Security researchers at Wiz have uncovered a sophisticated crypto-jacking attack targeting publically accessible API servers for several popular DevOps tools. Similarly, researchers at Sysdig have uncovered an attack on the popular AI tool Open WebUI using many of the same techniques and crypto-miners.
By Matt Saunders
Nature, Published online: 23 June 2025; doi:10.1038/d41586-025-01946-8
The uncontrolled deployment of machine learning in medicine can distort patient information and sacrifice long-term data reliability for short-term benefits.
When testing an AI model, it’s hard to tell if it is reasoning or just regurgitating answers from its training data. Xbench, a new benchmark developed by the Chinese venture capital firm HSG, or HongShan Capital Group, might help to sidestep that issue. That’s thanks to the way it evaluates models not only on the ability to pass arbitrary tests, like most other benchmarks, but also on the ability to execute real-world tasks, which is more unusual. It will be updated on a regular basis to try to keep it evergreen.
This week the company is making part of its question set open-source and letting anyone use for free. The team has also released a leaderboard comparing how mainstream AI models stack up when tested on Xbench. (ChatGPT o3 ranked first across all categories, though ByteDance’s Doubao, Gemini 2.5 Pro, and Grok all still did pretty well, as did Claude Sonnet.)
Development of the benchmark at HongShan began in 2022, following ChatGPT’s breakout success, as an internal tool for assessing which models are worth investing in. Since then, led by partner Gong Yuan, the team has steadily expanded the system, bringing in outside researchers and professionals to help refine it. As the project grew more sophisticated, they decided to release it to the public.
Xbench approached the problem with two different systems. One is similar to traditional benchmarking: an academic test that gauges a model’s aptitude on various subjects. The other is more like a technical interview round for a job, assessing how much real-world economic value a model might deliver.
Xbench’s methods for assessing raw intelligence currently include two components: Xbench-ScienceQA and Xbench-DeepResearch. ScienceQA isn’t a radical departure from existing postgraduate-level STEM benchmarks like GPQA and SuperGPQA. It includes questions spanning fields from biochemistry to orbital mechanics, drafted by graduate students and double-checked by professors. Scoring rewards not only the right answer but also the reasoning chain that leads to it.
DeepResearch, by contrast, focuses on a model’s ability to navigate the Chinese-language web. Ten subject-matter experts created 100 questions in music, history, finance, and literature—questions that can’t just be googled but require significant research to answer. Scoring favors breadth of sources, factual consistency, and a model’s willingness to admit when there isn’t enough data. A question in the publicized collection is “How many Chinese cities in the three northwestern provinces border a foreign country?” (It’s 12, and only 33% of models tested got it right, if you are wondering.)
On the company’s website, the researchers said they want to add more dimensions to the test—for example, aspects like how creative a model is in its problem solving, how collaborative it is when working with other models, and how reliable it is.
The team has committed to updating the test questions once a quarter and to maintain a half-public, half-private data set.
To assess models’ real-world readiness, the team worked with experts to develop tasks modeled on actual workflows, initially in recruitment and marketing. For example, one task asks a model to source five qualified battery engineer candidates and justify each pick. Another asks it to match advertisers with appropriate short-video creators from a pool of over 800 influencers.
The website also teases upcoming categories, including finance, legal, accounting, and design. The question sets for these categories have not yet been open-sourced.
ChatGPT-o3 again ranks first in both of the current professional categories. For recruiting, Perplexity Search and Claude 3.5 Sonnet take second and third place, respectively. For marketing, Claude, Grok, and Gemini all perform well.
“It is really difficult for benchmarks to include things that are so hard to quantify,” says Zihan Zheng, the lead researcher on a new benchmark called LiveCodeBench Pro and a student at NYU. “But Xbench represents a promising start.”
Around the world, countries are facing the challenges of aging populations, growing rates of chronic disease, and workforce shortages, leading to a growing burden on health care systems. From diagnosis to treatment, AI and other digital solutions can enhance the efficiency and effectiveness of health care, easing the burden on straining systems. According to the World Health Organization (WHO), spending an additional $0.24 per patient per year on digital health interventions could save more than two million lives from non-communicable diseases over the next decade.
To work most effectively, digital solutions need to be scaled and embedded in an ecosystem that ensures a high degree of interoperability, data security, and governance. If not, the proliferation of point solutions— where specialized software or tools focus on just one specific area or function—could lead to silos and digital canyons, complicating rather than easing the workloads of health care professionals, and potentially impacting patient treatment. Importantly, technologies that enhance workforce productivity should keep humans in the loop, aiming to augment their capabilities, rather than replace them.
Through a survey of 300 health care executives and a program of interviews with industry experts, startup leaders, and academic researchers, this report explores the best practices for success when implementing integrated digital solutions into health care, and how these can support decision-makers in a range of settings, including laboratories and hospitals.
Key findings include:
Health care is primed for digital adoption. The global pandemic underscored the benefits of value-based care and accelerated the adoption of digital and AI-powered technologies in health care. Overwhelmingly, 96% of the survey respondents say they are “ready and resourced” to use digital health, while one in four say they are “very ready.” However, 91% of executives agree interoperability is a challenge, with a majority (59%) saying it will be “tough” to solve. Two in five leaders say balancing security with usability is the biggest challenge for digital health. With the adoption of cloud solutions, organizations can enjoy the benefits of modernized IT infrastructure: 36% of the survey respondents believe scalability is the main benefit, followed by improved security (28%).
Digital health care can help health care institutions transform patient outcomes—if built on the right foundations. Solutions like AI-powered diagnostics, telemedicine, and remote monitoring can offer measurable impact across the patient journey, from improving early disease detection to reducing hospital readmission rates. However, these technologies can only support fully connected health care when scaled up and embedded in ecosystems with robust data governance, interoperability, and security.
Health care data has immense potential—but fragmentation and poor interoperability hinder impact. Health care systems generate vast quantities of data, yet much of it remains siloed or unusable due to inconsistent formats and incompatible IT systems, limiting scalability.
Digital tools must augment, not overload, the workforce. With global health care workforce shortages worsening, digital solutions like clinical decision support tools, patient prediction, and remote monitoring can be seen as essential aids rather than threats to the workforce. Successful deployment depends on usability, clinician engagement, and training.
Regulatory evolution, open data policies, and economic sustainability are key to scaling digital health. Even the best digital tools struggle to scale without reimbursement frameworks, regulatory support, and viable business models. Open data ecosystems are needed to unleash the clinical and economic value of innovation. Regulatory and reimbursement innovation is also critical to transitioning from pilot projects to high-impact, system-wide adoption.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Privacy only matters to those with something to hide. So goes one of the more inane and disingenuous justifications for mass government and corporate surveillance. There are others, of course, but the “nothing to hide” argument remains a popular way to rationalize or excuse what’s become standard practice in our digital age: the widespread and invasive collection of vast amounts of personal data.
One common response to this line of reasoning is that everyone, in fact, has something to hide, whether they realize it or not. If you’re unsure of whether this holds true for you, I encourage you to read Means of Control by Byron Tau.
Midway through his book, Tau, an investigative journalist, recalls meeting with a disgruntled former employee of a data broker—a shady company that collects, bundles, and sells your personal data to other (often shadier) third parties, including the government. This ex-employee had managed to make off with several gigabytes of location data representing the precise movements of tens of thousands of people over the course of a few weeks. “What could I learn with this [data]—theoretically?” Tau asks the former employee. The answer includes a laundry list of possibilities that I suspect would make even the most enthusiastic oversharer uncomfortable.
“If information is power, and America is a society that’s still interested in the guarantee of liberty, personal dignity, and the individual freedom of its citizens, a serious conversation is needed.”
Bryon Tau, author of Means of Control
Did someone in this group recently visit an abortion clinic? That would be easy to figure out, says the ex-employee. Anyone attend an AA meeting or check into inpatient drug rehab? Again, pretty simple to discern. Is someone being treated for erectile dysfunction at a sexual health clinic? If so, that would probably be gleanable from the data too. Tau never opts to go down that road, but as Means of Control makes very clear, others certainly have done so and will.
While most of us are at least vaguely aware that our phones and apps are a vector for data collection and tracking, both the way in which this is accomplished and the extent to which it happens often remain murky. Purposely so, argues Tau. In fact, one of the great myths Means of Control takes aim at is the very idea that what we do with our devices can ever truly be anonymized. Each of us has habits and routines that are completely unique, he says, and if an advertiser knows you only as an alphanumeric string provided by your phone as you move about the world, and not by your real name, that still offers you virtually no real privacy protection. (You’ll perhaps not be surprised to learn that such “anonymized ad IDs” are relatively easy to crack.)
“I’m here to tell you if you’ve ever been on a dating app that wanted your location, or if you ever granted a weather app permission to know where you are 24/7, there’s a good chance a detailed log of your precise movement patterns has been vacuumed up and saved in some data bank somewhere that tens of thousands of total strangers have access to,” writes Tau.
Unraveling the story of how these strangers—everyone from government intelligence agents and local law enforcement officers to private investigators and employees of ad tech companies—gained access to our personal information is the ambitious task Tau sets for himself, and he begins where you might expect: the immediate aftermath of 9/11.
At no other point in US history was the government’s appetite for data more voracious than in the days after the attacks, says Tau. It was a hunger that just so happened to coincide with the advent of new technologies, devices, and platforms that excelled at harvesting and serving up personal information that had zero legal privacy protections.
Over the course of 22 chapters, Tau gives readers a rare glimpse inside the shadowy industry, “built by corporate America and blessed by government lawyers,” that emerged in the years and decades following the 9/11 attacks. In the hands of a less skilled reporter, this labyrinthine world of shell companies, data vendors, and intelligence agencies could easily become overwhelming or incomprehensible. But Tau goes to great lengths to connect dots and plots, explaining how a perfect storm of business motivations, technological breakthroughs, government paranoia, and lax or nonexistent privacy laws combined to produce the “digital panopticon” we are all now living in.
Means of Control doesn’t offer much comfort or reassurance for privacy-minded readers, but that’s arguably the point. As Tau notes repeatedly throughout his book, this now massive system of persistent and ubiquitous surveillance works only because the public is largely unaware of it. “If information is power, and America is a society that’s still interested in the guarantee of liberty, personal dignity, and the individual freedom of its citizens, a serious conversation is needed,” he writes.
As another new book makes clear, this conversation also needs to include student data. Lindsay Weinberg’s Smart University: Student Surveillance in the Digital Age reveals how the motivations and interests of Big Tech are transforming higher education in ways that are increasingly detrimental to student privacy and, arguably, education as a whole.
By “smart university,” Weinberg means the growing number of public universities across the country that are being restructured around “the production and capture of digital data.” Similar in vision and application to so-called “smart cities,” these big-data-pilled institutions are increasingly turning to technologies that can track students’ movements around campus, monitor how much time they spend on learning management systems, flag those who seem to need special “advising,” and “nudge” others toward specific courses and majors. “What makes these digital technologies so seductive to higher education administrators, in addition to promises of cost cutting, individualized student services, and improved school rankings, is the notion that the integration of digital technology on their campuses will position universities to keep pace with technological innovation,” Weinberg writes.
Readers of Smart University will likely recognize a familiar logic at play here. Driving many of these academic tracking and data-gathering initiatives is a growing obsession with efficiency, productivity, and convenience. The result is a kind of Silicon Valley optimization mindset, but applied to higher education at scale. Get students in and out of university as fast as possible, minimize attrition, relentlessly track performance, and do it all under the guise of campus modernization and increased personalization.
Under this emerging system, students are viewed less as self-empowered individuals and more as “consumers to be courted, future workers to be made employable for increasingly smart workplaces, sources of user-generated content for marketing and outreach, and resources to be mined for making campuses even smarter,” writes Weinberg.
At the heart of Smart University seems to be a relatively straightforward question: What is an education for? Although Weinberg doesn’t provide a direct answer, she shows that how a university (or society) decides to answer that question can have profound impacts on how it treats its students and teachers. Indeed, as the goal of education becomes less to produce well-rounded humans capable of thinking critically and more to produce “data subjects capable of being managed and who can fill roles in the digital economy,” it’s no wonder we’re increasingly turning to the dumb idea of smart universities to get the job done.
If books like Means of Control and Smart University do an excellent job exposing the extent to which our privacy has been compromised, commodified, and weaponized (which they undoubtedly do), they can also start to feel a bit predictable in their final chapters. Familiar codas include calls for collective action, buttressed by a hopeful anecdote or two detailing previously successful pro-privacy wins; nods toward a bipartisan privacy bill in the works or other pieces of legislation that could potentially close some glaring surveillance loophole; and, most often, technical guides that explain how each of us, individually, might better secure or otherwise take control and “ownership” of our personal data.
The motivations behind these exhortations and privacy-centric how-to guides are understandable. After all, it’s natural for readers to want answers, advice, or at least some suggestion that things could be different—especially after reading about the growing list of degradations suffered under surveillance capitalism. But it doesn’t take a skeptic to start to wonder if they’re actually advancing the fight for privacy in the way that its advocates truly want.
For one thing, technology tends to move much faster than any one smartphone privacy guide or individual law could ever hope to keep up with. Similarly, framing rampant privacy abuses as a problem we each have to be responsible for addressing individually seems a lot like framing the plastic pollution crisis as something Americans could have somehow solved by recycling. It’s both a misdirection and a misunderstanding of the problem.
It’s to his credit, then, that Lowry Pressly doesn’t include a “What is to be done” section at the end of The Right to Oblivion: Privacy and the Good Life. In lieu of offering up any concrete technical or political solutions, he simply reiterates an argument he has carefully and convincingly built over the course of his book: that privacy is important “not because it empowers us to exercise control over our information, but because it protects against the creation of such information in the first place.”
For Pressly, a Stanford instructor, the way we currently understand and value privacy has been tainted by what he calls “the ideology of information.” “This is the idea that information has a natural existence in human affairs,” he writes, “and that there are no aspects of human life which cannot be translated somehow into data.” This way of thinking not only leads to an impoverished sense of our own humanity—it also forces us into the conceptual trap of debating privacy’s value using a framework (control, consent, access) established by the companies whose business model is to exploit it.
The way out of this trap is to embrace what Pressly calls “oblivion,” a kind of state of unknowing, ambiguity, and potential—or, as he puts it, a realm “where there is no information or knowledge one way or the other.” While he understands that it’s impossible to fully escape a modern world intent on turning us into data subjects, Pressly’s book suggests we can and should support the idea that certain aspects of our (and others’) subjective interior lives can never be captured by information. Privacy is important because it helps to both protect and produce these ineffable parts of our lives, which in turn gives them a sense of dignity, depth, and the possibility for change and surprise.
Reserving or cultivating a space for oblivion in our own lives means resisting the logic that drives much of the modern world. Our inclination to “join the conversation,” share our thoughts, and do whatever it is we do when we create and curate a personal brand has become so normalized that it’s practically invisible to us. According to Pressly, all that effort has only made our lives and relationships shallower, less meaningful, and less trusting.
Calls for putting our screens down and stepping away from the internet are certainly nothing new. And while The Right to Oblivion isn’t necessarily prescriptive about such things, Pressly does offer a beautiful and compelling vision of what can be gained when we retreat not just from the digital world but from the idea that we are somehow knowable to that world in any authentic or meaningful way.
If all this sounds a bit philosophical, well, it is. But it would be a mistake to think of The Right to Oblivion as a mere thought exercise on privacy. Part of what makes the book so engaging and persuasive is the way in which Pressly combines a philosopher’s knack for uncovering hidden assumptions with a historian’s interest in and sensitivity to older (often abandoned) ways of thinking, and how they can often enlighten and inform modern problems.
Pressly isn’t against efforts to pass more robust privacy legislation, or even to learn how to better protect our devices against surveillance. His argument is that in order to guide such efforts, you have to both ask the right questions and frame the problem in a way that gives you and others the moral clarity and urgency to act. Your phone’s privacy settings are important, but so is understanding what you’re protecting when you change them.
Bryan Gardiner is a writer based in Oakland, California.
iScience. 2025 May 15;28(6):112681. doi: 10.1016/j.isci.2025.112681. eCollection 2025 Jun 20.
ABSTRACT
Metabolic reprogramming is a defining feature of pancreatic cancer, influencing tumor progression and the tumor microenvironment. By integrating single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics, this study visualized the spatial co-localization of metabolites and gene expression within tumor samples, uncovering metabolic heterogeneity and intercellular interactions. Spatial transcriptomics identified distinct pathological regions, which were further characterized using single-cell transcriptomic data and pathologist annotations. Pseudotime trajectory analysis revealed metabolic shifts along the malignant progression, while single-cell Metabolism (scMetabolism) delineated metabolic differences between pathological regions, classifying them as hypermetabolic or hypometabolic. Notably, aberrant cell communication between cancer cells, macrophages, and fibroblasts was observed, with key receptor-ligand pairs significantly co-expressed in malignant regions and correlated with poor prognosis. Spatial metabolomics imaging identified signature metabolites, highlighting metabolic alterations in amino acid metabolism, polyamine metabolism, fatty acid synthesis, and phospholipid metabolism. This integrated analysis provides critical insights into pancreatic cancer metabolism, offering potential avenues for targeted therapeutic interventions.
PMID:40538442 | PMC:PMC12177182 | DOI:10.1016/j.isci.2025.112681
Cancer Treat Rev. 2025 Jun 14;139:102979. doi: 10.1016/j.ctrv.2025.102979. Online ahead of print.
ABSTRACT
Breast cancer is characterized by significant molecular heterogeneity; therefore, there are distinct clinical features, treatment modalities, and prognostic outcomes across its various molecular subtypes. In the era of precision medicine, liquid biopsy has emerged as a convenient and minimally invasive technique capable of dynamically representing the comprehensive tumor gene spectrum. This review systematically elaborates the clinical value of liquid biopsy as a breakthrough tool for precision diagnosis and treatment in breast cancer through dynamic detection of key biomarkers, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and non-coding RNA (ncRNA). Specific genetic mutations and methylation signatures in ctDNA can be applied to early breast cancer screening, minimal residual disease monitoring, and tracking drug resistance mechanisms. CTCs enumeration (≥1/7.5 mL in early-stage cancer or ≥ 5/7.5 mL in metastatic cancer) and PD-L1 expression levels demonstrate direct correlations with prognostic stratification and the efficacy of immunotherapy. As the specificity and sensitivity of liquid biopsy continue to improve, personalized treatment strategies, informed by biomarker analysis and targeted precision therapies, have unveiled new avenues of hope for patients with breast cancer. However, several challenges persist in the practical application of liquid biopsy. Despite persistent challenges, such as insufficient standardization and difficulties in resolving low-abundance variants, future advancements should focus on multi-omics integration and AI-driven technological breakthroughs to overcome bottlenecks in clinical translation. This review summarizes cutting-edge liquid biopsy technologies for identifying clinically significant molecular biomarkers, focusing on discussing critical challenges in the strategies to advance precision oncology applications for optimized treatment guidance and disease surveillance in breast cancer.
PMID:40540857 | DOI:10.1016/j.ctrv.2025.102979
Cytokine Growth Factor Rev. 2025 Jun 6:S1359-6101(25)00071-1. doi: 10.1016/j.cytogfr.2025.06.001. Online ahead of print.
ABSTRACT
Lung cancer is a leading cause of cancer-related mortality worldwide, characterized by the uncontrolled growth and spread of abnormal cells. Despite constant progress in diagnosis and treatment of lung cancer remains highly challenging. Present review explores the molecular underpinnings of lung cancer, emphasizing genetic mutations, altered metabolism, and the role of biomarkers in diagnosis and treatment. Advances in diagnostic methods, particularly liquid biopsies, have enabled non-invasive detection and monitoring of lung cancer. Therapeutic approaches have evolved significantly, with molecular-targeted therapies focusing on pathways like EGFR, ALK, KRAS, and MET. Caner Immunotherapy, including immune checkpoint inhibitors, artificial intelligence, and multi-omics analysis, including genomics and image data to predict treatment response, has further enhanced precision medicine by enabling earlier detection, better prognostic models, and efficient drug discovery. AI has a significant impact on various aspects of oncology, including improving the earlier diagnosis of lung cancer. Despite these advancements, challenges such as drug resistance, tumor heterogeneity, and limited efficacy of certain therapies persist, necessitating continued research and innovation.
PMID:40544104 | DOI:10.1016/j.cytogfr.2025.06.001
Cell Signal. 2025 Jun 18;134:111950. doi: 10.1016/j.cellsig.2025.111950. Online ahead of print.
ABSTRACT
FAAP100 plays an essential role in DNA damage repair, with dysregulation associated with elevated cancer susceptibility. Nevertheless, comprehensive pan-cancer analyses examining FAAP100 prognostic significance, immune correlations, and epigenetic regulation remains unexplored. This study systematically characterized FAAP100 across 33 cancer types utilizing multi-omics data from TCGA, UALCAN, cBioPortal, TIMER2.0, and CPTAC. Analytical assessments included expression profiles, prognostic significance, and diagnostic utility, alongside associations with DNA methylation, immune cell infiltration, immune checkpoint gene expression, tumor mutational load (TMB), microsatellite instability (MSI), and drug resistance. Findings revealed significant FAAP100 upregulation across multiple cancer types, exhibiting inverse correlations to patient survival. Genomic characterization identified associations between FAAP100 overexpression and both copy number amplification and promoter hypomethylation. Immune profiling demonstrated robust correlations with immune cell infiltration levels and checkpoint molecule activity. Functional assays utilizing PC9 and H1299 cells indicated that FAAP100 enhances cellular proliferation and migration while inhibiting apoptosis processes. In vivo studies confirmed tumor growth suppression upon FAAP100 knockdown. Collectively, this multi-omics investigation identifies FAAP100 as a pan-cancer oncogene driver, highlighting its potential as both a prognostic biomarker and therapeutic target. The integrated analysis of expression patterns, epigenetic modifications, immune characteristics, and genomic alterations elucidates the mechanistic involvement of FAAP100 in tumor progression, providing a foundation for clinical application in precision oncology approaches..
PMID:40541815 | DOI:10.1016/j.cellsig.2025.111950
J Gastrointest Cancer. 2025 Jun 19;56(1):139. doi: 10.1007/s12029-025-01249-1.
ABSTRACT
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor, with rising incidence and mortality rates posing a significant threat to global public health. Accurate prediction of liver cancer occurrence and progression is essential for improving patient prognosis. This study uses bibliometric methods to analyze the current state and future trends in liver cancer prediction research.
METHODS: A search was conducted in the Web of Science (WOS) database on October 22, 2023, identifying 1092 articles on liver cancer prediction. These articles were quantitatively analyzed using CiteSpace 6.2 software, with a focus on research hotspots, authors, countries, and keywords.
RESULTS: The study involved 114 countries, 4254 institutions, and 280 journals, with 48,788 citations. China (826 papers) and the USA (96 papers) dominate the field. Leading institutions include Sun Yat-sen University, Fudan University, Zhejiang University, and Yonsei University. The most cited journals were Hepatology (2209 citations) and Journal of Hepatology (946 citations). Frontiers in Oncology had the highest H-index (14). Key authors include Kim Seung Up (23 papers) and Ahn Sang Hoon (H-index = 14). Early research focused on risk factors and staging, while recent studies emphasize DNA methylation, immune microenvironments, and tumor metastasis. Future research will focus on multi-omics data integration and AI-driven predictive model optimization.
CONCLUSION: This study provides a comprehensive overview of liver cancer prediction research, highlighting key trends and the potential of multi-omics data and machine learning to enhance predictive models and clinical outcomes.
PMID:40537718 | DOI:10.1007/s12029-025-01249-1
AlphaWrite is a new framework designed to enhance creative writing with structure and measurable improvements. Developed by Toby Simonds, it employs an evolutionary process to iteratively boost storytelling quality during inference.
By Robert Krzaczyński
The Void IDE was recently released in beta, positioning itself as a privacy-focused and free alternative to popular closed-source AI editors like Cursor and GitHub Copilot. Void IDE is a fork of Visual Studio Code. While Microsoft recently announced plans to open Source its GitHub Copilot Chat Extension possibly in a few months, the beta release is available now for the community to fiddle with.
By Bruno Couriol
Cell Death Discovery, Published online: 20 June 2025; doi:10.1038/s41420-025-02550-5
Ferroptosis in periodontitis: mechanisms, impacts, and systemic connections
Support Care Cancer. 2025 Jun 19;33(7):596. doi: 10.1007/s00520-025-09670-9.
ABSTRACT
PURPOSE: Anorexia is a frequent and serious symptom in patients with lung cancer, often leading to malnutrition and cachexia, and negatively affecting quality of life and survival. This scoping review systematically synthesizes current evidence on biomarkers associated with cancer-related anorexia (CRA) in lung cancer, aiming to clarify biological mechanisms and inform targeted interventions.
METHODS: We performed a comprehensive literature search of studies evaluating the associations between CRA and various biomarkers in patients with lung cancer. Data were extracted and analyzed for pathway, genomic, transcriptomic, epigenetic, proteomic, metabolic, and composite biomarkers.
RESULTS: A total of 33 studies were included, identifying more than 100 biomarkers closely associated with CRA in lung cancer. These include inflammatory cytokines, energy metabolism markers, epigenetic and transcriptomic alterations, and disruptions in multiple cellular signaling pathways. Our analysis demonstrates that CRA is not the result of a single factor but reflects widespread dysregulation across metabolic, immune, and signaling networks. Some studies suggest that nutritional and anti-inflammatory interventions, such as n-3 fatty acid and antioxidant supplementation, can modulate biomarker profiles and potentially improve clinical outcomes.
CONCLUSION: CRA in lung cancer is a multifactorial syndrome involving complex interactions among inflammatory, metabolic, and signaling pathways. Multi-omics biomarker integration holds promise for early detection and individualized treatment, but larger, multi-center studies are needed to confirm clinical utility and optimize management strategies. Precision interventions based on biomarker profiles should be further explored in future research and practice.
PMID:40536584 | DOI:10.1007/s00520-025-09670-9
Front Genet. 2025 Jun 4;16:1610284. doi: 10.3389/fgene.2025.1610284. eCollection 2025.
ABSTRACT
INTRODUCTION: Lung cancer continues to pose significant global health burdens due to its high morbidity and mortality. This study aimed to systematically integrate biomedical datasets, particularly incorporating traditional Chinese medicine (TCM)-associated multi-omics data, employing advanced deep-learning methods enhanced by graph attention mechanisms. We sought to investigate molecular mechanisms underlying stage-wise lung cancer progression and identify pivotal stage-specific biomarkers to support precise cancer staging classification.
METHODS: We developed a novel multi-omics integrative model, named the Multi-Omics Lung Cancer Graph Network (MOLUNGN), based on Graph Attention Networks (GAT). Clinical datasets of non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), were analyzed to create omics-specific feature matrices comprising mRNA expression, miRNA mutation profiles, and DNA methylation data. MOLUNGN incorporated omics-specific GAT modules (OSGAT) combined with a Multi-Omics View Correlation Discovery Network (MOVCDN), effectively capturing intra- and inter-omics correlations. This framework enabled comprehensive classification of clinical cases into precise cancer stages, alongside the extraction of stage-specific biomarkers.
RESULTS: Evaluations utilizing publicly available datasets confirmed MOLUNGN's superior performance over existing methodologies. On the LUAD dataset, MOLUNGN achieved accuracy (ACC) of 0.84, Recall_weighted of 0.84, F1_weighted of 0.83, and F1_macro of 0.82. On the LUSC dataset, the model further improved, achieving ACC of 0.86, Recall_weighted of 0.86, F1_weighted of 0.85, and F1_macro of 0.84. Notably, critical stage-specific biomarkers with significant biological relevance to lung cancer progression were identified, facilitating robust gene-disease associations.
DISCUSSION: Our findings underscore the efficacy of MOLUNGN as an integrative framework in accurately classifying lung cancer stages and uncovering essential biomarkers. These biomarkers provide deep insights into lung cancer progression mechanisms and represent promising targets for future clinical validation. Integrating these biomarkers into the TCM-target-disease network enriches the understanding of TCM therapeutic potentials, laying a robust foundation for future precision medicine applications.
PMID:40534839 | PMC:PMC12174459 | DOI:10.3389/fgene.2025.1610284
Architectural success in decentralized systems depends more on how decisions are made than on system design alone. Replacing control with trust requires visible, structured practices—such as ADRs and advice forums—to build confidence and clarity.
By Lindsey Tibbitts
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