Transcriptional Profiling Data Research Articles

Comprehensive analysis of the relationship between RNA modification writers and immune microenvironment in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Four types of RNA modification writers (m6A, m1A, A-I editing, and APA) are widely involved in tumorigenesis and the TME. We aimed to comprehensively explore the role of the four RNA modification writers in the progression and immune microenvironment of HNSCC. We first obtained transcription profile data and transcriptional variation of the four types of RNA modification writers from The Cancer Genome Atlas (TCGA) database. HNSCC patients in TCGA dataset were divided into different clusters based on the four types of RNA modification writers. Univariate Cox and Least absolute shrinkage and selection operator (LASSO) analyses were performed to conduct a Writer-score scoring system, which was successfully verified in the GSE65858 dataset and our clinical sample dataset. Finally, we evaluated the relationship between different RNA modification clusters (Writer-score) and immunological characteristics of HNSCC. Two different RNA modification clusters (A and B) were obtained. These RNA modification clusters (Writer-score) were strongly associated with immunological characteristics (immunomodulators, cancer immunity cycles, infiltrating immune cells (TIICs), inhibitory immune checkpoints, and T cell inflamed score (TIS)) of HNSCC. This study identified two different RNA modification clusters and explored the potential relationship between RNA modification clusters (Writer-score) and immunological characteristics, offering a new theoretical basis for precision immunotherapy in patients with HNSCC.

B4GALT5 a sialylation-related genes associated with patient prognosis and immune microenvironment in ovarian cancer and pan-cancer

BackgroundOvarian cancer (OC) is the predominant primary tumor in the human reproductive system. Abnormal sialylation has a significant impact on tumor development, metastasis, immune evasion, angiogenesis, and treatment resistance. B4GALT5, a gene associated with sialylation, plays a crucial role in ovarian cancer, and may potentially affect clinicopathological characteristics and prognosis.MethodsWe conducted a comprehensive search across TIMER, GEPIA2, GeneMANIA, and Metascape to obtain transcription profiling data of ovarian cancer from The Cancer Genome Atlas (TCGA). The expression of B4GALT5 was test by immunohistochemistry. To investigate the impact of B4GALT5 on growth and programmed cell death in OC cells, we performed transwell assays and western blots.ResultsThe presence of B4GALT5 was strongly associated with an unfavorable outcome in OC. B4GALT5 significantly promoted the proliferation of OC cells. Upon analyzing gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), it was discovered that B4GALT5 played a crucial role in the extracellular matrix, particularly in collagen-containing structures, and exhibited correlations with ECM-receptor interactions, transcriptional dysregulation in cancer, as well as the interleukin-1 receptor signaling pathway. Furthermore, there is a clear link between B4GALT5 and the tumor immune microenvironment in OC. Moreover, B4GALT5 exhibits favorable expression levels across various types of cancers, including CHOL, KIRC, STAD and UCES.ConclusionIn conclusion, it is widely believed that B4GALT5 plays a pivotal role in the growth and progression of OC, with its heightened expression serving as an indicator of unfavorable outcomes. Moreover, B4GALT5 actively participates in shaping the cancer immune microenvironment within OC. This investigation has the potential to contribute significantly to a deeper understanding of the substantial involvement of B4GALT5 in human malignancies, particularly OCs.

Inflammatory corpuscle AIM2 facilitates macrophage foam cell formation by inhibiting cholesterol efflux protein ABCA1

The inflammatory corpuscle recombinant absents in melanoma 2 (AIM2) and cholesterol efflux protein ATP binding cassette transporter A1(ABCA1) have been reported to play opposing roles in atherosclerosis (AS) plaques. However, the relationship between AIM2 and ABCA1 remains unclear. In this study, we explored the potential connection between AIM2 and ABCA1 in the modulation of AS by bioinformatic analysis combined with in vitro experiments. The GEO database was used to obtain AS transcriptional profiling data; screen differentially expressed genes (DEGs) and construct a weighted gene co-expression network analysis (WGCNA) to obtain AS-related modules. Phorbol myristate acetate (PMA) was used to induce macrophage modelling in THP-1 cells, and ox-LDL was used to induce macrophage foam cell formation. The experiment was divided into Negative Control (NC) group, Model Control (MC) group, AIM2 overexpression + ox-LDL (OE AIM2 + ox-LDL) group, and AIM2 short hairpin RNA + ox-LDL (sh AIM2 + ox-LDL) group. The intracellular cholesterol efflux rate was detected by scintillation counting; high-performance liquid chromatography (HPLC) was used to detect intracellular cholesterol levels; apoptosis levels were detected by TUNEL kit; levels of inflammatory markers (IL-1β, IL-18, ROS, and GSH) were detected by ELISA kits; and levels of AIM2 and ABCA1 proteins were detected by Western blot. Bioinformatic analysis revealed that the turquoise module correlated most strongly with AS, and AIM2 and ABCA1 were co-expressed in the turquoise module with a trend towards negative correlation. In vitro experiments demonstrated that AIM2 inhibited macrophage cholesterol efflux, resulting in increased intracellular cholesterol levels and foam cell formation. Moreover, AIM2 had a synergistic effect with ox-LDL, exacerbating macrophage oxidative stress and inflammatory response. Silencing AIM2 ameliorated the above conditions. Furthermore, the protein expression levels of AIM2 and ABCA1 were consistent with the bioinformatic analysis, showing a negative correlation. AIM2 inhibits ABCA1 expression, causing abnormal cholesterol metabolism in macrophages and ultimately leading to foam cell formation. Inhibiting AIM2 may reverse this process. Overall, our study suggests that AIM2 is a reliable anti-inflammatory therapeutic target for AS. Inhibiting AIM2 expression may reduce foam cell formation and, consequently, inhibit the progression of AS plaques.

Abstract LB033: Identification of small molecule Pan-TEAD inhibitors disrupting YAP-TEAD protein-protein interaction and targeting gastric cancer cells

Abstract Because of the Hippo pathway’s unique ability to restrict TAZ/YAP-TEAD in promoting regeneration, any abnormality of its core components, may promote the migration, invasion, and malignancy of cancer cells. Aberrant overexpression of YAP/TAZ-TEAD promotes tumorigenesis and is therefore considered oncogenes in many solid tumors. Drug resistance is a major factor undermining the efficacy of cancer drugs. As YAP/TAZ-TEAD complex plays a role in intrinsic and acquired resistance to various chemotherapeutic and targeted therapy drugs, there is increasing interest in combining a TEAD inhibitor with various cancer therapies. Since elevated YAP/TAZ-TEAD activity has been implicated in multiple cancer types at various stages of cancer progression and cancer types, Drugging the TEAD hydrophobic pocket, which was discovered in 2015, remains as an attractive and proven strategy to modulate the activity. Despite the progresses, only 3 small molecule TEAD inhibitors are currently being tested in Phase I clinical trials which includes: VT3989b NCT04665206 from Vivacare, IK-930b NCT05228015 from Ikena Oncology and IAG-933, NCT0485737 from the Novartis Oncology. As such, there is a need to uncover alternative TEAD inhibitors for cancer therapeutics. We report novel small molecule inhibitors that form a covalent complex with the conserved cysteine of the TEAD palmitoylation site. Compounds inhibited TEAD transcriptional target genes in cancer cells. Cellular proliferation and colony forming assay data strongly displayed cellular sensitivity in Hippo signaling altered cancer cell lines. TEAD1 dependency of gastric cancer cell lines enhance cellular sensitivity in response to small molecule inhibitors. Transcriptional profiling and RNA seq data indicate that the covalent inhibitors specifically downregulate down-stream transcriptional target genes. With our exciting key data, we are exploring multiple other oncology targets to use our Pan-TEAD inhibitors either as a monotherapy or combination therapy. We expect to complete our Technology Disclosure on additional targets before the meeting and to add exciting data for the Cancer drug development community. Citation Format: Ramesh Kumar, Joel Toh, Joanne Thian, Noorul Farzana Mohideen, Jialin Sun, Sayan Chakraborty, Jayantha Gunaratne, Wanjin Hong. Identification of small molecule Pan-TEAD inhibitors disrupting YAP-TEAD protein-protein interaction and targeting gastric cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB033.

A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium

High throughput transcriptomics (HTTr) profiling has the potential to rapidly and comprehensively identify molecular targets of environmental chemicals that can be linked to adverse outcomes. We describe here the construction and characterization of a 50-gene expression biomarker designed to identify estrogen receptor (ER) active chemicals in HTTr datasets. Using microarray comparisons, the genes in the biomarker were identified as those that exhibited consistent directional changes when ER was activated (4 ER agonists; 4 ESR1 gene constitutively active mutants) and opposite directional changes when ER was suppressed (4 antagonist treatments; 4 ESR1 knockdown experiments). The biomarker was evaluated as a predictive tool using the Running Fisher algorithm by comparison to annotated gene expression microarray datasets including those evaluating the transcriptional effects of hormones and chemicals in MCF-7 cells. Depending on the reference dataset used, the biomarker had a predictive accuracy for activation of up to 96%. To demonstrate applicability for HTTr data analysis, the biomarker was used to identify ER activators in a set of 15 chemicals that are considered potential bisphenol A (BPA) alternatives examined at up to 10 concentrations in MCF-7 cells and analyzed by full-genome TempO-Seq. Using benchmark dose (BMD) modeling, the biomarker genes stratified the ER potency of BPA alternatives consistent with previous studies. These results demonstrate that the ER biomarker can be used to accurately identify ER activators in transcript profile data derived from MCF-7 cells.

Single-cell transcriptome analysis reveals characteristic transcription factors in polydactyly.

The study aims to provide guidance on the identification of multiple-digit malformations as potential biomarkers and therapeutic targets. Single-cell RNA sequencing (scRNA-seq) data of four multiple-finger malformation samples were downloaded from the GEO public database. Fibroblasts and keratinocytes were divided into cellular subpopulations and the transcription factors of different subpopulations were analyzed. The regulatory network of transcription factors and their target genes were constructed to analyze the functionality of regulons. Examination of the transcriptional profile data from 11,806 single cells uncovered significant associations between regulons and cell function in polydactyly. Specifically, the analysis highlighted the involvement of HOX family members and GLI2 transcription factors, including HOXD13, MSX2, LHX2, EMX2, LEF1, CREB3L2, and LHX2, in the polydactyly process within fibroblast cells. Furthermore, it sheds light on the roles of HES2 and GLIS1 in the formation and development of keratinocytes. Significant presence of transcription factors, especially HOXD13, MSX2, and LHX2, may be strongly related to the development of polydactyly.

Altered IL-7 signaling in CD4+ T cells from patients with visceral leishmaniasis.

CD4+ T cells play a central role in control of L. donovani infection, through IFN-γ production required for activation of macrophages and killing of intracellular parasites. Impaired control of parasites can in part be explained by hampered CD4+ T cells effector functions in visceral leishmaniasis (VL) patients. In a recent studies that defined transcriptional signatures for CD4+ T cells from active VL patients, we found that expression of the IL-7 receptor alpha chain (IL-7Rα; CD127) was downregulated, compared to CD4+ T cells from endemic controls (ECs). Since IL-7 signaling is critical for the survival and homeostatic maintenance of CD4+ T cells, we investigated this signaling pathway in VL patients, relative to ECs. CD4+ T cells were enriched from peripheral blood collected from VL patients and EC subjects and expression of IL7 and IL7RA mRNA was measured by real time qPCR. IL-7 signaling potential and surface expression of CD127 and CD132 on CD4+ T cell was analyzed by multicolor flow cytometry. Plasma levels of soluble IL-7 and sIL-7Rα were measured by ELISA. Transcriptional profiling data sets generated previously from our group showed lower IL7RA mRNA expression in VL CD4+ T cells as compared to EC. A significant reduction was, however not seen when assessing IL7RA mRNA by RT-qPCR. Yet, the levels of soluble IL-7Rα (sIL-7Rα) were reduced in plasma of VL patients compared to ECs. Furthermore, the levels of soluble IL-7 were higher in plasma from VL patients compared to ECs. Interestingly, expression of the IL-7Rα protein was higher on VL patient CD4+ T cells as compared to EC, with activated CD38+ CD4+ T cells showing higher surface expression of IL-7Rα compared to CD38- CD4+ T cells in VL patients. CD4+ T cells from VL patients had higher signaling potential baseline and after stimulation with recombinant human IL-7 (rhIL-7) compared to EC, as measured by phosphorylation of STAT5 (pSTAT5). Interestingly, it was the CD38 negative cells that had the highest level of pSTAT5 in VL patient CD4+ T cells after IL-7 stimulation. Thus, despite unaltered or potentially lowered IL7RA mRNA expression by CD4+ T cells from VL patients, the surface expression of the IL-7Rα was higher compared to EC and increased pSTAT5 was seen following exposure to rhIL-7. Accordingly, IL-7 signaling appears to be functional and even enhanced in VL CD4+ T cells and cannot explain the impaired effector function of VL CD4+ T cells. The enhanced plasma IL-7 may serve as part of homeostatic feedback mechanism regulating IL7RA expression in CD4+ T cells.

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer.

Breast cancer (BC), a leading malignant disease, affects women all over the world. Cancer associated fibroblasts (CAFs) stimulate epithelial-mesenchymal transition, and induce chemoresistance and immunosuppression. To establish a CAFs-associated prognostic signature to improve BC patient outcome estimation. We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas (TCGA) databases, and 3661 BC samples from the The Gene Expression Omnibus. CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm. CAF-associated gene identification was done by weighted gene co-expression network analysis. A CAF risk signature was established via univariate, least absolute shrinkage and selection operator regression, and multivariate Cox regression analyses. The receiver operating characteristic (ROC) and Kaplan-Meier curves were employed to evaluate the predictability of the model. Subsequently, a nomogram was developed with the risk score and patient clinical signature. Using Spearman's correlations analysis, the relationship between CAF risk score and gene set enrichment scores were examined. Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction (qRT-PCR). Employing an 8-gene (IL18, MYD88, GLIPR1, TNN, BHLHE41, DNAJB5, FKBP14, and XG) signature, we attempted to estimate BC patient prognosis. Based on our analysis, high-risk patients exhibited worse outcomes than low-risk patients. Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis. ROC analysis exhibited satisfactory nomogram predictability. The area under the curve showed 0.805 at 3 years, and 0.801 at 5 years in the TCGA cohort. We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes. CAF risk score was significantly correlated with most hallmark gene sets. Finally, the prognostic signature were further validated by qRT-PCR. We introduced a newly-discovered CAFs-associated gene signature, which can be employed to estimate BC patient outcomes conveniently and accurately.

Advancing the drug development process in metastatic prostate cancer through machine learning.

192 Background: Despite advances in the development of systemic therapeutics, metastatic prostate cancer (mPC) remains a lethal disease. The drug approval process is slow, and delays in approving active agents translate into lives lost, and increased morbidity for men living with mPC. Clinical benefit end points proximate to survival that could support regulatory approval have been sought. Methods that accelerate the prediction of overall survival (OS) from baseline and short-term clinical data could dramatically accelerate the "readout" timeline for registrational studies. Methods: Curated, anonymized data from 5,679 mPC patients (pts) across six completed clinical trials were used: COU-AA-301, COU-AA302, ACIS, GALAHAD, LATITUDE, and TITAN. These trials comprise heterogeneous pts across castrate sensitive (TITAN, LATITUDE) and castrate resistant (COU-AA-301, COU-AA-302, ACIS, GALAHAD) populations. Transcriptomic profiles of primary tumors were available from a subset of these pts (N = 605) in COU-AA-302, ACIS, TITAN using the DECIPHER platform (Decipher Biosciences, Inc.). We harmonized pts study data and are developing machine learning models in an attempt to predict long term outcomes using baseline clinical features, short term PSA kinetics, and transcriptional profiling data. Associations between clinical and transcriptional signature profiles and overall survival were assessed with the Cox proportional hazards model. Results: Pts were assigned to training (N = 3167) and test (N = 2,512) cohorts. Cohorts were balanced by age, self-reported race, ECOG performance status, extent of disease, and disease site, and had statistically indistinguishable survival curves. At month 3 on study, 97% of pts had PSA data available; 88% had ≥ 2 PSA values. Matched genomic data was evaluated in 504 and 101 pts in training and test sets respectively. Training cohort pts with a 50% reduction in PSA by month 3 (m3-PSA50) had significantly longer OS (HR 0.51 [0.38-0.67]; p = 1.2 x 106, median 26 vs. 38 months). ANOVA analysis identified expression signatures in the Decipher GRID that significantly improved OS model performance when added to a model including only m3-PSA50. We stratified pts by a combined m3-PSA50 and signature score and identified signatures that improved model fit over m3-PSA50 alone and significantly stratified pts in the test cohort (P < 0.05). Conclusions: In this early analysis (prior to applying machine learning), m3-PSA50 was significantly associated with OS in a heterogeneous group of mPC pts. Although the transcriptomic analysis was conducted on primary tumors obtained many years earlier, we identified transcriptional signatures that significantly improved OS models (independent of m3-PSA50) in men who subsequently developed mPC many years later. These results suggest adding genomic signature data to models for predicting outcome using PSA kinetics may provide improved predictive power.

Bladder Cancer Patients with Elevated SPRR1B Expression Experiencing a Poor Prognosis.

SPRR1B, a member of the small proline-rich protein family, is implicated in various epithelial cancers as a potential oncogene linked to tumour growth and poor survival outcomes. However, its role in urothelial bladder carcinoma (UBC) remains to be fully elucidated. Transcriptional profiling data from The Cancer Genome Atlas grouped UBC samples in accordance with SPRR1B expression. Bioinformatic analysis was conducted to evaluate whether SPRR1B is a prognostic factor and a survival factor in UBC. Gene set enrichment analysis (GSEA) was performed to study immune cells and pathways. Reverse transcription quantitative real-time polymerase chain reaction detected gene expression. Immunohistochemistry assessed protein expression. Spearman correlation test analysed the correlation between SPRR1B and the protein p53. The bioinformatics results indicated that the expression level of SPRR1B in UBC tissues was significantly increased compared with that in normal bladder tissues, correlating with clinical characteristics. A high expression predicted poor prognosis and survival. Univariate Cox statistics showed that a high expression level of SPRR1B was correlated with UBC patients having poor overall survival (OS) (p < 0.05). In addition, on the basis of the multivariate Cox analysis, SPRR1B expression was independently correlated with OS (p = 0.005). GSEA analysis revealed enrichment in the p53, apoptosis, and cell cycle signalling pathways, and an association with B cells, lymphocytes, and natural killer cells. In addition, SPRR1B was found to be associated with immune infiltration based on the analysis of immune cell infiltration. Performing corresponding verification on a small number of tissues collected from bladder cancer patients revealed that the expression of this protein was negatively correlated with the expression of p53. SPRR1B overexpression predicts poor UBC outcomes, suggesting its role as a prognostic marker and therapeutic target. Further research is necessary to elucidate its role in UBC progression.

RET Signaling Persists in the Adult Intestine and Stimulates Motility by Limiting PYY Release From Enteroendocrine Cells

RET tyrosine kinase is necessary for enteric nervous system development. Loss-of-function RET mutations cause Hirschsprung disease (HSCR), in which infants are born with aganglionic bowel. Despite surgical correction, patients with HSCR often experience chronic defecatory dysfunction and enterocolitis, suggesting that RET is important after development. To test this hypothesis, we determined the location of postnatal RET and its significance in gastrointestinal (GI) motility. RetCFP/+ mice and human transcriptional profiling data were studied to identify the enteric neuronal and epithelial cells that express RET. To determine whether RET regulates gut motility invivo, genetic, and pharmacologic approaches were used to disrupt RET in all RET-expressing cells, a subset of enteric neurons, or intestinal epithelial cells. Distinct subsets of enteric neurons and enteroendocrine cells expressed RET in the adult intestine. RET disruption in the epithelium, rather than in enteric neurons, slowed GI motility selectively in male mice. RET kinase inhibition phenocopied this effect. Most RET+ epithelial cells were either enterochromaffin cells that release serotonin or L-cells that release peptide YY (PYY) and glucagon-like peptide 1 (GLP-1), both of which can alter motility. RET kinase inhibition exaggerated PYY and GLP-1 release in a nutrient-dependent manner without altering serotonin secretion in mice and human organoids. PYY receptor blockade rescued dysmotility in mice lacking epithelial RET. RET signaling normally limits nutrient-dependent peptide release from L-cells and this activity is necessary for normal intestinal motility in male mice. These effects could contribute to dysmotility in HSCR, which predominantly affects males, and uncovers a mechanism that could be targeted to treat post-prandial GI dysfunction.

Harnessing large language models (LLMs) for candidate gene prioritization and selection

BackgroundFeature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection.MethodsIn this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene’s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene.ResultsOf the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module.ConclusionsTaken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.

Transcriptome profile of reserpine-induced locomotor behavioral changes in zebrafish (Danio rerio)

Reserpine is a drug that is commonly used as an antihypertensive and antipsychotic drug in clinical practice. During our previous research, we found that reserpine treatment in zebrafish larvae can cause depression-like behaviors, but the corresponding mechanisms are still unclear. In this study, we aimed to investigate the molecular mechanism by which reserpine exposure affects locomotor behaviors in larval zebrafish through transcriptome analysis. The gene enrichment results showed that the differentially highly expressed genes of zebrafish are mainly enriched in voltage-gated ion channels, dopaminergic synapses and wnt signaling pathways. Selected genes (apc2, cacna1aa, drd2b, dvl1a, fzd1, wnt1, wnt3a, wnt9a and wnt10a) by transcriptomic results was validated by real-time PCR. Consistently, Wnt signaling pathway inhibitor XAV939 may induce reduced behavioral changes in zebrafish larvae, while the Wnt signaling pathway agonist SB415286 reversed the reserpine-induced depressive effects. Our study provides gene transcriptional profile data for future research on reserpine-induced locomotor behavioral changes.

Response of leaf blade in Zanthoxylum planispinum var. dintanensis to differences altitudes through integrated metabolomics and transcriptomics analysis

To analyze the effects of altitude on Z. planispinum var. Dintanensisin, leaves of samples from different altitudes (H1:1054 m.a.s.l., H2:821 m.a.s.l., H3:645 m.a.s.l.) were collected for integrative analysis of the metabolome and transcriptome in the present study. According to transcriptome data, a total of 9,566 differentially expressed genes (DEGs) were identified from the three sample comparisons (H1 vs. H2, H1 vs. H3, H2 vs. H3). Fewer DEGs were observed in the H2 vs. H3 group than in the other two comparison groups, suggesting that differences in the transcriptome increased with elevation. Most genes were involved in “Biosynthesis of secondary metabolites”, “Plant hormone signal transduction” and “MAPK signaling pathway-plant” based on KEGG enrichment analysis. In total, 423 differential metabolites (DAMs) were determined in the three comparisons (mentioned above) by using metabolomics analysis, of which flavonoids (14.91%) were dominant. Interestingly, more accumulation of terpenoids, aldehydes and esters was observed at higher altitudinal levels, while higher percentages of flavonoids and polyphenols were obtained at lower altitudes based upon K-Means analysis. A unique pathway (p value<0.01) linked to “flavonoid biosynthesis” appeared to have consistent expression patterns in the comparison of H1 vs. H3 based on network analyses of DEGs and DAMs. Notably, regarding the direct products of catalytic reactions, naringenin chalcone, aromadendrin, and naringenin were positively regulated by genes encoding CHS, F3H and CHI, respectively. In addition, the transcription levels of nine candidate genes, including CHI, CHS, CCoAOMT, 2ODDs, ANS, ANR, F3H, DFR and HCT, were confirmed by RT‒qPCR, which were consistent with transcriptional profile data regarding RNA-seq and further demonstrated that these genes involved in flavonoid biosynthesis contribute to the environmental adaptation of the blade in Z. planispinum var. Dintanensis. In summary, our findings represent the first integration of transcriptome data with widely targeted metabolomic data from Z. planispinum var. Dintanensis grown in rocky desertification control areas, which provides novel insights into adaptation to environmental stress. This study has the potential to improve the cultivation and management of Z. planispinum var. Dintanensis through various technologies to gain maximum medicinal production in the future.

Development and validation of a ubiquitin-proteasome system gene signature for prognostic prediction and immune microenvironment evaluation in hepatocellular carcinoma.

The ubiquitin proteasome has a major role in the development of many tumors. However, the prognostic importance of ubiquitin proteasome-system genes (UPSGs) in hepatocellular carcinoma (HCC) is not fully defined. The TCGA and ICGC datasets were utilized to obtain transcriptional profiling data as well as clinicopathological information about HCC. The 3-UPSGs signature for the TCGA cohort was developed via univariate and LASSO Cox regression analyses. Differential expression of genes was demonstrated by qRT-PCR and immunohistochemistry (IHC). Biological pathways were studied using GSVA and GSEA. Six algorithms were used to compare immune infiltration between the two risk groups. Furthermore, drug sensitivity was measured using the "pRRophetic" R package. The predictive capacity of the 3-UPSGs signature for sensitivity to immunotherapy was also explored. Moreover, we performed a pan-cancer analysis of the 3-UPSGs signature. A risk model containing 3 UPSGs (DCAF13, CDC20 and PSMB5) was developed. IHC and qRT-PCR results showed that signature genes were significantly overexpressed in HCC tissues. The high-risk group had a worse prognosis, with a higher clinicopathological grade, higher levels of tumor mutation burden (TMB), elevated levels of immune checkpoint (IC) expression, as well as increased sensitivity to immunotherapy. The two risk groups also differ in their sensitivity to chemotherapeutic drugs. Furthermore, the three UPSGs may play crucial roles in the progression of multiple types of cancers. We created a 3-UPSGs signature to estimate the prognosis of HCC and to assist in individualized treatment.

Poster 101: Evaluation of Novel Treatment Agents on Rotator Cuff Tendon and Muscle Pathology in a Murine Subacromial Impingement Model

Poster 101: Evaluation of Novel Treatment Agents on Rotator Cuff Tendon and Muscle Pathology in a Murine Subacromial Impingement Model

Gut microbiota interspecies interactions shape the response of Clostridioides difficile to clinically relevant antibiotics.

In the human gut, the growth of the pathogen Clostridioides difficile is impacted by a complex web of interspecies interactions with members of human gut microbiota. We investigate the contribution of interspecies interactions on the antibiotic response of C. difficile to clinically relevant antibiotics using bottom-up assembly of human gut communities. We identify 2 classes of microbial interactions that alter C. difficile's antibiotic susceptibility: interactions resulting in increased ability of C. difficile to grow at high antibiotic concentrations (rare) and interactions resulting in C. difficile growth enhancement at low antibiotic concentrations (common). Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species Desulfovibrio piger increases the minimum inhibitory concentration (MIC) of metronidazole for C. difficile. Competition with species that display higher sensitivity to the antibiotic than C. difficile leads to enhanced growth of C. difficile at low antibiotic concentrations due to competitive release. A dynamic computational model identifies the ecological principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping C. difficile's response to antibiotics, which could inform therapeutic interventions.

BZW1 as an oncogene is associated with patient prognosis and the immune microenvironment in glioma

BackgroundGlioma is the most common primary tumor in the human central nervous system. This study was designed to explore the expression of BZW1 in glioma and its relevance to the clinicopathological features and outcome of glioma patients. MethodsGlioma transcription profiling data were obtained from The Cancer Genome Atlas (TCGA). TIMER2, GEPIA2, GeneMANIA, and Metascape were searched in the present study. Cell and animal experiments were conducted to verify the effect of BZW1 on glioma cell migration in vitro and in vivo. Transwell assays, western blotting and immunofluorescence assays were performed. ResultsWe found that BZW1 was highly expressed in gliomas and correlated with poor prognosis. BZW1 could promote glioma proliferation. GO/KEGG analysis revealed that BZW1 was involved in collagen-containing extracellular matrix and was correlated with ECM-receptor interactions, transcriptional misregulation in cancer and the IL-17 signaling pathway. In addition, BZW1 was also associated with the glioma tumor immune microenvironment. ConclusionBZW1 can promote glioma proliferation and progression, and its high expression is correlated with a poor prognosis. BZW1 is also associated with the tumor immune microenvironment of glioma. This study may facilitate further understanding of the critical role of BZW1 in human tumors, including gliomas.

Identification of four metabolic subtypes and key prognostic markers in lung adenocarcinoma based on glycolytic and glutaminolytic pathways

BackgroundGlucose and glutamine are the main energy sources for tumor cells. Whether glycolysis and glutaminolysis play a critical role in driving the molecular subtypes of lung adenocarcinoma (LUAD) is unknown. This study attempts to identify LUAD metabolic subtypes with different characteristics and key genes based on gene transcription profiling data related to glycolysis and glutaminolysis, and to construct prognostic models to facilitate patient outcome prediction.MethodsLUAD related data were obtained from the Cancer Genome Atlas and Gene Expression Omnibus, including TCGA-LUAD, GSE42127, GSE68465, GSE72094, GSE29013, GSE31210, GSE30219, GSE37745, GSE50081. Unsupervised consensus clustering was used for the identification of LUAD subtypes. Differential expression analysis, weighted gene co-expression network analysis (WGCNA) and CytoNCA App in Cytoscape 3.9.0 were used for the screening of key genes. The Cox proportional hazards model was used for the construction of the prognostic risk model. Finally, qPCR analysis, immunohistochemistry and immunofluorescence colocalization were used to validate the core genes of the model.ResultThis study identified four distinct characterized LUAD metabolic subtypes, glycolytic, glutaminolytic, mixed and quiescent types. The glycolytic type had a worse prognosis than the glutaminolytic type. Nine genes (CXCL8, CNR1, AGER, ALB, S100A7, SLC2A1, TH, SPP1, LEP) were identified as hub genes driving the glycolytic/glutaminolytic LUAD. In addition, the risk assessment model constructed based on three genes (SPP1, SLC2A1 and AGER) had good predictive performance and could be validated in multiple independent external LUAD cohorts. These three genes were differentially expressed in LUAD and lung normal tissues, and might be potential prognostic markers for LUAD.ConclusionLUAD can be classified into four different characteristic metabolic subtypes based on the glycolysis- and glutaminolysis-related genes. Nine genes (CXCL8, CNR1, AGER, ALB, S100A7, SLC2A1, TH, SPP1, LEP) may play an important role in the subtype-intrinsic drive. This metabolic subtype classification, provides new biological insights into the previously established LUAD subtypes.

PIMREG is a prognostic biomarker involved in immune microenvironment of clear cell renal cell carcinoma and associated with the transition from G1 phase to S phase.

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the world and affects human health seriously. PIMREG is a mitotic regulator which is essential to the metaphase-to-anaphase transition in cell cycle. Although PIMREG plays a crucial role in the malignant progression of tumors, there are few reports on its role in ccRCC. The transcriptional expression profile and clinical data of PIMREG were downloaded from TCGA database and verified by qRT-PCR. Kaplan-Meier plotter was used to analyze the effect of PIMREG on overall survival (OS), disease specific survival (DSS) and progression-free interval (PFI) of patients with ccRCC. Univariable and multivariable Cox regression analysis were used to determine the independent prognostic factors of ccRCC. The effects of PIMREG on cell migration and invasion were detected by wound healing assay and transwell invasion assay, and CCK-8 assay, colony formation assay and cell cycle assay were used to detect the effect of PIMREG on cell proliferation. In addition, the changes in cell cycle related proteins were detected by western blot. PIMREG was highly expressed in human ccRCC and was positively correlated with pathologic stage, TNM stage and histologic grade. In addition, patients with high expression of PIMREG had a poor prognosis. Univariable and multivariable Cox regression analysis identified that PIMREG was an independent prognostic factor of ccRCC. Additionally, PIMREG was also closely related to immune cell infiltration. Experiments in vitro identified that the knockdown of PIMREG could significantly inhibit the proliferation, migration and invasion abilities of ccRCC. The expression of cyclin D1, CDK4 and CDK6 was also significantly reduced after PIMREG knockdown. PIMREG plays a vital role in the development of ccRCC and may become a potential therapeutic target in the future.