Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors.

Diabetic nephropathy (DN) affects about 40% of diabetes mellitus (DM) patients and is the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) all over the world, especially in high- and middle-income countries. Most DN has been present for years before it is diagnosed. Currently, the treatment of DN is mainly to prevent or delay disease progression. Although many important molecules have been discovered in hypothesis-driven research over the past two decades, advances in DN management and new drug development have been very limited. Moreover, current animal/cell models could not replicate all the features of human DN, while the development of Epigenetics further demonstrates the complexity of the mechanism of DN progression. To capture the key pathways and molecules that actually affect DN progression from numerous published studies, we collected and analyzed human DN prognostic markers (independent risk factors for DN progression). One hundred and fifty-one DN prognostic markers were collected manually by reading 2365 papers published between 01/01/2002 and 12/15/2018. One hundred and fifteen prognostic markers of other four common CKDs were also collected. GO and KEGG enrichment analysis was done using g:Profiler, and a relationship network was built based on the KEGG database. Tissue origin distribution was derived mainly from The Human Protein Atlas (HPA), and a database of these prognostic markers was constructed using PHP Version 5.5.15 and HTML5. Several pathways were significantly enriched corresponding to different end point events. It is shown that the TNF signaling pathway plays a role through the process of DN progression and adipocytokine signaling pathway is uniquely enriched in ESRD. Molecules, such as TNF, IL6, SOD2, etc. are very important for DN progression, among which, it seems that “AGER” plays a pivotal role in the mechanism. A database, dbPKD, was constructed containing all the collected prognostic markers. This study developed a database for all prognostic markers of five common CKDs, offering some bioinformatics analyses of DN prognostic markers, and providing useful insights towards understanding the fundamental mechanism of human DN progression and for identifying new therapeutic targets.

Chronic spontaneous urticaria (CSU) comes with gut dysbiosis, but its relevance remains elusive. Here we use metagenomics sequencing and short-chain fatty acids metabolomics and assess the effects of human CSU fecal microbial transplantation, Klebsiella pneumoniae, Roseburia hominis, and metabolites in vivo. CSU gut microbiota displays low diversity and short-chain fatty acids production, but high gut Klebsiella pneumoniae levels, negatively correlates with blood short-chain fatty acids levels and links to high disease activity. Blood lipopolysaccharide levels are elevated, link to rapid disease relapse, and high gut levels of conditional pathogenic bacteria. CSU microbiome transfer and Klebsiella pneumoniae transplantation facilitate IgE-mediated mast cell(MC)-driven skin inflammatory responses and increase intestinal permeability and blood lipopolysaccharide accumulation in recipient mice. Transplantation of Roseburia hominis and caproate administration protect recipient mice from MC-driven skin inflammation. Here, we show gut microbiome alterations, in CSU, may reduce short-chain fatty acids and increase lipopolysaccharide levels, respectively, and facilitate MC-driven skin inflammation.

Adoptive cell therapy using T cell receptor-engineered T cells (TCR-T) is a promising approach for cancer therapy with an expectation of no significant side effects. In the human body, mature T cells are armed with an incredible diversity of T cell receptors (TCRs) that theoretically react to the variety of random mutations generated by tumor cells. The outcomes, however, of current clinical trials using TCR-T cell therapies are not very successful especially involving solid tumors. The therapy still faces numerous challenges in the efficient screening of tumor-specific antigens and their cognate TCRs. In this review, we first introduce TCR structure-based antigen recognition and signaling, then describe recent advances in neoantigens and their specific TCR screening technologies, and finally summarize ongoing clinical trials of TCR-T therapies against neoantigens. More importantly, we also present the current challenges of TCR-T cell-based immunotherapies, e.g., the safety of viral vectors, the mismatch of T cell receptor, the impediment of suppressive tumor microenvironment. Finally, we highlight new insights and directions for personalized TCR-T therapy.

Neoantigens can function as actual antigens to facilitate tumor rejection, which play a crucial role in cancer immunology and immunotherapy. Emerging evidence revealed that neoantigens can be used to develop personalized, cancer-specific vaccines. To date, large numbers of immunogenomic peptides have been computationally predicted to be potential neoantigens. However, experimental validation remains the gold standard for potential clinical application. Experimentally validated neoantigens are rare and mostly appear scattered among scientific papers and various databases. Here, we constructed dbPepNeo, a specific database for human leukocyte antigen class I (HLA-I) binding neoantigen peptides based on mass spectrometry (MS) validation or immunoassay in human tumors. According to the verification methods of these neoantigens, the collection of peptides was classified as 295 high confidence, 247 medium confidence and 407 794 low confidence neoantigens, respectively. This can serve as a valuable resource to aid further screening for effective neoantigens, optimize a neoantigen prediction pipeline and study T-cell receptor (TCR) recognition. Three applications of dbPepNeo are shown. In summary, this work resulted in a platform to promote the screening and confirmation of potential neoantigens in cancer immunotherapy. Database URL: www.biostatistics.online/dbPepNeo/.

The occurrence of hepatocellular carcinoma (HCC) related to liver cirrhosis is mostly accompanied by extensive immune infiltration. To reveal the infiltration immune cells landscape, single-cell RNA sequencing data from the healthy donor (HD), patients with liver cirrhosis (LC) and HCC were collected for analysis. By drawing a cell map and calculating the proportion of each cell type, total B cells were identified with a significant higher proportion in HCC (24.26%) than in LC (5.41%) and HD (5.82%), in which plasma cells account for 97.1% in HCC. While in HCC, TCGA datasets were taken for further investigation, and it was found that patients with lower proportion of plasma cells showed better prognosis. The pseudotime cell trajectory analysis of B cell population found that humoral immunity continuously changes during HD, LC and HCC, and humoral immune-related genes are highly expressed in the HCC stage. This suggests humoral immunity may play a key role in the development of LC-associated HCC. At the same time, single cell data of hepatocytes identified differentially expressed genes in HD/LC and LC/HCC groups, and a prognostic model constructed with six of the differential genes (FTCD, MARCKSL1, CXCL3, RGS5, KNG1, and S100A16) could classify HCC patients to two distinct risk groups (median survival time 2.46 years vs. 6.73 years, p < 0.001). Our study demonstrated the power of single-cell data analysis in dissecting tissues into infiltration and main body cells, it revealed the pivotal roles of humoral immunity infiltration in the landscape of HCC associated with cirrhosis, and the key tumor prognostic genes in hepatocytes themselves. These brought novel insights into studying microenvironment and tumor cells parallelly in cancer research. The interaction of both, rather than factors from one side may have caused tumorigenesis and progression.