Single-cell Long Non-coding RNA Landscape of T Cells in Human Cancer Immunity

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

Detection of cell-type-enriched long noncoding RNAs in atherosclerosis using single-cell techniques: A brief review

Cardiovascular diseases are the leading causes of mortality and morbidity worldwide. Atherosclerotic plaque underlies the predominant factors and is composed of various cell types, including structure cells, such as endothelial and smooth muscle cells, and immune cells, such as macrophages and T cells. Single-cell RNA sequencing (scRNA-seq) has been extensively applied to decipher these cellular heterogeneities to expand our understanding on the mechanisms of atherosclerosis (AS) and to facilitate identifying cell-type-specific long noncoding RNAs (LncRNAs). LncRNAs have been demonstrated to deeply regulate biological activities at the transcriptional and post-transcriptional levels. A group of well-documented functional lncRNAs in AS have been studied. In our review, we selectively described several lncRNAs involved in the critical process of AS. We highlighted four novel lncRNAs (lncRNA CARMN, LINC00607, PCAT19, LINC01235) detected in scRNA-seq datasets and their functions in AS. We also reviewed open web source and bioinformatic tools, as well as the latest methods to perform an in-depth study of lncRNAs. It is fundamental to annotate functional lncRNAs in the various biological activities of AS, as lncRNAs may represent promising targets in the future for treatment and diagnosis in clinical practice.

Regulation of T cell differentiation and function by long noncoding RNAs in homeostasis and cancer

Long noncoding RNAs (lncRNAs) increase in genomes of complex organisms and represent the largest group of RNA genes transcribed in mammalian cells. Previously considered only transcriptional noise, lncRNAs comprise a heterogeneous class of transcripts that are emerging as critical regulators of T cell-mediated immunity. Here we summarize the lncRNA expression landscape of different T cell subsets and highlight recent advances in the role of lncRNAs in regulating T cell differentiation, function and exhaustion during homeostasis and cancer. We discuss the different molecular mechanisms of lncRNAs and highlight lncRNAs that can serve as novel targets to modulate T cell function or to improve the response to cancer immunotherapies by modulating the immunosuppressive tumor microenvironment.

Comprehensive analysis of somatic mutator-derived and immune infiltrates related lncRNA signatures of genome instability reveals potential prognostic biomarkers involved in non-small cell lung cancer

Background: The function and features of long non-coding RNAs (lncRNAs) are already attracting attention and extensive research on their role as biomarkers of prediction in lung cancer. However, the signatures that are both related to genomic instability (GI) and tumor immune microenvironment (TIME) have not yet been fully explored in previous studies of non-small cell lung cancer (NSCLC).

Method: The clinical characteristics, RNA expression profiles, and somatic mutation information of patients in this study came from The Cancer Genome Atlas (TCGA) database. Cox proportional hazards regression analysis was performed to construct genomic instability-related lncRNA signature (GIrLncSig). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the potential functions of lncRNAs. CIBERSORT was used to calculate the proportion of immune cells in NSCLC.

Result: Eleven genomic instability-related lncRNAs in NSCLC were identified, then we established a prognostic model with the GIrLncSig ground on the 11 lncRNAs. Through the computed GIrLncSig risk score, patients were divided into high-risk and low-risk groups. By plotting ROC curves, we found that patients in the low-risk group in the test set and TCGA set had longer overall survival than those in the high-risk group, thus validating the survival predictive power of GIrLncSig. By stratified analysis, there was still a significant difference in overall survival between high and low risk groups of patients after adjusting for other clinical characteristics, suggesting the prognostic significance of GIrLncSig is independent. In addition, combining GIrLncSig with TP53 could better predict clinical outcomes. Besides, the immune microenvironment differed significantly between the high-risk and the low-risk groups, patients with low risk scores tend to have upregulation of immune checkpoints and chemokines. Finally, we found that high-risk scores were associated with increased sensitivity to chemotherapy.

Conclusion: we provided a new perspective on lncRNAs related to GI and TIME and revealed the worth of them in immune infiltration and immunotherapeutic response. Besides, we found that the expression of AC027288.1 is associated with PD-1 expression, which may be a potential prognostic marker in immune checkpoint inhibitor response to improve the prediction of clinical survival in NSCLC patients.

Identification and Validation of Immune-Related Long Non-Coding RNA Signature for Predicting Immunotherapeutic Response and Prognosis in NSCLC Patients Treated With Immunotherapy

Background

Numerous studies have reported that long non-coding RNAs (lncRNAs) play important roles in immune-related pathways in cancer. However, immune-related lncRNAs and their roles in predicting immunotherapeutic response and prognosis of non-small cell lung cancer (NSCLC) patients treated with immunotherapy remain largely unexplored.

Methods

Transcriptomic data from NSCLC patients were used to identify novel lncRNAs by a custom pipeline. ImmuCellAI was utilized to calculate the infiltration score of immune cells. The marker genes of immunotherapeutic response-related (ITR)-immune cells were used to identify immune-related (IR)-lncRNAs. A co-expression network was constructed to determine their functions. LASSO and multivariate Cox analyses were performed on the training set to construct an immunotherapeutic response and immune-related (ITIR)-lncRNA signature for predicting the immunotherapeutic response and prognosis of NSCLC. Four independent datasets involving NSCLC and melanoma patients were used to validate the ITIR-lncRNA signature.

Results

In total, 7,693 novel lncRNAs were identified for NSCLC. By comparing responders with non-responders, 154 ITR-lncRNAs were identified. Based on the correlation between the marker genes of ITR-immune cells and lncRNAs, 39 ITIR-lncRNAs were identified. A co-expression network was constructed and the potential functions of 38 ITIR-lncRNAs were annotated, most of which were related to immune/inflammatory-related pathways. Single-cell RNA-seq analysis was performed to confirm the functional prediction results of an ITIR-lncRNA, LINC01272. Four-ITIR-lncRNA signature was identified and verified for predicting the immunotherapeutic response and prognosis of NSCLC. Compared with non-responders, responders had a lower risk score in both NSCLC datasets (P<0.05). NSCLC patients in the high-risk group had significantly shorter PFS/OS time than those in the low-risk group in the training and testing sets (P<0.05). The AUC value was 1 of responsiveness in the training set. In melanoma validation datasets, patients in the high-risk group also had significantly shorter OS/PFS time than those in the low-risk group (P<0.05). The ITIR-lncRNA signature was an independent prognostic factor (P<0.001).

Conclusion

Thousands of novel lncRNAs in NSCLC were identified and characterized. In total, 39 ITIR-lncRNAs were identified, 38 of which were functionally annotated. Four ITIR-lncRNAs were identified as a novel ITIR-lncRNA signature for predicting the immunotherapeutic response and prognosis in NSCLC patients treated with immunotherapy.

Systematic Investigation of Immune-Related lncRNA Landscape Reveals a Potential Long Non-Coding RNA Signature for Predicting Prognosis in Renal Cell Carcinoma

Background: Renal cell carcinoma (RCC) is the predominant type of malignant tumor in kidney cancer. Finding effective biomarkers, particularly those based on the tumor immune microenvironments (TIME), is critical for the prognosis and diagnosis of RCC. Increasing evidence has revealed that long non-coding RNAs (lncRNAs) play a crucial role in cancer immunity. However, the comprehensive landscape of immune infiltration-associated lncRNAs and their potential roles in the prognosis and diagnosis of RCC remain largely unexplored.

Methods: Based on transcriptomic data of 261 RCC samples, novel lncRNAs were identified using a custom pipeline. RCC patients were classified into different immune groups using unsupervised clustering algorithms. Immune-related lncRNAs were obtained according to the immune status of RCC. Competing endogenous RNAs (ceRNA) regulation network was constructed to reveal their functions. Expression patterns and several tools such as miRanda, RNAhybrid, miRWalk were used to define lncRNAs-miRNAs-mRNAs interactions. Univariate Cox, LASSO, and multivariate Cox regression analyses were performed on the training set to construct a tumorigenesis-immune-infiltration-related (TIR)-lncRNA signature for predicting the prognosis of RCC. Independent datasets involving 531 RCC samples were used to validate the TIR-lncRNA signature.

Results: Tens of thousands of novel lncRNAs were identified in RCC samples. Comparing tumors with controls, 1,400 tumorigenesis-related (TR)-lncRNAs, 1269 TR-mRNAs, and 192 TR-miRNAs were obtained. Based on the infiltration of immune cells, RCC patients were classified into three immune clusters. By comparing immune-high with immune-low groups, 241 TIR-lncRNAs were identified, many of which were detected in urinary samples. Based on lncRNA-miRNA-mRNA interactions, we constructed a ceRNA network, which included 25 TR-miRNAs, 28 TIR-lncRNAs, and 66 TIR-mRNAs. Three TIR lncRNAs were identified as a prognostic signature for RCC. RCC patients in the high-risk group exhibited worse OS than those in the low-risk group in the training and testing sets (p &lt; 0.01). The AUC was 0.9 in the training set. Univariate and multivariate Cox analyses confirmed that the TIR-lncRNA signature was an independent prognostic factor in the training and testing sets.

Conclusion: Based on the constructed immune-related lncRNA landscape, 241 TIR-lncRNAs were functionally characterized, three of which were identified as a novel TIR-lncRNA signature for predicting the prognosis of RCC.

KaKs_Calculator 3.0: Calculating Selective Pressure on Coding and Non-coding Sequences

KaKs_Calculator 3.0 is an updated toolkit that is capable of calculating selective pressure on both coding and non-coding sequences. Similar to the nonsynonymous/synonymous substitution rate ratio for coding sequences, selection on non-coding sequences can be quantified as the ratio of non-coding nucleotide substitution rate to synonymous substitution rate of adjacent coding sequences. As testified on empirical data, KaKs_Calculator 3.0 shows effectiveness to detect the strength and mode of selection operated on molecular sequences, accordingly demonstrating its great potential to achieve genome-wide scan of natural selection on diverse sequences and identification of potentially functional elements at a whole-genome scale. The package of KaKs_Calculator 3.0 is freely available for academic use only at https://ngdc.cncb.ac.cn/biocode/tools/BT000001.

Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

Tumor immune microenvironment lncRNAs

Long non-coding ribonucleic acids (RNAs) (lncRNAs) are key players in tumorigenesis and immune responses. The nature of their cell type-specific gene expression and other functional evidence support the idea that lncRNAs have distinct cellular functions in the tumor immune microenvironment (TIME). To date, the majority of lncRNA studies have heavily relied on bulk RNA-sequencing data in which various cell types contribute to an averaged signal, limiting the discovery of cell type-specific lncRNA functions. Single-cell RNA-sequencing (scRNA-seq) is a potential solution for tackling this limitation despite the lack of annotations for low abundance yet cell type-specific lncRNAs. Hence, updated annotations and further understanding of the cellular expression of lncRNAs will be necessary for characterizing cell type-specific functions of lncRNA genes in the TIME. In this review, we discuss lncRNAs that are specifically expressed in tumor and immune cells, summarize the regulatory functions of the lncRNAs at the cell type level and highlight how a scRNA-seq approach can help to study the cell type-specific functions of TIME lncRNAs.