A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing

Abstract Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients’ prognosis. Thus, it’s critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at http://github.com/wangbo00129/classifybysklearn

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations. Availability: The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA

An Immune Signature for Risk Stratification and Therapeutic Prediction in Helicobacter pylori-Infected Gastric Cancer

Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC). Increasing evidence has clarified that tumor immune microenvironment (TIME) is closely related to the prognosis and therapeutic efficacy of HP-positive (HP+) GC patients. In this study, we aimed to construct a novel immune-related signature for predicting the prognosis and immunotherapy efficacy of HP+ GC patients. A total of 153 HP+ GC from three different cohorts were included in this study. An Immune-Related prognostic Signature for HP+ GC patients (IRSHG) was established using Univariate Cox regression, the LASSO algorithm, and Multivariate Cox regression. Univariate and Multivariate analyses proved IRSHG was an independent prognostic predictor for HP+ GC patients, and an IRSHG-integrated nomogram was established to quantitatively assessthe prognostic risk. The low-IRSHG group exhibited higher copy number load and distinct mutation profiles compared with the high-IRSHG group. In addition, the difference of hallmark pathways and immune cells infiltration between the two groups was investigated. Notably, tumor immune dysfunction and exclusion (TIDE) analysis indicated that the low-IRSHG group had a higher sensitivity to anti-PD-1 immunotherapy, which was validated by an external pabolizumab treatment cohort. Moreover, 98 chemotherapeutic drugs and corresponding potential biomarkers were identified for two groups, and several drugs with potential ability to reverse IRSHG score were identified using CMap analysis. Collectively, IRSHG may serve as a promising biomarker for survival outcome as well as immunotherapy efficacy. Furthermore, it can also help to prioritize potential therapeutics for HP+ GC patients, providing new insight for the personalized treatment of HP-infected GC

Whole-Transcriptome Analysis Identifies Gender Dimorphic Expressions of Mrnas and Non-Coding Rnas in Chinese Soft-Shell Turtle (Pelodiscus sinensis)

In aquaculture, the Chinese soft-shelled turtle (Pelodiscus sinensis) is an economically important species with remarkable gender dimorphism in its growth patterns. However, the underlying molecular mechanisms of this phenomenon have not been elucidated well. Here, we conducted a whole-transcriptome analysis of the female and male gonads of P. sinensis. Overall, 7833 DE mRNAs, 619 DE lncRNAs, 231 DE circRNAs, and 520 DE miRNAs were identified. Some “star genes” associated with sex differentiation containing dmrt1, sox9, and foxl2 were identified. Additionally, some potential genes linked to sex differentiation, such as bmp2, ran, and sox3, were also isolated in P. sinensis. Functional analysis showed that the DE miRNAs and DE ncRNAs were enriched in the pathways related to sex differentiation, including ovarian steroidogenesis, the hippo signaling pathway, and the calcium signaling pathway. Remarkably, a lncRNA/circRNA–miRNA–mRNA interaction network was constructed, containing the key genes associated with sex differentiation, including fgf9, foxl3, and dmrta2. Collectively, we constructed a gender dimorphism profile of the female and male gonads of P. sinensis, profoundly contributing to the exploration of the major genes and potential ncRNAs involved in the sex differentiation of P. sinensis. More importantly, we highlighted the potential functions of ncRNAs for gene regulation during sex differentiation in P. sinensis as well as in other turtles

CD177 drives the transendothelial migration of Treg cells enriched in human colorectal cancer

Abstract Objectives Regulatory T (Treg) cells regulate immunity in autoimmune diseases and cancers. However, immunotherapies that target tumor‐infiltrating Treg cells often induce unwanted immune responses and tissue inflammation. Our research focussed on exploring the expression pattern of CD177 in tumor‐infiltrating Treg cells with the aim of identifying a potential target that can enhance immunotherapy effectiveness. Methods Single‐cell RNA sequencing (scRNA‐seq) data and survival data were obtained from public databases. Twenty‐one colorectal cancer patient samples, including fresh tumor tissues, peritumoral tissues and peripheral blood mononuclear cells (PBMCs), were analysed using flow cytometry. The transendothelial activity of CD177+ Treg cells was substantiated using in vitro experiments. Results ScRNA‐seq and flow cytometry results indicated that CD177 was exclusively expressed in intratumoral Treg cells. CD177+ Treg cells exhibited greater activation status and expressed elevated Treg cell canonical markers and immune checkpoint molecules than CD177− Treg cells. We further discovered that both intratumoral CD177+ Treg cells and CD177‐overexpressing induced Treg (iTreg) cells had lower levels of PD‐1 than their CD177− counterparts. Moreover, CD177 overexpression significantly enhanced the transendothelial migration of Treg cells in vitro. Conclusions These results demonstrated that Treg cells with higher CD177 levels exhibited an enhanced activation status and transendothelial migration capacity. Our findings suggest that CD177 may serve as an immunotherapeutic target and that overexpression of CD177 may improve the efficacy of chimeric antigen receptor T (CAR‐T) cell therapy

Host–Guest Superstructures on Graphene-Based Kagome Lattice

The Kagome lattice of iron phthalocyanine (FePc) on the graphene moiré pattern is employed as host template for two kinds of guest molecules, FePc and <i>tert</i>-butyl zinc phthalocyanine ((<i>t</i>-Bu)₄–ZnPc), to fabricate stable host–guest molecular superstructures. Both FePc and (<i>t</i>-Bu)₄–ZnPc molecules prefer to occupy the nanoscale pores of the Kagome lattice. Ordered superstructures with alternate rows of FePc and (<i>t</i>-Bu)₄–ZnPc are formed after coadsorption of these two species with a ratio of 1:1 on the Kagome lattice. We elucidate that formation of ordered superstructures of guest FePc and (<i>t</i>-Bu)₄–ZnPc are controlled by long-range interaction between the guest molecules mediated by the host Kagome lattice with additional contribution from the graphene/Ru(0001) substrate