Objective: Relapse and lung metastasis of atypical type A thymoma are knotty problems in clinical treatment. The identification of specific biomarkers and novel therapeutic targets is critical for advancing the precision and efficacy of interventions against this disease. MicroRNAs (miRNAs), as pivotal regulators of gene expression, have emerged as key players in tumorigenesis and metastatic processes. In this study, we found that miR5700 was overexpressed in atypical type A thymoma, and miR5700 overexpression could promote lung cancer cell proliferation, migration, and invasion abilities. It gives us a clue that miR5700 could be a biomarker and therapeutic target for atypical type A thymoma.

Methods: miRNA microarray chip technology was applied to identify differentially expressed miRNAs in 20 pairs of atypical type A thymoma versus type A thymoma. Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) was performed to detect the expression of miR5700 in non-small cell lung cancer cells after lentiviral transfection. Subsequently, cell proliferation was examined by the real-time cellular analysis and clone formation assay. Cell migration and invasion abilities were evaluated by wound healing and Matrigel transwell assay, respectively. Besides, the effect of miR5700 on AKT, mTOR, and β-catenin was determined by western blotting.

Results: MiR5700 was dramatically increased in atypical A thymoma. The transfection of miR5700 into A549 and H2170 cells significantly promoted cell growth, migration, and invasion. Furthermore, miR5700 was confirmed to upregulate the expression of AKT, mTOR and β-catenin proteins, which were related to tumour progression by western blotting.

Conclusions: High expression of miR5700 is a new hallmark that could promote tumour progression. Additionally, it is hoped that miR5700 will become a potential target for the diagnosis of atypical type A thymoma through further research in the future.

Computational electrophysiology models are beginning to emerge as digital-twin–oriented representations of cancer cells, offering mechanistic insights that complement traditional patch-clamp experiments. In this study, we evaluate the ability of the earliest in-silico cancer electrophysiology model, an ion channel model based on Hidden Markov state transitions, to reproduce drug-modulated current densities in A549 lung adenocarcinoma cells. Using independent experimental data from Glaser et al. (2021), we characterised Ca²⁺-activated K⁺ channels, KCa1.1 and KCa3.1, in wild-type (WT) and erlotinib-resistant (ER) A549 cells under baseline conditions, as well as after activation with 1-EBIO (3-ethyl-1H-benzimidazol-2-one) and inhibition with paxilline and senicapoc. The in-silico model reproduced the qualitative order of current responses under all pharmacological conditions, quantitatively matching the paxilline- and senicapoc-blocked states while remaining within biologically reasonable channel expression limits. Reproducing 1-EBIO activation required higher-than-physiological effective channel numbers, indicating that ligand-dependent gating is not fully represented. Nevertheless, the model captured the overall electrophysiological behaviour of both WT and ER cells and successfully distinguished their phenotypes. In summary, the in-silico model already enables mechanistic interpretation of electrophysiological phenotypes and drug-modulated responses. With continued refinement, including the incorporation of ligand-modulated gating, improved calcium-feedback dynamics, and formal uncertainty quantification, this model has the potential to evolve into a predictive digital twin platform supporting ion-channel pharmacology, therapy optimisation and precision oncology.

Tumour heterogeneity, encompassing genetic, epigenetic, and microenvironmental diversity, remains a fundamental obstacle in precision oncology. Traditional bulk sequencing captures only averaged molecular profiles, thereby masking rare yet functionally critical subpopulations that drive malignant progression and therapeutic resistance. Recently, the emergence of single-cell sequencing technologies has overcome the limitations of bulk approaches, enabling high-resolution analyses of the genome, transcriptome, epigenome and proteome at the single-cell level. These advances have enabled detailed mapping of tumour ecosystems, identification of key cellular subtypes, reconstruction of evolutionary trajectories and elucidation of intercellular communication networks within the tumour microenvironment. Accumulating evidence demonstrates that single-cell technologies elucidate fundamental aspects of tumour biology and reveal potential diagnostic and therapeutic targets. This review systematically summarises the recent advances and applications of single-cell sequencing in the field of precision oncology, with particular emphasis on its applications in mechanistic discovery, diagnosis, therapy, and prognosis. Furthermore, we discuss current challenges related to technology, data analysis, and clinical translation, and outline future research directions. In summary, single-cell sequencing has profoundly reshaped our understanding of tumour biology and is propelling oncology into a new era of precision, prediction, and personalisation.

Breast cancer heterogeneity is still a primary concern, with a large variation in the prognosis necessitating the development of personalised treatment plans. Single biomarkers can't fully encompass the breast cancer complexity and variations, and therefore, multi-omics approaches offer a wide comprehension of the cancer-specific biology. In this study, we developed a multi-omics framework for the prediction of immune-active features framework integrating four-omics data, mainly genomic, proteomic and transcriptomic. By leveraging deep learning along with survival-based feature selection, we constructed an autoencoder that generated compressed multi-omics features to stratify the patients into two optimal immune subtypes with a significantly different overall survival (p < 0.002) and a high C-index of 0.74 (95% confidence interval: 0.62–0.83). An XGBoost classifier was trained to predict these subtypes by integrating all omics data as well as each omic individually, and was validated using both internal and external (the Cancer Genome Atlas and Gene Expression Omnibus) datasets. The integrated model achieved high predictive performance (ACC = 0.95). Omics-unique classifiers showed consistently strong validation on independent datasets, particularly the immunotherapy-treated cohort (GSE241876, p = 4.80×10⁻²). We further investigated the biological mechanisms across the clusters and discovered that the C2, low-risk cluster, exhibited an immune-active landscape, characterised by a high infiltration of cells and more immune-related pathways, making it a better candidate for a favourable immunotherapy response. On the other hand, the C1 cluster, the immune-cold group, displayed an immunosuppressive microenvironment and poor prognosis. This methodology demonstrated the promising potential of deep learning-driven multi-omics integration to support precision oncology by enhancing prognostic prediction and tailoring treatment decisions.

Background: As one of commonly used immune checkpoint inhibition therapies, PD1 monoclonal antibodies exhibit a promising cancer immunotherapy approach. However, their efficacy on tumour immunity needs to be augment, as large numbers of patients poorly respond to the treatment or suffer from recurrence in clinical. Although pan-PI3K is involved in the performance of PD1 on T cell immunity, the study for mechanisms of PI3K subunits involved in could be helpful for proposing a potential treatment strategy for lung cancer that combines anti-PD1 treatment with PI3Kγ inhibitor.

Materials & answers: Alterations of CD4⁺ and CD8⁺ T cell subpopulations in 11 types of peripheral blood mononuclear cells with healthy subjects and cancer patients were examined. The efficacy of different treatment strategies for lung cancer was then investigated, and the factors affecting the efficacy of anti-PD1 therapy in lung cancer were discussed.

Results: Lung cancer is characterized by widespread variation in T cell subsets, and anti-PD1 treatment is effective against CD4⁺ Tcm, CD4⁺ Tem and CD4⁺ Tn cell subsets. The involvement of PI3K in the effect of anti-PD1 was demonstrated using single-cell RNA sequencing. The PI3Kγ inhibitor CAY10505 was effective against CD4⁺ Tcm and CD4⁺ Tn subsets in lung cancer in vitro but not in pan-cancer therapy, indicating that the therapeutic effect of PI3Kγ on CD4⁺ T cells was lung cancer-specific. G-protein coupled receptor 183 (GPR183) was involved in migration and positioning of immune cells and associated with various immune-related diseases.

Discussion: We explored the regulatory role of GPR183 in the PI3K pathway and T cell subsets and identified potential lipids involved using lipidomics. We found that inhibition of PI3Kγ upregulated CD4⁺ Tcm and CD4⁺ Tn, potentially enhancing the therapeutic efficacy of anti-PD1 antibodies. Combining anti-PD1 treatment with PI3Kγ inhibitor could be a potential treatment strategy for lung cancer.

Background: Diabetes, metabolic disorders and feeding behaviours continue to pose significant public health challenges. Calcium/calmodulin-dependent protein kinase ID (CAMK1D) has recently emerged as a pivotal molecule potentially bridging peripheral metabolic control with central appetite regulation. Therefore, a comprehensive review was performed to critically evaluate and synthesize current evidence regarding the role of CAMK1D in diabetes, metabolic processes and feeding behaviours.

Main text: The review assessed both published results (263 non-duplicate studies; across Pubmed, WebOfScience and EMBASE) and the grey literature (including 14 patents, 3 clinical trials). Results from 43 unique studies, 2 patents and 5 genome-wide association studies were finally summarized. CAMK1D modulates both metabolic processes and feeding behaviours, exhibiting tissue-specific dynamics and diverging regulatory control either in the central nervous system (i.e., hypothalamic nuclei regulating appetite and satiety) or in the periphery (i.e., pancreatic beta cells). Genetic studies highlighted significant associations between CAMK1D polymorphisms and increased susceptibility to diabetes, obesity and altered feeding behaviours.

Conclusions: CAMK1D represents an emerging molecular target with promising implications for the treatment of a wide range of clinical conditions. However, further large-scale, mechanistic and longitudinal studies are warranted to validate its role across physiological and pathophysiological conditions, as well as to explore its future therapeutic potential.