LncRNADisease v3.0: an updated database of long non-coding RNA-associated diseases.

Systematic integration of lncRNA-disease associations is of great importance for further understanding their underlying molecular mechanisms and exploring lncRNA-based biomarkers and therapeutics. The database of long non-coding RNA-associated diseases (LncRNADisease) is designed for the above purpose. Here, an updated version (LncRNADisease v3.0) has curated comprehensive lncRNA (including circRNA) and disease associations from the burgeoning literatures. LncRNADisease v3.0 exhibits an over 2-fold increase in experimentally supported associations, with a total of 25440 entries, compared to the last version. Besides, each lncRNA-disease pair is assigned a confidence score based on experimental evidence. Moreover, all associations between lncRNAs/circRNAs and diseases are classified into general associations and causal associations, representing whether lncRNAs or circRNAs can directly lead to the development or progression of corresponding diseases, with 15721 and 9719 entries, respectively. In a case study, we used the data of LncRNADisease v3.0 to calculate the phenotypic similarity between human and mouse lncRNAs. This database will continue to serve as a valuable resource for potential clinical applications related to lncRNAs and circRNAs. LncRNADisease v3.0 is freely available at http://www.rnanut.net/lncrnadisease.

Deciphering gene contributions and etiologies of somatic mutational signatures of cancer.

Somatic mutational signatures (MSs) identified by genome sequencing play important roles in exploring the cause and development of cancer. Thus far, many such signatures have been identified, and some of them do imply causes of cancer. However, a major bottleneck is that we do not know the potential meanings (i.e. carcinogenesis or biological functions) and contributing genes for most of them. Here, we presented a computational framework, Gene Somatic Genome Pattern (GSGP), which can decipher the molecular mechanisms of the MSs. More importantly, it is the first time that the GSGP is able to process MSs from ribonucleic acid (RNA) sequencing, which greatly extended the applications of both MS analysis and RNA sequencing (RNAseq). As a result, GSGP analyses match consistently with previous reports and identify the etiologies for a number of novel signatures. Notably, we applied GSGP to RNAseq data and revealed an RNA-derived MS involved in deficient deoxyribonucleic acid mismatch repair and microsatellite instability in colorectal cancer. Researchers can perform customized GSGP analysis using the web tools or scripts we provide.

Defining the single base importance of human mRNAs and lncRNAs.

As the fundamental unit of a gene and its transcripts, nucleotides have enormous impacts on the gene function and evolution, and thus on phenotypes and diseases. In order to identify the key nucleotides of one specific gene, it is quite crucial to quantitatively measure the importance of each base on the gene. However, there are still no sequence-based methods of doing that. Here, we proposed Base Importance Calculator (BIC), an algorithm to calculate the importance score of each single base based on sequence information of human mRNAs and long noncoding RNAs (lncRNAs). We then confirmed its power by applying BIC to three different tasks. Firstly, we revealed that BIC can effectively evaluate the pathogenicity of both genes and single bases through single nucleotide variations. Moreover, the BIC score in The Cancer Genome Atlas somatic mutations is able to predict the prognosis of some cancers. Finally, we show that BIC can also precisely predict the transmissibility of SARS-CoV-2. The above results indicate that BIC is a useful tool for evaluating the single base importance of human mRNAs and lncRNAs.

The clinical relevance of epithelial-mesenchymal transition and its correlations with tumorigenic immune infiltrates in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a cancer with extremely high mortality. Epithelial-mesenchymal transition (EMT) may play an important role in the occurrence, invasion and prognosis of HCC; however, its relationship with immunity in HCC has not yet been studied. Therefore, we investigated the diagnostic and prognostic values of EMT and explored its potential connections with tumorigenic immune infiltrates in HCC. We first proposed a quantitative metric of EMT activity, the EMT score. After applying this metric to 20 datasets from the Integrative Molecular Database of Hepatocellular Carcinoma, the Cancer Genome Atlas, and the Gene Expression Omnibus, we explored the ability of the EMT score to stratify across sample types. We then applied the EMT score for survival analysis and to differentiate patients with/without vascular invasion to test its prognostic value. We also collected and calculated data on the abundance of immune cells and immune cell markers in HCC and investigated their correlations with EMT scores. Finally, we synthesized and analyzed 20 datasets and constructed an EMT-gene-immune linkage network. The results showed higher EMT scores in HCC samples than in cirrhotic and normal livers. The cases with higher EMT scores also showed poorer performance in terms of prognostic factors such as vascular invasion and overall survival time. Our research demonstrated a broad correlation between EMT and the tumor immune microenvironment, and we uncovered multiple potential linkers associated with both EMT and immunity. Studying EMT has clinical relevance and high diagnostic and prognostic value for HCC.

smORFunction: a tool for predicting functions of small open reading frames and microproteins.

BACKGROUND: Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. RESULTS: In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. CONCLUSIONS: We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at https://www.cuilab.cn/smorfunction .

Ancient genes can be served as pan-cancer diagnostic and prognostic biomarkers.

One important challenge for cancer is efficient biomarkers monitoring its formation and developments remain greatly limited. Although the accumulated big omics data provide great opportunities to the above purpose, the biomarkers identified by the data-driven strategy often do not work well in new datasets, which is one of the main bottlenecks limiting their utilities. Given that atavistic phenotype is generally observed in cancer cells, we have been suggested that the activity of progenitor genes in tumour could serve as an efficient cancer biomarker. For doing so, we first curated 77 progenitor genes and then proposed a quantitative score to evaluate cancer progenitorness. After applying progenitorness score to ~ 22 000 samples, 33 types of cancers from 81 datasets, this method generally performs well in the diagnosis, prognosis and therapy monitoring of cancers. This study proposed a potential pan-cancer biomarker and revealed a significant role of atavism in the formation and development of cancers.

The significance of glycolysis index and its correlations with immune infiltrates in Alzheimer's disease.

Alzheimer's disease (AD) is a common neurodegenerative disorder without an effective treatment, and results in an increasingly serious health problem. However, its pathogenesis is complex and poorly understood. Nonetheless, the exact role of dysfunctional glucose metabolism in AD pathogenesis remains unclear. We screened 28 core glycolysis-related genes and introduced a novel metric, the glycolysis index, to estimate the activation of glycolysis. The glycolysis index was significantly lower in the AD group in four different brain regions (frontal cortex, FC; temporal cortex, TC; hippocampus, HP; and entorhinal cortex, EC) than that in the control group. Combined with differential expression and over-representation analyses, we determined the clinical and pathological relevance of glycolysis in AD. Subsequently, we investigated the role of glycolysis in the AD brain microenvironment. We developed a glycolysis-brain cell marker connection network, which revealed a close relationship between glycolysis and seven brain cell types, most of which presented abundant variants in AD. Using immunohistochemistry, we detected greater infiltrated microglia and higher expression of glycolysis-related microglia markers in the APP/PS1 AD model than that in the control group, consistent with our bioinformatic analysis results. Furthermore, the excellent predictive value of the glycolysis index has been verified in different populations. Overall, our present findings revealed the clinical and biological significance of glycolysis and the brain microenvironment in AD.

AGTR2, One Possible Novel Key Gene for the Entry of SARS-CoV-2 Into Human Cells.

Recently, it was confirmed that ACE2 is the receptor of SARS-CoV-2, the pathogen causing the recent outbreak of severe pneumonia around the world. It is confused that ACE2 is widely expressed across a variety of organs and is expressed moderately but not highly in lung, which, however, is the major infected organ. Therefore, we hypothesized that there could be some other genes playing key roles in the entry of SARS-CoV-2 into human cells. Here we found that AGTR2 (angiotensin II receptor type 2), a G-protein coupled receptor, has interaction with ACE2 and is highly expressed in lung with a high tissue specificity. More importantly, simulation of 3D structure based protein-protein interaction reveals that AGTR2 shows a higher binding affinity with the Spike protein of SARS-CoV-2 than ACE2 (energy: -8.2 vs. -5.1 [kcal/mol]). A number of compounds, biologics and traditional Chinese medicine that could decrease the expression level of AGTR2 were predicted. Finally, we suggest that AGTR2 could be a putative novel gene for the entry of SARS-CoV-2 into human cells, which could provide different insight for the research of SARS-CoV-2 proteins with their receptors.

A Panel of Synapse-Related Genes as a Biomarker for Gliomas.

Gliomas are the most common primary brain cancers. In recent years, IDH mutation and 1p/19q codeletion have been suggested as biomarkers for the diagnosis, treatment, and prognosis of gliomas. However, these biomarkers are only effective for a part of glioma patients, and thus more biomarkers are still emergently needed. Recently, an electrochemical communication between normal neurons and glioma cells by neuro-glioma synapse has been reported. Moreover, it was discovered that breast-to-brain metastasis tumor cells have pseudo synapses with neurons, and these synapses were indicated to promote tumor progression and metastasis. Based on the above observations, we first curated a panel of 17 synapse-related genes and then proposed a metric, synapse score to quantify the "stemness" for each sample of 12 glioma gene expression datasets from TCGA, CGGA, and GEO. Strikingly, synapse score showed excellent predictive ability for the prognosis, diagnosis, and grading of gliomas. Moreover, being compared with the two established biomarkers, IDH mutation and 1p/19q codeletion, synapse score demonstrated independent and better predictive performance. In conclusion, this study proposed a quantitative method, synapse score, as an efficient biomarker for monitoring gliomas.

A novel workflow for cancer blood biomarker identification.

BACKGROUND: Over the last few years, great progress has been made in the development of key technologies to detect peripheral blood-based, tumor-specific biomarkers, such as circulating tumor cells (CTCs) and circulating cell free tumor DNA (ctDNA). Despite the considerable advances and their multiple clinical values, liquid biopsies are challenged by the very low concentrations of CTCs and ctDNA in blood samples. Additionally, blood biomarkers which were found using data-driven methods may only be effective in few datasets. METHODS: We firstly collected the genes which have expression correlations between blood and the other tissues/organs using Genotype-Tissue Expression (GTEx). Survival hazard genes and differential expression genes of each cancer type in The Cancer Genome Atlas (TCGA) were then selected by Cox regression model and Wilcoxon rank sum test, respectively. By combining the P values of two steps, several blood biomarkers can be inferred for each cancer type. After applying these potential blood biomarker sets to 13 datasets of blood samples from solid tumor patients using single sample gene set enrichment analyses (ssGSEA), we got an enrichment score (ES) for each sample. RESULTS: The inferred blood biomarker (BB infer) genes showed reliable predictive value in various malignancies. In all the blood samples that were analyzed, the ESs of positive BB Infer genes in cancer patients are higher than healthy people. Conversely, the ESs of negative BB Infer genes in cancer patients are lower than healthy people. Furthermore, lower ES of negative BB infer genes signify the dismal outcome of patients. CONCLUSIONS: We developed a novel solid tumor blood biomarker inference workflow for cancer screening and diagnosis. Moreover, we demonstrated the utility of this inference method in a series of blood sample datasets of solid tumor patients. These results suggested the potential value of this method in the screening, diagnosis and prognosis of cancers.

The Prognostic Value of EMT in Glioma and its Role in the Glioma Immune Microenvironment.

Diffuse glioma is the deadliest form of brain cancer, and the median survival of grade IV glioma (glioblastoma, GBM) is no more than 2 years even with maximal surgical resection followed by radiotherapy and chemotherapy, which are now the standard of care for GBM. Glioma shares common characteristics with most malignant tumours, such as invasiveness, rapid progression, resistance to various therapies and inevitable recurrence, while it also has its own unique features, such as high aggressiveness and immunotherapy resistance, which can be, respectively, attributed to epithelial-mesenchymal transition (EMT) and the immunosuppressive microenvironment. Here, we calculated the EMT score of glioma using The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA) and the Gene Expression Omnibus (GEO) datasets and validated its prognostic value. Then, we investigated its role in the glioma immune microenvironment, identified the enriched EMT-related immune genes and determined their specific biological functions in glioma. Furthermore, clinical relevance analysis showed the translational value of these EMT-related immune genes. In short, our findings reveal a critical link between EMT and the glioma immune microenvironment and offer important clues for further investigation of the underlying molecular mechanism.