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Received — 28 October 2025 ⏭ Favourites

Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review

✇cs.AI, q-bio.NC updates on arXiv.org
By: Stefan Julian Kooy · Jean Paul Sebastian Piest · Rob Henk Bemthuis
28 October 2025 at 12:00
arXiv:2510.22003v1 Announce Type: cross Abstract: Generative AI (GenAI) is reshaping enterprise architecture work in agile software organizations, yet evidence on its effects remains scattered. We report a systematic literature review (SLR), following established SLR protocols of Kitchenham and PRISMA, of 1,697 records, yielding 33 studies across enterprise, solution, domain, business, and IT architect roles. GenAI most consistently supports (i) design ideation and trade-off exploration; (ii) rapid creation and refinement of artifacts (e.g., code, models, documentation); and (iii) architectural decision support and knowledge retrieval. Reported risks include opacity and bias, contextually incorrect outputs leading to rework, privacy and compliance concerns, and social loafing. We also identify emerging skills and competencies, including prompt engineering, model evaluation, and professional oversight, and organizational enablers around readiness and adaptive governance. The review contributes with (1) a mapping of GenAI use cases and risks in agile architecting, (2) implications for capability building and governance, and (3) an initial research agenda on human-AI collaboration in architecture. Overall, the findings inform responsible adoption of GenAI that accelerates digital transformation while safeguarding architectural integrity.

PaperAsk: A Benchmark for Reliability Evaluation of LLMs in Paper Search and Reading

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yutao Wu · Xiao Liu · Yunhao Feng · Jiale Ding · Xingjun Ma
28 October 2025 at 12:00
arXiv:2510.22242v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly serve as research assistants, yet their reliability in scholarly tasks remains under-evaluated. In this work, we introduce PaperAsk, a benchmark that systematically evaluates LLMs across four key research tasks: citation retrieval, content extraction, paper discovery, and claim verification. We evaluate GPT-4o, GPT-5, and Gemini-2.5-Flash under realistic usage conditions-via web interfaces where search operations are opaque to the user. Through controlled experiments, we find consistent reliability failures: citation retrieval fails in 48-98% of multi-reference queries, section-specific content extraction fails in 72-91% of cases, and topical paper discovery yields F1 scores below 0.32, missing over 60% of relevant literature. Further human analysis attributes these failures to the uncontrolled expansion of retrieved context and the tendency of LLMs to prioritize semantically relevant text over task instructions. Across basic tasks, the LLMs display distinct failure behaviors: ChatGPT often withholds responses rather than risk errors, whereas Gemini produces fluent but fabricated answers. To address these issues, we develop lightweight reliability classifiers trained on PaperAsk data to identify unreliable outputs. PaperAsk provides a reproducible and diagnostic framework for advancing the reliability evaluation of LLM-based scholarly assistance systems.

Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Julia Bazinska · Max Mathys · Francesco Casucci · Mateo Rojas-Carulla · Xander Davies · Alexandra Souly · Niklas Pfister
28 October 2025 at 12:00
arXiv:2510.22620v1 Announce Type: cross Abstract: AI agents powered by large language models (LLMs) are being deployed at scale, yet we lack a systematic understanding of how the choice of backbone LLM affects agent security. The non-deterministic sequential nature of AI agents complicates security modeling, while the integration of traditional software with AI components entangles novel LLM vulnerabilities with conventional security risks. Existing frameworks only partially address these challenges as they either capture specific vulnerabilities only or require modeling of complete agents. To address these limitations, we introduce threat snapshots: a framework that isolates specific states in an agent's execution flow where LLM vulnerabilities manifest, enabling the systematic identification and categorization of security risks that propagate from the LLM to the agent level. We apply this framework to construct the $\operatorname{b}^3$ benchmark, a security benchmark based on 194331 unique crowdsourced adversarial attacks. We then evaluate 31 popular LLMs with it, revealing, among other insights, that enhanced reasoning capabilities improve security, while model size does not correlate with security. We release our benchmark, dataset, and evaluation code to facilitate widespread adoption by LLM providers and practitioners, offering guidance for agent developers and incentivizing model developers to prioritize backbone security improvements.

Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Stefan M. Fischer · Johannes Kiechle · Laura Daza · Lina Felsner · Richard Osuala · Daniel M. Lang · Karim Lekadir · Jan C. Peeken · Julia A. Schnabel
28 October 2025 at 12:00
arXiv:2510.23241v1 Announce Type: cross Abstract: In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the patch size during model training, resulting in an improved class balance for smaller patch sizes and accelerated convergence of the training process. We evaluate our curriculum approach in two settings: a resource-efficient mode and a performance mode, both regarding Dice score performance and computational costs across 15 diverse and popular 3D medical image segmentation tasks. The resource-efficient mode matches the Dice score performance of the conventional constant patch size sampling baseline with a notable reduction in training time to only 44%. The performance mode improves upon constant patch size segmentation results, achieving a statistically significant relative mean performance gain of 1.28% in Dice Score. Remarkably, across all 15 tasks, our proposed performance mode manages to surpass the constant patch size baseline in Dice Score performance, while simultaneously reducing training time to only 89%. The benefits are particularly pronounced for highly imbalanced tasks such as lesion segmentation tasks. Rigorous experiments demonstrate that our performance mode not only improves mean segmentation performance but also reduces performance variance, yielding more trustworthy model comparison. Furthermore, our findings reveal that the proposed curriculum sampling is not tied to a specific architecture but represents a broadly applicable strategy that consistently boosts performance across diverse segmentation models, including UNet, UNETR, and SwinUNETR. In summary, we show that this simple yet elegant transformation on input data substantially improves both Dice Score performance and training runtime, while being compatible across diverse segmentation backbones.

DataRater: Meta-Learned Dataset Curation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa M. Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, Andr\'as Gy\"orgy, Tom Schaul, Jeffrey Dean, Hado van Hasselt, David Silver
28 October 2025 at 12:00
arXiv:2505.17895v2 Announce Type: replace-cross Abstract: The quality of foundation models depends heavily on their training data. Consequently, great efforts have been put into dataset curation. Yet most approaches rely on manual tuning of coarse-grained mixtures of large buckets of data, or filtering by hand-crafted heuristics. An approach that is ultimately more scalable (let alone more satisfying) is to \emph{learn} which data is actually valuable for training. This type of meta-learning could allow more sophisticated, fine-grained, and effective curation. Our proposed \emph{DataRater} is an instance of this idea. It estimates the value of training on any particular data point. This is done by meta-learning using `meta-gradients', with the objective of improving training efficiency on held out data. In extensive experiments across a range of model scales and datasets, we find that using our DataRater to filter data is highly effective, resulting in significantly improved compute efficiency.

CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hyungyung Lee · Geon Choi · Jung-Oh Lee · Hangyul Yoon · Hyuk Gi Hong · Edward Choi
28 October 2025 at 12:00
arXiv:2505.18087v2 Announce Type: replace-cross Abstract: Recent progress in Large Vision-Language Models (LVLMs) has enabled promising applications in medical tasks, such as report generation and visual question answering. However, existing benchmarks focus mainly on the final diagnostic answer, offering limited insight into whether models engage in clinically meaningful reasoning. To address this, we present CheXStruct and CXReasonBench, a structured pipeline and benchmark built on the publicly available MIMIC-CXR-JPG dataset. CheXStruct automatically derives a sequence of intermediate reasoning steps directly from chest X-rays, such as segmenting anatomical regions, deriving anatomical landmarks and diagnostic measurements, computing diagnostic indices, and applying clinical thresholds. CXReasonBench leverages this pipeline to evaluate whether models can perform clinically valid reasoning steps and to what extent they can learn from structured guidance, enabling fine-grained and transparent assessment of diagnostic reasoning. The benchmark comprises 18,988 QA pairs across 12 diagnostic tasks and 1,200 cases, each paired with up to 4 visual inputs, and supports multi-path, multi-stage evaluation including visual grounding via anatomical region selection and diagnostic measurements. Even the strongest of 12 evaluated LVLMs struggle with structured reasoning and generalization, often failing to link abstract knowledge with anatomically grounded visual interpretation. The code is available at https://github.com/ttumyche/CXReasonBench

Fixing It in Post: A Comparative Study of LLM Post-Training Data Quality and Model Performance

✇cs.AI, q-bio.NC updates on arXiv.org
By: Aladin Djuhera · Swanand Ravindra Kadhe · Syed Zawad · Farhan Ahmed · Heiko Ludwig · Holger Boche
28 October 2025 at 12:00
arXiv:2506.06522v2 Announce Type: replace-cross Abstract: Recent work on large language models (LLMs) has increasingly focused on post-training and alignment with datasets curated to enhance instruction following, world knowledge, and specialized skills. However, most post-training datasets used in leading open- and closed-source LLMs remain inaccessible to the public, with limited information about their construction process. This lack of transparency has motivated the recent development of open-source post-training corpora. While training on these open alternatives can yield performance comparable to that of leading models, systematic comparisons remain challenging due to the significant computational cost of conducting them rigorously at scale, and are therefore largely absent. As a result, it remains unclear how specific samples, task types, or curation strategies influence downstream performance when assessing data quality. In this work, we conduct the first comprehensive side-by-side analysis of two prominent open post-training datasets: Tulu-3-SFT-Mix and SmolTalk. Using the Magpie framework, we annotate each sample with detailed quality metrics, including turn structure (single-turn vs. multi-turn), task category, input quality, and response quality, and we derive statistics that reveal structural and qualitative similarities and differences between the two datasets. Based on these insights, we design a principled curation recipe that produces a new data mixture, TuluTalk, which contains 14% fewer samples than either source dataset while matching or exceeding their performance on key benchmarks. Our findings offer actionable insights for constructing more effective post-training datasets that improve model performance within practical resource limits. To support future research, we publicly release both the annotated source datasets and our curated TuluTalk mixture.

OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chen Wang · Tianyu Peng · Wen Yang · Yinan Bai · Guangfu Wang · Jun Lin · Lanpeng Jia · Lingxiang Wu · Jinqiao Wang · Chengqing Zong · Jiajun Zhang
28 October 2025 at 12:00
arXiv:2507.05177v3 Announce Type: replace-cross Abstract: Empathetic interaction is a cornerstone of human-machine communication, due to the need for understanding speech enriched with paralinguistic cues and generating emotional and expressive responses. However, the most powerful empathetic LSLMs are increasingly closed off, leaving the crucial details about the architecture, data and development opaque to researchers. Given the critical need for transparent research into the LSLMs and empathetic behavior, we present OpenS2S, a fully open-source, transparent and end-to-end LSLM designed to enable empathetic speech interactions. Based on our empathetic speech-to-text model BLSP-Emo, OpenS2S further employs a streaming interleaved decoding architecture to achieve low-latency speech generation. To facilitate end-to-end training, OpenS2S incorporates an automated data construction pipeline that synthesizes diverse, high-quality empathetic speech dialogues at low cost. By leveraging large language models to generate empathetic content and controllable text-to-speech systems to introduce speaker and emotional variation, we construct a scalable training corpus with rich paralinguistic diversity and minimal human supervision. We release the fully open-source OpenS2S model, including the dataset, model weights, pre-training and fine-tuning codes, to empower the broader research community and accelerate innovation in empathetic speech systems. The project webpage can be accessed at https://casia-lm.github.io/OpenS2S
  • ✇npj Digital Medicine
  • Do we need AI guardians to protect us from health information overload? Arjun Mahajan · Stephen Gilbert
    npj Digital Medicine, Published online: 27 October 2025; doi:10.1038/s41746-025-02093-0The rise of digital health technologies has provided individuals with unprecedented access to biometric data and health insights. However, excess monitoring may contribute to fatigue, anxiety, and information overload, sometimes reducing engagement and worsening outcomes. This article explores how artificial intelligence-enabled assistants might help address this challenge by filtering, contextualizing, and pe
     
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Do we need AI guardians to protect us from health information overload?

✇npj Digital Medicine
By: Arjun Mahajan · Stephen Gilbert
27 October 2025 at 08:00

npj Digital Medicine, Published online: 27 October 2025; doi:10.1038/s41746-025-02093-0

The rise of digital health technologies has provided individuals with unprecedented access to biometric data and health insights. However, excess monitoring may contribute to fatigue, anxiety, and information overload, sometimes reducing engagement and worsening outcomes. This article explores how artificial intelligence-enabled assistants might help address this challenge by filtering, contextualizing, and personalizing health information, potentially supporting informed self-management while mitigating some unintended harms of digital health technologies.

When Models Outthink Their Safety: Mitigating Self-Jailbreak in Large Reasoning Models with Chain-of-Guardrails

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yingzhi Mao (Chinese Information Processing Laboratory · Institute of Software · Chinese Academy of Sciences · University of Chinese Academy of Sciences) · Chunkang Zhang (Chinese Information Processing Laboratory · Institute of Software · Chinese Academy of Sciences · University of Chinese Academy of Sciences) · Junxiang Wang (Chinese Information Processing Laboratory · Institute of Software · Chinese Academy of Sciences) · Xinyan Guan (Chinese Information Processing Laboratory · Institute of Software · Chinese Academy of Sciences · University of Chinese Academy of Sciences) · Boxi Cao (Chinese Information Processing Laboratory · Institute of Software · Chinese Academy of Sciences) · Yaojie Lu (Chinese Information Processing Laboratory · Institute of Software · Chinese Academy of Sciences) · Hongyu Lin (Chinese Information Processing Laboratory · Institute of Software · Chinese Academy of Sciences) · Xianpei Han (Chinese Information Processing Laboratory · Institute of Software · Chinese Academy of Sciences · University of Chinese Ac
27 October 2025 at 12:00
arXiv:2510.21285v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex reasoning tasks but remain vulnerable to severe safety risks, including harmful content generation and jailbreak attacks. Existing mitigation strategies rely on injecting heuristic safety signals during training, which often suppress reasoning ability and fail to resolve the safety-reasoning trade-off. To systematically investigate this issue, we analyze the reasoning trajectories of diverse LRMs and uncover a phenomenon we term Self-Jailbreak, where models override their own risk assessments and justify responding to unsafe prompts. This finding reveals that LRMs inherently possess the ability to reject unsafe queries, but this ability is compromised, resulting in harmful outputs. Building on these insights, we propose the Chain-of-Guardrail (CoG), a training framework that recomposes or backtracks unsafe reasoning steps, steering the model back onto safe trajectories while preserving valid reasoning chains. Extensive experiments across multiple reasoning and safety benchmarks demonstrate that CoG substantially improves the safety of current LRMs while preserving comparable reasoning ability, significantly outperforming prior methods that suffer from severe safety-reasoning trade-offs.

AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jonathan Bragg · Mike D'Arcy · Nishant Balepur · Dan Bareket · Bhavana Dalvi · Sergey Feldman · Dany Haddad · Jena D. Hwang · Peter Jansen · Varsha Kishore · Bodhisattwa Prasad Majumder · Aakanksha Naik · Sigal Rahamimov · Kyle Richardson · Amanpreet Singh · Harshit Surana · Aryeh Tiktinsky · Rosni Vasu · Guy Wiener · Chloe Anastasiades · Stefan Candra · Jason Dunkelberger · Dan Emery · Rob Evans · Malachi Hamada · Regan Huff · Rodney Kinney · Matt Latzke · Jaron Lochner · Ruben Lozano-Aguilera · Cecile Nguyen · Smita Rao · Amber Tanaka · Brooke Vlahos · Peter Clark · Doug Downey · Yoav Goldberg · Ashish Sabharwal · Daniel S. Weld
27 October 2025 at 12:00
arXiv:2510.21652v1 Announce Type: new Abstract: AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and even proposing new directions of inquiry; indeed, there are now many such agents, ranging from general-purpose "deep research" systems to specialized science-specific agents, such as AI Scientist and AIGS. Rigorous evaluation of these agents is critical for progress. Yet existing benchmarks fall short on several fronts: they (1) fail to provide holistic, product-informed measures of real-world use cases such as science research; (2) lack reproducible agent tools necessary for a controlled comparison of core agentic capabilities; (3) do not account for confounding variables such as model cost and tool access; (4) do not provide standardized interfaces for quick agent prototyping and evaluation; and (5) lack comprehensive baseline agents necessary to identify true advances. In response, we define principles and tooling for more rigorously benchmarking agents. Using these, we present AstaBench, a suite that provides the first holistic measure of agentic ability to perform scientific research, comprising 2400+ problems spanning the entire scientific discovery process and multiple scientific domains, and including many problems inspired by actual user requests to deployed Asta agents. Our suite comes with the first scientific research environment with production-grade search tools that enable controlled, reproducible evaluation, better accounting for confounders. Alongside, we provide a comprehensive suite of nine science-optimized classes of Asta agents and numerous baselines. Our extensive evaluation of 57 agents across 22 agent classes reveals several interesting findings, most importantly that despite meaningful progress on certain individual aspects, AI remains far from solving the challenge of science research assistance.
Received — 26 October 2025 ⏭ Favourites

Biomarkers for non-small cell lung cancer risk using multi-omics approaches: a nested case-control study

✇Omics In Lung
By: Youngmin Han · Keum Ji Jung · Seong Gyu Choi · Yeun Soo Yang · Kwangbae Lee · Sun Ha Jee
24 October 2025 at 18:00

Transl Lung Cancer Res. 2025 Sep 30;14(9):3645-3658. doi: 10.21037/tlcr-2025-603. Epub 2025 Sep 25.

ABSTRACT

BACKGROUND: Lung cancer poses a major public health challenge, accounting for the highest cancer-related mortality worldwide. This study aimed to identify non-invasive biomarkers for the early detection of non-small cell lung cancer (NSCLC) risk.

METHODS: We randomly selected 150 incident NSCLC cases during follow-up from the Korean Cancer Prevention Study-II. Controls (n=150) were matched to cases by age, gender, and the time of blood collection. Non-targeted metabolite screening by ultra-high-performance liquid chromatography (UHPLC)/mass spectrometry (MS) was conducted on the pre-diagnostic biological samples. The 11 reported lung cancer-associated single-nucleotide polymorphisms (SNPs) in Koreans were extracted from DNA genotyping data of the study population. Metabolite markers related to NSCLC risk were identified through clustering using hierarchical density-based spatial clustering of applications with noise. The associations between smoking, dietary factors, and NSCLC were also examined.

RESULTS: Six discriminative serum metabolites were identified as having an association with NSCLC incidence. Notably, the relationship between specific metabolite levels and NSCLC risk differed by rs7086803 genotype. Smoking status and occupational exposures appear to influence specific metabolite profiles, while dietary vegetable intake may modulate the risk of NSCLC among smokers.

CONCLUSIONS: The meaningful biomarkers revealed in the current research could be used to enhance the predictive ability for NSCLC risk. Furthermore, we suggest that the protective role of dietary vegetables against NSCLC may be attenuated or absent in smokers.

PMID:41133005 | PMC:PMC12541849 | DOI:10.21037/tlcr-2025-603

Received — 24 October 2025 ⏭ Favourites

AI PB: A Grounded Generative Agent for Personalized Investment Insights

✇cs.AI, q-bio.NC updates on arXiv.org
By: Daewoo Park · Suho Park · Inseok Hong · Hanwool Lee · Junkyu Park · Sangjun Lee · Jeongman An · Hyunbin Loh
24 October 2025 at 12:00
arXiv:2510.20099v1 Announce Type: new Abstract: We present AI PB, a production-scale generative agent deployed in real retail finance. Unlike reactive chatbots that answer queries passively, AI PB proactively generates grounded, compliant, and user-specific investment insights. It integrates (i) a component-based orchestration layer that deterministically routes between internal and external LLMs based on data sensitivity, (ii) a hybrid retrieval pipeline using OpenSearch and the finance-domain embedding model, and (iii) a multi-stage recommendation mechanism combining rule heuristics, sequential behavioral modeling, and contextual bandits. Operating fully on-premises under Korean financial regulations, the system employs Docker Swarm and vLLM across 24 X NVIDIA H100 GPUs. Through human QA and system metrics, we demonstrate that grounded generation with explicit routing and layered safety can deliver trustworthy AI insights in high-stakes finance.
Received — 23 October 2025 ⏭ Favourites

A Goal-Driven Survey on Root Cause Analysis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Aoyang Fang · Haowen Yang · Haoze Dong · Qisheng Lu · Junjielong Xu · Pinjia He
23 October 2025 at 12:00
arXiv:2510.19593v1 Announce Type: cross Abstract: Root Cause Analysis (RCA) is a crucial aspect of incident management in large-scale cloud services. While the term root cause analysis or RCA has been widely used, different studies formulate the task differently. This is because the term "RCA" implicitly covers tasks with distinct underlying goals. For instance, the goal of localizing a faulty service for rapid triage is fundamentally different from identifying a specific functional bug for a definitive fix. However, previous surveys have largely overlooked these goal-based distinctions, conventionally categorizing papers by input data types (e.g., metric-based vs. trace-based methods). This leads to the grouping of works with disparate objectives, thereby obscuring the true progress and gaps in the field. Meanwhile, the typical audience of an RCA survey is either laymen who want to know the goals and big picture of the task or RCA researchers who want to figure out past research under the same task formulation. Thus, an RCA survey that organizes the related papers according to their goals is in high demand. To this end, this paper presents a goal-driven framework that effectively categorizes and integrates 135 papers on RCA in the context of cloud incident management based on their diverse goals, spanning the period from 2014 to 2025. In addition to the goal-driven categorization, it discusses the ultimate goal of all RCA papers as an umbrella covering different RCA formulations. Moreover, the paper discusses open challenges and future directions in RCA.
Received — 22 October 2025 ⏭ Favourites
Received — 21 October 2025 ⏭ Favourites

Integrative Transcriptomic and Metabolomic Analysis Reveals Aberrant Glycosylation as a Hallmark of Lung Adenocarcinoma

✇Omics In Lung
By: Sanjukta Dasgupta
17 October 2025 at 18:00

OMICS. 2025 Oct 16. doi: 10.1177/15578100251387518. Online ahead of print.

ABSTRACT

Lung adenocarcinoma (LUAD) remains the most common subtype of lung cancer, characterized by high heterogeneity and poor survival outcomes. Although transcriptomic and metabolomic alterations have been individually studied, integrated multi-omics analyses are needed to uncover the convergent pathways that drive tumor progression. Differentially expressed genes (DEGs) were identified from the GSE229253 transcriptomic dataset comprising LUAD tumor and adjacent normal tissues, while significantly altered metabolites were obtained from the Lung Cancer Metabolome Database. The top 10 DEGs and metabolites were analyzed using the search tool for interacting chemicals (STITCH) to construct gene-metabolite networks, and Integrated Molecular Pathway Level Analysis (IMPaLA) was employed for integrated pathway enrichment to identify overlapping molecular processes. Transcriptomic profiling revealed 973 DEGs (410 upregulated and 563 downregulated), and metabolomic analysis identified significant alterations in metabolites linked to redox balance, amino acid derivatives, and nucleotide metabolism. Integration through STITCH generated a network of 16 nodes and 9 edges, highlighting gene-metabolite associations of probable biological relevance. Joint pathway enrichment analysis using IMPaLA consistently identified glycosylation-related pathways, particularly O-linked glycosylation of mucins, as major axes of convergence between transcriptomic and metabolomic alterations in LUAD (joint p = 0.00129-0.00434). Several genes (B3GNT6, FEZF1-AS1, and LCAL1) and metabolites (isoleucylleucine, leucylleucine, and isoleucylvaline) are probable novel candidates, warranting further investigation. These findings provide systems-level evidence that aberrant glycosylation is likely a central hallmark of LUAD, underscore the potential of glycosylation pathways as biomarkers and therapeutic targets, and demonstrate the utility of cross-omics approaches to unpack the molecular complexity of lung cancer.

PMID:41103242 | DOI:10.1177/15578100251387518

Comprehensive bioinformatics analysis of omics data to reveal molecular mechanisms and biomarkers in multiple cancers

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Mijan Mia · Tonima Rahman Tuli · Kazi Musfika Binte Absar · Arifa Afrose Rimi · Sumiya Ahmad Sejuti · Adrita Noumi Arshi · Md Emdadul Islam · Ahsan Habib
20 October 2025 at 18:00

In Silico Pharmacol. 2025 Oct 17;13(3):154. doi: 10.1007/s40203-025-00440-3. eCollection 2025.

ABSTRACT

Breast, ovarian, lung, cervical, and colorectal cancers are among the most prevalent malignancies affecting women worldwide. This study aimed to elucidate the common molecular mechanisms of tumorigenesis and identify potential biomarkers using an integrative bioinformatics and network-based approach. Integrative profiling of five microarray datasets identified 66 differentially expressed genes (DEGs) that are common across five cancer types. Gene ontology and KEGG pathway analyses of common DEGs were performed using the DAVID database. The cell cycle processes were the most enriched functions, and oocyte meiosis, oocyte maturation, the p53 signaling pathway, cancer pathways, and cellular senescence were the most important pathways identified. Protein-protein interaction (PPI) networks for the DEGs were constructed using the STRING database, and the resulting networks were visualized in Cytoscape. Through PPI network analysis, ten hub genes were identified, and subsequent survival analysis confirmed that CHEK1, DLGAP5, CCNB2, and CCNA2 are significantly associated with poor patient survivability, establishing them as common biomarkers across multiple cancer types. Subsequently, ten transcription factors (TFs) and ten post-transcriptional regulators were identified through the assessment of regulatory networks involving TFs-DEGs and miRNAs-DEGs. Finally, drug-gene association analysis from the GSCA library was used to anticipate drug-like compounds using the drug repurposing approach. Overall, this comprehensive investigation holds promise for future in vitro and in vivo studies, offering a molecular foundation for the diagnosis, prognosis, and treatment of malignant cancers.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00440-3.

PMID:41113171 | PMC:PMC12534660 | DOI:10.1007/s40203-025-00440-3

Parity and lactation induce T cell mediated breast cancer protection

✇Nature - Issue - nature.com science feeds
By: Balaji Virassamy · Franco Caramia · Peter Savas · Michael A. Harris · Jia-Wern Pan · Jianan Wang · Emmaline Brown · Megan M. R. O’Malley · Courtney T. van Geelen · Michael Hun · Thomas N. Burn · Sneha Sant · Jamieson D. Ballan · Jasmine Kay · Luis E. Lara Gonzalez · Kylie Clarke · Han Xian Aw Yeang · Rejhan Idrizi · Metta Jana · Damon J. Challice · Roberto Salgado · Heather Thorne · David Amor · Lesley Andrews · Yoland Antill · Rosemary Balleine · Jonathan Beesley · Ian Bennett · Michael Bogwitz · Simon Bodek · Leon Botes · Meagan Brennan · Melissa Brown · Michael Buckley · Jo Burke · Phyllis Butow · Liz Caldon · Ian Campbell · Michelle Cao · Anannya Chakrabarti · Deepa Chauhan · Manisha Chauhan · Georgia Chenevix-Trench · Alice Christian · Paul Cohen · Alison Colley · Ashley Crook · James Cui · Eliza Courtney · Margaret Cummings · Sarah-Jane Dawson · Anna deFazio · Martin Delatycki · Rebecca Dickson · Joanne Dixon · Stacey Edwards · Gelareh Farshid · Andrew Fellows · Georgina Fenton · Michael Field · James Flanagan · Peter Fong · Laura Forrest · Stephen Fox · Juliet F
20 October 2025 at 08:00

Nature, Published online: 20 October 2025; doi:10.1038/s41586-025-09713-5

Parity and lactation induce T cell mediated breast cancer protection
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