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Metabolic dysregulation: Its role in diabetes mellitus and cancers

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Shuang Dai · Jiacheng Li · Jing Xia · Yingtong Liu · Yan Li

Mol Aspects Med. 2026 Feb 23;108:101461. doi: 10.1016/j.mam.2026.101461. Online ahead of print.

ABSTRACT

Diabetes mellitus (DM) is a significant risk factor for several cancers, particularly cancers of the liver, pancreas, and endometrium. This review aims to understand the connections between diabetic pathophysiology and cancer biology. We synthesize how core metabolic disturbances-hyperinsulinemia, hyperglycemia, and inflammation-promote tumorigenesis by dysregulating canonical oncogenic pathways such as IGF-1 signaling, DNA damage repair, and immunometabolism. Subsequently, we focus on how key molecular integrators-such as p38 MAPK, Wnt/β-catenin, and the AGEs-RAGE axis-mediate metabolic stress to confer proliferative and invasive advantages to tumor cells. However, a direct translational application of these mechanisms, particularly in the context of repurposing antidiabetic drugs for cancer therapy, remains challenging due to inconsistent clinical outcomes. To address this gap, we suggest that a fundamental shift in approach is required. We propose that future research must move beyond simple pathway categorization and instead utilize spatial analysis techniques to reveal how diabetic metabolites reshape the tumor microenvironment (TME). By integrating single-cell and spatial omics technologies, the field can begin to map the precise cellular niches within tumors where diabetic metabolites exacerbate malignant progression and foster treatment resistance. This perspective is essential for developing targeted strategies to mitigate cancer risk and improve outcomes for the expanding population of patients with DM and cancer.

PMID:41734405 | DOI:10.1016/j.mam.2026.101461

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AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review

✇Journal of Medical Internet Research
By: Ander Cejudo · Markel Arrojo · Cristina Martín · Aitor Almeida
Background: Traditional cognitive screening relies on episodic clinical assessments and may miss early changes preceding cognitive impairment and dementia. Wearable and mobile health technologies enable continuous monitoring of sleep, physical activity, and circadian rhythms, generating digital biomarkers that may support scalable early detection and prevention. However, current evidence remains fragmented across devices, analytic approaches, and cognitive outcomes. Objective: This study synthesizes and critically evaluates recent evidence on wearable devices for early detection and prevention of cognitive impairment and dementia, focusing on device categories, cognitive outcomes, analytic approaches, and prevention relevance. Methods: We searched PubMed, Scopus, ACM Digital Library, and SpringerLink for peer-reviewed studies published between January 2020 and December 1, 2025. Eligible studies included human participants with a mean age ≥50 years, continuous wearable-derived data collected for ≥24 hours, and validated cognitive outcomes; reviews, protocols, smartphone-only studies, and pharmacological interventions were excluded. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Appraisal Tool for Cross-Sectional Studies, Newcastle-Ottawa Scale, Cochrane Risk of Bias tool, and Quality Assessment of Diagnostic Accuracy Studies-2. Owing to substantial heterogeneity in devices, outcomes, and analytic methods, quantitative meta-analysis was not feasible; a structured narrative synthesis was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidance. This study was not prospectively registered. Results: We included 49 studies, with sample sizes ranging from 14 to 91,948 participants (>200,000 total) and a median sample size of 145. Most used research-grade actigraphy (43/49, 87.8%), while fewer used commercial wearables (7/49, 14.3%). Cognitive outcomes most frequently relied on global screening instruments, including the Mini-Mental State Examination (18/49, 36.7%), followed by ICD-10 (International Statistical Classification of Diseases, Tenth Revision)–based clinical diagnoses (7/49, 14.3%) and the Montreal Cognitive Assessment (7/49, 14.3%). Analytic approaches were predominantly statistical (36/49, 73.5%), with fewer studies applying machine learning (7/49, 14.3%) or deep learning methods (6/49, 12.2%). Statistical analyses linked disrupted sleep, circadian rhythm fragmentation, and irregular activity patterns to worse cognitive outcomes, with modest-to-moderate effect sizes. Machine learning and deep learning approaches reported classification performance with area under the curve values between approximately 0.70 and 0.95. Approximately one-quarter of the studies (13/49, 26.5%) addressed early detection or prevention through longitudinal risk estimation or predictive modeling. Key limitations included small sample sizes, short monitoring durations, and limited external validation. Conclusions: Wearable-derived behavioral markers show promise for early risk stratification. This review advances the field by shifting from descriptive associations toward a digital phenotyping framework evaluating artificial intelligence–driven prediction in the preclinical window. Unlike prior reviews focused on established dementia, it differentiates direct predictive evidence from indirect correlational findings and critically assesses methodological maturity. Continuous, passive monitoring may enable scalable detection of subtle behavioral changes, supporting earlier and more personalized risk reduction strategies. Trial Registration:
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Preliminary Exploration of Fluvastatin Inhibiting Proliferation, Migration and Invasion of Lung Cancer Cells and Reversing Paclitaxel Resistance: Mechanism Exploration Based on Multi-Omics Analysis

✇Omics In Lung
By: Hongyu Xu · Zedong Du · Jia Shui · Xueting Li · Juan Tang · Guangquan Li

Drug Des Devel Ther. 2026 Feb 16;20:579427. doi: 10.2147/DDDT.S579427. eCollection 2026.

ABSTRACT

BACKGROUND: Lung cancer is one of the leading causes of cancer-related deaths, among which NSCLC accounts for approximately 80-85% of all lung cancer cases. Paclitaxel (TAX) is a commonly used chemotherapeutic drug, but it is easy to cause drug resistance. Fluvastatin has anti-cancer potential, but the mechanism of its reversal of drug resistance is unclear.

METHODS: The study was divided into four groups: the A549 control group, the A549/Tax control group, the A549 Fluvastatin-treated group, and the A549/Tax Fluvastatin-treated group. CCK-8, Transwell, and flow cytometry assays were used to detect fluvastatin's effects on cell proliferation, migration, invasion, and apoptosis. Transcriptomics, proteomics, and acetylomics were combined to explore the potential molecular mechanisms.

RESULTS: The preliminary results showed that fluvastatin inhibited the proliferation, migration and invasion of A549 and A549/Tax cells and promoted their apoptosis. Multi-omics analysis revealed that a large number of differentially expressed molecules were detected in both the A549-Fluvastatin vs A549-NC group and the A549/Tax-Fluvastatin vs A549/Tax-NC group, and these molecules were significantly enriched in multiple biological processes and signaling pathways. This suggests that fluvastatin may exert its effects through the synergistic regulation of multiple molecules and pathways. Integrated multi-omics analysis identified several key molecules (for example, HMGCR, RDH11, HSPB1) and acetylated protein-target gene pairs (for example, P09874-BCL2, P42224-PTGS2, P04150-CCND3), which may mediate the antitumor mechanism of fluvastatin.

CONCLUSION: This study indicates that fluvastatin has the potential to reverse TAX resistance in lung cancer, and the results of multi-omics analysis provide a theoretical basis for the exploration of potential therapeutic targets in the future.

PMID:41728357 | PMC:PMC12922964 | DOI:10.2147/DDDT.S579427

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A multi-omics approach elucidates the link between artificial food colorings and common cancers

✇Omics in Gastric
By: Xiang Feng · Biyuan Luo · Mengge Ding · Xianling Liu

Front Nutr. 2026 Feb 5;13:1743416. doi: 10.3389/fnut.2026.1743416. eCollection 2026.

ABSTRACT

BACKGROUND: Artificial food colorings (AFCs) are widely used, yet their potential links to cancer remain unclear. We investigated associations between commonly used AFCs and cancer-related molecular networks and prognosis.

METHODS: AFCs-related targets were collected from CTD, ChEMBL, SEA, and TargetNet, and cancer-related targets from GeneCards, OMIM, and CTD. Overlapping targets were subjected to STRING-based PPI analysis and Cytoscape visualization, followed by GO/KEGG enrichment. Core targets were evaluated for differential expression in GEO datasets of non-small cell lung cancer (NSCLC), colon adenocarcinoma (COAD), gastric cancer (GC), and breast cancer (BRCA), with GSEA for pathway characterization. Expression patterns were examined using GEPIA2. TCGA transcriptomic and clinical data were used to construct prognostic models via univariate Cox regression, LASSO selection, and multivariate Cox regression. Key genes were assessed using the Human Protein Atlas (HPA) and qPCR, and in vivo experiments evaluated tumor growth under AFCs exposure.

RESULTS: Four high-exposure AFCs were analyzed. We identified 108 shared AFCs-cancer targets and prioritized 50 core targets. Enrichment analyses highlighted cancer-relevant functional themes, including cell-cycle regulation (cyclin-dependent protein kinase holoenzyme complex) and oncogenic signaling (PI3K-Akt pathway). Multiple core targets were dysregulated in GEO tumor datasets, and GSEA identified consistently enriched pathways across cancer types. TCGA-derived signatures stratified patients into distinct risk groups with significantly different overall survival. HPA supported protein-level differences for selected targets, qPCR indicated that Allura Red AC or Tartrazine modulated prognostic gene expression in cancer cell lines, and AFCs exposure was associated with accelerated LLC tumor growth in mice.

CONCLUSION: This integrative analysis suggests that commonly used AFCs may be associated with cancer-related molecular networks and adverse prognosis in NSCLC, COAD, GC, and BRCA, informing future safety evaluation and regulation.

PMID:41727196 | PMC:PMC12916573 | DOI:10.3389/fnut.2026.1743416

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Generative AI analyzes medical data faster than human research teams

✇Latest Science News -- ScienceDaily
Researchers tested whether generative AI could handle complex medical datasets as well as human experts. In some cases, the AI matched or outperformed teams that had spent months building prediction models. By generating usable analytical code from precise prompts, the systems dramatically reduced the time needed to process health data. The findings hint at a future where AI helps scientists move faster from data to discovery.
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Evolving roles of liquid biopsy in precision medicine for colorectal cancer: from single-gene analysis to broad genomic profiling

✇MRD
By: Giulia Martini · Stefania Napolitano · Davide Ciardiello · Luca Boscolo Bielo · Erika Martinelli · Teresa Troiani · Maria Giulia Zampino · Nicola Fazio · Giuseppe Curigliano · Fortunato Ciardiello

Nat Rev Clin Oncol. 2026 Feb 20. doi: 10.1038/s41571-026-01126-1. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) is a heterogeneous malignancy, with various alterations in molecular signalling pathways driving disease progression and resistance to therapy. Liquid biopsy, as a source of circulating tumour DNA (ctDNA), has been utilized to characterize tumour molecular heterogeneity, facilitating the identification of actionable targets for precision medicine-guided therapies and the detection of emerging genomic drivers of drug resistance in patients with metastatic CRC. In addition, liquid biopsy-based analysis of ctDNA has been validated as a tool for detecting minimal residual disease (MRD) following locoregional treatment in patients with localized colon or rectal cancer, offering improved prognostic stratification and supporting the tailoring of adjuvant systemic therapy. Methodological evolution from PCR analysis of a few known mutations in one gene or a small panel of genes to the assessment of hundreds of genes and pathogenic variants by next-generation sequencing has enabled comprehensive genomic profiling (CGP), thereby improving knowledge of cancer molecular complexity at the individual patient level. In this respect, liquid biopsy-based CGP is an easily repeatable and minimally invasive approach that can provide a dynamic portrait of CRC molecular heterogeneity to guide personalized and adaptive treatment based on biomarkers of response and resistance. In this Review, we discuss current and potential roles of liquid biopsy-based ctDNA analysis in the clinical management of metastatic CRC. We also discuss the evidence supporting implementation of liquid biopsy-based assessment of MRD to refine the management of locoregional CRC and potentially improve cure rates while reducing overtreatment of many patients.

PMID:41720942 | DOI:10.1038/s41571-026-01126-1

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STAT+: Nature Medicine to investigate study that found cancer treatment is better in morning

✇STAT
By: Angus Chen

The notion that oncologists could boost immunotherapy responses simply by giving infusions in the morning, rather than late afternoon, is an attractive one. So when a clinical trial published in Nature Medicine this month showed that lung cancer patients treated in the morning had a massive reduction in the risk of progression compared to those treated in the afternoon, many scientists were intrigued, if skeptical.

Now that study is coming under fire, as multiple scientists and sleuths raise serious concerns about the data and point out inconsistencies in the trial.

These have called the study’s conclusions even further into question, which experts told STAT already lacked strong biological plausibility, and Nature Medicine appended a note on the study on Thursday that it is starting an investigation into the concerns.

Continue to STAT+ to read the full story…

© Jenny Kane/AP

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Gastric cancer occurrence and heterogeneity: integration of clinical data, multi-omics and tumor microenvironment

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Ping Li · Yafang Huang · Wanting Jiang · Haocheng Zheng · Runhua Chen · Xia Ding

Future Oncol. 2026 Feb 20:1-14. doi: 10.1080/14796694.2026.2631302. Online ahead of print.

ABSTRACT

Gastric cancer (GC) is an epithelial malignant tumor with high morbidity and mortality. In recent years, more and more studies have strengthened our understanding of how GC develops, including the origin of GC cells, precancerous lesions, gene mutations, transcriptional changes, protein translation and the tumor microenvironment. With the concept of accurate tumor therapy gradually applied to clinical practice, these data provide more reference and basis for early prevention, early screening, early detection and accurate treatment of GC.

PMID:41717787 | DOI:10.1080/14796694.2026.2631302

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MedClarify: An information-seeking AI agent for medical diagnosis with case-specific follow-up questions

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hui Min Wong · Philip Heesen · Pascal Janetzky · Martin Bendszus · Stefan Feuerriegel
arXiv:2602.17308v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for diagnostic tasks in medicine. In clinical practice, the correct diagnosis can rarely be immediately inferred from the initial patient presentation alone. Rather, reaching a diagnosis often involves systematic history taking, during which clinicians reason over multiple potential conditions through iterative questioning to resolve uncertainty. This process requires considering differential diagnoses and actively excluding emergencies that demand immediate intervention. Yet, the ability of medical LLMs to generate informative follow-up questions and thus reason over differential diagnoses remains underexplored. Here, we introduce MedClarify, an AI agent for information-seeking that can generate follow-up questions for iterative reasoning to support diagnostic decision-making. Specifically, MedClarify computes a list of candidate diagnoses analogous to a differential diagnosis, and then proactively generates follow-up questions aimed at reducing diagnostic uncertainty. By selecting the question with the highest expected information gain, MedClarify enables targeted, uncertainty-aware reasoning to improve diagnostic performance. In our experiments, we first demonstrate the limitations of current LLMs in medical reasoning, which often yield multiple, similarly likely diagnoses, especially when patient cases are incomplete or relevant information for diagnosis is missing. We then show that our information-theoretic reasoning approach can generate effective follow-up questioning and thereby reduces diagnostic errors by ~27 percentage points (p.p.) compared to a standard single-shot LLM baseline. Altogether, MedClarify offers a path to improve medical LLMs through agentic information-seeking and to thus promote effective dialogues with medical LLMs that reflect the iterative and uncertain nature of real-world clinical reasoning.
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Intent Laundering: AI Safety Datasets Are Not What They Seem

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shahriar Golchin · Marc Wetter
arXiv:2602.16729v1 Announce Type: cross Abstract: We systematically evaluate the quality of widely used AI safety datasets from two perspectives: in isolation and in practice. In isolation, we examine how well these datasets reflect real-world attacks based on three key properties: driven by ulterior intent, well-crafted, and out-of-distribution. We find that these datasets overrely on "triggering cues": words or phrases with overt negative/sensitive connotations that are intended to trigger safety mechanisms explicitly, which is unrealistic compared to real-world attacks. In practice, we evaluate whether these datasets genuinely measure safety risks or merely provoke refusals through triggering cues. To explore this, we introduce "intent laundering": a procedure that abstracts away triggering cues from attacks (data points) while strictly preserving their malicious intent and all relevant details. Our results indicate that current AI safety datasets fail to faithfully represent real-world attacks due to their overreliance on triggering cues. In fact, once these cues are removed, all previously evaluated "reasonably safe" models become unsafe, including Gemini 3 Pro and Claude Sonnet 3.7. Moreover, when intent laundering is adapted as a jailbreaking technique, it consistently achieves high attack success rates, ranging from 90% to over 98%, under fully black-box access. Overall, our findings expose a significant disconnect between how model safety is evaluated and how real-world adversaries behave.
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Be Wary of Your Time Series Preprocessing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sofiane Ennadir · Tianze Wang · Oleg Smirnov · Sahar Asadi · Lele Cao
arXiv:2602.17568v1 Announce Type: cross Abstract: Normalization and scaling are fundamental preprocessing steps in time series modeling, yet their role in Transformer-based models remains underexplored from a theoretical perspective. In this work, we present the first formal analysis of how different normalization strategies, specifically instance-based and global scaling, impact the expressivity of Transformer-based architectures for time series representation learning. We propose a novel expressivity framework tailored to time series, which quantifies a model's ability to distinguish between similar and dissimilar inputs in the representation space. Using this framework, we derive theoretical bounds for two widely used normalization methods: Standard and Min-Max scaling. Our analysis reveals that the choice of normalization strategy can significantly influence the model's representational capacity, depending on the task and data characteristics. We complement our theory with empirical validation on classification and forecasting benchmarks using multiple Transformer-based models. Our results show that no single normalization method consistently outperforms others, and in some cases, omitting normalization entirely leads to superior performance. These findings highlight the critical role of preprocessing in time series learning and motivate the need for more principled normalization strategies tailored to specific tasks and datasets.
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STAT+: Key study of Grail’s cancer detection test fails in setback for company

✇STAT
By: Matthew Herper and Angus Chen

A blood test for detecting cancer early being developed by the diagnostics firm Grail failed to meet its main goal in a giant study being conducted with England’s National Health Service, the company said Thursday.

Grail’s test has been the standard bearer for new technologies that promise a blood test can be used to detect many different types of cancer early and eventually even to indicate to scientists where in the body to look for tumors. The company already sells its test, called Galleri, for a list price of $1,000, although it is not yet approved by the Food and Drug Administration. Grail said Thursday it sold 185,000 tests in 2025, generating $136.8 million. 

The company’s shares were down 47% in after-hours trading.

Continue to STAT+ to read the full story…

© Adobe

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STAT+: AI-guided cancer treatments, telehealth usage, and other health tech news

✇STAT
By: Mario Aguilar

You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.

Good morning health tech readers!

Please join me in congratulating my colleagues as STAT, who have been honored with a fourth Polk Award for our coverage of the Trump administration’s impacts on the federal health department and American science. The award recognizes the whole newsroom and in particular the work of Lizzy Lawrence covering a dramatic year of changes at the Food and Drug Administration. 

Continue to STAT+ to read the full story…

© Adobe

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