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.
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.
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..
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.
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.
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.
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.
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.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Why it’s so hard to stop tech-facilitated abuse
After Gioia had her first child with her then husband, he installed baby monitors throughout their home—to “watch what we were doing,” she says, while he went to work. She’d turn them off; he’d get angry. By the time their third child turned seven, Gioia and her husband had divorced, but he still found ways to monitor her behavior. One Christmas, he gave their youngest a smartwatch. Gioia showed it to a tech-savvy friend, who found that the watch had a tracking feature turned on. It could be turned off only by the watch’s owner—her ex.
And Gioia is far from alone. In fact, tech-facilitated abuse now occurs in most cases of intimate partner violence—and we’re doing shockingly little to prevent it. Read the full story.
—Jessica Klein
This story is from the next print edition of MIT Technology Review, which explores power—who has it, and who wants it. It’s set to go live on Wednesday June 25, so subscribe & save 25% to read it and get a copy of the issue when it lands!
Why AI hardware needs to be open
—by Ayah Bdeir, a leader in the maker movement, champion of open source AI, and founder of littleBits, the hardware platform that teaches STEAM to kids through hands-on invention.
Once again, the future of technology is being engineered in secret by a handful of people and delivered to the rest of us as a sealed, seamless, perfect device. When technology is designed like this, we are reduced to consumers. We don’t shape the tools; they shape us.
However, this moment creates a chance to do things differently. Because away from the self-centeredness of Silicon Valley, a quiet, grounded sense of resistance is reactivating. Read the full story.
MIT Technology Review Narrated: Deepfakes of your dead loved ones are a booming Chinese business
In China, people are seeking help from AI-generated avatars to process their grief after a family member passes away. Our story about this trend is the latest to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Iran is going offline to avoid Israeli cyberattacks A government spokesperson said it plans to disconnect completely from the global internet this evening. (The Verge) + How attacks on Iran’s oil exports could hurt China. (WSJ $)
2 Trump is giving TikTok another reprieve from a US ban It’s been a full five years since he signed the original executive order telling Bytedance to sell it. (CNN) + Why Chinese manufacturers are going viral on TikTok. (MIT Technology Review)
3 Conspiracy theories about the Minnesota shooting are all over social media Whenever there’s an information vacuum, people are all too keen to fill it with noise and nonsense. (NBC) + The shooting suspect allegedly used data broker sites to find targets’ addresses. (Wired $)
4 Tensions between OpenAI and Microsoft are starting to boil over OpenAI has even threatened to report its formerly close partner to antitrust regulators. (WSJ $) + Here are the concessions OpenAI is seeking. (The Information $) + Inside the story that enraged OpenAI. (MIT Technology Review)
5 California cops are using AI cameras to investigate ICE protests And sharing license plate data with other agencies, a practice some experts say is illegal. (404 Media) + How a new type of AI is helping police skirt facial recognition bans. (MIT Technology Review)
6 Social media is now Americans’ primary news source It’s overtaken TV for the first time. (Reuters) + They watched more TV via streaming than cable last month, too. (NYT $)
7 Weight loss drugs may not work quite as well as hoped Researchers analysed data from 51,085 patients and found bariatric surgery delivered better, more sustainable results. (The Guardian)
8 What is AI doing to reading? Here’s what we stand to gain—and lose—when we outsource reading to machines. (New Yorker $)
9 India is relying on China to build up its EV market It’s taking a drastically different course to the US. (Rest of World) + Why EVs are (mostly) set for solid growth in 2025. (MIT Technology Review)
10 People are building AI tools to decipher cats’ meows Bet at least half of them are “feed me.” (Scientific American $)
Quote of the day
“Have we fallen so low? Have we no shame?”
—Remarks made by federal judge Williams G. Young this week as he voided some of the Trump administration’s cuts to National Institutes of Health grants, saying they were discriminatory, the New York Times reports.
One more thing
STEPHANIE ARNETT/MIT TECHNOLOGY REVIEW | GETTY
Why AI could eat quantum computing’s lunch
Tech companies have been funneling billions of dollars into quantum computers for years. The hope is that they’ll be a game changer for fields as diverse as finance, drug discovery, and logistics.
But while the field struggles with the realities of tricky quantum hardware, another challenger is making headway in some of these most promising use cases. AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all. Read the full story.
—Edd Gent
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
“The OpenAI Files,” an archival project from the Midas Project and the Tech Oversight Project, are a “collection of documented concerns with governance practices, leadership integrity, and organizational culture at OpenAI.”
Biomol Biomed. 2025 Jun 10. doi: 10.17305/bb.2025.12324. Online ahead of print.
ABSTRACT
Immune checkpoint inhibitors (ICIs) demonstrate substantial interpatient variability in clinical efficacy for unresectable non-small cell lung cancer (NSCLC), underscoring the unmet need for noninvasive biomarkers to predict early therapeutic responses and improve survival outcomes. To address this, we developed a CT-based deep learning model integrated with the systemic immune-inflammatory-nutritional index (SIINI) for early prediction of ICI response. In a retrospective multicenter study of 265 patients treated with ICIs (incorporating chest CT and laboratory data), the cohort was divided into training (70%), internal validation (30%), and external validation sets. The combined model-leveraging DenseNet121-derived deep radiomic features alongside SIINI-achieved strong predictive performance, with AUCs of 0.865 (95% CI: 0.7709-0.9595) in the internal validation cohort and 0.823 (95% CI: 0.6627-0.9827) in the external validation cohort. Gradient-weighted class activation mapping (Grad-CAM) highlighted key CT regions contributing to model predictions, enhancing interpretability for clinical application. These findings highlight the potential of integrating deep learning with inflammatory biomarkers to support personalized ICI therapy in unresectable NSCLC. Future directions include incorporating multi-omics biomarkers, expanding multicenter validation, and increasing sample sizes to further improve predictive accuracy and facilitate clinical translation.
After decades of pioneering institutional change in Chinese research, neuroscientist Rao Yi explains why US hesitation might offer China an opportunity to catch up internationally.
Infect Agent Cancer. 2025 Jun 15;20(1):37. doi: 10.1186/s13027-025-00672-0.
ABSTRACT
Mendelian Randomization (MR) is increasingly used in cancer research to infer causal relationships by leveraging genetic variants as instrumental variables. While the growth of genome-wide association studies and biobank data has expanded the utility of MR, this surge-particularly pronounced in China-raises concerns about methodological rigor. The widespread adoption may be partly driven by the Chinese translation of key MR literature. Recent advances such as multivariable MR, mediation analysis, and integration with AI and omics data have enhanced the robustness and biological interpretability of MR studies. However, challenges persist, including horizontal pleiotropy, weak instrument bias, and misinterpretation of biomarkers as causal exposures. To improve MR study credibility, frameworks like STROBE-MR and MR-GRADE are being adopted. This article reviews methodological improvements and persistent pitfalls in MR, especially within cancer epidemiology, and highlights strategies for ensuring validity in this rapidly evolving field.
BACKGROUND: The in silico analyses provide evidence supporting the potential of methylation-driven differentially expressed genes as therapeutic targets across cancer types. This leads us to identify novel targets and their associated drug compounds for further progress towards pancreatic cancer treatment.
OBJECTIVE: To identify targeted drugs based on methylation driven genes identified using bulk multi-omics data and single-cell level data to pinpoint important disease markers.
METHODS: The workflow involves screening using the TCGA and ICGC databases, followed by validation with GEO datasets. The study employs supervised learning algorithms like kNN and random forests, and constructs a prediction model using adaptive LASSO-Cox regression. The process also includes pathway analysis, evaluation of survival status, and immune profile deconvolution, as well as multistage evaluation of the methylation driven genes. We conducted drug targeting and molecular dynamic simulations, taking into account genes of interest.Lastly, molecular docking and dynamics simulations were used to find out if the key MEDEGs could be utilized as drug targets.
RESULTS: CD36, UGT1A1, TFF1, S100P, MUC13, CALHM3 and ANKRD44 were found to be top 7 methylation driven genes. The mutational profile was also documented along with pathway analysis, which showed concordance with our observation based on their significant enriched terms namely "Maintenance of Gastrointestinal Epithelium", and "Digestive System Homeostasis". CD36 had prognostic capabilities and was seen to significant in terms of survival and also showed significant immune dysregulation. Our novel findings suggest TFF1, S100P, and MUC13 were found to be associated with cell type specific expression as seen in single cell data and UGT1A1 was found to be suitable for probable drug targeting. CD36, UGT1A1, TFF1, S100P, and MUC13 showed concordance when observed at proteomics level and across other datasets. Apigenin-7-O-glucuronide emerged as the top binder for UDP-glucuronosyltransferase 1A1 (also known as UDP 1A1), forming stable complexes with favourable interactions. Catechin and epicatechin were identified as the best ligands for TFF1 and S100P, while rutin showed high-affinity binding to MUC13.
CONCLUSION: The study successfully identified and validated a panel of biomarkers specific to pancreatic cancer, with potential applications in early diagnosis and treatment. The findings highlight the importance of multi-omics data integration in cancer research and the potential of personalized medicine in improving patient outcomes. The in-silico drug targeting analysis provides a foundation for the development of novel drugs for PanCa treatment. Hence TFF1, S100P, MUC13, and UGT1A1 showcased themselves as most promising biomarkers and novel drug targets.
Signal Transduct Target Ther. 2025 Jun 15;10(1):186. doi: 10.1038/s41392-025-02243-6.
ABSTRACT
Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements in basic and clinical research. However, substantial unmet needs remain for patients worldwide, as drug resistance persists as an inevitable reality. Meanwhile, the journey towards amplifying the breadth and depth of the therapeutic effect requires comprehending and integrating diverse and profound progress. In this review, therefore, we aim to comprehensively present such progress that spans the various aspects of molecular pathology, encompassing elucidations of metastatic mechanisms, identification of therapeutic targets, and dissection of spatial omics. Additionally, we also highlight the numerous small molecule and antibody drugs, encompassing their application alone or in combination, across later-line, frontline, neoadjuvant or adjuvant settings. Then, we elaborate on drug resistance mechanisms, mainly involving targeted therapies and immunotherapies, revealed by our proposed theoretical models to clarify interactions between cancer cells and a variety of non-malignant cells, as well as almost all the biological regulatory pathways. Finally, we outline mechanistic perspectives to pursue innovative treatments of NSCLC, through leveraging artificial intelligence to incorporate the latest insights into the design of finely-tuned, biomarker-driven combination strategies. This review not only provides an overview of the various strategies of how to reshape available armamentarium, but also illustrates an example of clinical translation of how to develop novel targeted drugs, to revolutionize therapeutic landscape for NSCLC.
Front Immunol. 2025 May 29;16:1585505. doi: 10.3389/fimmu.2025.1585505. eCollection 2025.
ABSTRACT
BACKGROUND: Lung adenocarcinoma (LUAD) is a common and aggressive subtype of lung cancer associated with poor clinical outcomes. The role of mitochondrial dynamics (MD)-related genes in tumor progression and immune regulation remains poorly understood.
METHODS: Data from public databases were integrated, and subtypes were classified based on 23 MD-related genes. A five-gene prognostic model was constructed. Associations between the model and immune infiltration, tumor mutational burden (TMB), tumor stemness, and drug sensitivity were analyzed. The function of the key gene MTCH2 was validated through in vitro experiments.
RESULTS: Two distinct MD molecular subtypes were identified, exhibiting significant differences in prognosis and immune characteristics. A corresponding risk score model was established. Patients in the low-risk group showed better prognosis and enhanced immune activity, whereas the high-risk group displayed higher TMB and stemness scores. Drug sensitivity analysis revealed distinct responses to chemotherapeutic agents such as cisplatin and docetaxel between risk groups. Functional assays demonstrated that MTCH2 knockout significantly inhibited LUAD cell proliferation, migration, and invasion, and induced G0/G1 phase arrest, suggesting that MTCH2 may act as a potential adverse prognostic marker.
CONCLUSION: MD-related genes exhibit strong prognostic and immune subtyping value. The proposed risk model holds clinical potential, and MTCH2 may serve as a promising target for precision therapy in LUAD.
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