Long non-coding RNAs (lncRNAs) play crucial roles in regulating gene expression, and a growing number of researchers have focused on the identification of target genes of lncRNAs. However, no online repository is available to collect the information on target genes regulated by lncRNAs. To make it convenient for researchers to know what genes are regulated by a lncRNA of interest, we developed a database named lncRNA2Target to provide a comprehensive resource of lncRNA target genes in 2015. To update the database this year, we retrieved all new lncRNA–target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene, and is freely accessible at http://123.59.132.21/lncrna2target.

Pancreatic cancer (PC) is a great health burden to patients owing to its poor overall survival rate. Long noncoding RNAs (lncRNAs) interact with microRNAs (miRs) to participate in tumorigenesis. Therefore, we aim to uncover the role and related mechanism of LINC00473 in PC through the modulation of miR-195-5p and programmed death-ligand 1 (PD-L1). Increased LINC00473 and PD-L1 but declined miR-195-5p were determined in PC tissues and cell lines, and it was found that LINC00473 mainly situated in the cytoplasm. Also, miR-195-5p was verified to bind with both LINC00473 and PD-L1. Next, with the aim to examine the ability of LINC00473, miR-195-5p, and PD-L1 on the PC progression, the expression of LINC00473, miR-195-5p and PD-L1 were altered with mimics, inhibitors, overexpression vectors or siRNAs in PC cells and cocultured CD8 + T cells. It was demonstrated that LINC00473 sponged miR-195-5p to upregulate PD-L1 expression. More important, the obtained results revealed that LINC00473 silencing or miR-195-5p upregulation elevated the expression of Bcl-2 associated X protein (Bax), interferon (IFN)-γ, and interleukin (IL)-4 but reduced the expression of B-cell lymphoma-2 (Bcl-2), matrix metalloproteinase (MMP)-2, MMP-9, and IL-10, thus inducing the enhancement of the apoptosis as along with the inhibition of proliferation, invasion, and migration of the PC cells. LINC00473 silencing or miR-195-5p elevation activated the CD8 + T cells. Taken together, LINC00473 silencing blocked the PC progression through enhancing miR-195-5p-targeted downregulation of PD-L1. This finding offers new therapeutic options for treating this devastating disease. K E Y W O R D S CD8+ T cells, LINC00473, microRNA-195-5p, pancreatic cancer, programmed death-ligand 1

Although our knowledge of human diseases has increased dramatically, the molecular basis, phenotypic traits, and therapeutic targets of most diseases still remain unclear. An increasing number of studies have observed that similar diseases often are caused by similar molecules, can be diagnosed by similar markers or phenotypes, or can be cured by similar drugs. Thus, the identification of diseases similar to known ones has attracted considerable attention worldwide. To this end, the associations between diseases at the molecular, phenotypic, and taxonomic levels were used to measure the pairwise similarity in diseases. The corresponding performance assessment strategies for these methods involving the terms "category-based," "simulated-patient-based," and "benchmark-data-based" were thus further emphasized. Then, frequently used methods were evaluated using a benchmark-data-based strategy. To facilitate the assessment of disease similarity scores, researchers have designed dozens of tools that implement these methods for calculating disease similarity. Currently, disease similarity has been advantageous in predicting noncoding RNA (ncRNA) function and therapeutic drugs for diseases. In this article, we review disease similarity methods, evaluation strategies, tools, and their applications in the biomedical community. We further evaluate the performance of these methods and discuss the current limitations and future trends for calculating disease similarity. enous ncRNA that regulates several mRNAs to cause B cell lymphomas. 25,26 Based on the molecular basis of diseases, a large number of methods 27-33 have been designed for calculating disease similarity, using this as a metric. Recently, disease taxonomy has begun to play an important role in measuring disease similarity. One of the typical taxonomic classifiers for diseases is Disease Ontology (DO). 34 In this, each disease term represents a disease with different names, and two terms can be linked on the basis of a set of inclusive relationships. For example,

Given the chronic inflammatory nature of Parkinson’s disease (PD), T cell immunity may be important for disease onset. Here, we performed single-cell transcriptome and TCR sequencing, and conducted integrative analyses to decode composition, function and lineage relationship of T cells in the blood and cerebrospinal fluid of PD. Combined expression and TCR-based lineage tracking, we discovered a large population of CD8+ T cells showing continuous progression from central memory to terminal effector T cells in PD patients. Additionally, we identified a group of cytotoxic CD4+ T cells (CD4 CTLs) remarkably expanded in PD patients, which derived from Th1 cells by TCR-based fate decision. Finally, we screened putative TCR–antigen pairs that existed in both blood and cerebrospinal fluid of PD patients to provide potential evidence for peripheral T cells to participate in neuronal degeneration. Our study provides valuable insights and rich resources for understanding the adaptive immune response in PD.

The American Joint Committee on Cancer (AJCC) staging system is insufficiently prognostic for operable gastric cancer patients; therefore, complementary factors are under intense investigation. Although the focus is on immune markers, the prognostic impact of a single immune factor is minimal, due to complex antitumor immune responses. A more comprehensive evaluation may engender more accurate predictions. We analyzed immune factors by immunohistochemical staining in two independent cohorts. The association with patients' survival was analyzed by the Kaplan-Meier method. Our immunoscore system was constructed using Cox proportional hazard analysis. PD-L1 immune cells (IC), PD-L1 tumor cells (TC), PD-1, and CD8 were found among 33.33%, 31.37%, 33.33%, and 49%, respectively, of patients from the discovery cohort, and 41.74%, 17.4%, 38.26%, and 30.43% from the validation cohort. PD-L1 ICs and PD-1 ICs correlated with poorer overall survival (OS), but PD-L1 TCs correlated with better OS and clinical outcomes and infiltration of more CD8 T cells. These four factors were independently prognostic after tumor/lymph nodes/metastasis (TNM) stage adjustment. An immunoscore system based on hazard ratios of the four factors further separated gastric cancer patients with similar TNM staging into low-, medium-, or high-risk groups, with significantly different survival. Our prognostic model yielded an area under the receiver operating characteristic curve (AUC) of 0.856 for prediction of mortality at 5 years, superior to that of TNM staging (AUC of 0.676). Thus, this more comprehensive immunoscore system can provide more accurate prognoses and is an essential complement to the AJCC staging system for operable gastric cancer patients. .

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Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Increasing studies have revealed that long noncoding RNAs (lncRNAs) are not transcriptional noise but play important roles in the regulation of a wide range of biological processes, and the dysregulation of lncRNA genes is associated with disease development. Alzheimer's disease (AD) is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. However, little is known about the roles of lncRNA genes in AD and how the lncRNA genes are transcriptionally regulated. Herein, we analyzed RNA-seq data and ChIP-seq histone modification data from CK-p25 AD model and control mice and identified 72 differentially expressed lncRNA genes, 4,917 differential peaks of H3K4me3, and 1,624 differential peaks of H3K27me3 between AD and control samples, respectively. Furthermore, we found 92 differential peaks of histone modification H3K4me3 are located in the promoter of 39 differentially expressed lncRNA genes and 8 differential peaks of histone modification H3K27me3 are located upstream of 7 differentially expressed lncRNA genes, which suggest that the majority of lncRNA genes may be transcriptionally regulated by histone modification in AD.