Transcriptomic Analyses Revealed Systemic Alterations in Gene Expression in Circulation and Tumor Microenvironment of Colorectal Cancer Patients

Differential gene expression in PBMCs: Insights into the mechanism how pulmonary tuberculosis increases lung cancer risk

Pre-existing of pulmonary tuberculosis (PTB) poses increased lung cancer risk, yet the molecular mechanisms remain inadequately understood. This study sought to elucidate the potential mechanisms by performing comprehensive analyses of differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMCs) from patients with PTB, lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC). Microarray assays were employed to analyze the DEGs in PBMCs of these patients. The analyses revealed that, compared to healthy controls, the number of differentially expressed LncRNA in PBMCs from patients with PTB, LUAD, and LUSC were 801, 8,541, and 7,796, respectively. Similarly, the differentially expressed mRNA in PBMCs from patients with PTB, LUAD, and LUSC were 629, 4,865, and 4,438, respectively. These differentially expressed transcripts represent significant resources for the identifying diagnostic and differential diagnostic biomarkers for lung cancer and PTB. Pathways enriched by dysregulated mRNAs in patients with PTB, LUAD, and LUSC were identified through GO and KEGG pathway analyses. The results indicated that 9 pathways including the NOD-like receptor signaling pathway, pathways in cancer, and the MAPK signaling pathway were co-enriched across the PTB, LUAD, and LUSC groups, providing insights into the mechanisms by which PTB may increase the risk of cancer development and progression.

Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways

Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.

Therapeutic targeting of the TPX2/TTK network in colorectal cancer

BACKGROUND

While the increased screening, changes in lifestyle, and recent advances in treatment regimen have decreased colorectal cancer (CRC) mortality, metastatic disease and recurrence remains a major clinical challenge. In the era of precision medicine, the identification of actionable novel therapeutic targets could ultimately offer an alternative treatment strategy for CRC.

METHODS

RNA-Seq was conducted using the illumina platform, while bioinformatics analyses were conducted using CLC genomics workbench and iDEP.951. Colony forming unit, flow cytometry, and fluorescent microscopy were used to assess cell proliferation, cell cycle distribution, and cell death, respectively. The growth potential of CRC cells under 3-dimensional (3D) conditions was assessed using Matrigel. STRING database (v11.5) and Ingenuity Pathway Analysis (IPA) tool were used for network and pathway analyses. CRISPR-Cas9 perturbational effects database was used to identify potential therapeutic targets for CRC, through integration with gene-drug interaction database. Structural modeling and molecular docking were used to assess the interaction between candidate drugs and their targets.

RESULTS

In the current study, we investigated the therapeutic potential of targeting TPX2, TTK, DDX39A, and LRP8, commonly upregulated genes in CRC identified through differential expression analysis in CRC and adjacent non-cancerous tissue. Targeted depletion of TPX2 and TTK impaired CRC proliferation, cell cycle progression, and organoid formation under 3D culture conditions, while suppression of DDX39A and LRP8 had modest effects on CRC colony formation. Differential expression analysis and bioinformatics on TPX2 and TTK-deficient cells identified cell cycle regulation as the hallmark associated with loss of TPX2 and TTK. Elevated expression of TPX2 and TTK correlated with an oncogenic state in tumor tissue from patients with colon adenocarcinoma, thus corroborating an oncogenic role for the TPX2/TTK network in the pathogenesis of CRC. Gene set enrichment and pathway analysis of TPX2high/TTKhigh CRC identified numerous additional gene targets as integral components of the TPX2/TTK network. Integration of TPX2/TTK enriched network with CRISPR-Cas9 functional screen data identified numerous novel dependencies for CRC. Additionally, gene-drug interaction analysis identified several druggable gene targets enriched in the TPX2/TTK network, including AURKA, TOP2A, CDK1, BIRC5, and many others.

CONCLUSIONS

Our data has implicated an essential role for TPX2 and TTK in CRC pathogenesis and identified numerous potential therapeutic targets and their drug interactions, suggesting their potential clinical use as a novel therapeutic strategy for patients with CRC. Video Abstract.

Histone crotonylation of peripheral blood mononuclear cells is a potential biomarker for diagnosis of colorectal cancer

BACKGROUND

Blood-based tests have public appeal in screening cancers due to their minimally invasive nature, ability to integrate with other routine blood tests, and high compliance. This study aimed to investigate whether certain epigenetic modulation of peripheral blood mononuclear cells (PBMCs) could be a biomarker of colorectal cancer (CRC).

RESULTS

Western blotting of histones in the PBMCs from 40 colorectal cancer patients and 40 healthy controls was performed to identify the crotonylation sites of proteins. The correlation of crotonylation with tumor staging and diagnostic efficacy were analyzed. Crotonylation of H2BK12 (H2BK12cr) was identified significantly upregulated in the PBMCs of CRC patients compared to healthy controls, and were closely related to distant metastasis (P = 0.0478) and late TNM stage (P = 0.0201). Receiver operator characteristic curve (ROC) analysis demonstrated that the area under curve (AUC) of H2BK12cr was 0.8488, the sensitivity was 70%, and the specificity was 92.5%. The H2BK12cr parameter significantly increased the diagnostic effectiveness of CRC compared with the commercial carcinoembryonic antigen assays.

CONCLUSIONS

The H2BK12cr level in PBMCs of CRC patients has a potential to be a biomarker for distinguishing CRC patients from healthy controls with the advantages of easy operation and high diagnostic efficacy.

N6-methyladenosine modification of REG1α facilitates colorectal cancer progression via β-catenin/MYC/LDHA axis mediated glycolytic reprogramming

Aerobic glycolysis has been considered as a hallmark of colorectal cancer (CRC). However, the potential functional regulators of glycolysis in CRC remains to be elucidated. In the current study, we found that Regenerating islet-derived protein 1-alpha (REG1α) was significantly increased in both CRC tissues and serum, and positively associated with CRC patients' lymph node metastasis, advanced tumor stage, and unfavorable prognosis. Ectopic expression of REG1α contributed to various tumorigenic properties, including cell proliferation, cell cycle, migration, invasion, and glycolysis. In contrast, REG1α deficiency in CRC cells attenuated malignant properties and glucose metabolism. Mechanically, REG1α promoted CRC proliferation and metastasis via β-catenin/MYC axis-mediated glycolysis upregulation. Moreover, the malignant behaviors governed by REG1α could be effectively abolished by silencing of Wnt/β-catenin/MYC axis or glycolysis process using specific inhibitors. Besides, REG1α expression was mediated by METTL3 in an m6A-dependent manner. Overall, our work defines a novel regulatory model of the METTL3/REG1α/β-catenin/MYC axis in CRC, which indicates that REG1α could function as a novel biomarker and a potential therapeutic target for patients with CRC.

Research progress of circulating non-coding RNA in diagnosis and treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly malignant tumor that carries a significant risk of morbidity and mortality. This type of cancer is prevalent in Asia due to the widespread presence of risk factors. Unfortunately, HCC often goes undetected until it has reached an advanced stage, making early detection and treatment critical for better outcomes. Alpha-fetoprotein (AFP) is commonly used in clinical practice for diagnosing HCC, but its sensitivity and specificity are limited. While surgery and liver transplantation are the main radical treatments, drug therapy and local interventions are better options for patients with advanced HCC. Accurately assessing treatment efficacy and adjusting plans in a timely manner can significantly improve the prognosis of HCC. Non-coding RNA gene transcription products cannot participate in protein production, but they can regulate gene expression and protein function through the regulation of transcription and translation processes. These non-coding RNAs have been found to be associated with tumor development in various types of tumors. Noncoding RNA released by tumor or blood cells can circulate in the blood and serve as a biomarker for diagnosis, prognosis, and efficacy assessment. This article explores the unique role of circulating noncoding RNA in HCC from various perspectives.

Circulating Tumor Cells in Colorectal Cancer: Detection Systems and Clinical Utility

Colorectal cancer (CRC) is the third most common cancer worldwide. The high mortality from CRC is mainly related to metastasis affecting distant organs and their function. Dissemination of tumor cells from the primary tumor and hematogeneous spread are considered crucial in the formation of tumor metastases. The analysis of circulating tumor cells (CTCs) and CTC clusters in the blood can be used for the early detection of invasive cancer. Moreover, CTCs have a prognostic significance in the monitoring of a malignant disease or the response to chemotherapy. This work presents an overview of the research conducted on CTCs with the aim of finding suitable detection systems and assessing the possibility of clinical applications in patients with CRC.

Transcriptomic Analyses Reveal Long Non-Coding RNA in Peripheral Blood Mononuclear Cells as a Novel Biomarker for Diagnosis and Prognosis of Hepatocellular Carcinoma

Novel biomarkers are highly required for the diagnosis and predicting prognosis of hepatocellular carcinoma (HCC). In this study, we investigated the profiles of long non-coding RNAs (lncRNAs) obtained from the peripheral blood mononuclear cells (PBMCs) of patients with HCC and PBMCs from a co-culture model using transcriptomic analysis. The differentially expressed lncRNAs (DElncRNAs) were then characterized and integrated as cancer-induced lncRNAs. Among them, three up-regulating DElncRNAs including MIR4435-2HG, SNHG9 and lnc-LCP2-1 and one down-regulating, lnc-POLD3-2, were identified. The functional analysis showed that these enriched lncRNAs were mainly associated with carcinogenesis and immune responses. Following further validation in PBMCs samples (100 HBV-related HCC, 100 chronic hepatitis B and 100 healthy controls), MIR4435-2HG, lnc-POLD3-2 and their combination were revealed to be sensitive biomarkers in discriminating HCC from non-HCC (AUROC = 0.78, 0.80, and 0.87, respectively), particularly among individuals with normal serum alpha-fetoprotein levels. Additionally, high circulating SNHG9 expression was shown to be an independent prognostic factor of overall survival in patients with HCC. These results indicate that determining these lncRNAs in PBMCs could serve as novel diagnostic and prognostic biomarkers for HBV-related HCC.

Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer

Simple Summary

Using the covariate-adjusted tensor classification in the high-dimension (CATCH) model, we integrated adjusted radiomics-based CT images into RNA immune genomic expression data to achieve the accurate classification of recurrent CRC. The correlation between radiomic features and immune gene expression identifies potential therapeutic targets in CRC. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC.

Abstract

To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with PECAM1, PRDM1, AIF1, IL10, ISG20, and TLR8 expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets.

Peripheral Blood Transcriptome in Breast Cancer Patients as a Source of Less Invasive Immune Biomarkers for Personalized Medicine, and Implications for Triple Negative Breast Cancer

Transcriptome studies of peripheral blood cells can advance our understanding of the systemic immune response to the presence of cancer and the mechanisms underlying cancer onset and progression. This enables the identification of novel minimally invasive immune biomarkers for early cancer detection and personalized cancer management and may bring forward new immunotherapy options. Recent blood gene expression analyses in breast cancer (BC) identified distinct patient subtypes that differed in the immune reaction to cancer and were distinct from the clinical BC subtypes, which are categorized based on expression of specific receptors on tumor cells. Introducing new BC subtypes based on peripheral blood gene expression profiles may be appropriate, since it may assist in BC prognosis, the identification of patients likely to benefit from immunotherapy, and treatment efficacy monitoring. Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous, and difficult-to-treat disease, and identification of novel biomarkers for this BC is crucial for clinical decision-making. A few studies have reported TNBC-enriched blood transcriptional signatures, mostly related to strong inflammation and augmentation of altered immune signaling, that can differentiate TNBC from other classical BC subtypes and facilitate diagnosis. Future research is geared toward transitioning from expression signatures in unfractionated blood cells to those in immune cell subpopulations.

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.

Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast Cancer

Simple Summary

Breast cancer is the most commonly diagnosed cancer in women today and accounts for thousands of cancer-related deaths each year. While some breast cancer subtypes can be easily diagnosed and targeted for therapy, triple-negative breast cancer, which lacks receptor expression, is the most challenging to diagnose and treat. In this study, we use multiple RNA sequencing data to look specifically at long non-coding RNA (lncRNA) expression portraits at the transcript level and to identify lncRNA-based biomarkers associated with each breast cancer subtype. Receiver operating characteristic (ROC) analysis was used to validate their diagnostic potential, which was validated in two independent cohorts. Several lncRNA transcripts were found to be enriched in TNBC across all validation cohorts. Binary regression analysis identified a four lncRNA transcript signature with the highest diagnostic power for TNBC as potential novel biomarkers for diagnostic and therapeutic intervention. Interestingly, several of the identified lncRNAs were shown to have prognostic potential in TNBC.

Abstract

Breast cancer remains the world’s most prevalent cancer, responsible for around 685,000 deaths globally despite international research efforts and advances in clinical management. While estrogen receptor positive (ER+), progesterone receptor positive (PR+), and human epidermal growth factor receptor positive (HER2+) subtypes are easily classified and can be targeted, there remains no direct diagnostic test for triple-negative breast cancer (TNBC), except for the lack of receptors expression. The identification of long non-coding RNAs (lncRNAs) and the roles they play in cancer progression has recently proven to be beneficial. In the current study, we utilize RNA sequencing data to identify lncRNA-based biomarkers associated with TNBC, ER+ subtypes, and normal breast tissue. The Marker Finder algorithm identified the lncRNA transcript panel most associated with each molecular subtype and the receiver operating characteristic (ROC) analysis was used to validate the diagnostic potential (area under the curve (AUC) of ≥8.0 and p value < 0.0001). Focusing on TNBC, findings from the discovery cohort were validated in an additional two cohorts, identifying 13 common lncRNA transcripts enriched in TNBC. Binary regression analysis identified a four lncRNA transcript signature (ENST00000425820.1, ENST00000448208.5, ENST00000521666.1, and ENST00000650510.1) with the highest diagnostic power for TNBC. The ENST00000671612.1 lncRNA transcript correlated with worse refractory free survival (RFS). Our data provides a step towards finding a novel diagnostic lncRNA-based panel for TNBC with potential therapeutic implications.

25-Hydroxyvitamin D status is associated with interleukin-6 methylation in adipose tissue from patients with colorectal cancer

A dysfunctional visceral adipose tissue (VAT) is characterized by increased production of proinflammatory cytokines, which may increase the risk of colorectal cancer (CRC). However, the epigenetic contribution to the inflammatory status is poorly understood. In our study, we hypothesized that a dysfunctional VAT may be a risk factor for CRC, through epigenetic modifications. Therefore, we aimed to study the transcriptional/methylation profile of proinflammatory cytokines and genes related to vitamin D metabolism in VAT from CRC patients, and evaluate their association with serum 25-hydroxyvitamin D (25(OH)D). We included 129 participants (68 healthy participants and 61 CRC patients). We found that the majority of the studied genes are upregulated and hypomethylated in CRC patients, when compared to the healthy subjects (p < 0.05). In addition, serum 25(OH)D was associated with both mRNA gene expression and methylation of key genes, such as interleukin (IL)6, IL10, vitamin D receptor (VDR) or cytochrome P450 subfamily 27 type B1 (CYP27B1) (p < 0.05). Interestingly, while high IL6 expression was related to poor survival in CRC (p < 0.05), IL6 methylation was associated with an increased risk of CRC, in which 25(OH)D partially mediated this association (p < 0.05). Our study suggests a potential association between epigenetic regulation of inflammatory mediators in VAT - such as IL6 - in the CRC context, in which 25(OH)D may mediate this risk. Therefore, vitamin D could affect the epigenetic status of IL6, which can be considered for additional preventive strategies.

Transcriptional alterations of protein coding and noncoding RNAs in triple negative breast cancer in response to DNA methyltransferases inhibition

Background

DNA methylation plays a crucial role in multiple cellular processes such as gene regulation, chromatin stability, and genetic imprinting. In mammals, DNA methylation is achieved by DNA methyltransferases (DNMTs). A number of studies have associated alterations in DNMT activity to tumorigenesis; however, the exact role of DNMTs in shaping the genome in triple negative breast cancer (TNBC) is still being unraveled.

Methods

In the current study, we employed two DNMT inhibitors (Decitabine and 5-Azacytidine), two TNBC models (MDA-MB-231 and BT-549) and whole transcriptome RNA-Seq and characterized the transcriptional alterations associated with DNMT inhibition. Colony forming unit (CFU), flow cytometry, and fluorescent microscopy were used to assess cell proliferation, cell cycle distribution, and cell death, respectively. Ingenuity pathway analysis (IPA) was used for network and pathway analyses.

Results

Remarkably, DNMT inhibition induced the expression of genes involved in endoplasmic reticulum response to stress, response to unfolder protein, as well as cobalamin metabolic processes. In contrast, suppression of cellular processes related to cell cycle and mitosis were hallmarks of DNMT inhibition. Concordantly, DNMT inhibition led to significant inhibition of TNBC cell proliferation, G2-M cell cycle arrest and induction of cell death. Mechanistically, DNMT inhibition activated TP53, NUPR1, and NFkB (complex) networks, while RARA, RABL6, ESR1, FOXM1, and ERBB2 networks were suppressed. Our data also identified the long noncoding RNA (lncRNA) transcriptional portrait associated with DNMT inhibition and identified 25 commonly upregulated and 60 commonly downregulated lncRNAs in response to Decitabine and 5-Azacytidinec treatment in both TNBC models. TPT1-AS1 was the most highly induced (6.3 FC), while MALAT1 was the most highly suppressed (− 7.0 FC) lncRNA in response to DNMT inhibition.

Conclusions

Taken together, our data provides a comprehensive view of transcriptome alterations in the coding and noncoding transcriptome in TNBC in response to DNMT inhibition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02213-2.

Epigenetic regulation of triple negative breast cancer (TNBC) by TGF-β signaling

TGFβ signaling plays crucial role during development and cancer, however the role for TGFβ signaling in regulating the noncoding part of the human genome in triple negative breast cancer (TNBC) is still being unraveled. Herein, we provide the transcriptional landscape of TNBC in response to TGFβ activation and subsequent inhibition employing SB431542, selective TGFβ1 Receptor ALK5 Inhibitor. Our data revealed 72 commonly upregulated [fold change (FC) ≥ 2.0], including PLAU, TPM1, TAGLN, COL1A1, TGFBI, and SNAI1, and 53 downregulated (FC ≤ 2.0) protein coding genes in BT-549 and MDA-MB-231 models in response to TGFβ1 activation. Alignment to the geocode (V33) identified 41 upregulated (FC ≥ 2.0) and 22 downregulated (FC ≤ 2.0) long non-coding RNA (lncRNA) in response to TGFβ1 activation, which were inhibited by concurrent treatment with SB431542. To place our data from the in vitro models into their clinical context, we identified AC015909.1, AC013451.1, CYP1B1-AS1, AC004862.1, LINC01824, AL138828.1, B4GALT1-AS1, AL353751.1, AC090826.3, AC104695.4, ADORA2A-AS1, PTPRG-AS1, LINC01943, AC026954.3, TPM1-AS, ZFPM2-AS1, AC007362.1, AC112721.2, MALAT1, AL513314.2, AC112721.1, AC010343.3, LINC01711, and MAP3K2-DT lncRNA expression to positively correlate with TGFβ1 expression in a cohort of 360 TNBC patients. To provide mechanistic insight into lncRNA regulation by TGFβ signaling, SMAD2/3 ChIp-Seq data from BT-549 TNBC model retrieved from Gene Expression Omnibus (GEO) revealed direct binding of SMAD2/SMAD3 to the promoter of AC112721.1, AC112721.2, MALAT1, HHIP-AS1, LINC00472, and SLC7A11, suggesting their direct regulation by TGFβ1/SMAD2/SMAD3 pathway. Interestingly, AC112721.1, AC112721.2 exhibited higher expression in TNBC compared to normal breast tissue suggesting a possible role for those lncRNA in TNBC biology. Our miRNA analysis in the BT-549 model in response to exogenous TGFB1 revealed several affected miRNAs (2.0 ≤ FC ≤ 2.0), whose expression pattern was reversed in the presence of SB431542, suggesting those miRNA as plausible targets for TGFβ regulation. In particular, we observed hsa-miR-1275 to be downregulated in response to TGFB1 which was highly predicted to regulate PCDH1, FIBCD1, FXYD7, GDNF, STC1, EDN1, ZSWIM4, FGF1, PPP1R9B, NUAK1, PALM2AKAP2, IGFL3, and SPOCK1 whose expression were upregulated in response to TGFβ1 stimulus. On the other hand, hsa-miR-181b-5p was among the top upregulated miRNAs in response to TGFB1, which is also predicted to regulate CDKN1B, TNFRSF11B, SIM1, and ARSJ in the BT-549 model. Taken together, our data is the first to provide such in depth analysis of lncRNA and miRNA epigenetic changes in response to TGFβ signaling in TNBC.

Integrated whole transcriptome and small RNA analysis revealed multiple regulatory networks in colorectal cancer

Colorectal cancer (CRC) remains a global disease burden and a leading cause of cancer related deaths worldwide. The identification of aberrantly expressed messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA), and the resulting molecular interactions and signaling networks is essential for better understanding of CRC, identification of novel diagnostic biomarkers and potential development of therapeutic interventions. Herein, we performed microRNA (miRNA) sequencing on fifteen CRC and their non-tumor adjacent tissues and whole transcriptome RNA-Seq on six paired samples from the same cohort and identified alterations in miRNA, mRNA, and lncRNA expression. Computational analyses using Ingenuity Pathway Analysis (IPA) identified multiple activated signaling networks in CRC, including ERBB2, RABL6, FOXM1, and NFKB networks, while functional annotation highlighted activation of cell proliferation and migration as the hallmark of CRC. IPA in combination with in silico prediction algorithms and experimentally validated databases gave insight into the complex associations and interactions between downregulated miRNAs and upregulated mRNAs in CRC and vice versa. Additionally, potential interaction between differentially expressed lncRNAs such as H19, SNHG5, and GATA2-AS1 with multiple miRNAs has been revealed. Taken together, our data provides thorough analysis of dysregulated protein-coding and non-coding RNAs in CRC highlighting numerous associations and regulatory networks thus providing better understanding of CRC.

Identification of BHLHE40 expression in peripheral blood mononuclear cells as a novel biomarker for diagnosis and prognosis of hepatocellular carcinoma

Novel and sensitive biomarkers is highly required for early detection and predicting prognosis of hepatocellular carcinoma (HCC). Here, we investigated transcription profiles from peripheral blood mononuclear cells (PBMCs) of 8 patients with HCC and PBMCs from co-culture model with HCC using RNA-Sequencing. These transcription profiles were cross compared with published microarray datasets of PBMCs in HCC to identify differentially expressed genes (DEGs). A total of commonly identified of 24 DEGs among these data were proposed as cancer-induced genes in PBMCs, including 18 upregulated and 6 downregulated DEGs. The KEGG pathway showed that these enriched genes were mainly associated with immune responses. Five up-regulated candidate genes including BHLHE40, AREG, SOCS1, CCL5, and DDIT4 were selected and further validated in PBMCs of 100 patients with HBV-related HCC, 100 patients with chronic HBV infection and 100 healthy controls. Based on ROC analysis, BHLHE40 and DDIT4 displayed better diagnostic performance than alpha-fetoprotein (AFP) in discriminating HCC from controls. Additionally, BHLHE40 and DDIT4 had high sensitivity for detecting AFP-negative and early-stage HCC. BHLHE40 was also emerged as an independent prognostic factor of overall survival of HCC. Together, our study indicated that BHLHE40 in PBMCs could be a promising diagnostic and prognostic biomarker for HBV-related HCC.

Identification of PBMC-based molecular signature associational with COVID-19 disease severity

The longevity of COVID-19 as a global pandemic, and the devastating effects it has had on certain subsets of individuals thus far has highlighted the importance of identifying blood-based biomarkers associated with disease severity. We employed computational and transcriptome analyses of publicly available datasets from PBMCs from 126 patients with COVID-19 admitted to ICU (n = 50), COVID-19 not admitted to ICU (n = 50), non-COVID-19 admitted to ICU (n = 16) and non-COVID-19 not admitted to ICU (n = 10), and utilized the Gencode V33 assembly to analyze protein coding mRNA and long noncoding RNA (lncRNA) transcriptomes in the context of disease severity. Our data identified several aberrantly expressed mRNA and lncRNA based biomarkers associated with SARS-CoV-2 severity, which in turn significantly affected canonical, upstream, and disease functions in each group of patients. Immune, interferon, and antiviral responses were severely suppressed in COVID-19 patients admitted to ICU versus those who were not admitted to ICU. Our data suggests a possible therapeutic approach for severe COVID-19 through administration of interferon therapy. Delving further into these biomarkers, roles and their implications on the onset and disease severity of COVID-19 could play a crucial role in patient stratification and identifying varied therapeutic options with diverse clinical implications.

Molecular subtyping and functional validation of TTK, TPX2, UBE2C, and LRP8 in sensitivity of TNBC to paclitaxel

Triple-negative breast cancer (TNBC) patients exhibit variable responses to chemotherapy, suggesting an underlying molecular heterogeneity. In the current study, we analyzed publicly available transcriptome data from 360 TNBC and 88 normal breast tissues, which revealed activation of nucleosome and cell cycle as the hallmarks of TNBC. Mechanistic network analysis identified activation of FOXM1 and ERBB2, and suppression of TP53 and NURP1 networks in TNBC. Employing Iterative Clustering and Guide-gene Selection (ICGS), Uniform Manifold Approximation and Projection (UMAP), and dimensionality reduction analyses, we classified TNBC into seven molecular subtypes, each exhibiting a unique molecular signature, including immune infiltration (CD19, CD8, and macrophages) and mesenchymal signature, which correlated with variable disease outcomes in a larger cohort (1,070) of BC. Mechanistically, depletion of TTK, TPX2, UBE2C, CDCA7, MELK, NFE2L3, DDX39A, and LRP8 led to substantial inhibition of colony formation of TNBC models, which was further enhanced in the presence of paclitaxel. Our data provide novel insights into the molecular heterogeneity of TNBC and identified TTK, TPX2, UBE2C, and LRP8 as main drivers of TNBC tumorigenesis.

Single-cell long noncoding RNA (lncRNA) transcriptome implicates MALAT1 in triple-negative breast cancer (TNBC) resistance to neoadjuvant chemotherapy

Cumulative evidence suggests added benefit for neoadjuvant chemotherapy (NAC) in a subset of triple-negative breast cancer (TNBC) patients. Herein we identified the long noncoding RNA (lncRNA) transcriptional landscape associated with TNBC resistance to NAC, employing 1758 single cells from three extinction and three persistence TNBC patients. Using Iterative Clustering and Guide-gene Selection (ICGS) and uniform manifold approximation and projection (UMAP) dimensionality reduction analysis, we observed single cells derived from each patient to largely cluster together. Comparing the lncRNA transcriptome from single cells through the course of NAC treatment revealed minimal overlap based on lncRNA transcriptome, suggesting substantial effects of NAC on lncRNA transcription. The differential analysis revealed upregulation of 202 and downregulation of 19 lncRNAs in the persistence group, including upregulation of five different transcripts encoding for the MALAT1 lncRNA. CRISPR/Cas9-mediated MALAT1 promoter deletion in BT-549 TNBC model enhanced sensitivity to paclitaxel and doxorubicin, suggesting a role for MALAT1 in conferring resistance. Mechanistically, whole transcriptome analysis of MALAT1-KO cells revealed multiple affected mechanistic networks as well as oxidative phosphorylation canonical and angiogenesis functional category. Interestingly, lncRNA profiling of MALAT1-depleted TNBC also revealed a number of altered lncRNAs in response to MALAT1 deletion, suggesting a reciprocal relationship between MALAT1 and a number of lncRNAs, including NEAT1, USP3-AS1, and LINC-PINT, in TNBC. Elevated expression of MALAT1, USP3-AS1, and LINC-PINT correlated with worse clinical outcomes in BC patients. Our data revealed the lncRNA transactional portrait and highlighted a complex regulatory network orchestrated by MALAT1 in the context of TNBC resistance to NAC therapy.

RNA-Seq Analysis of Colorectal Tumor-Infiltrating Myeloid-Derived Suppressor Cell Subsets Revealed Gene Signatures of Poor Prognosis

Elevated levels of myeloid-derived suppressor cells (MDSCs), including polymorphonuclear MDSCs (PMN-MDSCs) and immature MDSCs (I-MDSCs), are usually associated with disease progression in cancer patients, including colorectal cancer (CRC). However, biological mechanisms and molecular pathways regulated by MDSC subpopulations in the CRC tumor microenvironment (TME) have not been fully investigated. In this study, we performed transcriptomic analysis of tumor-infiltrating I-MDSCs and PMN-MDSCs isolated from tumor tissues of six CRC patients, compared to antigen-presenting cells (APCs). We also compared the transcriptomic profiles of tumor-infiltrating PMN-MDSCs to I-MDSCs. Our results showed different molecular pathways regulated by each MDSC subset, potentially reflecting their phenotypical/molecular/functional characteristics in the CRC TME. Moreover, we identified gene signatures in PMN-MDSC and I-MDSC of poor overall survival (OS) and disease-free survival (DFS) using the Cancer Genome Atlas (TCGA) dataset from patients with colon adenocarcinoma (COAD). However, functional studies are required to validate these findings.

Single-Cell Transcriptome Analysis Highlights a Role for Neutrophils and Inflammatory Macrophages in the Pathogenesis of Severe COVID-19

Cumulative data link cytokine storms with coronavirus disease 2019 (COVID-19) severity. The precise identification of immune cell subsets in bronchoalveolar lavage (BAL) and their correlation with COVID-19 disease severity are currently being unraveled. Herein, we employed iterative clustering and guide-gene selection 2 (ICGS2) as well as uniform manifold approximation and projection (UMAP) dimensionality reduction computational algorithms to decipher the complex immune and cellular composition of BAL, using publicly available datasets from a total of 68,873 single cells derived from two healthy subjects, three patients with mild COVID-19, and five patients with severe COVID-19. Our analysis revealed the presence of neutrophils and macrophage cluster-1 as a hallmark of severe COVID-19. Among the identified gene signatures, IFITM2, IFITM1, H3F3B, SAT1, and S100A8 gene signatures were highly associated with neutrophils, while CCL8, CCL3, CCL2, KLF6, and SPP1 were associated with macrophage cluster-1 in severe-COVID-19 patients. Interestingly, although macrophages were also present in healthy subjects and patients with mild COVID-19, they had different gene signatures, indicative of interstitial and cluster-0 macrophage (i.e., FABP4, APOC1, APOE, C1QB, and NURP1). Additionally, MALAT1, NEAT1, and SNGH25 were downregulated in patients with mild and severe COVID-19. Interferon signaling, FCγ receptor-mediated phagocytosis, IL17, and Tec kinase canonical pathways were enriched in patients with severe COVID-19, while PD-1 and PDL-1 pathways were suppressed. A number of upstream regulators (IFNG, PRL, TLR7, PRL, TGM2, TLR9, IL1B, TNF, NFkB, IL1A, STAT3, CCL5, and others) were also enriched in BAL cells from severe COVID-19-affected patients compared to those from patients with mild COVID-19. Further analyses revealed genes associated with the inflammatory response and chemotaxis of myeloid cells, phagocytes, and granulocytes, among the top activated functional categories in BAL from severe COVID-19-affected patients. Transcriptome data from another cohort of COVID-19-derived peripheral blood mononuclear cells (PBMCs) revealed the presence of several genes common to those found in BAL from patients with severe and mild COVID-19 (IFI27, IFITM3, IFI6, IFIT3, MX1, IFIT1, OASL, IFI30, OAS1) or to those seen only in BAL from severe-COVID-19 patients (S100A8, IFI44, IFI44L, CXCL8, CCR1, PLSCR1, EPSTI1, FPR1, OAS2, OAS3, IL1RN, TYMP, BCL2A1). Taken together, our data reveal the presence of neutrophils and macrophage cluster-1 as the main immune cell subsets associated with severe COVID-19 and identify their inflammatory and chemotactic gene signatures, also partially reflected systemically in the circulation, for possible diagnostic and therapeutic interventions.

Computational and Transcriptome Analyses Revealed Preferential Induction of Chemotaxis and Lipid Synthesis by SARS-CoV-2

The continuous and rapid emergence of new viral strains calls for a better understanding of the fundamental changes occurring within the host cell upon viral infection. In this study, we analyzed RNA-seq transcriptome data from Calu-3 human lung epithelial cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) compared to five other viruses namely, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East Respiratory Syndrome (SARS-MERS), influenzavirus A (FLUA), influenzavirus B (FLUB), and rhinovirus (RHINO) compared to mock-infected cells and characterized their coding and noncoding RNA transcriptional portraits. The induction of interferon, inflammatory, and immune response was a hallmark of SARS-CoV-2 infection. Comprehensive bioinformatics revealed the activation of immune response and defense response to the virus as a common feature of viral infection. Interestingly however, the degree of functional categories and signaling pathways activation varied among different viruses. Ingenuity pathways analysis highlighted altered conical and casual pathways related to TNF, IL1A, and TLR7, which are seen more predominantly during SARS-CoV-2 infection. Nonetheless, the activation of chemotaxis and lipid synthesis was prominent in SARS-CoV-2-infected cells. Despite the commonality among all viruses, our data revealed the hyperactivation of chemotaxis and immune cell trafficking as well as the enhanced fatty acid synthesis as plausible mechanisms that could explain the inflammatory cytokine storms associated with severe cases of COVID-19 and the rapid spread of the virus, respectively.

Transcriptomic Profiling of Circulating HLA-DR- Myeloid Cells, Compared with HLA-DR+ Myeloid Antigen-presenting Cells

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of cells with potent immunosuppressive functions, which can inhibit the activation of immune responses under a steady-state condition and pathological conditions. We performed transcriptomic profiling of circulating CD33⁺HLA-DR⁺ myeloid antigen-presenting cells (APCs) and CD33⁺HLA-DR^- myeloid cells (potentially MDSCs) in healthy individuals. We sorted both subpopulations from peripheral blood mononuclear cells (PBMCs) of 10 healthy donors and performed RNA sequencing (RNA-Seq). We found that several signaling pathways associated with the positive regulation of immune responses, such as antigen presentation/processing, FcγR-mediated phagocytosis and immune cell trafficking, phosphoinositide 3-kinase (PI3K)/Akt signaling, DC maturation, triggering receptor expressed on myeloid cells 1 (TREM1) signaling, nuclear factor of activated T cells (NFAT) and IL-8 signaling were downregulated in CD33⁺HLA-DR^- myeloid cells. In contrast, pathways implicated in tumor suppression and anti-inflammation, including peroxisome proliferator-activated receptor (PPAR) and phosphatase and tensin homolog (PTEN), were upregulated in CD33⁺HLA-DR^- myeloid cells. These data indicate that PPAR/PTEN axis could be upregulated in myeloid cells to keep the immune system in check in normal physiological conditions. Our data reveal some of the molecular and functional differences between CD33⁺HLA-DR⁺ APCs and CD33⁺HLA-DR^- myeloid cells in a steady-state condition, reflecting the potential suppressive function of CD33⁺HLA-DR^- myeloid cells to maintain immune tolerance. For future studies, the same methodological approach could be applied to perform transcriptomic profiling of myeloid subsets in pathological conditions.

Protein Coding and Long Noncoding RNA (lncRNA) Transcriptional Landscape in SARS-CoV-2 Infected Bronchial Epithelial Cells Highlight a Role for Interferon and Inflammatory Response

The global spread of COVID-19, caused by pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need for an imminent response from medical research communities to better understand this rapidly spreading infection. Employing multiple bioinformatics and computational pipelines on transcriptome data from primary normal human bronchial epithelial cells (NHBE) during SARS-CoV-2 infection revealed activation of several mechanistic networks, including those involved in immunoglobulin G (IgG) and interferon lambda (IFNL) in host cells. Induction of acute inflammatory response and activation of tumor necrosis factor (TNF) was prominent in SARS-CoV-2 infected NHBE cells. Additionally, disease and functional analysis employing ingenuity pathway analysis (IPA) revealed activation of functional categories related to cell death, while those associated with viral infection and replication were suppressed. Several interferon (IFN) responsive gene targets (IRF9, IFIT1, IFIT2, IFIT3, IFITM1, MX1, OAS2, OAS3, IFI44 and IFI44L) were highly upregulated in SARS-CoV-2 infected NBHE cell, implying activation of antiviral IFN innate response. Gene ontology and functional annotation of differently expressed genes in patient lung tissues with COVID-19 revealed activation of antiviral response as the hallmark. Mechanistic network analysis in IPA identified 14 common activated, and 9 common suppressed networks in patient tissue, as well as in the NHBE cell model, suggesting a plausible role for these upstream regulator networks in the pathogenesis of COVID-19. Our data revealed expression of several viral proteins in vitro and in patient-derived tissue, while several host-derived long noncoding RNAs (lncRNAs) were identified. Our data highlights activation of IFN response as the main hallmark associated with SARS-CoV-2 infection in vitro and in human, and identified several differentially expressed lncRNAs during the course of infection, which could serve as disease biomarkers, while their precise role in the host response to SARS-CoV-2 remains to be investigated.

Transgelin is a poor prognostic factor associated with advanced colorectal cancer (CRC) stage promoting tumor growth and migration in a TGFβ-dependent manner

Colorectal cancer (CRC) is the fourth most common cancer type globally. Investigating the signaling pathways that maintain cancer cell phenotype can identify new biomarkers for targeted therapy. Aberrant transforming growth factor-β (TGFβ) signaling has been implicated in CRC progression, however, the exact mechanism by which TGFβ exerts its function is still being unraveled. Herein, we investigated TAGLN expression, prognostic value, and its regulation by TGFβ in CRC. While TAGLN was generally found to be downregulated in CRC, elevated expression of TAGLN was associated with advanced CRC stage and predicted poor overall survival (hazard ratio (HR) = 1.8, log-rank test P-value = 0.014) and disease-free survival (HR = 1.6, log-rank test P-value = 0.046), hence implicating TAGLN as poor prognostic factor in CRC. Forced expression of TAGLN was associated with enhanced CRC cell proliferation, clonogenic growth, cell migration and in vivo tumor formation in immunocompromised mice, while targeted depletion of TAGLN exhibited opposing biological effects. Global gene expression profiling of TAGLN-overexpressing or TAGLN-deficient CRC cell lines revealed deregulation of multiple cancer-related genes and signaling pathways. Transmission electron microscopy (TEM) revealed ultrastructural changes due to loss of TAGLN, including disruption of actin cytoskeleton organization and aberrant actin filament distribution. Hierarchical clustering, principle component, and ingenuity pathway analyses revealed distinct molecular profile associated with TAGLN^high CRC patients with remarkable activation of a number of mechanistic networks, including SMARCA4, TGFβ1, and P38 MAPK. The P38 MAPK was the top predicted upstream regulator network promoting cell movement through regulation of several intermediate molecules, including TGFβ1. Concordantly, functional categories associated with cellular movement and angiogenesis were also enriched in TAGLN^high CRC, supporting a model for the molecular mechanisms linking TGFβ-induced upregulation of TAGLN and CRC tumor progression and suggesting TAGLN as potential prognostic marker associated with advanced CRC pathological stage.