Integrating Expression Profiles Research Articles

Integrating expression profiling and computational modeling to elucidate PTEN‑miR‑141 interactions in oral squamous cell carcinoma

Integrating expression profiling and computational modeling to elucidate PTEN‑miR‑141 interactions in oral squamous cell carcinoma

Combined Transcriptome and Metabolome Analysis Reveals That Carbon Catabolite Repression Governs Growth and Pathogenicity in Verticillium dahliae.

Carbon catabolite repression (CCR) is a common transcriptional regulatory mechanism that microorganisms use to efficiently utilize carbon nutrients, which is critical for the fitness of microorganisms and for pathogenic species to cause infection. Here, we characterized two CCR genes, VdCreA and VdCreC, in Verticillium dahliae that cause cotton Verticillium wilt disease. The VdCreA and VdCreC knockout mutants displayed slow growth with decreased conidiation and microsclerotium production and reduced virulence to cotton, suggesting that VdCreA and VdCreC are involved in growth and pathogenicity in V. dahliae. We further generated 36 highly reliable and stable ΔVdCreA and ΔVdCreC libraries to comprehensively explore the dynamic expression of genes and metabolites when grown under different carbon sources and CCR conditions. Based on the weighted gene co-expression network analysis (WGCNA) and correlation networks, VdCreA is co-expressed with a multitude of downregulated genes. These gene networks span multiple functional pathways, among which seven genes, including PYCR (pyrroline-5-carboxylate reductase), are potential target genes of VdCreA. Different carbon source conditions triggered entirely distinct gene regulatory networks, yet they exhibited similar changes in metabolic pathways. Six genes, including 6-phosphogluconolactonase and 2-ODGH (2-oxoglutarate dehydrogenase E1), may serve as hub genes in this process. Both VdCreA and VdCreC could comprehensively influence the expression of plant cell wall-degrading enzyme (PCWDE) genes, suggesting that they have a role in pathogenicity in V. dahliae. The integrated expression profiles of the genes and metabolites involved in the glycolysis/gluconeogenesis and pentose phosphate pathways showed that the two major sugar metabolism-related pathways were completely changed, and GADP (glyceraldehyde-3-phosphate) may be a pivotal factor for CCR under different carbon sources. All these results provide a more comprehensive perspective for further analyzing the role of Cre in CCR.

Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics.

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 p73 and p53 p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

Comprehensive characterization of β-alanine metabolism-related genes in HCC identified a novel prognostic signature related to clinical outcomes.

Hepatocellular carcinoma (HCC) stands out as the most prevalent type of liver cancer and a significant contributor to cancer-related fatalities globally. Metabolic reprogramming, particularly in glucose, lipid, and amino acid metabolism, plays a crucial role in HCC progression. However, the functions of β-alanine metabolism-related genes (βAMRGs) in HCC remain understudied. Therefore, a comprehensive evaluation of βAMRGs is required, specifically in HCC. Initially, we explored the pan-cancer landscape of βAMRGs, integrating expression profiles, prognostic values, mutations, and methylation levels. Subsequently, scRNA sequencing results indicated that hepatocytes had the highest scores of β-alanine metabolism. In the process of hepatocyte carcinogenesis, metabolic pathways were further activated. Using βAMRGs scores and expression profiles, we classified HCC patients into three subtypes and examined their prognosis and immune microenvironments. Cluster 3, characterized by the highest βAMRGs scores, displayed the best prognosis, reinforcing β-alanine's significant contribution to HCC pathophysiology. Notably, immune microenvironment, metabolism, and cell death modes significantly varied among the β-alanine subtypes. We developed and validated a novel prognostic panel based on βAMRGs and constructed a nomogram incorporating risk degree and clinicopathological characteristics. Among the model genes, EHHADH has been identified as a protective protein in HCC. Its expression was notably downregulated in tumors and exhibited a close correlation with factors such as tumor staging, grading, and prognosis. Immunohistochemical experiments, conducted using HCC tissue microarrays, substantiated the validation of its expression levels. In conclusion, this study uncovers β-alanine's significant role in HCC for the first time, suggesting new research targets and directions for diagnosis and treatment.

Abstract 4880: Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics

Abstract This work aims to decipher p73-regulated biomarkers for a prompt diagnosis of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling technologies. Transcriptome profile of HCT116 cell line p53−/− p73+/+ and p53−/− p73 knockdown was performed to identify differentially expressed genes (DEGs) followed by cross-checking with three CRC tissue expression datasets available in Gene Expression Omnibus. KEGG and Gene ontology were performed on differentially expressed transcripts obtained via the transcriptome profile and intersected genes. The PPI network was constructed via Cytoscape to extract hub genes. KM plots assisted in investigating the prognostic significance of the hub genes. The clinicopathological relevance was explored using the GEPIA and UALCAN databases. Finally, machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1,289 upregulated and 1,897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. The analysis of gene ontology and KEGG showed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan Meier, GEPIA, and UALCAN analyses uncovered the prognostic and diagnostic relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. This is a novel study utilizing transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-relevant genes. These genes are found relevant regarding the patients’ prognosis and diagnosis. The unveiled biomarkers are also found robust in TNM-stage prediction. This is the first study where transcriptomics, publicly available tissue datasets, and machine learning are altogether employed to reveal key genes in CRC pathogenesis. Our research highlights the robust analysis of four independent data sets to find the key hub genes, further predicting the performance of the key genes in stage-wise classification. Citation Format: Chanchal Bareja, Kountay Dwivedi, Apoorva Uboveja, Ankit Mathur, Naveen Kumar, Daman Saluja. Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4880.

A data integration approach unveils a transcriptional signature of type 2 diabetes progression in rat and human islets.

Pancreatic islet failure is a key characteristic of type 2 diabetes besides insulin resistance. To get molecular insights into the pathology of islets in type 2 diabetes, we developed a computational approach to integrating expression profiles of Goto-Kakizaki and Wistar rat islets from a designed experiment with those of the human islets from an observational study. A principal gene-eigenvector in the expression profiles characterized by up-regulated angiogenesis and down-regulated oxidative phosphorylation was identified conserved across the two species. In the case of Goto-Kakizaki versus Wistar islets, such alteration in gene expression can be verified directly by the treatment-control tests over time, and corresponds to the alteration of α/β-cell distribution obtained by quantifying the islet micrographs. Furthermore, the correspondence between the dual sample- and gene-eigenvectors unveils more delicate structures. In the case of rats, the up- and down-trend of insulin mRNA levels before and after week 8 correspond respectively to the top two principal eigenvectors. In the case of human, the top two principal eigenvectors correspond respectively to the late and early stages of diabetes. According to the aggregated expression signature, a large portion of genes involved in the hypoxia-inducible factor signaling pathway, which activates transcription of angiogenesis, were significantly up-regulated. Furthermore, top-ranked anti-angiogenic genes THBS1 and PEDF indicate the existence of a counteractive mechanism that is in line with thickened and fragmented capillaries found in the deteriorated islets. Overall, the integrative analysis unravels the principal transcriptional alterations underlying the islet deterioration of morphology and insulin secretion along type 2 diabetes progression.

GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases

Chronic liver diseases usually developed through stepwise pathological transitions under the persistent risk factors. The molecular changes during liver transitions are pivotal to improve liver diagnostics and therapeutics yet still remain elusive. Cumulative large-scale liver transcriptomic studies have been revealing molecular landscape of various liver conditions at bulk and single-cell resolution, however, neither single experiment nor databases enabled thorough investigations of transcriptomic dynamics along the progression of liver diseases. Here we establish GepLiver, a longitudinal and multidimensional liver expression atlas integrating expression profiles of 2469 human bulk tissues, 492 mouse samples, 409,775 single cells from 347 human samples and 27 liver cell lines spanning 16 liver phenotypes with uniformed processing and annotating methods. Using GepLiver, we have demonstrated dynamic changes of gene expression, cell abundance and crosstalk harboring meaningful biological associations. GepLiver can be applied to explore the evolving expression patterns and transcriptomic features for genes and cell types respectively among liver phenotypes, assisting the investigation of liver transcriptomic dynamics and informing biomarkers and targets for liver diseases.

Cross-Platform Transcriptomic Data Integration, Profiling, and Mining in Vibrio cholerae.

A large number of transcriptome studies generate important data and information for the study of pathogenic mechanisms of pathogens, including Vibrio cholerae. V. cholerae transcriptome data include RNA-seq and microarray: microarray data mainly include clinical human and environmental samples, and RNA-seq data mainly focus on laboratory processing conditions, including different stresses and experimental animals in vivo. In this study, we integrated the data sets of both platforms using Rank-in and the Limma R package normalized Between Arrays function, achieving the first cross-platform transcriptome data integration of V. cholerae. By integrating the entire transcriptome data, we obtained the profiles of the most active or silent genes. By transferring the integrated expression profiles into the weighted correlation network analysis (WGCNA) pipeline, we identified the important functional modules of V. cholerae in vitro stress treatment, gene manipulation, and in vitro culture as DNA transposon, chemotaxis and signaling, signal transduction, and secondary metabolic pathways, respectively. The analysis of functional module hub genes revealed the uniqueness of clinical human samples; however, under specific expression patterning, the Δhns, ΔoxyR1 strains, and tobramycin treatment group showed high expression profile similarity with human samples. By constructing a protein-protein interaction (PPI) interaction network, we discovered several unreported novel protein interactions within transposon functional modules. IMPORTANCE We used two techniques to integrate RNA-seq data for laboratory studies with clinical microarray data for the first time. The interactions between V. cholerae genes were obtained from a global perspective, as well as comparing the similarity between clinical human samples and the current experimental conditions, and uncovering the functional modules that play a major role under different conditions. We believe that this data integration can provide us with some insight and basis for elucidating the pathogenesis and clinical control of V. cholerae.

Correction for: Integrated transcriptome expression profiling reveals a novel lncRNA associated with l-DOPA-induced dyskinesia in a rat model of Parkinson's disease.

Aging | doi:10.18632/aging.204596. Chun-Lei Han, Yun-Peng Liu, Yun-Peng Sui, Ning Chen, Ting-Ting Du, Ying Jiang, Chen-Jia Guo, Kai-Liang Wang, Qiao Wang, Shi-Ying Fan, Michitomo Shimabukuro, Fan-Gang Meng, Fang Yuan, Jian-Guo Zhang

Abstract P6-14-17: Endomucins and their expression in breast cancer and the cellular and therapeutic impact

Abstract Introduction. Endomucin-1 and endomucin-2 are two proteins structurally related, yet with low homology. The membrane bound sialoglycoproteins appear to play a key role in interfering with the formation of focal adhesion complexes (FAC) and matrix adhesiveness of cells, by mechanisms independent of the MUC1 repeat, which the endomucins do not possess. Endomucins are thought to be expressed at high levels in endothelium and haematopietic cell lineages, although the levels in mammary tissues also seem high. Despite the seeming importance of endomucins in the adhesion and migration of cells, including cancer cells, the clinical value of endomucin in clinical cancer, including breast cancer, is largely unknown. The present study examined the expression profile of endomucins, together with the focal adhesion kinase FAK, in breast cancer and aimed to explore the cellular impact of endomucin on cancer cells. Methods. Human breast cancer cells MCF-7 and MDA MB-231 and a range of other cell types were used. An endomucin overexpression cell model was created and subsequently used to evaluate the function of the cells. The expression profile of the endomucin-1 and endomucin-2 transcripts and FAK, in an existing fresh frozen breast cancer tissue cohort, were quantified. Results. High levels of endomucins, particularly endomucin-1, are good indicators for the overall survival of the patient, p=0.021 for endomucin-1 and p=0.15 for endomucin-2. When expression levels of FAK were integrated into the survival analysis model, patients with high levels of both endomucins and low levels of FAK had the most favourable outcome, compared with those with most unfavourable outcome who had low level of endomucin and high FAK (survival during the follow up period respectively at 100% and 54%, p=0.013, Harzoud Ratio 0.298). Together with the Nottingham Prognostic Index, which independently predicts a poor outcome (p=0.009, HR=7.6), the integrated expression profile of endomucin/FAK represents an independent prognostic indicator for favourable overall survival (p=0.003, HR=0.13), and indeed for a favourable disease free survival (p=0.008, HR=0.17). Mammary tissues, and indeed breast cancer cell lines, expressed high levels of endomucin-2 transcripts and low levels of endomucin-1 transcripts. High levels of endomucin-2 were also seen in fibroblasts and vascular endothelial cells. We created a breast cancer cell submodel with MCF-7, by overexpressing endomucin. It was shown that although endomucin over-expression had some marginal impact on the adhesiveness of breast cancer cells, the over-expression however, had significant impact on cells’ sensitivity to FAK inhibitor, with a markedly reduced adhesiveness to matrix (p< 0.001 control versus endomucin overexpression cells). Discussion. Endomucins have a reduced expression in breast cancers and the reduction, together with low levels of focal adhesion kinase, facilitate a favourable outcome for the patients. Together with the findings of in vitro cell models, it would suggest that the expression profile of endomucins and FAK may be a good indicator, not only for evaluating clinical outcomes, but also for choice of target therapies. Citation Format: Amber Xinyu Li, Tracey A. Martin, Lin Ye, Andrew J. Sanders, Fiona Ruge, Jane Lane, Eleri Davies, Wen G. Jiang. Endomucins and their expression in breast cancer and the cellular and therapeutic impact [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-14-17.

Abstract P2-26-11: Protein phosphatase 1 catalytic units, PPP1CA, PPP1CB and PPP1CC in breast cancer

Abstract Introduction. Protein phosphatase-1 (PP1) belongs to the Serine/threonine-protein phosphatase family and is known to participate in multiple functions in the cells including glycogen metabolism, cellular proliferation and cell receptor regulations. Protein phosphtase-1 has three catalytic units, namely PPP1CA, PPP1CB and PPP1CC. A PP1 protein complex is composed of one of the catalytic units and at least of one the various phosphatase regulatory units (PPP1Rs). The regulatory units of the phosphatase, abundant in numbers, also influence the functions of the protein phosphatase within cells. PP1 has been indicated to have a role in certain clinical conditions such as the Alzheimer’s and certain viral infections. The value of the PP1 catalytic units in clinical cancers including breast cancer is not well established, therefor this current study aimed to examine the expression profile of the three PP1 catalytic units in human breast cancer and attempted to establish the clinical relevance of the catalytic units in the disease progression of breast cancer. Methods. Fresh frozen normal mammary tissues and breast cancer tissues were investigated for the levels of gene transcripts of the three PP1 catalytic units were quantified and corrected with an epithelial cell marker. The expression and integrated expression profile of PPP1A, PPP1B and PPP1C in the tissues were assessed against the pathological, clinical and outcome of the patients as well as the hormone receptor status. Results. All three catalytic units of PP1 were positively expressed in normal and tumour breast tissues, with PPP1A at significantly higher levels in normal mammary tissues than tumours (p=0.01) and PPP1B and PPP1C showing no significant difference. Expression of PPP1B in breast cancer was significantly lower in high grade breast cancers (p< 0.05 vs low grade), in tumours from patients who died of breast cancer (p=0.047 vs those who survived) and with breast cancer related incidence (p=0.048 vs incidence free). The most significant finding of the present study was a correlation between high levels of all three catalytic units and favourable overall survival of the patients. Those patients with high levels of PPP1A, PPP1B and PPP1C in breast tumours had a mean survival time of 142.6, 141.6 and 140.3 months compared with those with low levels respectively at 111.8, 115.8 and 117.6 months. When the expression profiles of the three units were integrated, the integrated profile formed a highly valuable and independent indicator for both overall survival (p=0.008, HR=0.694) and for disease free survival (p=0.004, HR=0.702). The predictive value of PPP1A/PPP1B/PPP1C for survival was more significant for ER negative tumours than ER positive tumours (p=0.003), but appeared to be indifferent for Her2 status. Finally, our data indicated that the integrated expression of the three catalytic units had a significant predictive value in bone metastasis of the patients (p=0.008, HR=0.546). Discussion. Protein phosphatase-1 catalytic units, PPP1CA, PPP1CB and PPP1CC are expressed in human mammary tissues; raised levels of the catalytic units are a favourable clinical indicator for the survival of patients and for a decreased tendency to develop bone metastasis. Citation Format: Xuefei Dong, Tracey A. Martin, QingPing Dou, Wen G. Jiang, Eleri Davies. Protein phosphatase 1 catalytic units, PPP1CA, PPP1CB and PPP1CC in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-26-11.

Striatins and STRIPAK complex partners in clinical outcomes of patients with breast cancer and responses to drug treatment.

Striatins (STRNs) family, which contains three multi-domain scaffolding proteins, are cornerstones of the striatins interacting phosphatase and kinase (STRIPAK) complex. Although the role of the STRIPAK complex in cancer has become recognized in recent years, its clinical significance in breast cancer has not been fully established. Using a freshly frozen breast cancer tissue cohort containing both cancerous and adjacent normal mammary tissues, we quantitatively evaluated the transcript-level expression of all members within the STRIPAK complex along with some key interacting and regulatory proteins of STRNs. The expression profile of each molecule and the integrated pattern of the complex members were assessed against the clinical-pathological factors of the patients. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the breast cancer patients' response to chemotherapies. Four human breast cancer cell lines, MDA-MB-231, MDA-MB-361, MCF-7, and SK-BR-3, were subsequently adopted for in vitro work. Here we found that high-level expressions of STRIP2, calmodulin, CCM3, MINK1 and SLMAP were respectively associated with shorter overall survival (OS) of patients. Although the similar pattern observed for STRN3, STRN4 and a contrary pattern observed for PPP2CA, PPP2CB and PPPR1A were not significant, the integrated expression profile of STRNs group and PPP2 group members constitutes a highly significant prognostic indicator for OS [P<0.001, hazard ratio (HR)=2.04, 95% confidence interval (95% CI), 1.36-3.07] and disease-free survival (DFS) (P=0.003, HR=1.40, 95% CI, 1.12-1.75). Reduced expression of STRN3 has an influence on the biological functions including adhesiveness and migration. In line with our clinical findings, the breast cancer cells responded to STRN3 knockdown with changes in their chemo-sensitivity, of which the response is also breast cancer subtype dependent. Our results suggest a possible role of the STRIPAK complex in breast cancer development and prognosis. Among the members, the expression profile of STRN3 presents a valuable factor for assessing patients' responses to drug treatment.

ChIPBase v3.0: the encyclopedia of transcriptional regulations of non-coding RNAs and protein-coding genes.

Non-coding RNAs (ncRNAs) are emerging as key regulators of various biological processes. Although thousands of ncRNAs have been discovered, the transcriptional mechanisms and networks of the majority of ncRNAs have not been fully investigated. In this study, we updated ChIPBase to version 3.0 (https://rnasysu.com/chipbase3/) to provide the most comprehensive transcriptional regulation atlas of ncRNAs and protein-coding genes (PCGs). ChIPBase has identified ∼151 187 000 regulatory relationships between ∼171 600 genes and ∼3000 regulators by analyzing ∼55 000 ChIP-seq datasets, which represent a 30-fold expansion. Moreover, we de novo identified ∼29 000 motif matrices of transcription factors. In addition, we constructed a novel 'Enhancer' module to predict ∼1 837 200 regulation regions functioning as poised, active or super enhancers under ∼1300 conditions. Importantly, we constructed exhaustive coexpression maps between regulators and their target genes by integrating expression profiles of ∼65 000 normal and ∼15 000 tumor samples. We built a 'Disease' module to obtain an atlas of the disease-associated variations in the regulation regions of genes. We also constructed an 'EpiInter' module to explore potential interactions between epitranscriptome and epigenome. Finally, we designed 'Network' module to provide extensive and gene-centred regulatory networks. ChIPBase will serve as a useful resource to facilitate integrative explorations and expand our understanding of transcriptional regulation.

Proteotranscriptomics of ocular adnexal B-cell lymphoma reveals an oncogenic role of alternative splicing and identifies a diagnostic marker

BackgroundOcular adnexal B-cell lymphoma (OABL) is a rare subtype of non-Hodgkin lymphoma. The molecular characteristics of OABL remain poorly understood. We performed an integrated study to investigate the proteotranscriptome landscape and identify novel molecular characteristics and biomarkers of OABL.MethodsIntegrated quantitative proteome and transcriptome were performed on 40 OABL 12 idiopathic orbital inflammation, 6 reactive lymphoid hyperplasia, and 13 aesthetic orbital plastic surgery specimens. Complete clinicopathologic and prognostic data of the patients were recorded.ResultsWe identified high global protein-mRNA concordance as a novel characteristic of OABL. High concordance was related to OABL recurrence. By integrated expression profile, motif enrichment and trend analysis, we found that alternative splicing is inflammation-independently dysregulated in OABL. After portraying the aberrant alternative splicing event landscape, we demonstrated the oncogenic role of ADAR, a core splicing regulator that regulates the splicing of Rho GTPase and cell cycle members. We found that ADAR regulates cell proliferation and Rho GTPase inhibitor sensitivity of lymphoma. We identified DNAJC9 as a potential biomarker for OABL in proteomic analyses. Immunohistochemistry and immunofluorescent staining showed the nuclear staining of DNAJC9 was significantly higher in extranodal marginal zone lymphomas compared with inflammation specimens.ConclusionsThese results provide an integrated gene expression profiling and demonstrate that high global protein-mRNA concordance is a prognosis-related molecular characteristic of OABL. We portray the alternative splicing events landscape of OABL, and reveal the oncogenic role of ADAR. We identified strong nuclear staining of DNAJC9 as a promising pathology diagnostic biomarker for extranodal marginal zone lymphomas.

Deconvolution of a Large Cohort of Placental Microarray Data Reveals Clinically Distinct Subtypes of Preeclampsia.

It has been well established that the dysfunctional placenta plays an important role in the pathogenesis of preeclampsia (PE), a hypertensive disorder in pregnancy. However, it is not well understood how individual cell types in the placenta are involved in placenta dysfunction because of limited single-cell studies of placenta with PE. Given that a high-resolution single-cell atlas in the placenta is now available, deconvolution of publicly available bulk PE transcriptome data may provide us with the opportunity to investigate the contribution of individual placental cell types to PE. Recent benchmark studies on deconvolution have provided suggestions on the strategy of marker gene selection and the choice of methodologies. In this study, we experimented with these suggestions by using real bulk data with known cell-type proportions and established a deconvolution pipeline using CIBERSORT. Applying the deconvolution pipeline to a large cohort of PE placental microarray data, we found that the proportions of trophoblast cells in the placenta were significantly different between PE and normal controls. We then predicted cell-type-level expression profiles for each sample using CIBERSORTx and found that the activities of several canonical PE-related pathways were significantly altered in specific subtypes of trophoblasts in PE. Finally, we constructed an integrated expression profile for each PE sample by combining the predicted cell-type-level expression profiles of several clinically relevant placental cell types and identified four clusters likely representing four PE subtypes with clinically distinct features. As such, our study showed that deconvolution of a large cohort of placental microarray provided new insights about the molecular mechanism of PE that would not be obtained by analyzing bulk expression profiles.

Comprehensive Analysis of the Hsp20 Gene Family in Canavalia rosea Indicates Its Roles in the Response to Multiple Abiotic Stresses and Adaptation to Tropical Coral Islands

Heat shock protein 20 (Hsp20) is a major family of heat shock proteins that mainly function as molecular chaperones and are markedly accumulated in cells when organisms are subjected to environmental stress, particularly heat. Canavalia rosea is an extremophile halophyte with good adaptability to environmental high temperature and is widely distributed in coastal areas or islands in tropical and subtropical regions. In this study, we identified a total of 41 CrHsp20 genes in the C. rosea genome. The gene structures, phylogenetic relationships, chromosome locations, and conserved motifs of each CrHsp20 or encoding protein were analyzed. The promoters of CrHsp20s contained a series of predicted cis-acting elements, which indicates that the expression of different CrHsp20 members is regulated precisely. The expression patterns of the CrHsp20 family were analyzed by RNA sequencing both at the tissue-specific level and under different abiotic stresses, and were further validated by quantitative reverse transcription PCR. The integrated expression profiles of the CrHsp20s indicated that most CrHsp20 genes were greatly upregulated (up to dozens to thousands of times) after 2 h of heat stress. However, some of the heat-upregulated CrHsp20 genes showed completely different expression patterns in response to salt, alkaline, or high osmotic stresses, which indicates their potential specific function in mediating the response of C. rosea to abiotic stresses. In addition, some of CrHsp20s were cloned and functionally characterized for their roles in abiotic stress tolerance in yeast. Taken together, these findings provide a foundation for functionally characterizing Hsp20s to unravel their possible roles in the adaptation of this species to tropical coral reefs. Our results also contribute to the understanding of the complexity of the response of CrHsp20 genes to other abiotic stresses and may help in future studies evaluating the functional characteristics of CrHsp20s for crop genetic improvement.

A TRP Family Based Signature for Prognosis Prediction in Head and Neck Squamous Cell Carcinoma.

Purpose Head and neck squamous cell carcinoma (HNSCC) is a classical type of head and neck cancers, with heterogeneous clinical outcome. This project is set out to create a robust risk signature based on TRP family genes (TFGs) for prognosis evaluation in HNSCC. Methods Based on the HNSCC sample data from the TCGA website, we integrated expression profile of TFGs for 490 HNSCC cases. We explore the interactions among TFGs using STRING tool. The TFGs-based signature (TFBS) was created by Cox relative analyses. In addition, we conducted GSEA to identify the underlying signaling pathways of the specific TFGs in HNSCC. The immune landscape of HNSCC patients was analyzed by CIBERSORT and ssGSEA algorithms. Results A total of 6 TFGs (TRPC1, TRPC3, TRPC6, TRPV2, TRPV4, and TRPM8) closely associated with prognosis of HNSCC cases were screened to create TFBS. TFBS predicted that the TFBS-high group presented dismal patient outcome. Cox regression revealed the favorable independent value of TFBS. ROC analysis showed the robust power of TFBS for prognosis forecasting. GSEA determined several crucial pathways related with HNSCC, which are the p53 pathway, TNF-alpha signaling via NFKB, and hypoxia. Moreover, immune-related analysis showed that patients in the TFBS-high group were more likely in immunosuppressive status. Conclusion Our proposed TFBS could serve as a favorable indicator to forecast the survival outcome of HNSCC cases and offer prominent therapy guidance.

Long non-coding RNA CRNDE as potential biomarkers facilitate inflammation and apoptosis in alcoholic liver disease.

Due to persistent inconsistencies in the expression data of alcoholic liver disease (ALD), it is necessary to turn to “pre-laboratory” comprehensive analysis in order to accelerate effective precision medicine and transformation research. We screened pseudogene-derived lncRNA associated with ALD by comparative analysis of 2 independent data sets from GEO. Three lncRNAs (CRNDE, RBMS3-AS3, and LINC01088) were demonstrated to be potentially useful diagnostic markers in ALD. Among them, the expression of CRNDE is up-regulated. Therefore, we focus on CRNDE. Kyoto Encyclopedia of Genes and Genomes pathways analysis revealed higher CRNDE can activate MAPK signaling pathway, apoptosis, wnt signaling pathway, and hematopoietic cell lineage. Next, we established ALD animal model and verified the success of the modeling. The result showed ALD tissues in mice had significantly higher CRNDE levels than normal tissues. Moreover, the increase of IL-6 in the serum of mice in the low-dose group is related to the activation of inflammatory factors after alcohol-induced liver injury. In addition, alcohol can induce apoptosis, and knockdown of CRNDE can reduce apoptosis. Our integrated expression profiling identified CRNDE independently associated with ALD. CRNDE can facilitate inflammation and apoptosis in ALD.

Integration of chronological omics data reveals mitochondrial regulatory mechanisms during the development of hepatocellular carcinoma.

Mitochondria participate in multiple functions in eukaryotic cells. Although disruption of mitochondrial function has been associated with energetic deregulation in cancer, the chronological changes in mitochondria during cancer development remain unclear. With the aim to assess the role of mitochondria throughout cancer development, we analyzed samples chronologically obtained from induced hepatocellular carcinoma (HCC) in rats. In our analyses, we integrated mitochondrial proteomic data, mitochondrial metabolomic data and nuclear genome transcriptomic data. We used pathway over-representation and weighted gene co-expression network analysis (WGCNA) to integrate expression profiles of genes, miRNAs, proteins and metabolite levels throughout HCC development. Our results show that mitochondria are dynamic organelles presenting specific modifications in different stages of HCC development. We also found that mitochondrial proteomic profiles from tissues adjacent to nodules or tumor are determined more by the stage of HCC development than by tissue type, and we evaluated two models to predict HCC stage of the samples using proteomic profiles. Finally, we propose an omics integration pipeline to massively identify molecular features that could be further evaluated as key regulators, biomarkers or therapeutic targets. As an example, we show a group of miRNAs and transcription factors as candidates, responsible for mitochondrial metabolic modification in HCC.

Integrated expression profiles of mRNA and miRNA in a gerbil model of fatty liver fibrosis treated with exenatide

The morbidity of nonalcoholic fatty liver disease (NAFLD) has increased consistently in recent years. Exenatide could reverse liver fibrosis and lower the occurrence of fatty liver. The aim of the study was to identify and characterize mRNA and miRNA expression to elucidate the mechanism of exenatide in the gerbil model. Gerbils were fed a high-fat diet for 8 weeks to induce a fibrosis model; then, the gerbil models were treated with exenatide for 4 weeks. The total RNA extracted from the liver tissue samples was used to prepare the library and sequence on a HiSeq 2000. Bioinformatic methods were employed to analyze the sequence data to identify the mRNAs and miRNAs and to acquire the miRNA-mRNA regulatory network. By RNA-seq, 2344 differentially expressed genes (DEGs) and 72 miRNAs were found in the model group. Compared with the model group, 591 DEGs and 19 miRNAs were found in the quercetin group, whereas 876 DEGs and 18 miRNAs were found in the treatment group. The miRNA-mRNA regulatory network was constructed in a gerbil model. Immunohistochemistry and RNA sequencing confirmed that the therapeutic effect of exenatide may be derived from extrahepatic signal transduction. The key differential genes are CYP3A, CYP4A11, ACAA1, ACSM, PHX1, MAO, FMO, UGT, ACOX2, ABAT, PIK3C and PLCG1. The key miRNAs are miR-15a, miR-27b, miR-532-3P, miR-627, miR-3596, miR-142-3P, Let-7e-5p, miR-214-5, miR-101-3p, miR-378d. New miRNAs, such as novel_127, novel_143, novel_15, novel_204 are associated with liver fibrosis, while novel_127, novel_15, and novel_54 are associated with reverse treated with exenatide. Our research represents the first description of mRNA/miRNA profiles in a gerbil model of fatty liver fibrosis treated with exenatide, which may provide insights into the pathogenesis or treatment of the metabolic syndrome.