Background Characterized by rapid metastasis and a significant death rate, lung cancer presents a formidable challenge, which underscores the critical role of early detection in combating the disease. This study addresses the urgent need for early lung cancer detection using deep learning models applied to computed tomography (CT) images. Methods Our study introduced a unique non-cancer pneumonia dataset, a publicly available large-scale collection of high-quality pneumonia CT scans with detailed descriptions. We utilized this dataset to fine-tune nine pretrained models, including DenseNet121, MobileNetV2, InceptionV3, InceptionResNetV2, ResNet50, ResNet101, VGG16, VGG19, and Xception for the classification of lung cancer and pneumonia. Results ResNet50 demonstrated the highest accuracy and sensitivity (97.7% and 100%, respectively), while InceptionV3 excelled in precision (97.9%) and specificity (98.0%). The study also highlighted the contribution of the gradient-weighted class activation mapping (Grad-CAM) technique in examining the effectiveness of the model-training process via the visualization of features learned across different layers. Grad-CAM revealed that among the best-performed models, InceptionV3 successfully identified cancerous lesions in CT scans. Our findings demonstrated the potential of deep learning models in early lung cancer screening and improving the accuracy of the diagnosis procedure. Data availability The pneumonia CT scan dataset used in this study is extracted from peer-reviewed publications and can be accessed at https://github.com/ReiCHU31/CT-pneumonia-dataset

Breast cancer remains a prevalent global health challenge, with tumor-removal surgeries being among the most common treatments but often leading to aesthetic defects. Adipose-derived stem cell (ADSC)-enriched fat grafting in breast reconstruction offers promising therapeutic benefits. However, concerns about its oncological safety persist, particularly regarding the potential risks of promoting cancer recurrence. This study investigated the effects of ADSCs on breast cancer progression by coculturing ADSCs with the MCF-7 breast cancer cell line for a short cell cultivation period of 3 days. We performed an RNA-seq analysis to identify significant transcriptomic changes in cocultured MCF-7 cells and carried out functional enrichment analyses to uncover key biological pathways influenced by ADSCs. Our findings revealed that transcriptomic alterations in MCF-7 cells are linked to aggressive cancer traits, including the upregulation of epithelial–mesenchymal transition (EMT) and the HIF-1 signaling pathway, which indicate a shift toward aerobic glycolysis. Some of the observed gene expression changes also correlated with relapse risk and mortality. These findings underscore the need for further research to explore the implications of these genes and pathways in driving aggressive cancer phenotypes and assess the safety of ADSCs in clinical settings.

Wilms’ tumor (WT) is the most common form of pediatric kidney cancer, typically characterized by favorable outcomes but with a relatively high recurrence rate. The prognosis of recurrence often relies on histological features, tumor staging, and response to initial therapies. However, there is a need to incorporate molecular factors for more effective management of relapsed cases. In this study, we analyzed the transcriptomic profiles of WT using bulk RNA sequencing data obtained from The Cancer Genome Atlas. Gene expression profiles of recurrent tumors were compared with those of primary tumors, and primary tumors were compared to non-cancerous controls to identify dysregulation in gene expression programs. Our results revealed that recurrent WT exhibit transcriptional profiles distinct from those of primary tumors. While a subset of dysregulated genes was shared between primary and recurrent WT, several genes were uniquely differentially expressed in recurrent tumors. Functional enrichment analyses showed that these expression differences primarily affect immune response, cell adhesion, and extracellular matrix components. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression together with Cox and Kaplan-Meier analyses, a 20-gene risk signature was established, which is strongly associated with relapse probability. The model achieved its highest time-dependent area under the receiver-operating characteristic curve of 91.5% when predicting five-year recurrence survival. Our analyses also demonstrated that, along with the risk score, male gender and the favorable histological subtype of primary tumors are also linked to poorer disease-free survival. These findings serve as preliminary evidence toward establishing gene expression signatures that could aid risk stratification of WT.

Recent advancements in metagenomics, particularly in the studies of conservative 16S rRNA sequences, have significantly accelerated our understanding of the relationship between corals and their associated microbial communities. While bacteria are known to be closely linked with corals, there is limited understanding of the connections between archaea and corals. Unlike previous 16S rRNA studies conducted in similar tropical coral reef ecosystems, we analyzed both the archaeal and bacterial communities associated with Acropora sp. and Lobophyllia sp. corals, as well as the surrounding sediment and water columns in Phu Quoc Island, Kien Giang Province, Vietnam. The data collected were sequenced using the 16S rRNA sequencing method and further analyzed using bioinformatics tools in the R programming language, employing DADA2 and phyloseq pipelines. We examined the compositions and diversity of bacteria and archaea in coral, sediment, and water column samples to establish potential connections between these two domains. The results revealed that archaea constituted a small percentage of all samples, averaging 3.18% in coral mucus and reaching an average of 7.49% in sediment samples. Among the most abundant archaeal taxa were Crenarchaeota and Nanoarchaeota, alongside bacterial taxa Gammabacteria, Cyanobacteria, and Desulfobacteria, which are associated with important metabolic processes within coral hosts. Alpha and beta diversity analyses confirmed the highest archaeal diversity in sediment samples and the distinct existence of microbial communities in each biotope. These findings complement our knowledge of archaea’s presence and potential roles in the coral-associated microbiome.