Role of mitochondrial translation in remodeling of energy metabolism in ER/PR(+) breast cancer

Deregulation of mitochondrial gene expression in cancer: mechanisms and therapeutic opportunities

"Reprogramming of energy metabolism" was first considered an emerging hallmark of cancer in 2011 by Hanahan & Weinberg and is now considered a core hallmark of cancer. Mitochondria are the hubs of metabolism, crucial for energetic functions and cellular homeostasis. The mitochondrion's bacterial origin and preservation of their own genome, which encodes proteins and RNAs essential to their function, make them unique organelles. Successful generation of mitochondrial gene products requires coordinated functioning of the mitochondrial 'central dogma,' encompassing all steps necessary for mtDNA to yield mitochondrial proteins. Each of these processes has several levels of regulation, including mtDNA accessibility and protection through mtDNA packaging and epigenetic modifications, mtDNA copy number through mitochondrial replication, mitochondrial transcription through mitochondrial transcription factors, and mitochondrial translation through mitoribosome formation. Deregulation of these mitochondrial processes in the context of cancers has only recently been appreciated, with most studies being correlative in nature. Nonetheless, numerous significant associations of the mitochondrial central dogma with pro-tumor phenotypes have been documented. Several studies have even provided mechanistic insights and further demonstrated successful pharmacologic targeting strategies. Based on the emergent importance of mitochondria for cancer biology and therapeutics, it is becoming increasingly important that we gain an understanding of the underpinning mechanisms so they can be successfully therapeutically targeted. It is expected that this mechanistic understanding will result in mitochondria-targeting approaches that balance anticancer potency with normal cell toxicity. This review will focus on current evidence for the dysregulation of mitochondrial gene expression in cancers, as well as therapeutic opportunities on the horizon.

Spatial characterization and stratification of colorectal adenomas by deep visual proteomics

Colorectal adenomas (CRAs) are potential precursor lesions to adenocarcinomas, currently classified by morphological features. We aimed to establish a molecular feature-based risk allocation framework toward improved patient stratification. Deep visual proteomics (DVP) is an approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here, we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine male patients, immunohistologically stained for caudal-type homeobox 2 (CDX2), a protein implicated in colorectal cancer, enabling the characterization of cellular heterogeneity within distinct tissue regions and across patients. DVP identified DMBT1, MARCKS, and CD99 as protein markers linked to recurrence, suggesting their potential for risk assessment. It also detected a metabolic shift to anaerobic glycolysis in cells with high CDX2 expression. Our findings underscore the potential of spatial proteomics to refine early stage detection and contribute to personalized patient management strategies and provided novel insights into metabolic reprogramming.

node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking

Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks can be built to represent distinct conditions and compared to uncover graph-level differences-such as when comparing patterns of gene-gene interactions that change between biological states. Given the importance of the graph comparison problem, there is a clear and growing need for robust and scalable methods that can identify meaningful differences. We introduce node2vec2rank (n2v2r), a method for graph differential analysis that ranks nodes according to the disparities of their representations in joint latent embedding spaces. Improving upon previous bag-of-features approaches, we take advantage of recent advances in machine learning and statistics to compare graphs in higher-order structures and in a data-driven manner. Formulated as a multi-layer spectral embedding algorithm, n2v2r is computationally efficient, incorporates stability as a key feature, and can provably identify the correct ranking of differences between graphs in an overall procedure that adheres to veridical data science principles. By better adapting to the data, node2vec2rank clearly outperformed the commonly used node degree in finding complex differences in simulated data. In the real-world applications of breast cancer subtype characterization, analysis of cell cycle in single-cell data, and searching for sex differences in lung adenocarcinoma, node2vec2rank found meaningful biological differences enabling the hypothesis generation for therapeutic candidates. Software and analysis pipelines implementing n2v2r and used for the analyses presented here are publicly available.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

BACKGROUND

Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment.

METHODS

Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses.

RESULTS

Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions.

CONCLUSION

Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

The Role of Glucocorticoids in Breast Cancer Therapy

Glucocorticoids (GCs) are anti-inflammatory and immunosuppressive steroid molecules secreted by the adrenal gland and regulated by the hypothalamic-pituitary-adrenal (HPA) axis. GCs present a circadian release pattern under normal conditions; they increase their release under stress conditions. Their mechanism of action can be via the receptor-independent or receptor-dependent pathway. The receptor-dependent pathway translocates to the nucleus, where the ligand-receptor complex binds to specific sequences in the DNA to modulate the transcription of specific genes. The glucocorticoid receptor (GR) and its endogenous ligand cortisol (CORT) in humans, and corticosterone in rodents or its exogenous ligand, dexamethasone (DEX), have been extensively studied in breast cancer. Its clinical utility in oncology has mainly focused on using DEX as an antiemetic to prevent chemotherapy-induced nausea and vomiting. In this review, we compile the results reported in the literature in recent years, highlighting current trends and unresolved controversies in this field. Specifically, in breast cancer, GR is considered a marker of poor prognosis, and a therapeutic target for the triple-negative breast cancer (TNBC) subtype, and efforts are being made to develop better GR antagonists with fewer side effects. It is necessary to know the type of breast cancer to differentiate the treatment for estrogen receptor (ER)-positive, ER-negative, and TNBC, to implement therapies that include the use of GCs.