Analysis Of RNA Sequencing Data Research Articles

The Schwann cell-specific G-protein Gαo (Gnao1) is a cell-intrinsic controller contributing to the regulation of myelination in peripheral nerve system

Myelin sheath abnormality is the cause of various neurodegenerative diseases (NDDs). G-proteins and their coupled receptors (GPCRs) play the important roles in myelination. Gnao1, encoding the major Gα protein (Gαo) in mammalian nerve system, is required for normal motor function. Here, we show that Gnao1 restricted to Schwann cell (SCs) lineage, but not neurons, negatively regulate SC differentiation, myelination, as well as re-myelination in peripheral nervous system (PNS). Mice lacking Gnao1 expression in SCs exhibit faster re-myelination and motor function recovery after nerve injury. Conversely, mice with Gnao1 overexpression in SCs display the insufficient myelinating capacity and delayed re-myelination. In vitro, Gnao1 deletion in SCs promotes SC differentiation. We found that Gnao1 knockdown in SCs resulting in the elevation of cAMP content and the activation of PI3K/AKT pathway, both associated with SC differentiation. The analysis of RNA sequencing data further evidenced that Gnao1 deletion cause the increased expression of myelin-related molecules and activation of regulatory pathways. Taken together, our data indicate that Gnao1 negatively regulated SC differentiation by reducing cAMP level and inhibiting PI3K-AKT cascade activation, identifying a novel drug target for the treatment of demyelinating diseases.

Age-associated CD4+ T cells with B cell-promoting functions are regulated by ZEB2 in autoimmunity.

Aging is a significant risk factor for autoimmunity, and many autoimmune diseases tend to onset during adulthood. We conducted an extensive analysis of CD4+ T cell subsets from 354 patients with autoimmune disease and healthy controls via flow cytometry and bulk RNA sequencing. As a result, we identified a distinct CXCR3midCD4+ effector memory T cell subset that expands with age, which we designated "age-associated T helper (THA) cells." THA cells exhibited both a cytotoxic phenotype and B cell helper functions, and these features were regulated by the transcription factor ZEB2. Consistent with the highly skewed T cell receptor usage of THA cells, gene expression in THA cells from patients with systemic lupus erythematosus reflected disease activity and was affected by treatment with a calcineurin inhibitor. Moreover, analysis of single-cell RNA sequencing data revealed that THA cells infiltrate damaged organs in patients with autoimmune diseases. Together, our characterization of THA cells may facilitate improved understanding of the relationship between aging and autoimmune diseases.

UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment

The complexity of the tumor microenvironment (TME) is a crucial factor in lung adenocarcinoma (LUAD) progression. To gain deeper insights into molecular mechanisms of LUAD, we perform an integrative single-cell RNA sequencing (scRNA-seq) data analysis of 377,574 cells from 117 LUAD patient samples. By linking scRNA-seq data with bulk gene expression data, we identify a cluster of prognostic-related UPP1high tumor cells. These cells, primarily situated at the invasive front of tumors, display a stronger association with the immunosuppressive components in the TME. Our cytokine array analysis reveals that the upregulation of UPP1 in tumor cells leads to the increased release of various immunosuppressive cytokines, with TGF-β1 being particularly prominent. Furthermore, this UPP1 upregulation also elevates the expression of PD-L1 through the PI3K/AKT/mTOR pathway, which contributes to the suppression of CD8 + T cells. Cytometry by time-of-flight (CyTOF) analysis provides additional evidence of the role of UPP1 in shaping the immunosuppressive nature of the TME. Using patient-derived organoids (PDOs), we discover that UPP1high tumors exhibit relatively increased sensitivity to Bosutinib and Dasatinib. Collectively, our study highlights the immunosuppressive role of UPP1 in LUAD, and these findings may provide insights into the molecular features of LUAD and facilitate the development of personalized treatment strategies.

Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data Reveals Memory-like NK Cell Subset Associated with Mycobacterium tuberculosis Latency.

Natural killer (NK) cells are innate-like lymphocytes that belong to the family of type-1 innate lymphoid cells and rapidly respond to virus-infected and tumor cells. In this study, we have combined scRNA-seq data and bulk RNA-seq data to define the phenotypic and molecular characteristics of peripheral blood NK cells. While the role of NK cells in immune surveillance against virus infections and tumors has been well established, their contribution to protective responses to other intracellular microorganisms, such as Mycobacterium tuberculosis (Mtb), is still poorly understood. In this study, we have combined scRNA-seq data and bulk RNA-seq data to illuminate the molecular characteristics of circulating NK cells in patients with active tuberculosis (TB) disease and subjects with latent Mtb infection (LTBI) and compared these characteristics with those of healthy donors (HDs) and patients with non-TB other pulmonary infectious diseases (ODs). We show here that the NK cell cluster was significantly increased in LTBI subjects, as compared to patients with active TB or other non-TB pulmonary diseases and HD, and this was mostly attributable to the expansion of an NK cell population expressing KLRC2, CD52, CCL5 and HLA-DRB1, which most likely corresponds to memory-like NK2.1 cells. These data were validated by flow cytometry analysis in a small cohort of samples, showing that LTBI subjects have a significant expansion of NK cells characterized by the prevalence of memory-like CD52+ NKG2C+ NK cells. Altogether, our results provide some new information on the role of NK cells in protective immune responses to Mtb.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma.

Disulfidoptosis is an unconventional form of programmed cell death that distinguishes itself from well-established cell death pathways like ferroptosis, pyroptosis, and necroptosis. Initially, we conducted a single-cell analysis of the GSE131907 dataset from the GEO database to identify disulfidoptosis-related genes (DRGs). We utilized differentially expressed DRGs to classify TCGA samples with an unsupervised clustering algorithm. Prognostic models were built using Cox regression and LASSO regression. Two DRG-related clusters (C1 and C2) were identified based on the DEGs from single-cell sequencing data analysis. In comparison to C1, C2 exhibited significantly worse overall prognosis, along with lower expression levels of immune checkpoint genes (ICGs) and chemoradiotherapy sensitivity-related genes (CRSGs). Furthermore, C2 displayed a notable enrichment in metabolic pathways and cell cycle-associated mechanisms. C2 was also linked to the development and spread of tumors. We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs. Patients were categorized into high-risk and low-risk groups depending on their DRG score, with the former group being linked to a poorer prognosis and higher TMB score. Moreover, the DRG score displayed significant correlations with CRSGs, ICGs, the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapeutic sensitivity. Subsequently, we identified a significant correlation between the DRG score and monocyte macrophages. Additionally, crucial DRGs were additionally validated using qRT-PCR. Our new DRG score can predict the immune landscape and prognosis of LUAD, serving as a reference for immunotherapy and chemotherapy.

Synaptotagmin-4 induces anhedonic responses to chronic stress via BDNF signaling in the medial prefrontal cortex

Stressful circumstances are significant contributors to mental illnesses such as major depressive disorder. Anhedonia, defined as loss of the ability to enjoy pleasure in pleasurable situations, including rewarding activities or social contexts, is considered a key symptom of depression. Although stress-induced depression is associated with anhedonia in humans and animals, the underlying molecular mechanisms of anhedonic responses remain poorly understood. In this study, we demonstrated that synaptotagmin-4 (SYT4), which is involved in the release of neurotransmitters and neurotrophic factors, is implicated in chronic stress-induced anhedonia. Employing chronic unpredictable stress (CUS), we evaluated two subpopulations of mice, susceptible (SUS, anhedonic) and resilient (RES, nonanhedonic), based on sucrose preference, which was strongly correlated with social reward. The FosTRAP (targeted recombination in active populations) system and optogenetic approach revealed that neural activity in the medial prefrontal cortex (mPFC) was significantly associated with CUS-induced anhedonic behavioral phenotypes. By conducting weighted gene coexpression network analysis of RNA sequencing data from the mPFC of SUS and RES mice, we identified Syt4 as a hub gene in a gene network that was unique to anhedonia. We also confirmed that Syt4 overexpression in the mPFC was pro-susceptible, while Syt4 knockdown was pro-resilient; the pro-susceptible effects of SYT4 were mediated through a reduction in brain-derived neurotrophic factor (BDNF)-tropomyosin receptor kinase B (TrkB) signaling in the mPFC. These findings suggest that SYT4-BDNF interactions in the mPFC represent a crucial regulatory mechanism of anhedonic susceptibility to chronic stress.

CCR10-mediated Enhancement of T Cell Trafficking for Improved Tumor Immunotherapy.

The effectiveness of adoptive T cell therapy for solid tumors remains suboptimal, partly attributed to insufficient T cell infiltration into the tumor site. A promising strategy involves directing T cells towards the tumor utilizing tumor-specific chemokine receptors. We analyzed chemokine receptor expression in activated T cells and chemokine expression in breast and lung cancer using The Cancer Genome Atlas (TCGA) data. Subsequently, we generated 1G4 T cell receptor-engineered T (TCR-T) cells with CCR10 and performed in vitro and in vivo efficacy tests. CCR10 exhibited insufficient expression in various human T cells. Analysis of TCGA RNA sequencing data revealed elevated expression of the chemokine CCL28, the corresponding chemokine for CCR10, in breast and lung cancer. Consequently, we generated CCR10-1G4 TCR-T cells. CCR10-1G4 dual expressing TCR-T cells exhibited comparable cellular cytotoxicity but increased mobility compared to 1G4 TCR-T cells in vitro. Furthermore, injecting CCR10-1G4 dual expressing TCR-T cells into a xenograft tumor model demonstrated enhanced in vivo trafficking and a greater reduction of tumor burden. This study highlights the potential of CCR10 for developing efficient adoptive T-cell treatments targeting solid tumors.

CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.

Effective identification of cell types is of critical importance in single-cell RNA-sequencing (scRNA-seq) data analysis. To date, many supervised machine learning-based predictors have been implemented to identify cell types from scRNA-seq datasets. Despite the technical advances of these state-of-the-art tools, most existing predictors were single classifiers, of which the performances can still be significantly improved. It is therefore highly desirable to employ the ensemble learning strategy to develop more accurate computational models for robust and comprehensive identification of cell types on scRNA-seq datasets. We propose a two-layer stacking model, termed CTISL (Cell Type Identification by Stacking ensemble Learning), which integrates multiple classifiers to identify cell types. In the first layer, given a reference scRNA-seq dataset with known cell types, CTISL dynamically combines multiple cell-type-specific classifiers (i.e. support-vector machine and logistic regression) as the base learners to deliver the outcomes for the input of a meta-classifier in the second layer. We conducted a total of 24 benchmarking experiments on 17 human and mouse scRNA-seq datasets to evaluate and compare the prediction performance of CTISL and other state-of-the-art predictors. The experiment results demonstrate that CTISL achieves superior or competitive performance compared to these state-of-the-art approaches. We anticipate that CTISL can serve as a useful and reliable tool for cost-effective identification of cell types from scRNA-seq datasets. The webserver and source code are freely available at http://bigdata.biocie.cn/CTISLweb/home and https://zenodo.org/records/10568906, respectively.

Age-related dysregulation of intestinal epithelium fucosylation is linked to an increased risk of colon cancer.

Colon cancer affects people of all ages. However, its frequency, as well as the related morbidity and mortality, are high among older adults. The complex physiological changes in the aging gut substantially limit the development of cancer therapies. Here, we identify a potentially unique intestinal microenvironment that is linked with an increased risk of colon cancer in older adults. Our findings show that aging markedly influenced persistent fucosylation of the apical surfaces of intestinal epithelial cells, which resulted in a favorable environment for tumor growth. Furthermore, our findings shed light on the importance of the host-commensal interaction, which facilitates the dysregulation of fucosylation and promotes tumor growth as people get older. We analyzed colonic microbial populations at the species level to find changes associated with aging that could contribute to the development of colon cancer. Analysis of single-cell RNA-sequencing data from previous publications identified distinct epithelial cell subtypes involved in dysregulated fucosylation in older adults. Overall, our study provides compelling evidence that excessive fucosylation is associated with the development of colon cancer, that age-related changes increase vulnerability to colon cancer, and that a dysbiosis in microbial diversity and metabolic changes in the homeostasis of older mice dysregulate fucosylation levels with age.

Sperm exposure to accessory gland secretions alters the transcriptomic response of the endometrium in cattle

In cattle, mating to intact, but not vasectomised, bulls has been shown to modify the endometrial transcriptome, suggesting an important role of sperm in the modulation of the uterine environment in this species. However, it is not clear whether these changes are driven by intrinsic sperm factors, or by factors of accessory gland (AG) origin that bind to sperm at ejaculation. Therefore, the aim of the present study was to determine whether ejaculated sperm, which are suspended in the secretions of the AGs, elicit a different endometrial transcriptomic response than epididymal sperm, which have never been exposed to AG factors. To this end, bovine endometrial explants collected from heifers in oestrus were (co-)incubated for 6 h alone (control), or with epididymal sperm or ejaculated sperm, following which transcriptomic changes in the endometrium were evaluated. Epididymal sperm elicited a more dramatic endometrial response than ejaculated sperm, in terms of the number of differentially expressed genes (DEGs). Indeed, RNA-sequencing data analysis revealed 1912 DEGs in endometrial explants exposed to epididymal sperm compared with control explants, whereas 115 DEGs were detected between endometrial explants exposed to ejaculated sperm in comparison to control explants. The top pathways associated with genes upregulated by epididymal sperm included T cell regulation and TNF, NF-KB and IL17 signalling. Interestingly, ejaculated sperm induced downregulation of genes associated with T cell immunity and Th17 differentiation, and upregulation of genes involved in NF-KB signalling, in comparison to epididymal sperm. These data indicate that factors of AG origin modulate the interaction between sperm and the endometrium in cattle.

Transcriptomic Analysis of Metastatic Colorectal Tumor with Low Mutational Burden

Objective: To identify potential drug targets for metastasis colorectal cancer (CRC) patients with low mutational burden by examining differences in immune-related gene expression. Methods: For this study, 623 samples were collected from The Cancer Genome Atlas (TCGA) database, comprising tumor mutational burden (TMB), RNA sequencing (RNA-Seq), and clinical data. Differential gene expression analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the identified genes were conducted using the R package. Additionally, a comparative analysis of immune infiltrating cell composition in metastatic and non-metastatic groups was performed. Hub genes, exhibiting high levels of interaction, were selected using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The Drug Gene Interaction Database (DGIdb) was then utilized to estimate drugs targeting the identified hub genes. Results: The transcriptome data of 326 colorectal cancer patients with low TMB were analyzed, comprising 58 patients with metastasis and 268 patients without metastasis. Among the differential expression in 1,111 genes for patients with metastasis compared to those without metastasis, 733 genes were upregulated, and 378 genes were downregulated. KEGG and GO enrichment analysis indicated significant differences in gene expression in CRC metastatic patients with low TMB compared to non-metastasis patients with low TMB. Enriched pathways included humoral immuneresponse, immunoglobulin production, and regulation of AMPA receptor activity. Two genes related to interleukin-12 were identified through secondary enrichment for immune-related genes. Analysis of tumor-infiltrating immune cell data revealed significant differences in memory-activated T cell CD4 and T cell CD8. Conclusions: This analysis of RNA sequencing data and immune-filtrating cell data revealed significant differences between metastatic colorectal cancer patients with low TMB and their non-metastatic counterparts. These distinctions suggest the possibility of identifying more effective drugs or therapies for metastatic colorectal cancer patients with low TMB.

ScHybridBERT: integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering.

Graph learning models have received increasing attention in the computational analysis of single-cell RNA sequencing (scRNA-seq) data. Compared with conventional deep neural networks, graph neural networks and language models have exhibited superior performance by extracting graph-structured data from raw gene count matrices. Established deep neural network-based clustering approaches generally focus on temporal expression patterns while ignoring inherent interactions at gene-level as well as cell-level, which could be regarded as spatial dynamics in single-cell data. Both gene-gene and cell-cell interactions are able to boost the performance of cell type detection, under the framework of multi-view modeling. In this study, spatiotemporal embedding and cell graphs are extracted to capture spatial dynamics at the molecular level. In order to enhance the accuracy of cell type detection, this study proposes the scHybridBERT architecture to conduct multi-view modeling of scRNA-seq data using extracted spatiotemporal patterns. In this scHybridBERT method, graph learning models are employed to deal with cell graphs and the Performer model employs spatiotemporal embeddings. Experimental outcomes about benchmark scRNA-seq datasets indicate that the proposed scHybridBERT method is able to enhance the accuracy of single-cell clustering tasks by integrating spatiotemporal embeddings and cell graphs.

Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning.

Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still low. In this work, we proposed a novel Gaussian noise augmentation-based scRNA-seq contrastive learning method (GsRCL) to learn a type of discriminative feature representations for cell type identification tasks. A large-scale computational evaluation suggests that GsRCL successfully outperformed other state-of-the-art predictive methods on difficult cell type identification tasks, while the conventional random genes masking augmentation-based contrastive learning method also improved the accuracy of easy cell type identification tasks in general.

SRNAflow: A Tool for the Analysis of Small RNA-Seq Data.

The analysis of small RNA sequencing data across a range of biofluids is a significant research area, given the diversity of RNA types that hold potential diagnostic, prognostic, and predictive value. The intricate task of segregating the complex mixture of small RNAs from both human and other species, including bacteria, fungi, and viruses, poses one of the most formidable challenges in the analysis of small RNA sequencing data, currently lacking satisfactory solutions. This study introduces sRNAflow, a user-friendly bioinformatic tool with a web interface designed for the analysis of small RNAs obtained from biological fluids. Tailored to the unique requirements of such samples, the proposed pipeline addresses various challenges, including filtering potential RNAs from reagents and environment, classifying small RNA types, managing small RNA annotation overlap, conducting differential expression assays, analysing isomiRs, and presenting an approach to identify the sources of small RNAs within samples. sRNAflow also encompasses an alternative alignment-free analysis of RNA-seq data, featuring clustering and initial RNA source identification using BLAST. This comprehensive approach facilitates meaningful comparisons of results between different analytical methods.

Abstract B115: Single-cell transcriptomic analysis reveals both shared and separate functions for GATA6 and KRT17 in pancreatic cancer

Abstract Human pancreatic ductal carcinomas (PDACs) are transcriptionally heterogeneous and can be grouped into two classes, the classical subtype associated with GATA6 expression and the basal subtype associated with KRT17 expression. Accordingly, GATA6 and KRT17 are generally thought to have opposing functions in PDAC. However, through our analysis of single cell RNA sequencing data, we have found that many of the genes that are the highly correlated with KRT17 expression are also highly correlated with GATA6 expression, including KLF5, which encodes a transcription factor that promotes PDAC development, and several genes that encode components of intercellular junctions (e.g. CDH1, CLDN4, CLDN18, JUP, and SHROOM3). These results suggest that KRT17 and GATA6 share certain biological functions including the maintenance or modulation of intercellular junctions. We also found evidence for separate functions. Genes with expression that correlated specifically with GATA6 included transcription factor genes involved in normal pancreatic differentiation along with genes encoding components of the Hippo signaling pathway. On the other hand, genes with expression that correlated specifically with KRT17 included several components of PIK3-Akt signaling. Further analysis of scRNA-seq data should reveal more about the mechanisms by which these genes are involved in influencing PDAC progression and maintenance. Citation Format: Brian J. Nelson, Natalie Marchenko, Luisa F. Escobar-Hoyos, Kenneth Shroyer, Scott Powers, Thomas MacCarthy. Single-cell transcriptomic analysis reveals both shared and separate functions for GATA6 and KRT17 in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B115.

Cytoplasmatic Localization of Six1 in Male Testis and Spermatogonial Stem Cells.

Sine oculis homeobox 1 (Six1) is an important factor for embryonic development and carcinoma malignancy. However, the localization of Six1 varies due to protein size and cell types in different organs. In this study, we focus on the expression and localization of Six1 in male reproductive organ via bioinformatics analysis and immunofluorescent detection. The potential interacted proteins with Six1 were also predicted by protein-protein interactions (PPIs) and Enrichr analysis. Bioinformatic data from The Cancer Genome Atlas and Genotype-Tissue Expression project databases showed that SIX1 was highly expressed in normal human testis, but low expressed in the testicular germ cell tumor sample. Human Protein Atlas examination verified that SIX1 level was higher in normal than that in cancer samples. The sub-localization of SIX1 in different reproductive tissues varies but specifically in the cytoplasm and membrane in testicular cells. In mouse cells, single cell RNA-sequencing data analysis indicated that Six1 expression level was higher in mouse spermatogonial stem cells (mSSCs) and differentiating spermatogonial than in other somatic cells. Immunofluorescence staining showed the cytoplasmic localization of Six1 in mouse testis and mSSCs. Further PPIs and Enrichr examination showed the potential interaction of Six1 with bone morphogenetic protein 4 (Bmp4) and catenin Beta-1 (CtnnB1) and stem cell signal pathways. Cytoplasmic localization of Six1 in male testis and mSSCs was probably associated with stem cell related proteins Bmp4 and CtnnB1 for stem cell development.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed T cell marker genes based molecular sub-types and a prognostic signature in lung adenocarcinoma

Immunotherapy has emerged as a promising modality for addressing advanced or conventionally drug-resistant malignancies. When it comes to lung adenocarcinoma (LUAD), T cells have demonstrated significant influence on both antitumor activity and the tumor microenvironment. However, their specific contributions remain largely unexplored. This investigation aimed to delineate molecular subtypes and prognostic indicators founded on T cell marker genes, thereby shedding light on the significance of T cells in LUAD prognosis and precision treatment. The cellular phenotypes were identified by scrutinizing the single-cell data obtained from the GEO repository. Subsequently, T cell marker genes derived from single-cell sequencing analyses were integrated with differentially expressed genes from the TCGA repository to pinpoint T cell-associated genes. Utilizing Cox analysis, molecular subtypes and prognostic signatures were established and subsequently verified using the GEO dataset. The ensuing molecular and immunological distinctions, along with therapy sensitivity between the two sub-cohorts, were examined via the ESTIMATE, CIBERSORT, and ssGSEA methodologies. Compartmentalization, somatic mutation, nomogram development, chemotherapy sensitivity prediction, and potential drug prediction analyses were also conducted according to the risk signature. Additionally, real-time qPCR and the HPA database corroborated the mRNA and protein expression patterns of signature genes in LUAD tissues. In summary, this research yielded an innovative T cell marker gene-based signature with remarkable potential to prognosis and anticipate immunotherapeutic outcomes in LUAD patients.

A novel disulfidptosis-related lncRNAs signature for predicting survival and immune response in hepatocellular carcinoma.

The accumulation of intracellular disulfides induces a novel and unique form of metabolic-related cell death known as disulfidptosis. A previous study revealed the prognostic value of a risk model of disulfidptosis-related genes in hepatocellular carcinoma (HCC). However, to date, no studies have investigated the relationship between disulfidptosis-related long non-coding RNAs (DRLs) and HCC. In this study, we collected and analyzed RNA sequencing data from 370 HCC samples to explore the DRLs in the tumorigenesis and development of HCC. By employing Lasso Cox regression and multivariate Cox regression analyses, we identified five prognostic DRLs, which were used to construct a prognostic signature. The signature was subsequently validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, Cox regression analyses, nomograms, and calibration curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed, revealing that the DRLs signature was associated with HCC and several cancer-related pathways. Furthermore, the DRLs signature showed correlations with the infiltration of M0 and M1 macrophages, immune-related functions, and multiple immune checkpoints, including PDCD1, LAG3, CTLA4, TIGIT, CD47, and others. Analysis using the tumor immune dysfunction and exclusion (TIDE) approach demonstrated that the DRLs signature could predict the response to immunotherapy. Finally, we screened potential chemotherapy drugs that could sensitize HCC. In conclusion, our novel DRLs signature provides valuable insights into predicting patient survival and immunotherapy responses.

Identification of Breast Cancer Subtypes Based on Endoplasmic Reticulum Stress-Related Genes and Analysis of Prognosis and Immune Microenvironment in Breast Cancer Patients.

Introduction: Endoplasmic reticulum stress (ERS) was a response to the accumulation of unfolded proteins and plays a crucial role in the development of tumors, including processes such as tumor cell invasion, metastasis, and immune evasion. However, the specific regulatory mechanisms of ERS in breast cancer (BC) remain unclear. Methods: In this study, we analyzed RNA sequencing data from The Cancer Genome Atlas (TCGA) for breast cancer and identified 8 core genes associated with ERS: ELOVL2, IFNG, MAP2K6, MZB1, PCSK6, PCSK9, IGF2BP1, and POP1. We evaluated their individual expression, independent diagnostic, and prognostic values in breast cancer patients. A multifactorial Cox analysis established a risk prognostic model, validated with an external dataset. Additionally, we conducted a comprehensive assessment of immune infiltration and drug sensitivity for these genes. Results: The results indicate that these eight core genes play a crucial role in regulating the immune microenvironment of breast cancer (BRCA) patients. Meanwhile, an independent diagnostic model based on the expression of these eight genes shows limited independent diagnostic value, and its independent prognostic value is unsatisfactory, with the time ROC AUC values generally below 0.5. According to the results of logistic regression neural networks and risk prognosis models, when these eight genes interact synergistically, they can serve as excellent biomarkers for the diagnosis and prognosis of breast cancer patients. Furthermore, the research findings have been confirmed through qPCR experiments and validation. Conclusion: In conclusion, we explored the mechanisms of ERS in BRCA patients and identified 8 outstanding biomolecular diagnostic markers and prognostic indicators. The research results were double-validated using the GEO database and qPCR.

Computational analysis of peripheral blood RNA sequencing data unravels disrupted immune patterns in Alzheimer's disease

<abstract> <p>The central nervous system (CNS) and the immune system collectively coordinate cellular functionalities, sharing common developmental mechanisms. Immunity-related molecules exert an influence on brain development, challenging the conventional view of the brain as immune-privileged. Chronic inflammation emerges as a key player in the pathophysiology of Alzheimer's disease (AD), with increased stress contributing to the disease progression and potentially exacerbating existing symptoms. In this study, the most significant gene signatures from selected RNA-sequencing (RNA-seq) data from AD patients and healthy individuals were obtained and a functional analysis and biological interpretation was conducted, including network and pathway enrichment analysis. Important evidence was reported, such as enrichment in immune system responses and antigen processes, as well as positive regulation of T-cell mediated cytotoxicity and endogenous and exogenous peptide antigen, thus indicating neuroinflammation and immune response participation in disease progression. These findings suggest a disturbance in the immune infiltration of the peripheral immune environment, providing new challenges to explore key biological processes from a molecular perspective that strongly participate in AD development.</p> </abstract>