Independent Gene Expression Research Articles

Transgenic Drosophila lines for LexA-dependent gene and growth regulation.

Conditional expression of short hairpin RNAs with binary genetic systems is an indispensable tool for studying gene function. Addressing mechanisms underlying cell–cell communication in vivo benefits from simultaneous use of 2 independent gene expression systems. To complement the abundance of existing Gal4/UAS-based resources in Drosophila, we and others have developed LexA/LexAop-based genetic tools. Here, we describe experimental and pedagogical advances that promote the efficient conversion of Drosophila Gal4 lines to LexA lines, and the generation of LexAop-short hairpin RNA lines to suppress gene function. We developed a CRISPR/Cas9-based knock-in system to replace Gal4 coding sequences with LexA, and a LexAop-based short hairpin RNA expression vector to achieve short hairpin RNA-mediated gene silencing. We demonstrate the use of these approaches to achieve targeted genetic loss-of-function in multiple tissues. We also detail our development of secondary school curricula that enable students to create transgenic flies, thereby magnifying the production of well-characterized LexA/LexAop lines for the scientific community. The genetic tools and teaching methods presented here provide LexA/LexAop resources that complement existing resources to study intercellular communication coordinating metazoan physiology and development.

Prognostic Autophagy-Related Model Revealed by Integrating Single-Cell RNA Sequencing Data and Bulk Gene Profiles in Gastric Cancer

Autophagy has been associated with tumor progression, prognosis, and treatment response. However, an autophagy-related model and their clinical significance have not yet been fully elucidated. In the present study, through the integrative analysis of bulk RNA sequencing and single-cell RNA sequencing, an autophagy-related risk model was identified. The model was capable of distinguishing the worse prognosis of patients with gastric cancer (GC), which was validated in TCGA and two independent Gene Expression Omnibus cohorts utilizing the survival analysis, and was also independent of other clinical covariates evaluated by multivariable Cox regression. The clinical value of this model was further assessed using a receiver operating characteristic (ROC) and nomogram analysis. Investigation of single-cell RNA sequencing uncovered that this model might act as an indicator of the dysfunctional characteristics of T cells in the high-risk group. Moreover, the high-risk group exhibited the lower expression of immune checkpoint markers (PDCD1 and CTLA4) than the low-risk group, which indicated the potential predictive power to the current immunotherapy response in patients with GC. In conclusion, this autophagy-associated risk model may be a useful tool for prognostic evaluation and will facilitate the potential application of this model as an indicator of the predictive immune checkpoint biomarkers.

Iterative bicluster-based Bayesian principal component analysis and least squares for missing-value imputation in microarray and RNA-sequencing data.

Microarray and RNA-sequencing (RNA-seq) techniques each produce gene expression data that can be expressed as a matrix that often contains missing values. Thus, a process of missing-value imputation that uses coherence information of the dataset is necessary. Existing imputation methods, such as iterative bicluster-based least squares (bi-iLS), use biclustering to estimate the missing values because genes are only similar under correlative experimental conditions. Also, they use the row average to obtain a temporary complete matrix, but the use of the row average is considered to be a flaw. The row average cannot reflect the real structure of the dataset because the row average only uses the information of an individual row. Therefore, we propose the use of Bayesian principal component analysis (BPCA) to obtain the temporary complete matrix instead of using the row average in bi-iLS. This alteration produces new missing values imputation method called iterative bicluster-based Bayesian principal component analysis and least squares (bi-BPCA-iLS). Several experiments have been conducted on two-dimension independent gene expression datasets, which are microarray (e.g., cell-cycle expression dataset of yeast saccharomyces cerevisiae) and RNA-seq (gene expression data from schizosaccharomyces pombe) datasets. In the case of the microarray dataset, our proposed bi-BPCA-iLS method showed a significant overall improvement in the normalized root mean square error (NRMSE) values of 10.6% from the local least squares (LLS) and 0.6% from the bi-iLS. In the case of the RNA-seq dataset, our proposed bi-BPCA-iLS method showed an overall improvement in the NRMSE values of 8.2% from the LLS and 3.1% from the bi-iLS. The additional computational time of bi-BPCA-iLS is not significant compared to bi-iLS.

An 8-Gene Signature for Classifying Major Subtypes of Non-Small-Cell Lung Cancer.

Motivation:The precise diagnosis of the major subtypes, lung adenocarcinoma and lung squamous cell carcinoma, of non-small-cell lung cancer is of practical importance as some treatments are subtype-specific. However, in some cases diagnosis via the commonly-used method, that is staining the specimen using immunohistochemical markers, may be challenging. Hence, having a computational method that complements the diagnosis is desirable. In this paper, we propose a gene signature for this purpose.Results:We developed an expression-based method that systematically suggests a huge set of candidate gene signatures and finds the best candidate. By applying this method to a training set, the optimal gene signature was found by considering close to 765 billion candidate signatures. The 8-gene signature found for classifying the 2 aforementioned subtypes comprises TP63, CALML3, KRT5, PKP1, TESC, SPINK1, C9orf152, and KRT7. The signature achieved a high overall prediction accuracy of 0.936 when tested using 34 independent gene expression datasets obtained using different technologies and comprising 2556 adenocarcinoma and 1630 squamous cell carcinoma samples. Additionally, the signature performed well in clinically challenging cases, that is poorly differentiated tumors and specimens obtained from biopsies. In comparison with 2 previously reported signatures, our signature performed better in terms of overall accuracy and especially accuracy of classifying lung squamous cell carcinoma.Conclusions:Our signature is easy to use and accurate regardless of the technology used to obtain the gene expression profiles. It performs well even in clinically challenging cases and thus can assist pathologists in diagnosis of the ambiguous cases.

FKBP1A upregulation correlates with poor prognosis and increased metastatic potential of HNSCC.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy globally. The etiology of HNSCC is multifactorial, including cellular stress induced by a tobacco smoking, tobacco chewing excess alcohol consumption, and human papillomavirus infection. The induction of stress includes autophagy as one of the response pathways in maintaining homeostatic equilibrium. We evaluated the expression of autophagy-related genes in HNSCC tissues from RNA sequencing datasets and identified 19 genes correlated with poor prognosis and 18 genes correlated with improved prognosis of HNSCC patients. Further analysis of independent gene expression datasets revealed that ATG12, HSP90AB1, and FKBP1A are overexpressed in HNSCC and correlate with poor prognosis, whereas the overexpression of ANXA1, FOS, and ULK3 correlates with improved prognosis. Using independent datasets, we also found that ATG12, HSP90AB1, and FKBP1A expression increased with an increase in the T-stage of HNSCC. Among all the datasets analyzed, FKBP1A was overexpressed in HNSCC and was strongly associated with lymph node metastasis in multiple in silico datasets. In conclusion, our analysis indicates dynamic alterations in autophagy genes during HNSCC and warrants further investigation, specifically on FKBP1A and its role in tumor progression and metastasis.

Innate response of rainbow trout gill epithelial (RTgill-W1) cell line to ultraviolet-inactivated VHSV and FliC and rhabdovirus infection

Gills reportedly play a crucial role in induction of an antiviral immune response in fish. We investigated the expression of innate response genes in the rainbow trout gill epithelial cell line RTgill-W1 36 h after pretreatment with ultraviolet-inactivated viral hemorrhagic septicemia virus (UV-VHSV), flagellin C protein from Edwardsiella tarda (FliC), VHSV and SVCV using an Agilent 4 × 44k cGRASP salmonid microarray. RTgill-W1 cells pretreated with UV-VHSV, triggered an independent gene expression profile from those treated with a recombinant flagellin C protein from Edwardsiella tarda. In addition, exposure of RTgill-W1 cells to live viruses spring viremia of carp virus and viral hemorrhagic septicemia virus induced a less robust transcriptional change of 24 and 22 gene probes, respectively, when compared to 123 genes for UV-VHSV. Further the pretreatment of RTgill-W1 cells with (UV-VHSV) significantly reduced VHSV genome copy number at 6 d post infection (dpi) relative to the FliC-treated and untreated control. A quantitative PCR was used to study the transcriptional modulation of a set of 25 innate immune-related genes highlighted by the microarray data and a panel of 7 established antiviral genes in the protected cells. Notably, the expression of ifn1, ifn2, mx1 and mx3 were expressed more in untreated cells than in UV-VHSV-treated cells where virus replication was inhibited. The results from this study shed light on the mechanisms and pathways used by teleost gill epithelium innate immunity in combating viral and bacterial infection.

Integrative Analysis Identifies Multi-Omics Signatures That Drive Molecular Classification of Uveal Melanoma.

Simple SummaryUveal melanoma (UM) is a heterogeneous disease driven by accumulative alterations at multi-omics levels including genomics, epigenomics and transcriptomics. It is of great clinical interest to identify UM molecular subtypes and subtype-specific biomarkers that could be used for prognosis and targeted therapy. Integrative clustering (iCluster) analysis is a new approach designed to identify cancer integrative subtypes (iSubtypes) and multi-omics signatures that drive molecular classification of cancer. In this study, we performed an iCluster analysis of UM multi-omics data and identified four UM iSubtypes, which formed two major iSubtypes (denoted by M3 and D3) with distinct multi-omics landscapes. We showed that the integrative molecular classification of UM was determined by concordant alterations at multi-omics levels including DNA copy number, DNA methylation, gene expression and somatic mutation. We further derived a gene panel that can be used to classify UM into high- or low-risk groups for metastasis.By iCluster analysis, we found that the integrative molecular classification of the UM was primarily driven by DNA copy number variation on chromosomes 3, 6 and 8, differential methylation and expression of genes involved in the immune system, cell morphogenesis, movement and migration, and differential mutation of genes including GNA11, BAP1, EIF1AX, SF3B1 and GNAQ. Integrative analysis revealed that pathways including IL6/JAK/STAT3 signaling, angiogenesis, allograft rejection, inflammatory response and interferon gamma response were hypomethylated and up-regulated in the M3 iSubtype, which was associated with a worse overall survival, compared to the D3 iSubtype. Using two independent gene expression datasets, we demonstrated that the subtype-driving genes had an excellent prognostic power in classifying UM into high- or low-risk groups for metastasis. Integrative analysis of UM multi-omics data provided a comprehensive view of UM biology for understanding the underlying mechanism leading to UM metastasis. The concordant molecular alterations at multi-omics levels revealed by our integrative analysis could be used for patient stratification towards personalized management and surveillance.

Proliferation Cycle Transcriptomic Signatures are Strongly associated With Gastric Cancer Patient Survival.

Gastric cancer is one of the most heterogeneous tumors with multi-level molecular disturbances. Sustaining proliferative signaling and evading growth suppressors are two important hallmarks that enable the cancer cells to become tumorigenic and ultimately malignant, which enable tumor growth. Discovering and understanding the difference in tumor proliferation cycle phenotypes can be used to better classify tumors, and provide classification schemes for disease diagnosis and treatment options, which are more in line with the requirements of today’s precision medicine. We collected 691 eligible samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, combined with transcriptome data, to explore different heterogeneous proliferation cycle phenotypes, and further study the potential genomic changes that may lead to these different phenotypes in this study. Interestingly, two subtypes with different clinical and biological characteristics were identified through cluster analysis of gastric cancer transcriptome data. The repeatability of the classification was confirmed in an independent Gene Expression Omnibus validation cohort, and consistent phenotypes were observed. These two phenotypes showed different clinical outcomes, and tumor mutation burden. This classification helped us to better classify gastric cancer patients and provide targeted treatment based on specific transcriptome data.

Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting.

Systemic lupus erythematosus (SLE) is a chronic, multisystem, autoimmune inflammatory disease with genomic and non-genomic contributions to risk. We hypothesize that epigenetic factors are a significant contributor to SLE risk and may be informative for identifying pathogenic mechanisms and therapeutic targets. To test this hypothesis while controlling for genetic background, we performed an epigenome-wide analysis of DNA methylation in genomic DNA from whole blood in three pairs of female monozygotic (MZ) twins of European ancestry, discordant for SLE. Results were replicated on the same array in four cell types from a set of four Danish female MZ twin pairs discordant for SLE. Genes implicated by the epigenetic analyses were then evaluated in 10 independent SLE gene expression datasets from the Gene Expression Omnibus (GEO). There were 59 differentially methylated loci between unaffected and affected MZ twins in whole blood, including 11 novel loci. All but two of these loci were hypomethylated in the SLE twins relative to the unaffected twins. The genes harboring these hypomethylated loci exhibited increased expression in multiple independent datasets of SLE patients. This pattern was largely consistent regardless of disease activity, cell type, or renal tissue type. The genes proximal to CpGs exhibiting differential methylation (DM) in the SLE-discordant MZ twins and exhibiting differential expression (DE) in independent SLE GEO cohorts (DM-DE genes) clustered into two pathways: the nucleic acid-sensing pathway and the type I interferon pathway. The DM-DE genes were also informatically queried for potential gene–drug interactions, yielding a list of 41 drugs including a known SLE therapy. The DM-DE genes delineate two important biologic pathways that are not only reflective of the heterogeneity of SLE but may also correlate with distinct IFN responses that depend on the source, type, and location of nucleic acid molecules and the activated receptors in individual patients. Cell- and tissue-specific analyses will be critical to the understanding of genetic factors dysregulating the nucleic acid-sensing and IFN pathways and whether these factors could be appropriate targets for therapeutic intervention.

A Novel Mitochondrial-Related Nuclear Gene Signature Predicts Overall Survival of Lung Adenocarcinoma Patients.

Background: Lung cancer is the leading cause of cancer-related death worldwide, of which lung adenocarcinoma (LUAD) is one of the main histological subtypes. Mitochondria are vital for maintaining the physiological function, and their dysfunction has been found to be correlated with tumorigenesis and disease progression. Although, some mitochondrial-related genes have been found to correlate with the clinical outcomes of multiple tumors solely. The integrated relationship between nuclear mitochondrial genes (NMGs) and the prognosis of LUAD remains unclear.Methods: The list of NMGs, gene expression data, and related clinical information of LUAD were downloaded from public databases. Bioinformatics methods were used and obtained 18 prognostic related NMGs to construct a risk signature.Results: There were 18 NMGs (NDUFS2, ATP8A2, SCO1, COX14, COA6, RRM2B, TFAM, DARS2, GARS, YARS2, EFG1, GFM1, MRPL3, MRPL44, ISCU, CABC1, HSPD1, and ETHE1) identified by LASSO regression analysis. The mRNA expression of these 18 genes was positively correlated with their relative linear copy number alteration (CNA). Meanwhile, the established risk signature could effectively distinguish high- and low-risk patients, and its predictive capacity was validated in three independent gene expression omnibus (GEO) cohorts. Notably, a significantly lower prevalence of actionable EGFR alterations was presented in patients with high-risk NMGs signature but accompanied with a more inflame immune tumor microenvironment. Additionally, multicomponent Cox regression analysis showed that the model was stable when risk score, tumor stage, and lymph node stage were considered, and the 1-, 3-, and 5-year AUC were 0.74, 0.75, and 0.70, respectively.Conclusion: Together, this study established a signature based on NMGs that is a prognostic biomarker for LUAD patients and has the potential to be widely applied in future clinical settings.

IHC-based Ki67 as response biomarker to tamoxifen in breast cancer window trials enrolling premenopausal women

Window studies are gaining traction to assess (molecular) changes in short timeframes. Decreased tumor cell positivity for the proliferation marker Ki67 is often used as a proxy for treatment response. Immunohistochemistry (IHC)-based Ki67 on tissue from neo-adjuvant trials was previously reported to be predictive for long-term response to endocrine therapy for breast cancer in postmenopausal women, but none of these trials enrolled premenopausal women. Nonetheless, the marker is being used on this subpopulation. We compared pathologist assessed IHC-based Ki67 in samples from pre- and postmenopausal women in a neo-adjuvant, endocrine therapy focused trial (NCT00738777), randomized between tamoxifen, anastrozole, or fulvestrant. These results were compared with (1) IHC-based Ki67 scoring by AI, (2) mitotic figures, (3) mRNA-based Ki67, (4) five independent gene expression signatures capturing proliferation, and (5) blood levels for tamoxifen and its metabolites as well as estradiol. Upon tamoxifen, IHC-based Ki67 levels were decreased in both pre- and postmenopausal breast cancer patients, which was confirmed using mRNA-based cell proliferation markers. The magnitude of decrease of Ki67 IHC was smaller in pre- versus postmenopausal women. We found a direct relationship between post-treatment estradiol levels and the magnitude of the Ki67 decrease in tumors. These data suggest IHC-based Ki67 may be an appropriate biomarker for tamoxifen response in premenopausal breast cancer patients, but anti-proliferative effect size depends on estradiol levels.

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses.

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature’s ability to detect the disease in its early stages in patients’ biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

Construction of circRNA‐based ceRNA network and its prognosis‐associated subnet of clear cell renal cell carcinoma

Circular RNAs (circRNAs) are novel biomarkers of various cancers. CircRNAs can sponge miRNAs and regulate target mRNAs, which was called competing endogenous RNAs (ceRNA). This study was designed to identify circRNAs related to patients with clear cell renal cell carcinoma (ccRCC) and the first to select three independent Gene Expression Omnibus microarrays covering circRNAs, miRNAs, and mRNAs for multiple analyses. The data of clinical cases applied in our study were obtained from The Cancer Genome Atlas. We successfully conducted a circRNA/miRNA/mRNA ceRNA network related to ccRCC patients via R software and Cytoscape including 8 circRNAs, 6 miRNAs, and 49 mRNAs. The prognosis‐associated subnet covered 8 circRNAs, 6 miRNAs, and 22 mRNAs. Quantitative real‐time PCR was applied to measure our prediction in three renal cell lines and 23 pairs of tissues. Small interfering RNA targeting the back‐splice region of hsa_circ_0001167 was further implied to confirm the regulation. Ultimately, hsa_circ_0001167/hsa‐miR‐595/CCDC8 regulatory axis was identified in this study, which may serve as prognostic indicators. Lower levels of hsa_circ_0001167 and CCDC8 were potentially correlated with worse patient survival.

Reciprocal YAP1 loss and INSM1 expression in neuroendocrine prostate cancer.

Neuroendocrine prostate cancer (NEPC) is a rare but aggressive histologic variant of prostate cancer that responds poorly to androgen deprivation therapy. Hybrid NEPC-adenocarcinoma (AdCa) tumors are common, often eluding accurate pathologic diagnosis and requiring ancillary markers for classification. We recently performed an outlier-based meta-analysis across a number of independent gene expression microarray datasets to identify novel markers that differentiate NEPC from AdCa, including up-regulation of insulinoma-associated protein 1 (INSM1) and loss of Yes-associated protein 1 (YAP1). Here, using diverse cancer gene expression datasets, we show that Hippo pathway-related genes, including YAP1, are among the top down-regulated gene sets with expression of the neuroendocrine transcription factors, including INSM1. In prostate cancer cell lines, transgenic mouse models, and human prostate tumor cohorts, we confirm that YAP1 RNA and YAP1 protein expression are silenced in NEPC and demonstrate that the inverse correlation of INSM1 and YAP1 expression helps to distinguish AdCa from NEPC. Mechanistically, we find that YAP1 loss in NEPC may help to maintain INSM1 expression in prostate cancer cell lines and we further demonstrate that YAP1 silencing likely occurs epigenetically, via CpG hypermethylation near its transcriptional start site. Taken together, these data nominate two additional markers to distinguish NEPC from AdCa and add to data from other tumor types suggesting that Hippo signaling is tightly reciprocally regulated with neuroendocrine transcription factor expression. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Comparison of merging strategies for building machine learning models on multiple independent gene expression data sets

AbstractHigh‐dimensional gene expression data are regularly studied for their ability to separate different groups of samples by means of machine learning (ML) models. Meanwhile, a large number of such data are publicly available. Several approaches for meta‐analysis on independent sets of gene expression data have been proposed, mainly focusing on the step of feature selection, a typical step in fitting a ML model. Here, we compare different strategies of merging the information of such independent data sets to train a classifier model. Specifically, we compare the strategy of merging data sets directly (strategy A), and the strategy of merging the classification results (strategy B). We use simulations with pure artificial data as well as evaluations based on independent gene expression data from lung fibrosis studies to compare the two merging approaches. In the simulations, the number of studies, the strength of batch effects, and the separability are varied. The comparison incorporates five standard ML techniques typically used for high‐dimensional data, namely discriminant analysis, support vector machines, least absolute shrinkage and selection operator, random forest, and artificial neural networks. Using cross‐study validations, we found that direct data merging yields higher accuracies when having training data of three or four studies, and merging of classification results performed better when having only two training studies. In the evaluation with the lung fibrosis data, both strategies showed a similar performance.

Single-cell analysis of a tumor-derived exosome signature correlates with prognosis and immunotherapy response

BackgroundTumor-derived exosomes (TEXs) are involved in tumor progression and the immune modulation process and mediate intercellular communication in the tumor microenvironment. Although exosomes are considered promising liquid biomarkers for disease diagnosis, it is difficult to discriminate TEXs and to develop TEX-based predictive biomarkers.MethodsIn this study, the gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) database, IMvigor210 cohorts, and six independent Gene Expression Omnibus datasets. A TEXs-associated signature named TEXscore was established to predict overall survival in multiple cancer types and in patients undergoing immune checkpoint blockade therapies.ResultsBased on exosome-associated genes, we first constructed a tumor-derived exosome signature named TEXscore using a principal component analysis algorithm. In single-cell RNA-sequencing data analysis, ascending TEXscore was associated with disease progression and poor clinical outcomes. In the TCGA Pan-Cancer cohort, TEXscore was elevated in tumor samples rather than in normal tissues, thereby serving as a reliable biomarker to distinguish cancer from non-cancer sources. Moreover, high TEXscore was associated with shorter overall survival across 12 cancer types. TEXscore showed great potential in predicting immunotherapy response in melanoma, urothelial cancer, and renal cancer. The immunosuppressive microenvironment characterized by macrophages, cancer-associated fibroblasts, and myeloid-derived suppressor cells was associated with high TEXscore in the TCGA and immunotherapy cohorts. Besides, TEXscore-associated miRNAs and gene mutations were also identified. Further experimental research will facilitate the extending of TEXscore in tumor-associated exosomes.ConclusionsTEXscore capturing tumor-derived exosome features might be a robust biomarker for prognosis and treatment responses in independent cohorts.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

New Insights Into Mitochondrial Dysfunction at Disease Susceptibility Loci in the Development of Type 2 Diabetes.

This study investigated the potential genetic mechanisms which underlie adipose tissue mitochondrial dysfunction in Type 2 diabetes (T2D), by systematically identifying nuclear-encoded mitochondrial genes (NEMGs) among the genes regulated by T2D-associated genetic loci. The target genes of these ‘disease loci’ were identified by mapping genetic loci associated with both disease and gene expression levels (expression quantitative trait loci, eQTL) using high resolution genetic maps, with independent estimates co-locating to within a small genetic distance. These co-locating signals were defined as T2D-eQTL and the target genes as T2D cis-genes. In total, 763 cis-genes were associated with T2D-eQTL, of which 50 were NEMGs. Independent gene expression datasets for T2D and insulin resistant cases and controls confirmed that the cis-genes and cis-NEMGs were enriched for differential expression in cases, providing independent validation that genetic maps can identify informative functional genes. Two additional results were consistent with a potential role of T2D-eQTL in regulating the 50 identified cis-NEMGs in the context of T2D risk: (1) the 50 cis-NEMGs showed greater differential expression compared to other NEMGs and (2) other NEMGs showed a trend towards significantly decreased expression if their expression levels correlated more highly with the subset of 50 cis-NEMGs. These 50 cis-NEMGs, which are differentially expressed and associated with mapped T2D disease loci, encode proteins acting within key mitochondrial pathways, including some of current therapeutic interest such as the metabolism of branched-chain amino acids, GABA and biotin.

Upregulated NLGN1 predicts poor survival in colorectal cancer

BackgroundNeuroligin1 (NLGN1) is a main component of excitatory glutamatergic synapses complex and is important for synapse assembly and function. The clinical value of NLGN1 in colorectal cancer (CRC) is not clear.MethodsWe obtained the expression data of 1143 CRC patients from 3 independent Gene Expression Omnibus (GEO) datasets (GSE32323, GSE24551, GSE39582) and The Cancer Genome Atlas (TCGA) to make the comparison of the NLGN1 expression level between CRC tissues and matched noncancerous tissues, and to evaluate its value in predicting survival of CRC patients. At the protein level, these results were further confirmed by immunohistochemical staining of 52 CRC samples in our own centre. Finally, the function of NLGN1 was explored by gene set enrichment analysis (GSEA).ResultsIncreased mRNA and protein levels of NLGN1 expression were associated with worse overall survival or recurrence-free survival in CRC patients from 2 GEO datasets, the TCGA database, and our cohort. In addition, multivariate regression analysis showed that NLGN1 was an independent poor prognostic factor of survival in patients with CRC in TCGA database (OR = 2.524, P = 0.010). Functional analysis revealed that NLGN1 was correlated with function involving the Hedgehog signaling pathway, mismatch repair process, and some material metabolism processes.ConclusionsThis study is the first to implicate and verify NLGN1 as a new poor prognostic marker for CRC.

Abstract 2456: SOX4 and SMARCA4 upregulate glycolysis-driven tumor proliferation through Hexokinase 2 in triple negative breast cancer

Abstract Triple-negative breast cancer (TNBC) accounts for nearly 1-in-4 breast cancer related deaths in the United States yearly despite representing 10-12% of newly diagnosed cases. This aggressive disease, which is largely synonymous with the basal-like molecular subtype, predominantly affects younger and African American women. Despite recent advances, overall survival has not drastically improved, highlighting the need to identify novel therapeutic opportunities. We determined that the oncogenic transcription factor SOX4 is overexpressed in ~88.1% of basal-like tumors relative to adjacent normal tissue. Immunoprecipitation follow by MS was identified the SWI/SNF ATPase SMARCA4 as an essential SOX4 cofactor that is concurrently overexpressed in basal-like tumors. Mechanistic studies showed that this complex is required to maintain an active open chromatin conformation at regulatory regions upstream of SOX4-regulated genes. To identify the functional impact of overexpression in TNBC, RNAseq and ChIPseq analyses were used to identify SMARCA4-dependent and -independent SOX4 gene expression programs. Analyses of 3,000+ tumor samples from the METABRIC and TCGA cohorts was used to investigate the functional and clinical implications of these gene expression programs in human tumors. Investigation of SOX4 and SMARCA4 binding to promoters and subsequent gene transcription identified SMARCA4-dependent down-stream signaling programs associated with overall worse prognosis and key metabolomic processes. This correlation was confirmed in vitro by demonstrating that both SOX4 and SMARCA4 could mediate changes in intracellular glucose consumption. Metabolomic profiling of breast cancer cell lines demonstrated that SOX4 overexpression resulted in increased glycolysis. Mechanistic studies have determined that SOX4/SMARCA4 transcriptionally regulate expression of Hexokinase 2 (HK2) which catalyzes the first step in glucose metabolism, and HK2 activity is essential for SOX4/SMARCA4-regulation of glycolysis. Given that our data demonstrate that SOX4 or SMARCA4 can regulate cell proliferation/survival and that glycolysis has been shown to modulate cell proliferation, we hypothesized that SOX4-SMARCA4 cooperatively regulate the proliferative capacity of TNBC cells through altered glucose consumption. Consistent with this premise, in vitro studies showed that SOX4 and SMARCA4 are essential for breast tumor cell proliferation, survival and colony formation. Importantly, genetic or pharmacological inhibition of HK2 results in reduced TNBC cell proliferation and growth. Collectively these data demonstrate that the SOX4/SMARCA4 complex mediates the proliferative capacity of TNBC cells by modulating metabolic processes. Given the drug-ability of the glycolysis pathway, these results may have important clinical implications for TNBC patient treatment. Citation Format: Pooja Khanna, Gaurav Mehta, Vrushank Bhatt, Jessie Y. Guo, Michael L. Gatza. SOX4 and SMARCA4 upregulate glycolysis-driven tumor proliferation through Hexokinase 2 in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2456.