Bulk RNA-seq Data Research Articles

Integrative analysis of bulk and single-cell RNA sequencing reveals the gene expression profile and the critical signaling pathways of type II CPAM

BackgroudType II congenital pulmonary airway malformation (CPAM) is a rare pulmonary microcystic developmental malformation. Surgical excision is the primary treatment for CPAM, although maternal steroids and betamethasone have proven effective in reducing microcystic CPAM. Disturbed intercellular communication may contribute to the development of CPAM. This study aims to investigate the expression profile and analyze intercellular communication networks to identify genes potentially associated with type II CPAM pathogenesis and therapeutic targets.MethodsRNA sequencing (RNA-seq) was performed on samples extracted from both the cystic area and the adjacent normal tissue post-surgery in CPAM patients. Iterative weighted gene correlation network analysis (iWGCNA) was used to identify genes specifically expressed in type II CPAM. Single-cell RNA-seq (scRNA-seq) was integrated to unveil the heterogeneity in cell populations and analyze the communication and interaction within epithelial cell sub-populations.ResultsA total of 2,618 differentially expressed genes were identified, primarily enriched in cilium-related biological process and inflammatory response process. Key genes such as EDN1, GPR17, FPR2, and CHRM1, involved in the G protein-coupled receptor (GPCR) signaling pathway and playing roles in cell differentiation, apoptosis, calcium homeostasis, and the immune response, were highlighted based on the protein-protein interaction network. Type II CPAM-associated modules, including ciliary function-related genes, were identified using iWGCNA. By integrating scRNA-seq data, AGR3 (related to calcium homeostasis) and SLC11A1 (immune related) were identified as the only two differently expressed genes in epithelial cells of CPAM. Cell communication analysis revealed that alveolar type 1 (AT1) and alveolar type 2 (AT2) cells were the predominant communication cells for outgoing and incoming signals in epithelial cells. The ligands and receptors between epithelial cell subtypes included COLLAGEN genes enriched in PI3K-AKT singaling and involved in epithelial to mesenchymal transition.ConclusionsIn summary, by integrating bulk RNA-seq data of type II CPAM with scRNA-seq data, the gene expression profile and critical signaling pathways such as GPCR signaling and PI3K-AKT signaling pathways were revealed. Abnormally expressed genes in these pathways may disrupt epithelial-mesenchymal transition and contribute to the development of CPAM. Given the effectiveness of prenatal treatments of microcystic CPAM using maternal steroids and maternal betamethasone administration, targeting the genes and signaling pathways involved in the development of CPAM presents a promising therapeutic strategy.

Medaka liver developed Human NAFLD-NASH transcriptional signatures in response to ancestral bisphenol A exposure.

The progression of fatty liver disease to non-alcoholic steatohepatitis (NASH) is a leading cause of death in humans. Lifestyles and environmental chemical exposures can increase the susceptibility of humans to NASH. In humans, the presence of bisphenol A (BPA) in urine is associated with fatty liver disease, but whether ancestral BPA exposure leads to the activation of human NAFLD-NASH-associated genes in the unexposed descendants is unclear. In this study, using medaka fish as an animal model for human NAFLD, we investigated the transcriptional signatures of human NAFLD-NASH and their associated roles in the pathogenesis of the liver of fish that were not directly exposed, but their ancestors were exposed to BPA during embryonic and perinatal development three generations prior. Comparison of bulk RNA-Seq data of the liver in BPA lineage male and female medaka with publicly available human NAFLD-NASH patient data revealed transgenerational alterations in the transcriptional signature of human NAFLD-NASH in medaka liver. Twenty percent of differentially expressed genes (DEGs) were upregulated in both human NAFLD patients and medaka. Specifically in females, among the total shared DEGs in the liver of BPA lineage fish and NAFLD patient groups, 27.69% were downregulated, and 20% were upregulated. Of all DEGs, 52.31% of DEGs were found in ancestral BPA-lineage females, suggesting that NAFLD in females shared the majority of human NAFLD gene networks. Pathway analysis revealed beta-oxidation, lipoprotein metabolism, and HDL/LDL-mediated transport processes linked to downregulated DEGs in BPA lineage males and females. In contrast, the expression of genes encoding lipogenesis-related proteins was significantly elevated in the liver of BPA lineage females only. BPA lineage females exhibiting activation of myc, atf4, xbp1, stat4, and cancerous pathways, as well as inactivation of igf1, suggest their possible association with an advanced NAFLD phenotype. The present results suggest that gene networks involved in the progression of human NAFLD and the transgenerational NAFLD in medaka are conserved and that medaka can be an excellent animal model to understand the development and progression of liver disease and environmental influences in the liver.

Integrated single-cell and bulk RNA sequencing revealed an epigenetic signature predicts prognosis and tumor microenvironment colorectal cancer heterogeneity.

Colorectal cancer (CRC) prognosis prediction is currently a major challenge. Epigenetic regulation has been widely reported for its role in cancer development. To construct a robust prognostic signature, we used developed and validated across datasets. After constructing the signature, the prognostic value of the signature was evaluated in the TCGA cohort and six independent datasets (GSE17526, GSE17537, GSE33113, GSE37892, GSE39048 and GSE39582). The clinical, genomic and transcriptomic features related to the signature were identified. The correlations of the signature score with immune cell infiltration and cell-cell interactions were analyzed. The correlations between the signature score and the sensitivity to different drugs were also predicted. In the TCGA cohort, patients in the low-risk group according to the signature score had longer survival than those in the high-risk group, and this finding was validated in the validation datasets. The signature was a prognostic factor independent of age and sex and was correlated with stage and PD-1/PD-L1 expression. Area under the receiving operating characteristic curve was 0.72. Genomic association analyses revealed that samples from high-risk patients exhibited chromosomal instability. Transcriptomic analyses revealed that the signature score was significantly associated with multiple cellular pathways. Bulk RNA-seq and single-cell sequencing data revealed that the signature reflected differences in infiltrating immune cell-tumor cell interactions, especially for macrophages. The signature also predicted the putative drug sensitivity of CRC samples. The signature is a valuable biomarker for predicting CRC prognosis and reflects multiple features of CRC, especially macrophage infiltration in the microenvironment.

Single-cell RNA sequencing reveals a high-resolution cell atlas of petals in Prunus mume at different flowering development stages.

Prunus mume (mei), a traditional ornamental plant in China, is renowned for its fragrant flowers, primarily emitted by its petals. However, the cell types of mei petals and where floral volatile synthesis occurs are rarely reported. The study used single-cell RNA sequencing to characterize the gene expression landscape in petals of P. mume 'Fenhong Zhusha' at budding stage (BS) and full-blooming stage (FS). Six major cell types of petals were identified: epidermal cells (ECs), parenchyma cells (PCs), xylem parenchyma cells, phloem parenchyma cells, xylem vessels and fibers, and sieve elements and companion cells complex. Cell-specific marker genes in each cell type were provided. Floral volatiles from mei petals were measured at four flowering development stages, and their emissions increased from BS to FS, and decreased at the withering stage. Fifty-eight differentially expressed genes (DEGs) in benzenoid/phenylpropanoid pathway were screened using bulk RNA-seq data. Twenty-eight DEGs expression increased from BS to FS, indicating that they might play roles in floral volatile synthesis in P. mume, among which PmBAHD3 would participate in benzyl acetate synthesis. ScRNA-seq data showed that 27 DEGs mentioned above were expressed variously in different cell types. In situ hybridization confirmed that PmPAL2, PmCAD1, PmBAHD3,5, and PmEGS1 involved in floral volatile synthesis in mei petals are mainly expressed in EC, PC, and most vascular tissues, consistent with scRNA-seq data. The result indicates that benzyl acetate and eugenol, the characteristic volatiles in mei, are mostly synthesized in these cell types. The first petal single-cell atlas was constructed, offering new insights into the molecular mechanism of floral volatile synthesis.

Tumor Subtype Classification Tool for HPV-associated Head and Neck Cancers.

Molecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized and have been shown to have distinct mechanisms of carcinogenesis, clinical outcomes, and potentially differing optimal treatment strategies. Currently, no standardized method exists to subtype a new HPV+ HNSCC tumor. Our paper introduces a machine learning-based classifier and webtool to reliably subtype HPV+ HNSCC tumors using the IMU/KRT paradigm and highlights the importance of subtype in HPV+ HNSCC. To develop a robust, accurate machine learning-based classification tool that standardizes the process of subtyping HPV+ HNSCC, and to investigate the clinical, demographic, and molecular features associated with subtype in a meta-analysis of four patient cohorts. We conducted RNA-seq on 67 HNSCC FFPE blocks from University of Michigan hospital. Combining this with three publicly available datasets, we utilized a total of 229 HPV+ HNSCC RNA-seq samples. All participants were HPV+ according to RNA expression. An ensemble machine learning approach with five algorithms and three different input training gene sets were developed, with final subtype determined by majority vote. Several additional steps were taken to ensure rigor and reproducibility throughout. The classifier was trained and tested using 84 subtype-labeled HPV+ RNA-seq samples from two cohorts: University of Michigan (UM; n=18) and TCGA-HNC (n=66). The classifier robustness was validated with two independent cohorts: 83 samples from the HPV Virome Consortium and 62 additional samples from UM. We revealed 24 of 39 tested clinicodemographic and molecular variables significantly associated with subtype. The classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigating the relationship between subtype and 39 molecular and clinicodemographic variables revealed IMU is associated with epithelial-mesenchymal transition (p=2.25×10-4), various immune cell types, and lower radiation resistance (p=0.0050), while KRT is more highly keratinized (p=2.53×10-8), and more likely female than IMU (p=0.0082). This study provides a reliable classifier for subtyping HPV+ HNSCC tumors as either IMU or KRT based on bulk RNA-seq data, and additionally, improves our understanding of the HPV+ HNSCC subtypes.

Altered Inflammatory State and Mitochondrial Function Identified by Transcriptomics in Paediatric Congenital Heart Patients Prior to Surgical Repair.

Congenital heart disease (CHD) remains the most common birth defect, with surgical intervention required in complex cases. Right ventricle (RV) function is known to be a major predictor of sustained cardiac health in these patients; thus, by elucidating the divergent profiles between CHD and the control through tissue analysis, this study aims to identify new avenues of investigation into the mechanisms surrounding reduced RV function. Transcriptomic profiling, in-silico deconvolution and functional network analysis were conducted on RV biopsies, identifying an increase in the mitochondrial dysfunction genes RPPH1 and RMPR (padj = 4.67 × 10-132, 2.23 × 10-107), the cytotoxic T-cell markers CD8a, LAGE3 and CD49a (p = 0.0006, p < 0.0001, and p = 0.0118) and proinflammatory caspase-1 (p = 0.0055) in CHD. Gene-set enrichment identified mitochondrial dysfunctional pathways, predominately changes within oxidative phosphorylation processes. The negative regulation of mitochondrial functions and metabolism was identified in the network analysis, with dysregulation of the mitochondrial complex formation. A histological analysis confirmed an increase in cellular bodies in the CHD RV tissue and positive staining for both CD45 and CD8, which was absent in the control. The deconvolution of bulk RNAseq data suggests a reduction in CD4+ T cells (p = 0.0067) and an increase in CD8+ T cells (p = 0.0223). The network analysis identified positive regulation of the immune system and cytokine signalling clusters in the inflammation functional network, as there were lymphocyte activation and leukocyte differentiation. Utilising RV tissue from paediatric patients undergoing CHD cardiac surgery, this study identifies dysfunctional mitochondrial pathways and an increase in inflammatory T-cell presence prior to reparative surgery.

DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data.

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types.

Exploring the role of DNMT1 in dental papilla cell fate specification during mouse tooth germ development through integrated single-cell transcriptomics and bulk RNA sequencing

ObjectivesThis study aimed to investigate the regulatory mechanisms governing dental mesenchymal cell commitment during tooth development, focusing on odontoblast differentiation and the role of epigenetic regulation in this process. MethodsWe performed single-cell RNA sequencing (scRNA-seq) of dental cells from embryonic day 14.5 (E14.5) mice to understand the heterogeneity of developing tooth germ cells. Computational analyses including gene regulatory network (GRN) assessment were conducted.We validated our findings using immunohistochemistry (IHC) and in vitro loss-of-function analyses using the DNA methyltransferase 1 (DNMT1) inhibitor Gsk-3484862 in primary dental mesenchymal cells (DMCs) isolated from E14.5 mouse tooth germs. Bulk RNA-seq of Gsk-3484862-treated DMCs was performed to identify potential downstream targets of DNMT1. ResultsscRNA-seq analysis revealed diverse cell populations within the tooth germs, including epithelial, mesenchymal, immune, and muscle cells. Using single-cell regulatory network inference and clustering (SCENIC), we identified Dnmt1 as a key regulator of early odontoblast development. IHC analysis showed the ubiquitous expression of DNMT1 in the dental papilla and epithelium. Bulk RNA-seq of cultured DMCs showed that Gsk-3484862 treatment upregulated odontoblast-related genes, whereas genes associated with cell division and the cell cycle were downregulated. Integrated analysis of bulk RNA-seq data with scRNA-seq SCENIC profiles was used to identify the potential Dnmt1 target genes. ConclusionsDnmt1 may negatively affect odontoblast commitment and differentiation during tooth development. These findings contribute to a better understanding of the molecular mechanisms underlying tooth development and future development of hard-tissue regenerative therapies.

Anandamide modulation of monocyte-derived Langerhans cells: implications for immune homeostasis and skin inflammation.

The endocannabinoid system (ECS), named after the chemical compounds found in the cannabis plant, is a regulatory network of neurotransmitters, receptors, and enzymes that plays crucial roles in skin health and disease. Endogenous ligands of the ECS, called endocannabinoids, have proven to be important regulators of immune responses. One of the most prevalent endocannabinoids, arachidonoylethanolamide (also known as anandamide), is known for its anti-inflammatory effects. Langerhans cells (LCs) are the sole antigen-presenting cells present in the human epidermis. They serve as the first line of defense against pathogens and are essential for the skin's specific immune responses and play a critical role in maintaining tissue homeostasis; however, little is known about the effect of endocannabinoids on these cells. Our research aimed to provide the connection between monocyte-derived Langerhans cells (moLCs) and the ECS, shedding light on their collaborative roles in immune homeostasis and inflammation. Human monocytes were differentiated into moLCs using established protocols. Anandamide was applied during the differentiation process to test its effect on the viability, marker expression, and cytokine production of the cells, as well as in short term treatments for intracellular calcium measurement. TLR ligands applied after the differentiation protocol were used to activate moLCs. The impact of anandamide on the functionality of moLCs was further assessed using differential gene expression analysis of bulk RNA-Seq data, moLC-T cell cocultures, while ELISpot was employed to determine polarization of T cells activated in the aforementioned cocultures. Anandamide did not significantly affect the viability of moLCs up to 10 µM. When applied during the differentiation process it had only a negligible effect on CD207 expression, the prototypic marker of LCs; however, there was an observed reduction in CD1a expression by moLCs. Anandamide had no significant effects on the maturation status of moLCs, nor did it affect the maturation induced by TLR3 and TLR7/8 agonists. MoLCs differentiated in the presence of anandamide did however show decreased production of CXCL8, IL-6, IL-10 and IL-12 cytokines induced by TLR3 and TLR7/8 activation. Anandamide-treated moLCs showed an increased capability to activate naïve T cells; however, not to the level seen with combined TLR agonism. RNA sequencing analysis of moLCs differentiated with anandamide showed modest changes compared to control cells but did reveal an inhibitory effect on oxidative phosphorylation specifically in activated moLCs. Anandamide also promoted the polarization of naïve T cells towards a Th1 phenotype. Our results show that anandamide has nuanced effects on the differentiation, maturation, cytokine secretion, metabolism and function of activated moLCs. Among these changes the decrease in CD1a expression on moLCs holds promise to selectively dampen inflammation induced by CD1a restricted T cells, which have been implicated as drivers of inflammation in common inflammatory skin conditions such as psoriasis, atopic dermatitis and contact dermatitis.

Establishment and validation of a novel CD8+ T cell-associated prognostic signature for predicting clinical outcomes and immunotherapy response in hepatocellular carcinoma via integrating single-cell RNA-seq and bulk RNA-seq

CD8+ T lymphocytes are critical in the immune response against neoplasms, yet the prognostic relevance of CD8+ T cell-associated genes in hepatocellular carcinoma (HCC) is not fully understood. We sourced single-cell RNA-sequencing (scRNA-seq) and bulk RNA-seq data for HCC from the GSE98638 dataset and The Cancer Genome Atlas (TCGA) repository. We utilized Weighted Gene Correlation Network Analysis (WGCNA) to identify CD8+ T cell-related genes. A clinical prognostic model for risk stratification was then constructed via Cox-Lasso regression analysis. The Immunophenotypic Score (IPS) was utilized to evaluate the potential of immunotherapeutic interventions in the categorized cohorts. Validation of the expression of CD8+ T cell-associated risk genes was performed using quantitative reverse transcription PCR (qRT-PCR). Integrating scRNA-seq with RNA-seq data, we identified five CD8+ T cell-related signature genes: IKBKE, ATP1B3, MSC, ADA, and BATF. Notably, HCC patients in the high-risk group had markedly decreased overall survival. Elevated infiltration levels of CD8+ T cells, B cells, and macrophages were observed in the high-risk group. Moreover, there was a positive correlation between the risk score and immune checkpoints (ICPs), including PDCD1, CD274, and CTLA4. Patients within the high-risk group subject to PD1 and CTLA4 blockade exhibited higher IPS levels. Additionally, the expression of the five risk genes was upregulated in HCC cell lines and tissues compared to normal cells and tissues. Our findings establish a prognostic signature based on CD8+ T cells, offering a potent predictive model for clinical outcomes and responsiveness to immunotherapy in HCC patients.

Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy.

Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized. We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database. We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups. Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

NNICE: a deep quantile neural network algorithm for expression deconvolution

The composition of cell-type is a key indicator of health. Advancements in bulk gene expression data curation, single cell RNA-sequencing technologies, and computational deconvolution approaches offer a new perspective to learn about the composition of different cell types in a quick and affordable way. In this study, we developed a quantile regression and deep learning-based method called Neural Network Immune Contexture Estimator (NNICE) to estimate the cell type abundance and its uncertainty by automatically deconvolving bulk RNA-seq data. The proposed NNICE model was able to successfully recover ground-truth cell type fraction values given unseen bulk mixture gene expression profiles from the same dataset it was trained on. Compared with baseline methods, NNICE achieved better performance on deconvolve both pseudo-bulk gene expressions (Pearson correlation R = 0.9) and real bulk gene expression data (Pearson correlation R = 0.9) across all cell types. In conclusion, NNICE combines statistic inference with deep learning to provide accurate and interpretable cell type deconvolution from bulk gene expression.

Exploring the immune landscape and drug prediction of an M2 tumor-associated macrophage-related gene signature in EGFR-negative lung adenocarcinoma.

Improving immunotherapy efficacy for EGFR-negative lung adenocarcinoma (LUAD) patients remains a critical challenge, and the therapeutic effect of immunotherapy is largely determined by the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the top-ranked immune infiltrating cells in the TME, and M2-TAMs exert potent roles in tumor promotion and chemotherapy resistance. An M2-TAM-based prognostic signature was constructed by integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to reveal the immune landscape and select drugs in EGFR-negative LUAD. M2-TAM-based biomarkers were obtained from the intersection of bulk RNA-seq data and scRNA-seq data. After consensus clustering of EGFR-negative LUAD into different clusters based on M2-TAM-based genes, we compared the prognosis, clinical features, estimate scores, immune infiltration, and checkpoint genes among the clusters. Next, we combined univariate Cox and LASSO regression analyses to establish an M2-TAM-based prognostic signature. CCL20, HLA-DMA, HLA-DRB5, KLF4, and TMSB4X were verified as prognostic M2-like TAM-related genes by univariate Cox and LASSO regression analyses. IPS and TMB analyses revealed that the high-risk group responded better to common immunotherapy. The study shows the potential of the M2-like TAM-related gene signature in EGFR-negative LUAD, explores the immune landscape based on M2-like TAM-related genes, and predict immunotherapy response of patients with EGFR-negative LUAD, providing a new insight for individualized treatment.

Exploring the Molecular Mechanism of Comorbidity of Type 2 Diabetes Mellitus and Colorectal Cancer: Insights from Bulk Omics and Single-Cell Sequencing Validation.

The relationship between type 2 diabetes mellitus (T2DM) and colorectal cancer (CRC) has long been extensively recognized, but their crosstalk mechanisms based on gene regulation remain elusive. In our study, for the first time, bulk RNA-seq and single-cell RNA-seq data were used to explore the shared molecular mechanisms between T2DM and CRC. Moreover, Connectivity Map and molecular docking were employed to determine potential drugs targeting the candidate targets. Eight genes (EVPL, TACSTD2, SOX4, ETV4, LY6E, MLXIPL, ENTPD3, UGP2) were identified as characteristic comorbidity genes for T2DM and CRC, with EVPL and ENTPD3 further identified as core comorbidity genes. Our results demonstrated that upregulation of EVPL and downregulation of ENTPD3 were intrinsic molecular features throughout T2DM and CRC and were significantly associated with immune responses, immune processes, and abnormal immune landscapes in both diseases. Single-cell analysis highlighted a cancer-associated fibroblast (CAF) subset that specifically expressed ENTPD3 in CRC, which exhibited high heterogeneity and unique tumor-suppressive features that were completely different from classical cancer-promoting CAFs. Furthermore, ENTPD3+ CAFs could notably predict immunotherapy response in CRC, holding promise to be an immunotherapy biomarker at the single-cell level. Finally, we identified that droperidol may be a novel drug simultaneously targeting EVPL and ENTPD3. In conclusion, previous studies have often focused solely on metabolic alterations common to T2DM and CRC. Our study establishes EVPL and ENTPD3 as characteristic molecules and immune biomarkers of comorbidity in T2DM and CRC patients, and emphasizes the importance of considering immunological mechanisms in the co-development of T2DM and CRC.

Characterization of tumor endothelial cells (TEC) in gastric cancer and development of a TEC-based risk signature using single-cell RNA-seq and bulk RNA-seq data.

Tumor endothelial cells (TECs) are essential participants in tumorigenesis. This study is focused on elucidating the TEC traits in gastric cancer (GC) and constructing a prognostic risk model to predict the clinical outcome of GC patients. Single-cell RNA sequencing (scRNA-seq) data were obtained from the GEO database. Using specific markers, the Seurat R package aided in processing scRNA-seq data and identifying TEC clusters. Based on TEC cluster-associated genes identified by Pearson correlation analysis, TEC-related prognostic genes were screened by lasso-Cox regression analysis, thereby constructing a risk signature. A nomogram was created by combining the risk signature with clinicopathological features. Based on the scRNA-seq data, 5 TEC clusters were discovered in GC, with 3 of them showing prognostic associations in GC. A total of 163 genes were pinpointed among 3302 DEGs as significantly linked to TEC clusters, leading to the formulation of a risk signature comprising 8 genes. Furthermore, there was a notable correlation between the risk signature and the immune cell infiltration. Multivariate analysis findings indicated that the risk signature served as an independent prognostic factor for GC. Moreover, its efficacy in forecasting immune response was validated. TEC-based risk model is highly effective in predicting the survival outcomes of GC patients and can forecast the immune response. Targeting TECs may significantly inhibit tumor progression and enhance the efficacy of immunotherapy.

A Transcriptomic Dataset of Embryonic Murine Telencephalon

Sex bias is known in the prevalence/pathology of neurodevelopmental disorders. Sex-dependent differences of the certain brain areas are known to emerge perinatally through the exposure to sex hormones, while gene expression patterns in the rodent embryonic brain does not seem to be completely the same between male and female. To investigate potential sex differences in gene expression and cortical organization during the embryonic period in mice, we conducted a comprehensive analysis of gene expression for the telencephalon at embryonic day (E) 11.5 (a peak of neural stem cell expansion) and E14.5 (a peak of neurogenesis) using bulk RNA-seq data. As a result, our data showed the existence of notable sex differences in gene expression patterns not obviously at E11.5, but clearly at E14.5 when neurogenesis has become its peak. These data can be useful for exploring potential contribution of genes exhibiting sex differences to the divergence in brain development. Additionally, our data underscore the significance of studying the embryonic period to gain a deeper understanding of sex differences in brain development.

Unveiling the Links Between Microbial Alteration and Host Gene Disarray in Crohn's Disease via TAHMC.

A compelling correlation method linking microbial communities and host gene expression in tissues is currently absent. A novel pipeline is proposed, dubbed Transcriptome Analysis of Host-Microbiome Crosstalk (TAHMC), designed to concurrently restore both host gene expression and microbial quantification from bulk RNA-seq data. Employing this approach, it discerned associations between the tissue microbiome and host immunity in the context of Crohn's disease (CD). Further, machine learning is utilized to separately construct networks of associations among host mRNA, long non-coding RNA, and tissue microbes. Unique host genes and tissue microbes are extracted from these networks for potential utility in CD diagnosis. Experimental validation of the predicted host gene regulation by microbes from the association network is achieved through the co-culturing of Faecalibacterium prausnitzii with Caco-2 cells. Collectively, the TAHMC pipeline accurately recovers both host gene expression and microbial quantification from CD RNA-seq data, thereby illuminating potential causal links between shifts in microbial composition as well as diversity within CD mucosal tissues and aberrant host gene expression.

Complex immune microenvironment of chordoma: a road map for future treatment

BackgroundChordoma, a rare bone tumor, presents limited treatment options and patients typically exhibit poor survival outcomes. While immunotherapy has shown promising results in treating various tumors, research on the immune...

Exploration of cancer biological characteristics in people with different five movements and six climates constitution based on pan-cancer Bulk RNA-Seq

According to the theory of five movements and six climates, the innate constitution plays a crucial role in determining the underlyingpa thological mechanisms of diseases later in life. Previous studies have demonstrated a close association between the constitution, as defined by the theory of five movements and six climates, and the development of various types of tumors. Furt hermore,the tumorsubtype determined by the constitution has prognostic implications. This highlights the potential of utilizing the fivemovements and six climates theory to guide the implementation of precision medicine strategies in thefield of oncology. However, no resear ch has yet been conducted to investigate the use of this theory in guiding the development of tumor molecular classification and precisi onmedicine strategies. The objective of this research is to uncover the biological characteristics of each constitution within a pancanc ercohort and identify potential anti-tumor drugs that are applicable to patients with different constitutional types. By doing so, we aimto c ontribute to the establishment of a precision medicine strategy for tumors derived from the original concepts of traditional Chi nesemedicine(TCM). In this study, we obtainedpan-cancer Bulk RNA-Seq data from UCSC Xena, GWAS cohort data from the UKBiobank, and cis-eQTLs data from eQ TLGen and GTEx V8. We employed machine learning methods to screen for hub genes associated with each constitution. Subsequently, we utilized informatics tools to explore the biological characteristics of each constitut iondefined by the theory of five movements and six bioclimates. Further, potential anti-tumor drugs suitable for patients with differen tconstitutional types were identified through mendelian randomization, molecular docking, and drug-like prediction techniques. Withinthe pan-cancer cohort, significant differences were observed among different constitutions in terms of progression-free interval, biological f unctions, immune cell abundance, tumor drug sensitivity, and immunotherapy response. These findings suggest that the five movements and six climates theory can guide tumor molecular classification and the development of precision medicine strategies. Moreover,the biological characteristics inherent to each constitution partially shed light on the scientific implications of Chinese medicinetheories, offering a fresh perspective towards clinical cancer treatment. Through molecular docking and drug-like prediction, several po tential anti-tumor drugs such as 17-beta-estradiol, serotonin, trans-resveratrol, and linoleic acid were identified. Overall, the util izationof multi-omics approaches pro vides a powerful tool to unravel the scientific foundations of TCM theories. The elucidation of themu lti-omics features associated witheach constitution in tumors serves as the basis for applying the five movements and six climates theoryto tumor molecular classification and the development of precision medicine strategies.

Identification of PATL1 as a prognostic and immunotherapeutic predictive factor for nasal-type natural killer/T-cell lymphoma and head and neck squamous cell carcinoma

This research examines the function of protein associated with topoisomerase II homolog 1 (PATL1) in nasal-type natural killer/T-cell lymphoma (NKTCL) and head and neck squamous cell carcinoma (HNSCC). We analyzed bulk RNA-seq data from NKTCL, nasal polyps, and normal nasal mucosa, identifying 439 differentially expressed genes. Machine learning algorithms highlighted PATL1 as a hub gene. PATL1 exhibited significant upregulation in NKTCL and HNSCC tumor samples in comparison to normal tissues, showing high diagnostic accuracy (AUC = 1.000) for NKTCL. Further analysis of local hospital data identified PATL1 as an independent prognostic risk factor for NKTCL. Data analysis of TCGA and GEO datasets revealed that high PATL1 expression correlated with poorer prognosis in HNSCC patients (p < 0.05). We also constructed a PATL1-based nomogram, which emerged as an independent prognostic predictor for HNSCC after addressing missing values. Additionally, we found a strong correlation between PATL1 and various immune cell infiltrates (e.g., activated.CD4 T cell), and a significant association with the expression of 37 immune checkpoints genes (e.g., CTLA4, PDCD1) and 20 N6-methyladenosine-related genes (e.g., ZC3H13, METTL3) (all p < 0.05). Both TCIA and TIDE algorithms suggested that PATL1 could potentially predict immunotherapy efficacy (p < 0.05). Cellular experiments demonstrated that transfection with a silencing plasmid of PATL1 significantly inhibited the malignant behaviors of SNK6 and FaDu cell lines(p < 0.05). In conclusion, our findings suggest that PATL1 may serve as a valuable prognostic and predictive biomarker in NKTCL and HNSCC, highlighting its significant role in these cancers.