An integrated RNA-Seq and network pharmacology approach for exploring the preventive effect of Corydalis bungeana Turcz. Extract and Acetylcorynoline on LPS-induced acute lung injury.

ETHNOPHARMACOLOGICAL RELEVANCE: Corydalis bungeana Turcz. (KDD) is a Chinese herbal medicine with anti-inflammatory, lung cleansing, detoxification and other functions. Clinically, it is commonly used to treat respiratory infections. This study uses ALI as the research model, which is consistent with the clinical use of KDD. Acetylcorynoline (AC) is the main alkaloid component of the KDD extracts, and network pharmacology studies suggest that it may be the main active ingredient in the prevention of ALI. AIM OF THE STUDY: The aim of this study is to explore the underlying mechanisms and to study the efficacy material basis of KDD in anti-ALI effect by LPS-induced mice and using a combination of RNA sequencing (RNA-Seq) technology and network pharmacology. MATERIALS AND METHODS: Establish a mouse model of ALI by intraperitoneal injection of LPS (5 mg/kg). The main active ingredients of KDD were identified and analyzed by high performance liquid chromatography with quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) and network pharmacology. IL-18, IL-1ß, and IL-6 levels in serum and bronchoalveolar lavage fluid (BALF), lung histopathological changes, and lung myeloperoxidase (MPO) activity were assessed. We investigated the possible molecular mechanisms of KDD and AC in an LPS-induced mouse ALI models with RNA-Seq technology. In addition, the anti-inflammatory effect of AC was verified in vitro by establishing an LPS-stimulated RAW264.7 inflammation model. Molecular docking further validated AC as the efficacy material basis of KDD in anti-ALI. RESULTS: Based on HPLC-QTOF-MS technology and network pharmacology, KDD is more strongly associated with lung tissue, and that AC may be the main active ingredient of KDD. Subsequently, in vivo experiments results showed that KDD and AC reduced the levels of pro-inflammatory cytokines in serum and BALF, reduced MPO levels and reduced inflammatory damage in the lungs. To elucidate its underlying mechanism, based on RNA-Seq analysis techniques performed in lung tissue, enrichment analysis showed that KDD and AC intervened through the NLR signaling pathway, thereby mitigating LPS-induced ALI. Then, RT-qPCR, IF, WB and other technologies were used to verify the anti-ALI core difference genes of KDD and AC from the gene transcription and protein expression levels of the NLR signaling pathway, and confirmed the anti-ALI. In vitro experimental results also showed that AC has anti-inflammatory effects in RAW264.7. Finally, the biotransformation and molecular docking results also further indicated that AC is the active ingredient of KDD in anti-ALI. CONCLUSIONS: Studies have shown that KDD has a good therapeutic effect on ALI, and AC is the main pharmacodynamic material basis for its therapeutic effect in ALI.

A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data.

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy.

Bulk RNA-Seq Combined with Single-Cell Transcriptome Sequencing Reveals the Possible Mechanisms by Which HDGFL3 Involves in Prostate Cancer Growth and Metastasis.

BACKGROUND: Based on publicly available transcriptome and single-cell sequencing data, the current study aimed to explore the molecular mechanisms underlying the involvement of hepatocellular carcinoma-derived growth factor-like 3 (HDGFL3) in prostate cancer (PCA) growth and metastasis. METHODS: The Gene Expression Omnibus database was used to download the single cell transcriptome of PCA (GSE193337). Single-cell RNA sequencing (scRNA-seq) data were examined to identify which genes are essential for endothelial cell function. The Cancer Genome Atlas Prostate Adenocarcinoma database provided the RNA sequencing data, and univariate COX regression analysis was introduced to identify the genes that were associated with the prognosis of patients with PCA. Human PCA cell lines PC-3 and DU145 were used in in vitro cellular studies to test the effect of silencing HDGFL3. The results were validated using Transwell® assay, scratch assay, and cell counting kit-8 assay. To support the role of HDGFL3 in PCA, an in vivo animal model of PCA transplantation tumor in nude mice was established. Quantitative reverse transcription polymerase chain reaction was introduced to measure HDGFL3 messenger ribonucleic acid (mRNA) expression levels in tumor tissues from nude mice, and Hematoxylin and Eosin staining was used to identify lung metastasis. Immunohistochemical staining was employed to identify the expression levels of HDGFL3 and hematopoietic progenitor cell antigen CD34+. RESULTS: It was discovered through analysis of the scRNA-seq dataset that HDGFL3, a gene specific to endothelial cells, is linked to a poor prognosis in men with PCA. In addition, HDGFL3 and the expression of genes linked to angiogenesis have a substantial association. Studies on cells in vitro revealed that silencing HDGFL3 prevented PC-3 and DU145 cells from proliferation, migrating and invading. Silencing HDGFL3 decreased the weight of prostate tumors, the number of lung metastases, and the area occupied by CD34+ vascular endothelial cells, according to in vivo investigations. CONCLUSIONS: This study identified HDGFL3 as a key gene in endothelial cells that may stimulate tumor angiogenesis to increase the growth and spread of PCA. These results imply that HDGFL3 may represent a possible target for antiangiogenic therapy in the management of PCA.

Shared Molecular Pathways in Glaucoma and Other Neurodegenerative Diseases: Insights from RNA-Seq Analysis and miRNA Regulation for Promising Therapeutic Avenues.

Advances in RNA-sequencing technologies have led to the identification of molecular biomarkers for several diseases, including neurodegenerative diseases, such as Alzheimer's, Parkinson's, Huntington's diseases and Amyotrophic Lateral Sclerosis. Despite the nature of glaucoma as a neurodegenerative disorder with several similarities with the other above-mentioned diseases, transcriptional data about this disease are still scarce. microRNAs are small molecules (~17-25 nucleotides) that have been found to be specifically expressed in the CNS as major components of the system regulating the development signatures of neurodegenerative diseases and the homeostasis of the brain. In this review, we sought to identify similarities between the functional mechanisms and the activated pathways of the most common neurodegenerative diseases, as well as to discuss how those mechanisms are regulated by miRNAs, using RNA-Seq as an approach to compare them. We also discuss therapeutically suitable applications for these disease hallmarks in clinical future studies.

Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Data to Explore the Key Role of Fatty Acid Metabolism in Breast Cancer.

Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines.

RNA-Seq and Comparative Transcriptomic Analyses of Asian Soybean Rust Resistant and Susceptible Soybean Genotypes Provide Insights into Identifying Disease Resistance Genes.

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is one of the most destructive foliar diseases that affect soybeans. Developing resistant cultivars is the most cost-effective, environmentally friendly, and easy strategy for controlling the disease. However, the current understanding of the mechanisms underlying soybean resistance to P. pachyrhizi remains limited, which poses a significant challenge in devising effective control strategies. In this study, comparative transcriptomic profiling using one resistant genotype and one susceptible genotype was performed under infected and control conditions to understand the regulatory network operating between soybean and P. pachyrhizi. RNA-Seq analysis identified a total of 6540 differentially expressed genes (DEGs), which were shared by all four genotypes. The DEGs are involved in defense responses, stress responses, stimulus responses, flavonoid metabolism, and biosynthesis after infection with P. pachyrhizi. A total of 25,377 genes were divided into 33 modules using weighted gene co-expression network analysis (WGCNA). Two modules were significantly associated with pathogen defense. The DEGs were mainly enriched in RNA processing, plant-type hypersensitive response, negative regulation of cell growth, and a programmed cell death process. In conclusion, these results will provide an important resource for mining resistant genes to P. pachyrhizi infection and valuable resources to potentially pyramid quantitative resistance loci for improving soybean germplasm.

Ficus carica Latex Modulates Immunity-Linked Gene Expression in Human Papillomavirus Positive Cervical Cancer Cell Lines: Evidence from RNA Seq Transcriptome Analysis.

Cervical carcinogenesis is the leading cause of cancer-related deaths in women, and the role of high-risk human papillomavirus (HR-HPV) as a possible risk factor in the development of this cancer is well recognized. Despite the availability of multi-therapeutic approaches, there is still major concern regarding the prevention of metastatic dissemination and excessive tissue injuries. Therefore, it is imperative to develop a safer and more efficient treatment modality. Ficus carica, a natural plant, has shown potential therapeutic properties through its fruit latex when applied to HPV-positive cervical cancer cell lines. However, the mechanisms of action of Ficus carica (fig) latex are not well understood. This study aims to provide a deeper insight into the biological activities of fig latex on human cervical cancer cell lines expressing high-risk HPV types 16 and 18. The data obtained from this study reveal that fig latex influences the expression of genes involved in "Class I MHC-mediated antigen presentation" as well as "Antigen processing: Ubiquitination and Proteasome degradation". These genes play a crucial role in host immune surveillance and the resolution of infection. Notably, Western blot analysis corroborated these findings, demonstrating an increase in the expression of MHC class I in HeLa cells after fig latex treatment. Findings from this study suggest that fig latex may enhance T cell responses against oncogenic HPV, which could be beneficial for the clearance of early-stage cancer.

Genome-Wide RNA Tomography in the Mouse Whole Olfactory Mucosa.

Spatial transcriptomics allows for the genome-wide profiling of topographic gene expression patterns within a tissue of interest. Here, we describe our methodology to generate high-quality RNA-seq libraries from cryosections from fresh frozen mouse whole olfactory mucosae. This methodology can be extended to virtually any vertebrate organ or tissue sample.

PyDESeq2: a python package for bulk RNA-seq differential expression analysis.

SUMMARY: We present PyDESeq2, a python implementation of the DESeq2 workflow for differential expression analysis on bulk RNA-seq data. This re-implementation yields similar, but not identical, results: it achieves higher model likelihood, allows speed improvements on large datasets, as shown in experiments on TCGA data, and can be more easily interfaced with modern python-based data science tools. AVAILABILITY AND IMPLEMENTATION: PyDESeq2 is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/owkin/PyDESeq2 and documented at https://pydeseq2.readthedocs.io. PyDESeq2 is part of the scverse ecosystem.

Paired ATAC- and RNA-seq offer insight into the impact of HIV on alveolar macrophages: a pilot study.

People with HIV remain at greater risk for both infectious and non-infectious pulmonary diseases even after antiretroviral therapy initiation and CD4 cell count recovery. These clinical risks reflect persistent HIV-mediated defects in innate and adaptive immunity, including in the alveolar macrophage, a key innate immune effector in the lungs. In this proof-of-concept pilot study, we leveraged paired RNA-seq and ATAC-seq analyses of human alveolar macrophages obtained with research bronchoscopy from people with and without HIV to highlight the potential for recent methodologic advances to generate novel hypotheses about biological pathways that may contribute to impaired pulmonary immune function in people with HIV. In addition to 35 genes that were differentially expressed in macrophages from people with HIV, gene set enrichment analysis identified six gene sets that were differentially regulated. ATAC-seq analysis revealed 115 genes that were differentially accessible for people with HIV. Data-driven integration of the findings from these complementary, high-throughput techniques using xMWAS identified distinct clusters involving lipoprotein lipase and inflammatory pathways. By bringing together transcriptional and epigenetic data, this analytic approach points to several mechanisms, including previously unreported pathways, that warrant further exploration as potential mediators of the increased risk of pulmonary disease in people with HIV.

Coixendide efficacy in combination with temozolomide in glioblastoma and transcriptome analysis of the mechanism.

The purpose of this study was to explore the role of coixendide (Coix) combine with temozolomide (TMZ) in the treatment of Glioblastoma (GBM) and explore its possible mechanism. CCK-8 was used to determine the inhibitory rate of Coix group, TMZ group and drug combination group on GBM cells, and the combination index (CI) was calculated to determine whether they had synergistic effect. Then RNA was extracted from each group, transcriptome sequencing was performed, and differentially expressed genes (DEGs) were identified. The possible mechanism was analyzed by GO enrichment analysis and KEGG enrichment analysis. The CI of Coix and TMZ indicating a synergistic effect when TMZ concentration is 0.1 mg/ml and Coix concentration is 2 mg/ml. Transcriptome sequencing analysis showed that interferon (IFN) related genes were down-regulated by Coix and up-regulated by TMZ and combined drugs, however, the up-regulation induced by combined drugs was less than that of TMZ. Besides IFN related genes, cholesterol metabolism pathway were also been regulated. Coix and TMZ have synergistic effects in the treatment of GBM at certain doses. RNA-Seq results suggested that the abnormal on genetic materials caused by DNA damage induced by TMZ treatment can be sensed by IFN related genes and activates antiviral IFN signaling, causing the activation of repairing mechanism and drug resistance. Coix inhibits IFN related genes, thereby inhibits drug resistance of TMZ. In addition, the activation of ferroptosis and the regulation of DEGs in cholesterol metabolism pathway were also contributed to the synergistic effects of Coix and TMZ.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer.

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Public RNA-seq data-based identification and functional analyses reveal that MXRA5 retains proliferative and migratory abilities of dental pulp stem cells.

Dental pulp stem cells (DPSC) usually remain quiescent in the dental pulp tissue; however, once the dental pulp tissue is injured, DPSCs potently proliferate and migrate into the injury microenvironment and contribute to immuno-modulation and tissue repair. However, the key molecules that physiologically support the potent proliferation and migration of DPSCs have not been revealed. In this study, we searched publicly available transcriptome raw data sets, which contain comparable (i.e., equivalently cultured) DPSC and mesenchymal stem cell data. Three data sets were extracted from the Gene Expression Omnibus database and then processed and analyzed. MXRA5 was identified as the predominant DPSC-enriched gene associated with the extracellular matrix. MXRA5 is detected in human dental pulp tissues. Loss of MXRA5 drastically decreases the proliferation and migration of DSPCs, concomitantly with reduced expression of the genes associated with the cell cycle and microtubules. In addition to the known full-length isoform of MXRA5, a novel splice variant of MXRA5 was cloned in DPSCs. Recombinant MXRA5 coded by the novel splice variant potently induced the haptotaxis migration of DPSCs, which was inhibited by microtubule inhibitors. Collectively, MXRA5 is a key extracellular matrix protein in dental pulp tissue for maintaining the proliferation and migration of DPSCs.

Cytological observation and RNA-seq analysis reveal novel miRNAs high expression associated with the pollen fertility of neo-tetraploid rice.

BACKGROUND: Neo-tetraploid rice lines exhibit high fertility and strong heterosis and harbor novel specific alleles, which are useful germplasm for polyploid rice breeding. However, the mechanism of the fertility associated with miRNAs remains unknown. In this study, a neo-tetraploid rice line, termed Huaduo21 (H21), was used. Cytological observation and RNA-sequencing were employed to identify the fertility-related miRNAs in neo-tetraploid rice. RESULTS: H21 showed high pollen fertility (88.08%), a lower percentage of the pollen mother cell (PMC) abnormalities, and lower abnormalities during double fertilization and embryogenesis compared with autotetraploid rice. A total of 166 non-additive miRNAs and 3108 non-additive genes were detected between H21 and its parents. GO and KEGG analysis of non-additive genes revealed significant enrichments in the DNA replication, Chromosome and associated proteins, and Replication and repair pathways. Comprehensive multi-omics analysis identified 32 pairs of miRNA/target that were associated with the fertility in H21. Of these, osa-miR408-3p and osa-miR528-5p displayed high expression patterns, targeted the phytocyanin genes, and were associated with high pollen fertility. Suppression of osa-miR528-5p in Huaduo1 resulted in a low seed set and a decrease in the number of grains. Moreover, transgenic analysis implied that osa-MIR397b-p3, osa-miR5492, and osa-MIR5495-p5 might participate in the fertility of H21. CONCLUSION: Taken together, the regulation network of fertility-related miRNAs-targets pairs might contribute to the high seed setting in neo-tetraploid rice. These findings enhance our understanding of the regulatory mechanisms of pollen fertility associated with miRNAs in neo-tetraploid rice.

INTEGRATE-Circ and INTEGRATE-Vis: unbiased detection and visualization of fusion-derived circular RNA.

MOTIVATION: Backsplicing of RNA results in circularized rather than linear transcripts, known as circular RNA (circRNA). A recently discovered and poorly understood subset of circRNAs that are composed of multiple genes, termed fusion-derived circular RNAs (fcircRNAs), represent a class of potential biomarkers shown to have oncogenic potential. Detection of fcircRNAs eludes existing analytical tools, making it difficult to more comprehensively assess their prevalence and function. Improved detection methods may lead to additional biological and clinical insights related to fcircRNAs. RESULTS: We developed the first unbiased tool for detecting fcircRNAs (INTEGRATE-Circ) and visualizing fcircRNAs (INTEGRATE-Vis) from RNA-Seq data. We found that INTEGRATE-Circ was more sensitive, precise and accurate than other tools based on our analysis of simulated RNA-Seq data and our tool was able to outperform other tools in an analysis of public lymphoblast cell line data. Finally, we were able to validate in vitro three novel fcircRNAs detected by INTEGRATE-Circ in a well-characterized breast cancer cell line. AVAILABILITY AND IMPLEMENTATION: Open source code for INTEGRATE-Circ and INTEGRATE-Vis is available at https://www.github.com/ChrisMaherLab/INTEGRATE-CIRC and https://www.github.com/ChrisMaherLab/INTEGRATE-Vis.

Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM.

BACKGROUND: Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies. RESULTS: We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data. CONCLUSION: DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.

Recapitulation of patient-specific 3D chromatin conformation using machine learning.

Regulatory networks containing enhancer-gene edges define cellular states. Multiple efforts have revealed these networks for reference tissues and cell lines by integrating multi-omics data. However, the methods developed cannot be applied for large patient cohorts due to the infeasibility of chromatin immunoprecipitation sequencing (ChIP-seq) for limited biopsy material. We trained machine-learning models using chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture combined with chromatin immunoprecipitation (HiChIP) data that can predict connections using only assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq data as input, which can be generated from biopsies. Our method overcomes limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers or between active vs. poised states in different samples, a hallmark of network rewiring in cancer. Application of our model on 371 samples across 22 cancer types revealed 1,780 enhancer-gene connections for 602 cancer genes. Using CRISPR interference (CRISPRi), we validated enhancers predicted to regulate ESR1 in estrogen receptor (ER)+ breast cancer and A1CF in liver hepatocellular carcinoma.

Bulk RNA-seq and scRNA-seq reveal SLC7A11, a key regulatory molecule of ferroptosis, is a prognostic-related biomarker and highly related to the immune system in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer. Ferroptosis is an iron-dependent, non-apoptotic cell death mode, highly correlated with the tumorigenesis and progression of multiple cancers. Solute carrier family 7 member 11 (SLC7A11) maintains the anti-porter activity of cysteine and glutamate to regulate ferroptosis. We collected bulk RNA-seq and scRNA-seq from The Cancer Genome Altas and Gene Expression Omnibus databases. Then, we extracted the expression level of SLC7A11 to perform the differential expression analysis between normal tissues and LUAD tissues. Then, we applied survival, univariate, and multivariate Cox regression analyses to investigate the predictive value of SLC7A11 in LUAD. Gene set enrichment analysis was used to explore the underlying molecular mechanisms of SLC7A11 in LUAD. Finally, we analyzed the relationship of SLC7A11 to the immune status and the curative effect of immunotherapy. The expression level of SLC7A11 in LUAD tissues was markedly increased. The survival analysis, univariate and multivariate Cox regression analysis showed that SLC7A11 was a negative factor for the prognosis of LUAD patients. Gene set enrichment analysis revealed that several immune-related pathways were enriched in the low-level group. The lower SLC7A11 level has a better therapeutic effect of immunotherapy and less probability of immune escape and dysfunction. SLC7A11 was a prognostic-related biomarker and closely correlated with the immune status and therapeutic effect of immunotherapy in LUAD, which could be an effective biomarker for evaluating the prognosis and the therapeutic efficacy of immunotherapy.

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures.

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.

TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing.

Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis. We present an integrated computational toolkit named TAGET for Iso-seq full-length transcript data analyses, including transcript alignment, annotation, gene fusion detection, and quantification analyses such as differential expression gene analysis and differential isoform usage analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients. TAGET gives significantly more precise novel splice site prediction and enables more accurate novel isoform and gene fusion discoveries, as validated by experimental validations and comparisons with RNA-seq data. We identify and experimentally validate a differential isoform usage gene ECM1, and further show that its isoform ECM1b may be a tumor-suppressor in laryngocarcinoma. Our results demonstrate that TAGET provides a valuable computational toolkit and can be applied to many full-length transcriptome studies.