Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow.

Type 2 diabetes (T2D) is a rapidly growing epidemic, which leads to increased mortality rates and health care costs. Nutrients (namely, carbohydrates, fat, protein, mineral substances, and vitamin), sensing, and management are central to metabolic homeostasis, therefore presenting a leading factor contributing to T2D. Understanding the comprehensive effects and the underlying mechanisms of nutrition in regulating glucose metabolism and the interactions of diet with genetics, epigenetics, and gut microbiota is helpful for developing new strategies to prevent and treat T2D. In this review, we discuss different mechanistic pathways contributing to T2D and then summarize the current researches concerning associations between different nutrients intake and glucose homeostasis. We also explore the possible relationship between nutrients and genetic background, epigenetics, and metagenomics in terms of the susceptibility and treatment of T2D. For the specificity of individual, precision nutrition depends on the person’s genotype, and microbiota is vital to the prevention and intervention of T2D.

Hitherto, the recognition of the microbiota role in tumorigenesis and clinical studies mostly focused on the intestinal flora. In contrast to the gut microbiome, microorganisms resident in tumor tissue are in close contact with cancer cells and therefore have the potential to have similar or even different functional patterns to the gut flora. Some investigations have shown intratumoral bacteria, which might come from commensal microbiota in mucosal areas including the gastrointestinal tract and oral cavity, or from nearby normal tissues. The existence, origin, and interactions of intratumoral bacteria with the tumor microenvironment all contribute to intratumoral microorganism heterogeneity. Intratumoral bacteria have a significant role in tumor formation. They can contribute to cancer at the genetic level by secreting poisons that directly damage DNA and also intimately related to immune system response at the systemic level. Intratumoral bacteria have an impact on chemotherapy and immunotherapy in cancer. Importantly, various properties of bacteria such as targeting and ease of modification make them powerful candidates for precision therapy, and combining microbial therapies with other therapies is expected to improve the effectiveness of cancer treatment. In this review, we mainly described the heterogeneity and potential sources of intratumoral bacteria, overviewed the important mechanisms by which they were involved in tumor progression, and summarized their potential value in oncology therapy. At last, we highlight the problems of research in this field, and look forward to a new wave of studies using the various applications of intratumoral microorganisms in cancer therapy.

The identification of linear B-cell epitopes is important for developing epitope-based vaccines. Recently, machine learning techniques have been used in the epitope prediction, but the existing encoding schemes usually neglected valuable discriminative information. In this paper, we proposed a novel encoding scheme which combines several groups of sequence-derived structural and physicochemical features, and support vector machine was used to construct the prediction models. When applied to the benchmark dataset, our proposed method demonstrated better results than benchmark methods. Moreover, the study indicated incorporating more discriminative features may contribute to the higher prediction performance.

Cancer is a major cause of incidence rate and mortality worldwide. In recent years, cancer immunotherapy has made great progress in the preclinical and clinical treatment of advanced malignant tumors. However, cancer patients will have transient cancer suppression reaction and serious immune related adverse reactions when receiving immunotherapy. In recent years, nanoparticle-based immunotherapy, which can accurately deliver immunogens, activate antigen presenting cells (APCs) and effector cells, provides a new insight to solve the above problems. In this review, we discuss the research progress of nanomaterials in immunotherapy including nanoparticle-based delivery systems, nanoparticle-based photothermal and photodynamic immunotherapy, nanovaccines, nanoparticle-based T cell cancer immunotherapy and nanoparticle-based bacteria cancer immunotherapy. We also put forward the current challenges and prospects of immunomodulatory therapy.

Early screening and detection of non-small cell lung cancer (NSCLC) is crucial due to the significantly low survival rate in advanced stages. Blood-based liquid biopsy is non-invasive test to assistant disease diagnosis, while cell-free RNA is one of the promising biomarkers in blood. However, the disease related signatures have not been explored completely for most cell-free RNA transcriptome sequencing (cfRNA-Seq) datasets. To address this gap, we developed a comprehensive cfRNA-Seq pipeline for data analysis and constructed a machine learning model to facilitate noninvasive early diagnosis of NSCLC. The results of our study have demonstrated the identification of differential mRNA, lncRNAs and miRNAs from cfRNA-Seq, which have exhibited significant association with development and progression of lung cancer. The classifier based on gene expression signatures achieved an impressive area under the curve (AUC) of up to 0.9, indicating high specificity and sensitivity in both cross-validation and independent test. Furthermore, the analysis of T cell and B cell immune repertoire extracted from cfRNA-Seq have provided insights into the immune status of cancer patients, while the microbiome analysis has revealed distinct bacterial and viral profiles between NSCLC and normal samples. In our future work, we aim to validate the existence of cancer associated T cell receptors (TCR)/B cell receptors (BCR) and microorganisms, and subsequently integrate all identified signatures into diagnostic model to improve the prediction accuracy. This study not only provided a comprehensive analysis pipeline for cfRNA-Seq dataset but also highlights the potential of cfRNAs as promising biomarkers and models for early NSCLC diagnosis, emphasizing their importance in clinical settings.