Autophagy is a fundamental cellular process that maintains homeostasis by degrading damaged components and regulating stress responses. It plays a crucial role in cancer biology, including tumor progression, metastasis, and therapeutic resistance. Oxidative stress, similarly, is key to maintaining cellular balance by regulating oxidants and antioxidants, with its disruption leading to molecular damage. The interplay between autophagy and oxidative stress is particularly significant, as reactive oxygen species (ROS) act as both inducers and by-products of autophagy. While autophagy can function as a tumor suppressor in early cancer stages, it often shifts to a pro-tumorigenic role in advanced disease, aiding cancer cell survival under adverse conditions such as hypoxia and nutrient deprivation. This dual role is mediated by several signaling pathways, including PI3K/AKT/mTOR, AMPK, and HIF-1α, which coordinate the balance between autophagic activity and ROS production. In this review, we explore the mechanisms by which autophagy and oxidative stress interact across different hematological malignancies. We discuss how oxidative stress triggers autophagy, creating a feedback loop that promotes tumor survival, and how autophagic dysregulation leads to increased ROS accumulation, exacerbating tumorigenesis. We also examine the therapeutic implications of targeting the autophagy–oxidative stress axis in cancer. Current strategies involve modulating autophagy through specific inhibitors, enhancing ROS levels with pro-oxidant compounds, and combining these approaches with conventional therapies to overcome drug resistance. Understanding the complex relationship between autophagy and oxidative stress provides critical insights into novel therapeutic strategies aimed at improving cancer treatment outcomes.

Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

Autophagy is a fundamental cellular process that maintains homeostasis by degrading damaged components and regulating stress responses. It plays a crucial role in cancer biology, including tumor progression, metastasis, and therapeutic resistance. Oxidative stress, similarly, is key to maintaining cellular balance by regulating oxidants and antioxidants, with its disruption leading to molecular damage. The interplay between autophagy and oxidative stress is particularly significant, as reactive oxygen species (ROS) act as both inducers and by-products of autophagy. While autophagy can function as a tumor suppressor in early cancer stages, it often shifts to a pro-tumorigenic role in advanced disease, aiding cancer cell survival under adverse conditions such as hypoxia and nutrient deprivation. This dual role is mediated by several signaling pathways, including PI3K/AKT/mTOR, AMPK, and HIF-1α, which coordinate the balance between autophagic activity and ROS production. In this review, we explore the mechanisms by which autophagy and oxidative stress interact across different hematological malignancies. We discuss how oxidative stress triggers autophagy, creating a feedback loop that promotes tumor survival, and how autophagic dysregulation leads to increased ROS accumulation, exacerbating tumorigenesis. We also examine the therapeutic implications of targeting the autophagy-oxidative stress axis in cancer. Current strategies involve modulating autophagy through specific inhibitors, enhancing ROS levels with pro-oxidant compounds, and combining these approaches with conventional therapies to overcome drug resistance. Understanding the complex relationship between autophagy and oxidative stress provides critical insights into novel therapeutic strategies aimed at improving cancer treatment outcomes.

Uterine pathologies pose a challenge to women’s health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

An early diagnosis of cancer is fundamental not only in regard to reducing its mortality rate but also in terms of counteracting the progression of the tumor in the initial stages. Breast cancer (BC) is the most common tumor pathology in women and the second deathliest cancer worldwide, although its survival rate is increasing thanks to improvements in screening programs. However, the most common techniques to detect a breast tumor tend to be time-consuming, unspecific or invasive. Herein, the use of untargeted hydrophilic interaction liquid chromatography−mass spectrometry analysis appears as an analytical technique with potential use for the early detection of biomarkers in liquid biopsies from BC patients. In this research, plasma samples from 134 BC patients were compared with 136 from healthy controls (HC), and multivariate statistical analyses showed a clear separation between four BC phenotypes (LA, LB, HER2, and TN) and the HC group. As a result, we identified two candidate biomarkers that discriminated between the groups under study with a VIP > 1 and an AUC of 0.958. Thus, targeting the specific aberrant metabolic pathways in future studies may allow for better molecular stratification or early detection of the disease.