several oncogenes and tumor suppressor genes have been implicated in the progression of ovarian cancer. In this review, we highlight the genetic alterations undergone by the key oncogenes and tumor suppressor genes responsible for the development of ovarian cancer. We also summarize the role of these oncogenes and tumor suppressor genes and their association with a deregulated network of fatty acid, glycolysis, tricarboxylic acid and amino acid metabolism in ovarian cancers. Identification of genomic and metabolic circuits will be useful in clinical stratification of patients with complex etiologies and in identifying drug targets for personalized therapies against cancer.

Pathway analysis of smoking-induced changes in buccal mucosal gene expression

Background

Cigarette smoking is the leading preventable cause of death worldwide, and it is the most common cause of oral cancers. This study aims to provide a deeper understanding of the molecular pathways in the oral cavity that are altered by exposure to cigarette smoke.

Methods

The gene expression dataset (accession number GSE8987, GPL96) of buccal mucosa samples from smokers (n = 5) and never smokers (n = 5) was downloaded from The National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository. Differential expression was ascertained via NCBI's GEO2R software, and Ingenuity Pathway Analysis (IPA) software was used to perform a pathway analysis.

Results

A total of 459 genes were found to be significantly differentially expressed in smoker buccal mucosa (p  < 0.05). A total of 261 genes were over-expressed while 198 genes were under-expressed. The top canonical pathways predicted by IPA were nitric oxide and reactive oxygen production at macrophages, macrophages/fibroblasts and endothelial cells in rheumatoid arthritis, and thyroid cancer pathways. The IPA upstream analysis predicted that the TP53, APP, SMAD3, and TNF proteins as well as dexamethasone drug would be top transcriptional regulators.

Conclusions

IPA highlighted critical pathways of carcinogenesis, mainly nitric oxide and reactive oxygen production at macrophages, and confirmed widespread injury in the buccal mucosa due to exposure to cigarette smoke. Our findings suggest that cigarette smoking significantly impacts gene pathways in the buccal mucosa and may highlight potential targets for treating the effects of cigarette smoking.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43042-022-00268-y.

Unraveling Autocrine Signaling Pathways through Metabolic Fingerprinting in Serous Ovarian Cancer Cells

Focusing on defining metabolite-based inter-tumoral heterogeneity in ovarian cancer, we investigated the metabolic diversity of a panel of high-grade serous ovarian carcinoma (HGSOC) cell-lines using a metabolomics platform that interrogate 731 compounds. Metabolic fingerprinting followed by 2-dimensional and 3-dimensional principal component analysis established the heterogeneity of the HGSOC cells by clustering them into five distinct metabolic groups compared to the fallopian tube epithelial cell line control. An overall increase in the metabolites associated with aerobic glycolysis and phospholipid metabolism were observed in the majority of the cancer cells. A preponderant increase in the levels of metabolites involved in trans-sulphuration and glutathione synthesis was also observed. More significantly, subsets of HGSOC cells showed an increase in the levels of 5-Hydroxytryptamine, γ-aminobutyrate, or glutamate. Additionally, 5-hydroxytryptamin synthesis inhibitor as well as antagonists of γ-aminobutyrate and glutamate receptors prohibited the proliferation of HGSOC cells, pointing to their potential roles as oncometabolites and ligands for receptor-mediated autocrine signaling in cancer cells. Consistent with this role, 5-Hydroxytryptamine synthesis inhibitor as well as receptor antagonists of γ-aminobutyrate and Glutamate-receptors inhibited the proliferation of HGSOC cells. These antagonists also inhibited the three-dimensional spheroid growth of TYKNU cells, a representative HGSOC cell-line. These results identify 5-HT, GABA, and Glutamate as putative oncometabolites in ovarian cancer metabolic sub-type and point to them as therapeutic targets in a metabolomic fingerprinting-based therapeutic strategy.