Effect of high variation in transcript expression on identifying differentially expressed genes in RNA-seq analysis.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Racial/ethnic disparity in cancer refers to the disproportionate incidence of and/or mortality from various cancers among population groups. In addition to differences in social, lifestyle and structural determinants of health, there is accumulating evidence for a biologic contribution to racial/ethnic disparity in cancer. To understand further the biologic mechanisms underlying racial/ethnic disparity in cancer, the analysis of differential aggregate gene expression and mutation among cancer patients of different population groups is important. Our recently published work reported differential RNA splicing as a critical mechanism underlying prostate cancer aggressiveness and drug response in African American (AA) patients. Here, we use R to analyze the Genomic Data Commons for differential aggregate gene expression (2-fold mean change, p < 0.001, Wilcoxon rank sum test) and TCGASpliceSeq to analyze TCGA for differential RNA splicing (20% median change, percent spliced in) between breast (BRCA, 183 AA and 761 white), colon (COAD, 56 AA and 194 white), squamous and adenocarcinoma lung (LUSC and LUAD, 34 and 57 AA and 392 and 444 white, respectively), and prostate (PRAD, 49 AA and 307 white) cancer specimens from AA and white patients. Our analysis identified 698 differentially expressed genes (DEGs) and 114 alternative RNA splicing events (ARSs) in BRCA, 107 DEGs and 57 ARSs in COAD, 25 and 117 DEGs and 115 and 53 ARSs in LUSC and LUAD, respectively, and 25 DEGs and 71 ARSs in PRAD from AA and white patients. Notably, comparing the lists of DEGs or ARSs from each cancer type with each of the other cancer types yields a minority of overlapping DEGs or ARSs among cancer types, indicating that race-related DEGs and ARSs are largely specific to cancer type. Pathway analysis of DEGs and ARSs reveals that the majority of these genes function in pathways relevant to cancer development and progression, such as programmed cell death, DNA repair, signal transduction, gene expression and metabolism. These analyses increase understanding of molecular mechanisms underlying racial/ethnic disparity in cancer. Upon further study of the function of these variants, such DEGs and ARSs have the potential to become candidates for development of new precision medicine interventions. Citation Format: Muthana Al Abo, Daniel J. George, Jennifer A. Freedman, Steven R. Patierno. Identification of differentially expressed and spliced genes between breast, colon, lung, and prostate cancer from African American and white patients [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B050.

A significantly different transcriptome is found between sperm samples from patients undergoing homologous intrauterine insemination (IUI) achieving or not pregnancy

Objective To screen and identify the differential expression genes on peripheral blood cells of mice based on the experimental animal model of radon exposure. Methods BALB/c mice were exposed in a type HD-3 multifunctional radon-room, with the accumulative doses of radon-exposure group at 105 WLM and control group at 1 WLM. Total RNA was extracted from peripheral blood cells and the methods of SMART for dscDNA synthesis and SSH for gene screening was applied. With the construction of the cDNA library enriched with differentially expressed genes, the pMD 18-T plasmid containing LacZ operator at the multiple cloning site was used to allow a blue-white screening. The TA clones were amplified by nested PCR and the reverse Northern blot was used to identify up and down regulation of the clones. The differently expressed cDNA was then sequenced and analyzed. Results The subtracted cDNA libraries were successfully constructed. A total of 390 recombinant white colonies were randomly selected. Among the 312 cDNA monoelones selected from bath forward- and reverse-subtracted libraries,41 clones were chosen to sequence for their differential expressions based on reverse Northern blot. Among the 41 sequenced clones, 10 clones with known function/annotation and 3 new ESTs with the GenBank accession numbers were obtained. Most of the known function/annotation genes were revealed to be related with cell proliferation, metabolism, cellular apoptosis and carcinogenesis. Conclusions The animal model of radon exposure was established and the cDNA library of peripheral blood cells was suceessfully constructed. Radon exposure could up- and down-regulate a series of genes. Differentially expressed genes could be identified by using SSH technique and the results may help exploring mechanisms of random exposure. Key words: Radon; Suppression subtractive hybridization; Differential expression genes; Bioinformatics

Breast cancer is one of the most common cancers causing a high mortality worldwide. This study aimed to identify differential methylation and expression genes with prognostic value in breast cancer. DNA methylation and gene expression profiles (GSE60185, GSE42568, GSE21653, GSE58812, and GSE52865) were downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) databases. The differentially expressed genes (DEGs) and differential methylation genes were identified between breast cancer samples and normal samples. Functional analysis was performed using DAVID (Database for Annotation, Visualization, and Integrated Discovery) tool. Furthermore, functional epigenetic modules (FEM) were analyzed to identify critical genes with prognostic values. A large amount of DEGs and aberrant methylation genes were identified between breast cancer samples and normal samples. These genes were mainly associated with several GO (Gene Ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, such as neuroactive ligand-receptor interaction, alcoholism, gamma-aminobutyric acid signaling pathway, and G-protein-coupled receptor signaling pathway. Additionally, 10 DEGs with differential methylation levels were significantly correlated with survival outcomes in breast cancer patients. FEM analysis revealed that several DEGs (e.g., GABRA4, GABRG1, and GABRA1) in module GABRA4 were identified as potential biomarkers in breast cancer patients. Several DEGs identified were associated with breast cancer prognosis. These DEGs might act as prognostic and diagnostic markers in breast cancer.

The Iso-Seq method of full-length cDNA sequencing is suitable to quantify differentially expressed genes (DEGs), transcripts (DETs) and transcript usage (DTU). However, the higher cost of Iso-Seq relative to RNA-seq has limited the comparison of both methods. Transcript abundance estimated by RNA-seq and deep Iso-Seq data for fetal liver from two cattle subspecies were compared to evaluate concordance. Inter-sample correlation of gene- and transcript-level abundance was higher within technology than between technologies. Identification of DEGs between the cattle subspecies depended on sequencing method with only 44 genes identified by both that included 6 novel genes annotated by Iso-Seq. There was a pronounced difference between Iso-Seq and RNA-seq results at transcript-level wherein Iso-Seq revealed several magnitudes more transcript abundance and usage differences between subspecies. Factors influencing DEG identification included size selection during Iso-Seq library preparation, average transcript abundance, multi-mapping of RNA-seq reads to the reference genome, and overlapping coordinates of genes. Some DEGs called by RNA-seq alone appear to be sequence duplication artifacts. Among the 44 DEGs identified by both technologies some play a role in immune system, thyroid function and cell growth. Iso-Seq revealed hidden transcriptional complexity in DEGs, DETs and DTU genes between cattle subspecies previously missed by RNA-seq.

Background: Heterogeneity between different breast cancer tumors plays an important role in patients’ survival outcomes, and such tumor heterogeneity unveiled by transcriptome and proteome are often in disagreement. We hypothesize that alternative splicing of mRNA could explain heterogeneity within intrinsic breast cancer subtypes. Methods: Cases used in this study were drawn from the Clinical Breast Care Project where breast cancer patients were consented using an IRB-approved protocol. Fifty breast tumors were selected and processed by laser microdissection. RNA and protein were extracted from tissues using the Illustra triplePrep kit (GE Healthcare). Paired-end mRNA sequencing was performed using the Illumina HiSeq platform. Paired-end reads were preprocessed using PRINSEQ and splice-aligned to the genome using GSNAP software. Gene counts were measured using HTSeq while Exon counts used the DEXSeq. Differential expression was called at 10% false discovery rate. Proteome Discoverer with Byonic node was used for analyzing the quantitative global proteomics dataset, and we were able to quantitate 8600 proteins. All other analyses were performed using Perl and R. Results: The number of sequencing reads for the 50 cases ranged from 60 million to 410 million reads. After preprocessing, an average of 93% of reads was mapped, and 36,700 genes were identified among 50 tumor samples. PAM50 algorithm was used for intrinsic subtype calls, yielding 16 Basal, 8 Her2+, 19 Luminal A (LA) and 7 Luminal B tumors. Unsupervised clustering of the 8 Her2+ cases using the PAM50 genes and highly varying proteins resulted in different clustering patterns, with the latter clustering 6 of the 8 Her2+ cases with two distinct 3-case sub-clusters. Between these two sub-clusters, there were 10 differentially expressed (DEX) genes and 25 DEX proteins, but none of the 25 proteins were mapped to the 10 DEX genes. This motivated us to investigate the DEX exons, and we found 7,076 DEX exons between the Her2+ sub-clusters, and 9 of the 25 DEX proteins were matched to genes bearing DEX exons. For comparison, we performed the same analyses between similarly clustered Basal and LA sub-clusters. Even though the number of DEX genes between Basal (8) and LA (18) sub-clusters were comparable to that between the Her2+ sub-clusters, DEX exons were much lower for both subtypes (616 & 157), and none of the DEX proteins (3 & 7) mapped to DEX exons or genes for either subtype. Conclusions: Our findings imply that there is more proteomic-level heterogeneity in the Her2+ subtype than in Basal and LA subtypes, which could be due to alternative usage of exons (alternative splicing). If such heterogeneity is associated with patients’ survival outcomes then our results will further stress the importance of alternative splicing in breast cancer. The views expressed in this article are those of the author and do not reflect the official policy of the Department of Defense, or U.S. Government. Citation Format: Praveen Kumar Raj Kumar, Tao Liu, Lori A. Sturtz, Albert Kovatich, Marina A. Gritsenko, Vladislav A. Petyuk, Brenda Deyarmin, Viswanadham Sridhara, James Craig, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Leonid Kvecher, Jianfang Liu, Jennifer Kane, Jennifer Melley, Stella Somiari, Joji Iida, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Karin D. Rodland, Craig D. Shriver, Hai Hu. Differential exon usage in the HER2 subtype of breast cancer identified with RNA-seq and proteomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3372. doi:10.1158/1538-7445.AM2017-3372

Background: Nucleolar and spindle-associated protein 1 (NUSAP1) was previously reported to be associated with poor prognosis in multiple cancers. In the present study, we comprehensively investigated the clinicopathological features and potential prognostic value of NUSAP1 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).Methods: The expression profiles of the genes were extracted from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Cancer Cell Line Encyclopedia (CCLE), Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas databases. The association between clinicopathological characteristics and NUSAP1 was analyzed using logistic regression in TCGA patients and receiver operating characteristic (ROC) curve analysis for GSE7803, GSE9750, and GSE63514 datasets. The prognostic value of NUSAP1 in TCGA patients was evaluated using the Kaplan-Meier method and Cox regression. Gene set enrichment analysis (GSEA) was conducted using TCGA dataset.Results: A total of 68 differentially expressed genes (DEGs) were identified in CESC. ROC analysis of NUSAP1 suggested that the area under the ROC curve was 0.968. Kaplan-Meier survival analysis indicated that CESC with low expression of NUSAP1 has a worse prognosis than CESC with high NUSAP1 expression (P = 0.005). The logistic regression revealed that low NUSAP1 expression in CESC was related to advanced tumor stage in TCGA database. Moreover, Cox regression analysis showed that NUSAP1 expression correlated significantly with prognosis in the case of patients in TCGA database. GSEA demonstrated that CESC patients with high expression of NUSAP1 were enriched in the G2M checkpoint, MYC targets, and breast cancer ZNF217.Conclusion: The results suggest that identification of DEGs might enhance our understanding of the causes and molecular mechanisms underlying the development of CESC. Moreover, NUSAP1 may play an important role in CESC progression and prognosis and may serve as a valuable indicator of poor survival in CESC.

KRT15, INHBA, MATN3, and AGT are aberrantly methylated and differentially expressed in gastric cancer and associated with prognosis

Glioblastoma (GBM) is the most frequent malignant brain tumor in adults. Our study focused on uncovering differentially expressed genes (DEGs) and their methylation in order to identify novel diagnostic biomarkers and potential treatment targets. Using GBM RNA-sequencing data from The Cancer Genome Atlas (TCGA) database, DEGs between GBM samples and paracancer tissue samples were analyzed. Enrichment analysis for DEGs and transcription factors (TFs) was performed. A total of 1029 upregulated genes and 1542 downregulated genes were identified, which were associated mainly with multiple tumor-related and immune-related pathways such as cell cycle, mitogen-activated protein kinase signaling pathway, leukocyte transendothelial migration, and autoimmune thyroid disease. These DEGs were enriched for 174 TFs, and six TFs were differentially expressed and identified as key TFs in GBM: HOXA3, EN1, ZIC1, and FOXD3 were upregulated, while HLF and EGR3 were downregulated. A total of 1978 DEGs were involved in the regulatory networks of the six key differentially expressed TFs. High expression of EN1 was associated with shorter overall survival, while high expression of EGR3 was associated with shorter recurrence-free survival. The six TFs were differentially methylated in GBM samples compared with paracancer tissues. Our study identifies numerous DEGs and their associated pathways as potential contributors to GBM, particularly the TFs EN1, EGR3, HOXA3, ZIC1, FOXD3, and HLF. The differential expression of these TFs may be unlikely driven by aberrant methylation. These TFs may be useful as diagnostic markers and treatment targets in GBM, and EN1 and EGR3 may have predictive prognostic value.

Overdue treatment and prognostic evaluation lead to low survival rates in patients with lung adenocarcinoma (LUAD). To date, effective biomarkers for prognosis are still required. The aim of the present study was to screen differentially expressed genes (DEGs) as biomarkers for prognostic evaluation of LUAD. DEGs in tumor and normal samples were identified and analyzed for Kyoto Encyclopedia of Genes and Genomes/Gene Ontology functional enrichments. The common genes that are up and downregulated were selected for prognostic analysis using RNAseq data in The Cancer Genome Atlas. Differential expression analysis was performed with 164 samples in GSE10072 and GSE7670 datasets. A total of 484 DEGs that were present in GSE10072 and GSE7670 datasets were screened, including secreted phosphoprotein 1 (SPP1) that was highly expressed and DEGs ficolin 3, advanced glycosylation end-product specific receptor (AGER), transmembrane protein 100 that were lowly expressed in tumor tissues. These four key genes were subsequently verified using an independent dataset, GSE19804. The gene expression model was consistent with GSE10072 and GSE7670 datasets. The dysregulation of highly expressed SPP1 and lowly expressed AGER significantly reduced the median survival time of patients with LUAD. These findings suggest that SPP1 and AGER are risk factors for LUAD, and these two genes may be utilized in the prognostic evaluation of patients with LUAD. Additionally, the key genes and functional enrichments may provide a reference for investigating the molecular expression mechanisms underlying LUAD.

Panicle degeneration, sometimes known as abortion, causes heavy losses in grain yield. However, the mechanism of naturally occurring panicle abortion is still elusive. In a previous study, we characterized a mutant, apical panicle abortion1331 (apa1331), exhibiting abortion in apical spikelets starting from the 6 cm stage of panicle development. In this study, we have quantified the five phytohormones, gibberellins (GA), auxins (IAA), abscisic acid (ABA), cytokinins (CTK), and brassinosteroids (BR), in the lower, middle, and upper parts of apa1331 and compared these with those exhibited in its wild type (WT). In apa331, the lower and middle parts of the panicle showed contrasting concentrations of all studied phytohormones, but highly significant changes in IAA and ABA, compared to the upper part of the panicle. A comparative transcriptome of apa1331 and WT apical spikelets was performed to explore genes causing the physiological basis of spikelet abortion. The differential expression analysis revealed a significant downregulation and upregulation of 1587 and 978 genes, respectively. Hierarchical clustering of differentially expressed genes (DEGs) revealed the correlation of gene ontology (GO) terms associated with antioxidant activity, peroxidase activity, and oxidoreductase activity. KEGG pathway analysis using parametric gene set enrichment analysis (PGSEA) revealed the downregulation of the biological processes, including cell wall polysaccharides and fatty acids derivatives, in apa1331 compared to its WT. Based on fold change (FC) value and high variation in expression during late inflorescence, early inflorescence, and antherdevelopment, we predicted a list of novel genes, which presumably can be the potential targets of inflorescence development. Our study not only provides novel insights into the role of the physiological dynamics involved in panicle abortion, but also highlights the potential targets involved in reproductive development.

Background: Differences in ER+ and ER- breast cancer tumor biology are well-documented, but little is known about the field of background benign breast in which those cancers arise. We evaluated the transcriptome of benign breast tissues from women with concurrent ipsilateral ER+ and ER- breast cancers (BC) to characterize coding and non-coding RNA profiles. This pilot study provides insight into the transcriptomic landscapes of benign breast tissues in patients with BC, and the microenvironment of the at-risk benign tissue. Methods: With institutional approval, cryobanked breast tissues from patients with concurrent ipsilateral ER+ BC (benign ER+BC, N=14) or ER- BC (benign ER-BC, N=10), were selected for benign tissues with similar epithelial:stromal ratios and grouped into pre (preM) and post-menopausal (PM) groups. Following RNA sequencing (Illumina TruSeq Stranded mRNA kit & Illumina HiSeq 4000), reads were processed (MAP-RSeq v3.0.0) and aligned (STAR aligner; hg38). Differential expression (DE) analysis (edgeR 2.6.2) identified DE genes from normalized RPKM reads (absolute log2 fold change (FC) > 1 and false discovery rate (FDR) < 0.10), corrected for intra-group biases. Over-representation analysis [Ingenuity pathway analysis (IPA), Ingenuity® Systems] and gene set enrichment analysis [(GSEA), GeneTrail 2.0] identified significantly-enriched pathways. Results: In the PM group, there were 144 DE transcripts between benign ER+BC and benign ER-BC, including coding RNAs (40%), antisense RNAs (35%) and lncRNAs (7%). In contrast, the preM group had no significantly DE genes between benign ER+BC and benign ER-BC. In the PM DE coding gene set, the top DE transcripts in benign ER+BC included many genes implicated in BC development# or ER+ BC progression (*) (e.g. up-regulated: KAAG1*, DNAJB7/HSP40*, TMEM151B, ZBTB32# p < 0.001 ; down-regulated: CPB1*, FOS, TPPP3#, CLEC3B#p < 0.001). Top canonical pathways altered in benign ER+BC included MAPK, PI3K, and acute phase response pathways (p<0.05). GSEA of the entire gene set (N=15,223; ranked in order of 144 DE genes) identified 72 altered pathways (P < 0.005); those with the highest normalization enrichment scores (NES) (> 0.4) functionally grouped as immune function-related (T cell function and antigen presentation). Depleted pathways with NES > 0.4, (N=6) functionally grouped into proteasome-related, fatty acid biosynthesis and mitochondrial energy metabolism. Among the non-coding DE gene set, notably, the entire DE antisense RNA gene set (N=51 transcripts) was up-regulated in benign ER+ BC compared to benign ER-BC (P< 0.001) with a subset (N=11) showing marked up-regulation (> 4 log2FC). Among the DE antisense RNAs, 70% have reported roles in carcinogenesis or BC progression (e.g. KRT7-AS, NAV2-AS2, CCDC144NL-AS1, RP11-66B24.4, HSA-MIR4454). Conclusion: The benign breast transcriptome differs between postmenopausal women with ER+ vs ER- BC, with distinctive coding and non-coding RNA signatures. In postmenopausal women with ER+ BC, benign breast expresses a unique antisense RNA set and is enriched in genes implicated in BC development or progression. These data provide insight into at-risk benign breast and facilitate identification of potential biomarkers of carcinogenesis. Citation Format: Carter JM, Nair AA, Davila JI, Heinzen EP, Hoskin TL, Winham SJ, Radisky DC, Visscher DW, Degnim AC. A unique coding and non-coding benign breast transcriptome in post-menopausal ER+ breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-08-10.

To integrate transcriptomic and genomic data to identify lung cancer biomarkers, evaluate their prognostic significance, and explore their potential in guiding personalized therapy. We integrated transcriptomic and genomic data to uncover genes associated with lung cancer risk using differential expression and Mendelian randomization (MR) analyses. Gene expression profiles from three GEO datasets (GSE19804, GSE18842, GSE19188) were batch-corrected with ComBat and analyzed to identify differentially expressed genes (DEGs). Two-sample MR was performed using lung cancer GWAS summary statistics. Functional enrichment, immune infiltration, survival analysis, and drug sensitivity prediction were conducted. The results were validated using The Cancer Genome Atlas (TCGA) lung cancer cohort. A total of 1,193 DEGs (409 upregulated, 784 downregulated) were detected between examined groups. MR analysis revealed 253 genes associated with lung cancer risk, including 118 increasing and 135 decreasing genes. Eleven genes overlapped between DEGs and MR results, with SAPCD2 and SPP1 being found to be associated with increased risk of lung cancer, whereas GPX3, C1orf162, CTSW, TPSAB1, CD93, HK3, TFPI, NEBL, and STEAP4 exhibiting inverse association. Enrichment analysis highlighted functions in vesicle lumen, carbohydrate metabolism, and immune processes. SAPCD2 and SPP1 were correlated with pro-tumor immune infiltration. Seven genes were associated with overall survival, and a derived prognostic score effectively stratified patients and correlated with TIDE and drug response profiles. This integrative genomic-transcriptomic analysis uncovered genes with suggestive causal and prognostic relevance in lung cancer, emphasizing their immune roles and therapeutic potential. These insights advance the understanding of lung cancer biology and support precision oncology strategies.

THU0029 IDENTIFICATION AND COMPARING OF HUB GENES IN SYSTEMIC LUPUS ERYTHEMATOSUS T AND B CELLS BY BIOINFORMATIC ANALYSIS