Genome-wide expression analysis of Middle Eastern colorectal cancer reveals FOXM1 as a novel target for cancer therapy

Therapeutic Landscape of FOXM1 in Triple-Negative Breast Cancer and Aggressive Solid Cancers

Triple-negative breast cancer (TNBC) is one of the most aggressive forms of breast cancer, lacking common treatment targets such as estrogen (ER), progesterone (PR), and HER2 receptors. This subtype is associated with significant heterogeneity, chemoresistance, early recurrence, metastasis, and poor patient survival. FOXM1 is a cancer-promoting transcription factor that plays a critical role in TNBC and other highly aggressive cancers by driving cell proliferation, invasion, metastasis, and drug resistance. In TNBC, mutations in the TP53 gene-detected in approximately 80% of patients-lead to the overexpression of FOXM1, making it a promising therapeutic target. Beyond TNBC, FOXM1 is implicated in other solid cancers, such as brain (glioblastoma), lung, and pancreatic cancers, and is considered an Achilles' heel of aggressive cancers. Despite its potential as a therapeutic target, there are currently no FDA-approved FOXM1 inhibitors, and none have advanced to clinical trials. This review explores the role of FOXM1 in cancer progression and highlights the current status of efforts to develop effective FOXM1 inhibitors.

Insights of Expression Profile of Chemokine Family in Inflammatory Bowel Diseases and Carcinogenesis

Chemokines are integral components of the immune system and deeply involved in the pathogenesis and progression of inflammatory bowel disease (IBD) and colorectal cancer (CRC). Although a considerable amount of transcriptome data has been accumulated on these diseases, most of them are limited to a specific stage of the disease. The purpose of this study is to visually demonstrate the dynamic changes in chemokines across various stages of bowel diseases by integrating relevant datasets. Integrating the existing datasets for IBD and CRC, we compare the expression changes of chemokines across different pathological stages. This study collected 11 clinical databases from various medical centers around the world. Patients: Data of patient tissue types were classified into IBD, colorectal adenoma, primary carcinoma, metastasis, and healthy control according to the publisher's annotation. The expression changes in chemokines in various pathological stages are statistically analyzed. The chemokines were clustered by different expression patterns. The chemokine family was clustered into four distinct expression patterns, which correspond to varying expression changes in different stages of colitis and tumor development. Certain chemokines and receptors associated with inflammation and tumorigenesis have been identified. Furthermore, it was confirmed that the 2,4,6-trinitrobenzenesulfonic acid (TNBS)-induced colitis model and the azoxymethane (AOM)/ dextran sulfate sodium (DSS)-induced colon cancer model shows stronger correlations with the clinical data in terms of chemokine expression levels. This study paints a panoramic picture of the expression profiles of chemokine families at multiple stages from IBD to advanced colon cancer, facilitating a comprehensive understanding of the regulation patterns of chemokines and guiding the direction of drug development. This study provides researchers with a clear atlas of chemokine expression in the pathological processes of inflammatory bowel disease and colon cancer.

Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Its complexity is influenced by various signal transduction networks that govern cellular proliferation, survival, differentiation, and apoptosis. The pathogenesis of CRC is a testament to the dysregulation of these signaling cascades, which culminates in the malignant transformation of colonic epithelium. This review aims to dissect the foundational signaling mechanisms implicated in CRC, to elucidate the generalized principles underpinning neoplastic evolution and progression. We discuss the molecular hallmarks of CRC, including the genomic, epigenomic and microbial features of CRC to highlight the role of signal transduction in the orchestration of the tumorigenic process. Concurrently, we review the advent of targeted and immune therapies in CRC, assessing their impact on the current clinical landscape. The development of these therapies has been informed by a deepening understanding of oncogenic signaling, leading to the identification of key nodes within these networks that can be exploited pharmacologically. Furthermore, we explore the potential of integrating AI to enhance the precision of therapeutic targeting and patient stratification, emphasizing their role in personalized medicine. In summary, our review captures the dynamic interplay between aberrant signaling in CRC pathogenesis and the concerted efforts to counteract these changes through targeted therapeutic strategies, ultimately aiming to pave the way for improved prognosis and personalized treatment modalities in colorectal cancer.

Novel benzothiazole/benzothiazole thiazolidine-2,4-dione derivatives as potential FOXM1 inhibitors: In silico, synthesis, and in vitro studies

The oncogenic transcription factor FOXM1 overexpressed in breast and other solid cancers, is a key driver of tumor growth and progression through complex interactions, making it an attractive molecular target for the development of targeted therapies. Despite the availability of small-molecule inhibitors, their limited specificity, potency, and efficacy hinder clinical translation. To identify effective FOXM1 inhibitors, we synthesized novel benzothiazole derivatives (KC10-KC13) and benzothiazole hybrids with thiazolidine-2,4-dione (KC21-KC36). These compounds were evaluated for FOXM1 inhibition. Molecular docking and molecular dynamics simulation analysis revealed their binding patterns and affinities for the FOXM1-DNA binding domain. The interactions with key amino acids such as Asn283, His287, and Arg286, crucial for FOXM1 inhibition, have been determined with the synthesized compounds. Additionally, the molecular modeling study indicated that KC12, KC21, and KC30 aligned structurally and interacted similarly to the reference compound FDI-6. In vitro studies with the MDA-MB-231 breast cancer cell line demonstrated that KC12, KC21, and KC30 significantly inhibited FOXM1, showing greater potency than FDI-6, with IC50 values of 6.13, 10.77, and 12.86 µM, respectively, versus 20.79 µM for FDI-6. Our findings suggest that KC12, KC21, and KC30 exhibit strong activity as FOXM1 inhibitors and may be suitable for in vivo animal studies.

PTOV1 facilitates colorectal cancer cell proliferation through activating AKT1 signaling pathway

Background

Colorectal cancer is a predominant contributor to global cancer-related morbidity and mortality. The oncogene PTOV1 has been linked to various human malignancies, yet its specific role in CRC pathogenesis requires further elucidation.

Methods

Our study used a comprehensive array of authoritative bioinformatics tools, such as TIMER, UCSC Xena, GEO, Human Protein Atlas, UALCAN, CIBERSORTx and others which used to investigate the complex effects of PTOV1 on gene expression profiles, diagnostic and prognostic biomarkers, tumor immunology, signaling pathways, epigenetic alterations, and genetic mutations. Gene expression validation was conducted using Western blot and qRT-PCR. The in vitro proliferative and migratory potentials of CRC cells were evaluated using CCK-8 assays, colony formation, and transwell migration assays, respectively. MSP was applied to assess the methylation status of the PTOV1 promoter region.

Results

Our results reveal a significant association between increased PTOV1 expression, driven by promoter hypomethylation, and poor patient prognosis in CRC. Elevated PTOV1 levels were positively correlated with the enrichment of diverse immune cell subsets and immune-related molecules within the tumor microenvironment. In vitro assays demonstrated that PTOV1 knockdown markedly reduced CRC cell proliferation, colony formation, and migration, while ectopic PTOV1 expression had the opposite effect. Importantly, PTOV1 was shown to regulate the PI3K-AKT signaling pathway, significantly influencing the phosphorylation of AKT1 and the expression of cell cycle regulators P21 and P27. The pharmacological inhibition of AKT1 phosphorylation using MK2206 effectively counteracted the proliferative effects induced by PTOV1 overexpression.

Conclusion

The ability of PTOV1 to enhance CRC cell proliferation via modulation of the AKT1 signaling pathway establishes it as a potential therapeutic target and a promising biomarker for prognostic stratification in CRC.

Expression of Forkhead Box M1 and Anticancer Effects of FOXM1 Inhibition in Epithelioid Sarcoma

Epithelioid sarcoma (ES) is a rare aggressive sarcoma that, unlike most soft-tissue sarcomas, shows a tendency toward local recurrence and lymph node metastasis. Novel antitumor agents are needed for ES patients. Forkhead box transcription factor 1 (FOXM1) is a member of the Forkhead transcription factor family and is associated with multiple oncogenic functions; FOXM1 is known to be overexpressed and correlated with pathogenesis in various malignancies. In this study, we immunohistochemically analyzed FOXM1 expression levels and their clinical, clinicopathologic, and prognostic significance in 38 ES specimens. In addition, to investigate potential correlations between FOXM1 downregulation and oncologic characteristics, we treated ES cell lines with thiostrepton, a naturally occurring antibiotic that inhibits both small interfering RNA (siRNA) and FOXM1. In the analyses using ES samples, all 38 specimens were diagnosed as positive for FOXM1 by immunohistochemistry. We separated specimens into high (n = 19) and low (n = 19) FOXM1-protein expression groups by staining index score, and into large (n = 12), small (n = 25), and unknown (n = 1) tumor-size groups using a cutoff of 5 cm maximum diameter. Although there were significantly more samples with high FOXM1 expression in the large tumor group (P = .013), there were no significant differences with respect to age (P = 1.00), sex (P = .51), primary site of origin (P = .74), histologic subtypes (P = 1.00), depth (P = .74), or survival rate (P = .288) between the high and low FOXM1-protein expression groups. In the in vitro experiments using ES cell lines, FOXM1 siRNA and thiostrepton successfully downregulated FOXM1 mRNA and protein expression. Furthermore, downregulation of FOXM1 inhibited cell proliferation, drug resistance against chemotherapeutic agents, migration, and invasion and caused cell cycle arrest in the ES cell lines. Finally, cDNA microarray analysis data showed that FOXM1 regulated cIAP2, which is one of the apoptosis inhibitors activated by the TNFα-mediated NF-κB pathway. In conclusion, the FOXM1 gene may be a promising therapeutic target for ES.

Integrated edge information and pathway topology for drug-disease associations

Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.

Clinicopathological Impact of FOXM1 and MMP-9 Immunohistochemical Expression in Different Grades of Intracranial Meningioma

OBJECTIVES

To find predictive biomarkers for recurrence and progression of meningioma.

BACKGROUND

Despite great advances in meningioma treatment, the prognosis remained unfavorable due to the high recurrence rate.

METHODS

In this study, we evaluated the immunohistochemical expression of FOXM1, MMP-9, and Ki67 in 50 cases of intracranial meningioma to detect its potential role in meningioma progression, recurrence, and patients' survival.

RESULTS

Strong FOXM1 expression was detected in 20% of the cases and was significantly associated with meningioma grade ( P = 0.002) and peritumoral brain edema (PTBE; P <0.001). Strong MMP-9 expression was noted in 32% of the cases and was significantly associated with meningioma grade and PTBE ( P <0.001, P <0.001, respectively). High Ki67 was noted in 50% and significantly associated with tumor grade and PTBE ( P <0.001, P = 0.002, respectively). The follow-up period revealed that meningiomas with strong FOXM1, strong MMP-9, and high Ki67 expression were associated with tumor recurrence, shorter OS, and recurrence-free survival. Furthermore, up-regulation of FOXM1 and MMP-9 expression had a significant relation with poor clinical response to the therapy ( P = 0.010, P = 0. 001, respectively). However, high Ki67 cases were more sensitive to clinical therapy ( P = 0.005).

CONCLUSION

Strong FOXM1, strong MMP-9, and high Ki67 in meningiomas indicate highly aggressive tumors with a shortened survival rate, dismal outcome, and high risk of recurrence after the standard protocol of therapy.

A drug repurposing study identifies novel FOXM1 inhibitors with in vitro activity against breast cancer cells

FOXM1, a proto-oncogenic transcription factor, plays a critical role in cancer development and treatment resistance in cancers, particularly in breast cancer. Thus, this study aimed to identify potential FOXM1 inhibitors through computational screening of drug databases, followed by in vitro validation of their inhibitory activity against breast cancer cells. In silico studies involved pharmacophore modeling using the FOXM1 inhibitor, FDI-6, followed by virtual screening of DrugBank and Selleckchem databases. The selected drugs were prepared for molecular docking, and the crystal structure of FOXM1 was pre-processed for docking simulations. In vitro studies included MTT assays to assess cytotoxicity, and Western blot analysis to evaluate protein expression levels. Our study identified Pantoprazole and Rabeprazole as potential FOXM1 inhibitors through in silico screening and molecular docking. Molecular dynamics simulations confirmed stable interactions of these drugs with FOXM1. In vitro experiments showed both Pantoprazole and Rabeprazole exhibited strong FOXM1 inhibition at effective concentrations and that showed inhibition of cell proliferation. Rabeprazole showed the inhibitor activity at 10 µM in BT-20 and MCF-7 cell lines. Pantoprazole exhibited FOXM1 inhibition at 30 µM and in BT-20 cells and at 70 µM in MCF-7 cells, respectively. Our current study provides the first evidence that Rabeprazole and Pantoprazole can bind to FOXM1 and inhibit its activity and downstream signaling, including eEF2K and pEF2, in breast cancer cells. These findings indicate that rabeprazole and pantoprazole inhibit FOXM1 and breast cancer cell proliferation, and they can be used for FOXM1-targeted therapy in breast or other cancers driven by FOXM1.

DEPDC5 protects CD8+ T cells from ferroptosis by limiting mTORC1-mediated purine catabolism

Peripheral CD8+ T cell number is tightly controlled but the precise molecular mechanism regulating this process is still not fully understood. In this study, we found that epilepsy patients with loss of function mutation of DEPDC5 had reduced peripheral CD8+ T cells, and DEPDC5 expression positively correlated with tumor-infiltrating CD8+ T cells as well as overall cancer patient survival, indicating that DEPDC5 may control peripheral CD8+ T cell homeostasis. Significantly, mice with T cell-specific Depdc5 deletion also had reduced peripheral CD8+ T cells and impaired anti-tumor immunity. Mechanistically, Depdc5-deficient CD8+ T cells produced high levels of xanthine oxidase and lipid ROS due to hyper-mTORC1-induced expression of ATF4, leading to spontaneous ferroptosis. Together, our study links DEPDC5-mediated mTORC1 signaling with CD8+ T cell protection from ferroptosis, thereby revealing a novel strategy for enhancing anti-tumor immunity via suppression of ferroptosis.

Small-molecule inhibitors targeting FOXM1: Current challenges and future perspectives in cancer treatments

Forkhead box (FOX) protein M1 (FOXM1) is a critical proliferation-associated transcription factor (TF) that is aberrantly overexpressed in the majority of human cancers and has also been implicated in poor prognosis. A comprehensive understanding of various aspects of this molecule has revealed its role in, cell proliferation, cell migration, invasion, angiogenesis and metastasis. The FOXM1 as a TF directly or indirectly regulates the expression of several target genes whose dysregulation is associated with almost all hallmarks of cancer. Moreover, FOXM1 expression is associated with chemoresistance to different anti-cancer drugs. Several studies have confirmed that suppression of FOXM1 enhanced the drug sensitivity of various types of cancer cells. Current data suggest that small molecule inhibitors targeting FOXM1 in combination with anticancer drugs may represent a novel therapeutic strategy for chemo-resistant cancers. In this review, we discuss the clinical utility of FOXM1, further, we summarize and discuss small-molecule inhibitors targeting FOXM1 and categorize them according to their mechanisms of targeting FOXM1. Despite great progress, small-molecule inhibitors targeting FOXM1 face many challenges, and we present here all small-molecule FOXM1 inhibitors in different stages of development. We discuss the current challenges and provide insights on the future application of FOXM1 inhibition to the clinic.

In vitro evaluation of Neosetophomone B inducing apoptosis in cutaneous T cell lymphoma by targeting the FOXM1 signaling pathway

BACKGROUND

Cutaneous T cell lymphoma (CTCL) is a T cell-derived non-Hodgkin lymphoma primarily affecting the skin, with treatment posing a significant challenge and low survival rates.

OBJECTIVE

In this study, we investigated the anti-cancer potential of Neosetophomone B (NSP-B), a fungal-derived secondary metabolite, on CTCL cell lines H9 and HH.

METHODS

Cell viability was measured using Cell counting Kit-8 (CCK8) assays. Apoptosis was measured by annexin V/PI dual staining. Immunoblotting was performed to examine the expression of proteins. Applied Biosystems' high-resolution Human Transcriptome Array 2.0 was used to examine gene expression.

RESULTS

NSP-B induced apoptosis in CTCL cells by activating mitochondrial signaling pathways and caspases. We observed downregulated expression of BUB1B, Aurora Kinases A and B, cyclin-dependent kinases (CDKs) 4 and 6, and polo-like kinase 1 (PLK1) in NSP-B treated cells, which was further corroborated by Western blot analysis. Notably, higher expression levels of these genes showed reduced overall and progression-free survival in the CTCL patient cohort. FOXM1 and BUB1B expression exhibited a dose-dependent reduction in NSP-B-treated CTCL cells.FOXM1 silencing decreased cell viability and increased apoptosis via BUB1B downregulation. Moreover, NSP-B suppressed FOXM1-regulated genes, such as Aurora Kinases A and B, CDKs 4 and 6, and PLK1. The combined treatment of Bortezomib and NSP-B showed greater efficacy in reducing CTCL cell viability and promoting apoptosis compared to either treatment alone.

CONCLUSION

Our findings suggest that targeting the FOXM1 pathway may provide a promising therapeutic strategy for CTCL management, with NSP-B offering significant potential as a novel treatment option.

A drug repurposing method based on inhibition effect on gene regulatory network

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

Drug Repurposing Analysis for Colorectal Cancer through Network Medicine Framework: Novel Candidate Drugs and Small Molecules

This study aimed to reveal the drug-repurposing candidates for colorectal cancer (CRC) via drug-repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol (PEG), gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.

Identification of the prognostic biomarkers and their correlations with immune infiltration in colorectal cancer through bioinformatics analysis and in vitro experiments

Colorectal cancer (CRC) is the third most diagnosed malignancy and the second leading cause of cancer death. The objective was to identify novel hub genes that were helpful for prognosis and targeted therapy in CRC. GSE23878, GSE24514, GSE41657, GSE81582 were filtered from the gene expression omnibus (GEO). Differentially expressed genes (DEGs) were identified through GEO2R, which were enriched in the GO term and KEGG pathway in DAVID. PPI network was constructed and analyzed using STRING and hub genes were screened out. The relationships between hub genes and prognoses in CRC were evaluated in GEPIA based on the cancer genome atlas (TCGA) and genotype-tissue expression (GTEx). The transcription factors and miRNA-mRNA interaction networks for hub genes were performed using miRnet and miRTarBase. The relationship between hub genes and tumor-infiltrating lymphocytes were analyzed in TIMER. The protein levels of hub genes were identified in HPA. The expression levels of hub gene in CRC and its effect on the biological effect of CRC cells were identified in vitro. As hub genes, the mRNA levels of BIRC5, CCNB1, KIF20A, NCAPG, and TPX2 were highly expressed in CRC and had excellent prognostic value. The BIRC5, CCNB1, KIF20A, NCAPG, and TPX2 were closely associated with transcription factors, miRNAs, tumor-infiltrating lymphocytes, suggesting their involvement in the regulation of CRC. BIRC5 highly expressed in CRC tissues and cells, and promoted the proliferation, migration, and invasion of CRC cells. BIRC5, CCNB1, KIF20A, NCAPG, and TPX2 are hub genes that serve as promising prognostic biomarkers in CRC. BIRC5 plays an important role in the development and progression of CRC.

KLF5-mediated CDCA5 expression promotes tumor development and progression of epithelial ovarian carcinoma

Cell division cycle associated 5 (CDCA5) is correlated with the development and progression of many malignant tumors. However, little is known about its role in epithelial ovarian cancer (EOC) progression. In this study, the clinical value, biological function and underlying mechanisms of CDCA5 in EOC were evaluated. CDCA5 mRNA and protein levels were substantially upregulated in EOC and had a significant positive correlation with adverse clinicopathological characteristics and a poor prognosis. CDCA5 facilitated proliferation, invasion, and metastasis and disrupted mitochondrial-mediated endogenous apoptosis by activating the cell cycle pathway and inhibiting the P53 pathway in EOC cells. Conversely, knockdown of CDCA5 expression blocked the malignant activities of EOC cells and suppressed the growth of xenograft tumors in vivo. Mechanistically, the transcription factor KLF5 bound to a specific site in the CDCA5 promoter and promoted CDCA5 expression. Moreover, KLF5 overexpression rescued the negative regulation of inhibited CDCA5 expression on EOC cell proliferation. In conclusion, our findings revealed that CDCA5 promoted tumor progression of EOC via the KLF5/CDCA5/cell cycle and P53 axes, which might provide new insights into the roles of CDCA5 in EOC.

Transcriptomic and Cellular Content Analysis of Colorectal Cancer by Combining Multiple Independent Cohorts

INTRODUCTION

By linking cellular content and molecular subtypes of colorectal cancer (CRC), we aim to uncover novel features useful for targeted therapy. Our first goal was to evaluate gene expression alterations linked to CRC pathogenesis, and then, we aimed to evaluate the cellular composition differences between normal colon mucosa and tumor and between different colon cancer molecular subtypes.

METHODS

We collected microarray and RNA sequencing data of patients with CRC from the Genome Expression Omnibus and The Cancer Genome Atlas. We combined all cases and performed quantile normalization. Genes with a fold change of >2 were further investigated. We used xCell for cellular decomposition and CMScaller for molecular subtyping. For statistical analyses, the Kruskal-Wallis H test and Mann-Whitney U tests were performed with Bonferroni correction.

RESULTS

We established an integrated database of normal colon and CRC using transcriptomic data of 1,082 samples. By using this data set, we identified genes showing the highest differential expression in colon tumors. The top genes were linked to calcium signaling, matrix metalloproteinases, and transcription factors. When compared with normal samples, CD4+ memory T cells, CD8+ naive T cells, CD8+ T cells, Th1 cells, Th2 cells, and regulatory T cells were enriched in tumor tissues. The ImmuneScore was decreased in tumor samples compared with normal samples. The CMS1 and CMS4 molecular subtypes were the most immunogenic, with the highest ImmuneScore but also high infiltration by CD8+ T cells, Th1 cells, and Th2 cells in CMS1 and B-cell subtypes and CD8+ T cells in CMS4.

DISCUSSION

Our analysis uncovers features enabling advanced treatment selection and the development of novel therapies in CRC.

Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer

Background: Colorectal cancer (CRC) is the third most common cancer and third leading cause of cancer-associated deaths worldwide. Diagnosing CRC patients reliably at an early and curable stage is of utmost importance to reduce the risk of mortality. Methods: We identified global differentially expressed genes with copy number alterations in patients with CRC. We then identified genes that are also expressed in blood, which resulted in a blood-based gene signature. We validated the gene signature's diagnostic and prognostic potential using independent datasets of gene expression profiling from over 800 CRC patients with detailed clinical data. Functional enrichment, gene interaction networks and pathway analyses were also performed. Results: The analysis revealed a 17-gene signature that is expressed in blood and demonstrated that it has diagnostic potential. The 17-gene SVM classifier displayed 99 percent accuracy in predicting the patients with CRC. Moreover, we developed a prognostic model and defined a risk-score using 17-gene and validated that high risk score is strongly associated with poor disease outcome. The 17-gene signature predicted disease outcome independent of other clinical factors in the multivariate analysis (HR = 2.7, 95% CI = 1.3-5.3, p = 0.005). In addition, our gene network and pathway analyses revealed alterations in oxidative stress, STAT3, ERK/MAPK, interleukin and cytokine signaling pathways as well as potentially important hub genes, including BCL2, MS4A1, SLC7A11, AURKA, IL6R, TP53, NUPR1, DICER1, DUSP5, SMAD3, and CCND1. Conclusion: Our results revealed alterations in various genes and cancer-related pathways that may be essential for CRC transformation. Moreover, our study highlights diagnostic and prognostic value of our gene signature as well as its potential use as a blood biomarker as a non-invasive diagnostic method. Integrated analysis transcriptomic data coupled with copy number aberrations may provide a reliable method to identify key biological programs associated with CRC and lead to improved diagnosis and therapeutic options.

Dysregulated FOXM1 signaling in the regulation of cancer stem cells

Since the introduction of the cancer stem cell (CSC) paradigm, significant advances have been made in understanding the functional and biological plasticity of these elusive components in malignancies. Endowed with self-renewing abilities and multilineage differentiation potential, CSCs have emerged as cellular drivers of virtually all facets of tumor biology, including metastasis, tumor recurrence/relapse, and drug resistance. The functional and biological characteristics of CSCs, such as self-renewal, cell fate decisions, survival, proliferation, and differentiation are regulated by an array of extracellular factors, signaling pathways, and pluripotent transcriptional factors. Besides the well-characterized regulatory role of transcription factors OCT4, SOX2, NANOG, KLF4, and MYC in CSCs, evidence for the central role of Forkhead box transcription factor FOXM1 in the establishment, maintenance, and functions of CSCs is accumulating. Conventionally identified as a master regulator of the cell cycle, a comprehensive understanding of this molecule has revealed its multifarious oncogenic potential and uncovered its role in angiogenesis, invasion, migration, self-renewal, and drug resistance. This review compiles the large body of literature that has accumulated in recent years that provides evidence for the mechanisms by which FOXM1 expression promotes stemness in glioblastoma, breast, colon, ovarian, lung, hepatic, and pancreatic carcinomas. We have also compiled the data showing the association of stem cell mediators with FOXM1 using TCGA mRNA expression data. Further, the prognostic importance of FOXM1 and other stem cell markers is presented. The delineation of FOXM1-mediated regulation of CSCs can aid in the development of molecularly targeted pharmacological approaches directed at the selective eradication of CSCs in several human malignancies.

m6A Methyltransferase KIAA1429 Regulates the Cisplatin Sensitivity of Gastric Cancer Cells via Stabilizing FOXM1 mRNA

Simple Summary

N6-methyladenosine (m⁶A) is involved in the development of drug resistance in various cancer types. The role of N6-methyladenosine (m⁶A) methyltransferase, KIAA1429, in the resistance of gastric cancer to cisplatin is largely unknown. In this study, the KIAA1429 expression level as well as m⁶A content were found to be higher in cisplatin resistant gastric cancer cells, and KIAA1429 regulated the sensitivity of gastric cancer cells to cisplatin treatment. We then identified p65 as the regulator of KIAA1429 expression. Mechanistically, KIAA1429 regulated the sensitivity of gastric cancer cells to cisplatin by stabilizing FOXM1 mRNA via YTHDF1. The findings from this study suggest that KIAA1429 could be a therapeutic target of cisplatin resistance in gastric cancer.

Abstract

Although cisplatin is frequently used to treat gastric cancer, the resistance is the main obstacle for effective treatment. mRNA modification, N6-methyladenosine (m⁶A), is involved in the tumorigenesis of many types of cancer. As one of the largest m⁶A methyltransferase complex components, KIAA1429 bridges the catalytic m⁶A methyltransferase components, such as METTL3. In gastric cancer, KIAA1429 was reported to promote cell proliferation. However, whether KIAA1429 is involved in the resistance of gastric cancer to cisplatin remains unclear. Here, we generated cisplatin resistant gastric cancer cell lines, and compared the m⁶A content between resistant cells and wild type cells. The m⁶A content as well as KIAA1429 expression are higher in resistant cells. Interestingly, the expression of KIAA1429 was significantly increased after cisplatin treatment. We then used shRNA to knockdown KIAA1429 and found that resistant cells responded more to cisplatin treatment after KIAA1429 depletion, while overexpression of KIAA1429 decreased the sensitivity. Moreover, we identified a putative p65 binding site on the promoter area of KIAA1429 and ChIP assay confirmed the binding. p65 depletion decreased the expression of KIAA1429. YTHDF1 is the most abundant m⁶A “reader” that interacts with m⁶A modified mRNA. Mechanistically, YTHDF1 was recruited to the 3′-untranslated Region (3′-UTR) of transcriptional factor, FOXM1 by KIAA1429 and stabilized FOXM1 mRNA. More importantly, KIAA1429 knockdown increased the sensitivity of resistant cells to cisplatin in vivo. In conclusion, our results demonstrated that KIAA1429 facilitated cisplatin resistance by stabilizing FOXM1 mRNA in gastric cancer cells.

HDAC6-dependent deacetylation of AKAP12 dictates its ubiquitination and promotes colon cancer metastasis

Aberrant expression of histone deacetylase 6 (HDAC6) is greatly involved in neoplasm metastasis, which is a leading cause of colon cancer related death. Thus, deep understanding of the regulatory mechanisms of HDAC6 in the metastasis of colon cancer is warranted. In this study, we firstly found that HDAC6 expression was highly expressed in metastatic colon cancer tissues and inhibition or knockdown of HDAC6 suppressed colon cancer metastasis. Next, based on proteomic analysis we uncovered A-kinase anchoring protein 12 (AKAP12) was a novel substrate of HDAC6. HDAC6 interacted with AKAP12 and deacetylated the K526/K531 residues of AKAP12. Moreover, deacetylation of AKAP12 at K531 by HDAC6 increased its ubiquitination level, which facilitated AKAP12 proteasome-dependent degradation. Importantly, we observed an inverse correlation between AKAP12 and HDAC6 protein levels with human colon cancer specimens. Further deletion of AKAP12 in HDAC6 knockdown cells restored the cell motility defects and reactivated the protein kinase C isoforms, repression of which were responsible for the inhibition of cancer metastasis of AKAP12. Our study identified AKAP12 was a new interactor and substrate of HDAC6 and uncovered a novel mechanism through which HDAC6-dependent AKAP12 deacetylation led to its ubiquitination mediated degradation and promoted colon cancer metastasis.

The Underlying Roles of Exosome-Associated PIGR in Fatty Acid Metabolism and Immune Signaling in Colorectal Cancer

The polymeric immunoglobulin receptor (PIGR), an exosome-associated glycoprotein, plays an important role in the occurrence and development of different tumors. This study aimed to investigate whether PIGR is essential for colorectal cancer (CRC). Comprehensive bioinformatics analysis and immunohistochemistry (IHC) revealed that expression of PIGR was significantly decreased in CRC patients. Upregulated PIGR displayed favorable prognostic values in CRC patients. Several algorithms, such as TISIDB and TIMER, were used to evaluate the roles of PIGR expression in the regulation of immune response in CRC. Moreover, GSEA enrichment analysis indicated the underlying role of PIGR in the regulation of fatty acid metabolism in CRC. Taken together, our findings might provide a new potential prognostic and immune-associated biomarker for CRC and supply a new destination for PIGR-related immunotherapy in clinical treatment.

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Downregulation of LRRC19 Is Associated with Poor Prognosis in Colorectal Cancer

OBJECTIVE

Colorectal cancer (CRC) is globally one of the most often diagnosed cancers with high mortality rates. This study aimed to explore novel biomarkers for the diagnosis and prognosis of CRC.

METHODS

We collected 4 datasets about CRC in GEO and sought differentially expressed genes (DEGs) with GEO2R. Leucine-rich repeat-containing protein 19 (LRRC19) expression was assessed through the Oncomine and TIMER database analyses, which was further confirmed by qRT-PCR of CRC samples. We used online survival analysis tools (GEPIA, PrognoScan, and Kaplan-Meier plotter) to examine the prognostic value of LRRC19 in CRC and other malignancies. GO and KEGG enrichment analyses were employed to explore the biological functions of LRRC19. Finally, we conducted network prediction by STRING and further validation on the GEPIA to discover other molecules that might interact with LRRC19.

RESULTS

A total of 21 upregulated and 46 downregulated DEGs were identified from the 4 datasets. The TIMER and Oncomine online analyses showed lower mRNA of LRRC19 in CRC tissues compared with adjacent normal tissues, which was validated by qRT-PCR in CRC patient samples. The survival analysis through the GEPIA and PrognoScan websites revealed that low LRRC19 expression was significantly correlated with poor prognosis in CRC patients. The Kaplan-Meier plotter survival analysis indicated that low LRRC19 expression was significantly associated with the disease progression of patients with ovarian cancer, gastric cancer, breast cancer, and lung cancer. The enrichment analysis suggested that low expression of LRRC19 could be involved in the retinol metabolism and the zymogen granule membrane. Through STRING and GEPIA, it was found that LRRC19 is clearly associated with ZCCHC10, MOB3B, IMMP2L, and TRMT11.

CONCLUSION

LRRC19 mRNA was prominently decreased in human CRC tissues and was significantly associated with shorter survival in CRC patients. LRRC19 might serve as a possible target for early diagnosis and prognosis assessment in CRC.

Ferroptosis-Related Gene MT1G as a Novel Biomarker Correlated With Prognosis and Immune Infiltration in Colorectal Cancer

Ferroptosis, a newly discovered way of cell death, has been proved to be involved in the oncogenesis and development of cancers, including colorectal cancer (CRC). Here, by identifying the differentially expressed genes (DEGs) from three CRC transcriptome microarray datasets (GSE20842, GSE23878, and GSE25070), we found that the expression of MT1G was significantly decreased in CRC tissues, and the patients with a high level of MT1G displayed a poor prognosis. Quantitative PCR (qPCR) further confirmed the downregulated MT1G in two CRC cells, HCT8 and HCT116. The colony-forming assay indicated that the MT1G overexpression exhibited a remarkable inhibition of cell proliferation in HCT8 and HCT116 cells. In addition, we explored the co-expressed genes of MT1G to gain a better understanding of its potential signaling pathways. Aberrantly expressed MT1G also affected the immune response of CRC patients. Collectively, these findings might deepen our comprehension on the potential biological implications of MT1G in CRC.

Single-cell and spatial analysis reveal interaction of FAP+ fibroblasts and SPP1+ macrophages in colorectal cancer

Colorectal cancer (CRC) is among the most common malignancies with limited treatments other than surgery. The tumor microenvironment (TME) profiling enables the discovery of potential therapeutic targets. Here, we profile 54,103 cells from tumor and adjacent tissues to characterize cellular composition and elucidate the potential origin and regulation of tumor-enriched cell types in CRC. We demonstrate that the tumor-specific FAP⁺ fibroblasts and SPP1⁺ macrophages were positively correlated in 14 independent CRC cohorts containing 2550 samples and validate their close localization by immuno-fluorescent staining and spatial transcriptomics. This interaction might be regulated by chemerin, TGF-β, and interleukin-1, which would stimulate the formation of immune-excluded desmoplasic structure and limit the T cell infiltration. Furthermore, we find patients with high FAP or SPP1 expression achieved less therapeutic benefit from an anti-PD-L1 therapy cohort. Our results provide a potential therapeutic strategy by disrupting FAP⁺ fibroblasts and SPP1⁺ macrophages interaction to improve immunotherapy.

Tumour microenvironment profiling during colorectal cancer progression may enable the discovery of therapeutic targets. Here, single cell and spatial RNA sequencing of tumour and adjacent normal tissues reveals an interaction between FAP⁺ fibroblasts and SPP1⁺ macrophages that could be disrupted as an immunotherapy strategy.

Identification of four novel hub genes as monitoring biomarkers for colorectal cancer

Background

It must be admitted that the incidence of colorectal cancer (CRC) was on the rise all over the world, but the related treatment had not caught up. Further research on the underlying pathogenesis of CRC was conducive to improving the survival status of current CRC patients.

Methods

Differentially expressed genes (DEGs) screening were conducted based on “limma” and “RobustRankAggreg” package of R software. Weighted gene co-expression network analysis (WGCNA) was performed in the integrated DEGs that from The Cancer Genome Atlas (TCGA), and all samples of validation were from Gene Expression Omnlbus (GEO) dataset.

Results

The terms obtained in the functional annotation for primary DEGs indicated that they were associated with CRC. The MEyellow stand out whereby showed the significant correlation with clinical feature (disease), and 4 hub genes, including ABCC13, AMPD1, SCNN1B and TMIGD1, were identified in yellow module. Nine datasets from Gene Expression Omnibus database confirmed these four genes were significantly down-regulated and the survival estimates for the low-expression group of these genes were lower than for the high-expression group in Kaplan-Meier survival analysis section. MEXPRESS suggested that down-regulation of some top hub genes may be caused by hypermethylation. Receiver operating characteristic curves indicated that these genes had certain diagnostic efficacy. Moreover, tumor-infiltrating immune cells and gene set enrichment analysis for hub genes suggested that there were some associations between these genes and the pathogenesis of CRC.

Conclusion

This study identified modules that were significantly associated with CRC, four novel hub genes, and further analysis of these genes. This may provide a little new insights and directions into the potential pathogenesis of CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00216-7.

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression

Transcriptional profiling of pre- and post-malignant colorectal cancer (CRC) lesions enable temporal monitoring of molecular events underlying neoplastic progression. However, the most widely used transcriptomic dataset for CRC, TCGA-COAD, is devoid of adenoma samples, which increases reliance on an assortment of disparate microarray studies and hinders consensus building. To address this, we developed a microarray meta-dataset comprising 231 healthy, 132 adenoma, and 342 CRC tissue samples from twelve independent studies. Utilizing a stringent analytic framework, select datasets were downloaded from the Gene Expression Omnibus, normalized by frozen robust multiarray averaging and subsequently merged. Batch effects were then identified and removed by empirical Bayes estimation (ComBat). Finally, the meta-dataset was filtered for low variant probes, enabling downstream differential expression as well as quantitative and functional validation through cross-platform correlation and enrichment analyses, respectively. Overall, our meta-dataset provides a robust tool for investigating colorectal adenoma formation and malignant transformation at the transcriptional level with a pipeline that is modular and readily adaptable for similar analyses in other cancer types.

Thiostrepton inhibits growth and induces apoptosis by targeting FoxM1/SKP2/MTH1 axis in B-precursor acute lymphoblastic leukemia cells

Forkhead box M1 (FoxM1) is a transcription factor that plays an important role in the etiology of many cancers, however, its role has not been elucidated in B-precursor acute lymphoblastic leukemia (B-pre-ALL). In the current study, we showed that the downregulation of FoxM1 by its inhibitor thiostrepton inhibited cell viability and induced caspase-dependent apoptosis in a panel of B-pre-ALL cell lines. Thiostrepton led downregulation of FoxM1 accompanied by decreased expression of Aurora kinase A, B, matrix metalloproteinases, and oncogene SKP2 as well as MTH1. Downregulation of the FoxM1/SKP2/MTH1 axis led to increase in the Bax/Bcl2 ratio and suppression of antiapoptotic proteins. Thiostrepton-mediated apoptosis was prevented by N-acetyl cysteine, a scavenger of reactive oxygen species. Co-treatment of B-pre-ALL with subtoxic doses of thiostrepton and bortezomib potentiated the proapoptotic action. Altogether, our results suggest that targeting FoxM1expression could be an attractive strategy for the treatment of B-pre-ALL.

Expression and Clinical Significance of Lactate Dehydrogenase A in Colon Adenocarcinoma

Lactate dehydrogenase A (LDHA) is an important glycolytic enzyme that promotes glycolysis and plays a crucial role in cancer cell invasion and immune infiltration. However, the relevance of LDHA in colon adenocarcinoma (COAD) remains unclear. In this study, we analyzed the correlation between the expression of LDHA and clinicopathological characteristics in COAD using immunohistochemistry analysis, and then used integrative bioinformatics analyses to further study the function and role of LDHA in COAD. We found that LDHA was highly expressed in COAD tissues compared with adjacent normal tissues, and that COAD patients with high LDHA expression levels showed poor survival. In addition, LDHA expression was closely associated with the immune infiltrating levels of CD8+ T cells, neutrophils, and dendritic cells. Our findings highlight the potential role of LDHA in the tumorigenesis and prognosis of COAD. Furthermore, our results indicate that COAD is a novel immune checkpoint in the diagnosis and treatment of COAD.

Ordering of Omics Features Using Beta Distributions on Montecarlo p-Values

The current trend in genetic research is the study of omics data as a whole, either combining studies or omics techniques. This raises the need for new robust statistical methods that can integrate and order the relevant biological information. A good way to approach the problem is to order the features studied according to the different kinds of data so a key point is to associate good values to the features that permit us a good sorting of them. These values are usually the p-values corresponding to a hypothesis which has been tested for each feature studied. The Montecarlo method is certainly one of the most robust methods for hypothesis testing. However, a large number of simulations is needed to obtain a reliable p-value, so the method becomes computationally infeasible in many situations. We propose a new way to order genes according to their differential features by using a score defined from a beta distribution fitted to the generated p-values. Our approach has been tested using simulated data and colorectal cancer datasets from Infinium methylationEPIC array, Affymetrix gene expression array and Illumina RNA-seq platforms. The results show that this approach allows a proper ordering of genes using a number of simulations much lower than with the Montecarlo method. Furthermore, the score can be interpreted as an estimated p-value and compared with Montecarlo and other approaches like the p-value of the moderated t-tests. We have also identified a new expression pattern of eighteen genes common to all colorectal cancer microarrays, i.e., 21 datasets. Thus, the proposed method is effective for obtaining biological results using different datasets. Our score shows a slightly smaller type I error for small sizes than the Montecarlo p-value. The type II error of Montecarlo p-value is lower than the one obtained with the proposed score and with a moderated p-value, but these differences are highly reduced for larger sample sizes and higher false discovery rates. Similar performances from type I and II errors and the score enable a clear ordering of the features being evaluated.

Evaluating the transcriptional fidelity of cancer models

BACKGROUND

Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors.

METHODS

We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence.

RESULTS

We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity.

CONCLUSIONS

CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package ( https://github.com/pcahan1/cancerCellNet ) and as a web application ( http://www.cahanlab.org/resources/cancerCellNet_web ) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

PRMT5 promotes colorectal cancer growth by interaction with MCM7

Protein arginine methyltransferase 5 (PRMT5) is a type of methyltransferase enzyme that can catalyse arginine methylation of histones and non‐histone proteins. Accumulating evidence indicates that PRMT5 promotes cancer development and progression. However, its function in colorectal cancer (CRC) is poorly understood. In this study, we revealed the oncogenic roles of PRMT5 in CRC. We found that PRMT5 promoted CRC cell proliferation, migration and invasion in vitro and in vivo. We identified minichromosome maintenance‐7 (MCM7) as the direct PRMT5‐binding partner. A co‐immunoprecipitation (co‐IP) assay indicated that PRMT5 physically interacted with MCM7 and that the direct binding domain was located between residues 1‐248 in MCM7. In addition, our results from analysis of 99 CRC tissues and 77 adjacent non‐cancerous tissues indicated that the PRMT5 and MCM7 expression levels were significantly higher in CRC tissues than in control tissues, which was further confirmed by bioinformatic analysis using TCGA and GEO datasets. We also found that MCM7 promoted CRC cell proliferation, migration and invasion in vitro. Furthermore, we observed that increased PRMT5 expression predicted unfavourable patient survival in CRC patients and in the subgroup of patients with a tumour size of ≤5 cm. These data suggested that PRMT5 and MCM7 might be novel potential targets for the treatment of CRC.

Chronic exposure to microcystin-LR increases the risk of prostate cancer and induces malignant transformation of human prostate epithelial cells

Microcystins-LR (MC-LR) acts as a possible carcinogen for humans and causes a serious risk to public environmental health. The current study aimed to evaluate the interaction between MC-LR exposure and prostate cancer development and elucidate the underlying mechanism. In this study, mice were exposed to MC-LR at various doses for 180 days. MC-LR was able to induce the progression of prostatic intraepithelial neoplasia (PIN) and microinvasion. Furthermore, MC-LR notably increased angiogenesis and susceptibility to prostate cancer in vivo. In vitro, over 25 weeks of MC-LR exposure, normal human prostate epithelial (RWPE-1) cells increased secretion of matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), and colony formation, features typical for cancer cells. These MC-LR-transformed prostate epithelial cells displayed increased expression of forkhead box M1 (FOXM1) and cyclooxygenase-2 (COX-2); abrogation of FOXM1 or COX-2 activity by specific inhibitors could abolish the invasion and migration of MC-LR-treated cells. In conclusion, we have provided compelling evidence demonstrating the induction of a malignant phenotype in human prostate epithelial cells and the in vivo development of prostate cancer by exposure to MC-LR, which might be a potential tumor promoter in the progression of prostate cancer.

Long noncoding RNA DLEU2 affects the proliferative and invasive ability of colorectal cancer cells

Emerging evidence indicates that long noncoding RNAs (lncRNAs) are closely associated with colorectal cancer (CRC) tumorigenesis. One example is lncRNA Deleted in Lymphocytic Leukemia 2 (DLEU2). However, how DLEU2 contributes to CRC is still poorly understood. This study sought to investigate the effects of DLEU2 on CRC pathogenesis, and the underlying mechanism involved. Using a quantitative real-time polymerase chain reaction (qRT-PCR) assay, we demonstrated that the expression levels of DLEU2 in 45 pairs of CRC tissues were higher than those in the corresponding normal colon mucosal tissues. In addition, CRC patients with high DLEU2 expression levels exhibited poor overall survival (OS) and recurrence-free survival (RFS), as determined by analyses and measurements from the GEO and GEPIA databases. When DLEU2 was silenced using short interfering RNA (siRNA) in CRC cell line, the results demonstrated that DLEU2 silencing suppressed CRC cell tumorigenesis in vitro, which was associated with decreased expression of cyclin dependent kinase 6(CDK6), ZEB1, and ZEB2 as well as enhancing the expression of Cyclin-dependent kinase inhibitor 1A (CDKN1A). Taken together, the results of this study suggested that DLEU2 may play critical roles in the progression of CRC and may serve as a prognostic biomarker for CRC.

Transcription factors in colorectal cancer: molecular mechanism and therapeutic implications

Colorectal cancer (CRC) is a major cause of cancer mortality worldwide, however, the molecular mechanisms underlying the pathogenesis of CRC remain largely unclear. Recent studies have revealed crucial roles of transcription factors in CRC development. Transcription factors essential for the regulation of gene expression by interacting with transcription corepressor/enhancer complexes and they orchestrate downstream signal transduction. Deregulation of transcription factors is a frequent occurrence in CRC, and the accompanying drastic changes in gene expression profiles play fundamental roles in multistep process of tumorigenesis, from cellular transformation, disease progression to metastatic disease. Herein, we summarized current and emerging key transcription factors that participate in CRC tumorigenesis, and highlighted their oncogenic or tumor suppressive functions. Moreover, we presented critical transcription factors of CRC, emphasized the major molecular mechanisms underlying their effect on signal cascades associated with tumorigenesis, and summarized of their potential as molecular biomarkers for CRC prognosis therapeutic response, as well as drug targets for CRC treatment. A better understanding of transcription factors involved in the development of CRC will provide new insights into the pathological mechanisms and reveal novel prognostic biomarkers and therapeutic strategies for CRC.

Omics Integration Analysis Unravel the Landscape of Driving Mechanisms of Colorectal Cancer

Colorectal cancer (CRC) is one of the most malignant cancers and results in a substantial rate of morbidity and mortality. Diagnosis of this malignancy in early stages increases the chance of effective treatment. High-throughput data analyses reveal omics signatures and also provide the possibility of developing computational models for early detection of this disease. Such models would be able to use as complementary tools for early detection of different types of cancers including CRC. In this study, using gene expression data, the Flux balance analysis (FBA) applied to decode metabolic fluxes in cancer and normal cells. Moreover, transcriptome and genome analyses revealed driver agents of CRC in a biological network scheme. By applying comprehensive publicly available data from TCGA, different aspect of CRC regulome including the regulatory effect of gene expression, methylation, microRNA, copy number aberration and point mutation profile over protein levels investigated and the results provide a regulatory picture underlying CRC. Compiling omics profiles indicated snapshots of changes in different omics levels and flux rate of CRC. In conclusion, considering obtained CRC signatures and their role in biological operating systems of cells, the results suggest reliable driver regulatory modules that could potentially serve as biomarkers and therapeutic targets and furthermore expand our understanding of driving mechanisms of this disease.
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The forkhead box M1 (FOXM1) expression and antitumor effect of FOXM1 inhibition in malignant rhabdoid tumor

PURPOSE

Malignant rhabdoid tumor (MRT) is a rare, highly aggressive sarcoma with an uncertain cell of origin. Despite the existing standard of intensive multimodal therapy, the prognosis of patients with MRT is very poor. Novel antitumor agents are needed for MRT patients. Forkhead box transcription factor 1 (FOXM1) is overexpressed and is correlated with the pathogenesis in several human malignancies. In this study, we identified the clinicopathological and prognostic values of the expression of FOXM1 and its roles in the progression of MRT.

METHODS

We investigated the FOXM1 expression levels and their clinical significance in 23 MRT specimens using immunohistochemistry and performed clinicopathologic and prognostic analyses. We also demonstrated correlations between the downregulation of FOXM1 and oncological characteristics using small interfering RNA (siRNA) and FOXM1 inhibitor in MRT cell lines.

RESULTS

Histopathological analyses revealed that primary renal MRTs showed significantly low FOXM1 protein expression levels (p = 0.032); however, there were no significant differences in other clinicopathological characteristics or the survival rate. FOXM1 siRNA and FOXM1 inhibitor (thiostrepton) successfully downregulated the mRNA and protein expression of FOXM1 in vitro and the downregulation of FOXM1 inhibited cell proliferation, drug resistance to chemotherapeutic agents, migration, invasion, and caused the cell cycle arrest and apoptosis of MRT cell lines. A cDNA microarray analysis showed that FOXM1 regulated FANCD2 and NBS1, which are key genes for DNA damage repair.

CONCLUSION

This study demonstrates that FOXM1 may serve as a promising therapeutic target for MRT.

Identifying disease-associated signaling pathways through a novel effector gene analysis

Background

Signaling pathway analysis methods are commonly used to explain biological behaviors of disease cells. Effector genes typically decide functional attributes (associated with biological behaviors of disease cells) by abnormal signals they received. The signals that the effector genes receive can be quite different in normal vs. disease conditions. However, most of current signaling pathway analysis methods do not take these signal variations into consideration.

Methods

In this study, we developed a novel signaling pathway analysis method called signaling pathway functional attributes analysis (SPFA) method. This method analyzes the signal variations that effector genes received between two conditions (normal and disease) in different signaling pathways.

Results

We compared the SPFA method to seven other methods across 33 Gene Expression Omnibus datasets using three measurements: the median rank of target pathways, the median p-value of target pathways, and the percentages of significant pathways. The results confirmed that SPFA was the top-ranking method in terms of median rank of target pathways and the fourth best method in terms of median p-value of target pathways. SPFA’s percentage of significant pathways was modest, indicating a good false positive rate and false negative rate. Overall, SPFA was comparable to the other methods. Our results also suggested that the signal variations calculated by SPFA could help identify abnormal functional attributes and parts of pathways. The SPFA R code and functions can be accessed at https://github.com/ZhenshenBao/SPFA.

Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).

Transcriptomic analysis on the effects of melatonin in gastrointestinal carcinomas

BACKGROUND

Melatonin has been shown with anticancer property and therapeutic potential for tumors. However, there lacks a systematic study on the molecular pathways of melatonin and its antitumor effects in gastrointestinal carcinomas.

METHODS

Using the gene expression profiles of four cancer cell lines from three types of gastrointestinal carcinomas before and after melatonin treatment, including gastric carcinoma (GC), colorectal carcinoma (CRC) and hepatocellular carcinoma (HCC), differentially expressed genes (DEGs) and biological pathways influenced by melatonin were identified. The qRT-PCR analyses were performed to validate the effects of melatonin on 5-FU resistance-related genes in CRC.

RESULTS

There were 17 pathways commonly altered by melatonin in the three cancer types, including FoxO signaling pathways enriched by the upregulated DEGs and cell cycle signaling pathways enriched by the downregulated DEGs, confirmed the dual role of melatonin to tumor growth, pro-apoptosis and anti-proliferation. DEGs upregulated in the three types of cancer tissues but reversely downregulated by melatonin were commonly enriched in RNA transport, spliceosome and cell cycle signaling pathways, which indicate that melatonin might exert antitumor effects through these pathways. Our results further showed that melatonin can downregulate the expression levels of 5-FU resistance-related genes, such as thymidylate synthase in GC and ATR, CHEK1, BAX and MYC in CRC. The qRT-PCR results demonstrated that melatonin enhanced the sensitivity of CRC 5-FU resistant cells by decreasing the expression of ATR.

CONCLUSIONS

Melatonin exerts the effects of pro-apoptosis and anti-proliferation on gastrointestinal carcinomas, and might increase the sensitivity of 5-FU in GC and CRC patients.

Down-regulated Solute Carrier Family 4 Member 4 Predicts Poor Progression in Colorectal Cancer

Aim: To identify potential key candidate genes, whose expression and clinical significance was further assessed in colorectal cancer (CRC).

Methods: Three original microarray datasets (GSE41328, GSE22598, and GSE23878) from NCBI-GEO were used to analyze differentially expressed genes (DEGs) in CRC. Online database analyses through Oncomine and GEIPA were performed to evaluate SLC4A4 expression and explore the prognostic merit of SLC4A4 expression, which was further confirmed by analyses from QPCR based cDNA array and IHC based tissue microarray (TMA). STRING website was used to explore the interaction between SLC4A4 with other DEGs based on the protein-protein interaction (PPI) networks.

Results: Analysis of three original microarray datasets from GEO identified 82 shared, differentially expressed genes (28 upregulated and 54 down-regulated) in CRC tissues. Online analyses from Oncomine and GEIPA revealed lower SLC4A4 mRNA expression in CRC tissues compared to adjacent normal tissues, which were further confirmed by QPCR based cDNA array and IHC based TMA analyses on both mRNA and protein levels. Survival analyses through GEIPA and from TMA demonstrated that low SLC4A4 expression is correlated with worse overall survival among patients with CRC. Survival analysis from Kaplan-meier plotter demonstrated that low SLC4A4 expression is significantly associated with poor progression (including relapse-free survival, overall survival, distant metastasis-free survival, post-progression survival) of patients with breast cancer, lung cancer, gastric cancer, and ovarian cancer. PPI analysis found that SLC4A4 is highly correlated with various genes, including SLC9A3, SLC26A6, ENSG00000214921, SLC26A4, SLC9A3R1, and SLC9A1.

Conclusion: The mRNA and protein levels of SLC4A4 were decreased in CRC tissues, and low expression of SLC4A4 significantly correlated with shorter survival of CRC patients and poorer progression of patients with breast cancer, lung cancer, gastric cancer and ovarian cancer, suggesting potential role of SLC4A4 on tumor suppression and prognostic prediction in multiple malignancies including CRC.

E3 ligase RCHY1 negatively regulates HDAC2

HDAC2, one of the class I histone deacetylase regulates epigenetic landscape through histone modification. Because HDAC2 is overexpressed in many cancers, cancer therapeutics against HDAC2 have been developed. Here we show novel mechanism of HDAC2 regulation by E3 ligase RCHY1. We found inverse correlation RCHY1 and HDAC2 levels in tumor tissue from six independent dataset using meta-analysis. Ectopic expression of RCHY1 decreased the level of HDAC2 from cancer cells including p53 wildtype, mutant and null cells. In addition, HDAC2 was increased by RCHY1 knockdown. RCHY1 directly interacts with HDAC2. Ectopic expression of wild type but not RING mutant RCHY1 increased HDAC2 levels. These data provide an evidence that RCHY1 negatively regulates HDAC2.

Deep learning with evolutionary and genomic profiles for identifying cancer subtypes

Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated to contain informative signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains largely unclear. Here, we identified the evolutionarily conserved genes (i.e. core genes) and observed that they are primarily involved in cellular pathways relevant to cell growth and metabolisms. By using these core genes, we developed two novel strategies, namely a feature-based strategy (FES) and an image-based strategy (IMS) by integrating their evolutionary and genomic profiles with the deep learning algorithm. In comparison with the FES using the random set and the strategy using the PAM50 classifier, the core gene set-based FES achieved a higher accuracy for identifying breast cancer subtypes. The IMS and FES using the core gene set yielded better performances than the other strategies, in terms of classifying both breast cancer subtypes and multiple cancer types. Moreover, the IMS is reproducible even using different gene expression data (i.e. RNA-seq and microarray). Comprehensive analysis of eight cancer types demonstrates that our evolutionary conservation-based models represent a valid and helpful approach for identifying cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.

FOXM1 promotes the growth and metastasis of colorectal cancer via activation of β-catenin signaling pathway

Purpose

Our previous study proved that FOXM1 regulates colorectal cancer (CRC) cell metastasis through epithelial–mesenchymal transition program. The aim of this study is to further explore the underlying mechanism of FOXM1 in CRC.

Materials and methods

In this study, we detected the mRNA and protein expressions of FOXM1 and β-catenin in CRC tissues and their corresponding normal-appearing tissues (NATs) by quantitative reverse transcription-PCR and western blot analysis, respectively. Then the potential link between FOXM1 and β-catenin in CRC tissues was analyzed. Furthermore, we systematically analyzed the biological functions of FOXM1 in CRC cells after reconstitution of FOXM1 expression in vitro. Moreover, the mechanism of FOXM1-promoted CRC progression by improving β-catenin nuclear translocation was also discussed.

Results

Our data demonstrated that FOXM1 and β-catenin were upregulated in CRC tissues compared with the corresponding NATs (P<0.05). Clinicopathologic analysis revealed that increased FOXM1 (or β-catenin) expression positively correlated with some clinicopathologic features, such as tumor size, TNM stage, lymphatic metastasis, and distant metastasis (P<0.05). Meanwhile, the possible relationships between FOXM1 and β-catenin in CRC samples were evaluated using SPSS software, and a significant positive correlation was found (P<0.05). In vitro data demonstrate that elevated FOXM1 expression exerted oncogenic effects on CRC via activation of β-catenin signaling pathway. The inhibition of β-catenin by siRNAs significantly attenuates FOXM1-induced malignant activities.

Conclusion

The data suggested that FOXM1/β-catenin is critical for malignancy of CRC, which may constitute a potential therapeutic strategy for CRC.