The Importance of Sex in the Discovery of Colorectal Cancer Prognostic Biomarkers

Hases, Linnea; Ibrahim, Ahmed Atef; Chen, Xinsong; Liu, Yanghong; Hartman, Johan; Williams, Cecilia

doi:10.3390/ijms22031354

Cited by 21 publications

(18 citation statements)

References 35 publications

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“…While both are derived from primary colon adenocarcinomas, the HT29 cell line is derived from a likely premenopausal (44-year-old) woman, whereas SW480 originates from a 50-year-old man (57,58). We have also reported sex differences in the non-tumor and tumor transcriptome of CRC patients, which impacted biomarker discovery (59). The different female-male origin of the cell lines used here, may indeed impact the different regulation of p65 cistrome or its modulation by ERb.…”

Section: Discussionmentioning

confidence: 74%

Estrogen Receptor Beta Influences the Inflammatory p65 Cistrome in Colon Cancer Cells

Indukuri

Hases²,

Archer³

et al. 2021

Front. Endocrinol.

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Inflammation is a primary component of both initiation and promotion of colorectal cancer (CRC). Cytokines secreted by macrophages, including tumor necrosis factor alpha (TNFα), activates the pro-survival transcription factor complex NFκB. The precise mechanism of NFκB in CRC is not well studied, but we recently reported the genome-wide transcriptional impact of TNFα in two CRC cell lines. Further, estrogen signaling influences inflammation in a complex manner and suppresses CRC development. CRC protective effects of estrogen have been shown to be mediated by estrogen receptor beta (ERβ, ESR2), which also impacts inflammatory signaling of the colon. However, whether ERβ impacts the chromatin interaction (cistrome) of the main NFκB subunit p65 (RELA) is not known. We used p65 chromatin immunoprecipitation followed by sequencing (ChIP-Seq) in two different CRC cell lines, HT29 and SW480, with and without expression of ERβ. We here present the p65 colon cistrome of these two CRC cell lines. We identify that RELA and AP1 motifs are predominant in both cell lines, and additionally describe both common and cell line-specific p65 binding sites and correlate these to transcriptional changes related to inflammation, migration, apoptosis and circadian rhythm. Further, we determine that ERβ opposes a major fraction of p65 chromatin binding in HT29 cells, but enhances p65 binding in SW480 cells, thereby impacting the p65 cistrome differently in the two cell lines. However, the biological functions of the regulated genes appear to have similar roles in both cell lines. To our knowledge, this is the first time the p65 CRC cistrome is compared between different cell lines and the first time an influence by ERβ on the p65 cistrome is investigated. Our work provides a mechanistic foundation for a better understanding of how estrogen influences inflammatory signaling through NFκB in CRC cells.

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Section: Discussionmentioning

confidence: 74%

Estrogen Receptor Beta Influences the Inflammatory p65 Cistrome in Colon Cancer Cells

Indukuri

Hases²,

Archer³

et al. 2021

Front. Endocrinol.

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“…However, in CRC, not only the male but also the female CRC patients with rs131451 “TC + CC” genotype were significantly associated with advanced tumor T status and perineural invasion ( Table 4 ). In contrast, in the aspect of CRC prognostic biomarkers, it was suggested that there are clear sex differences in CRC characteristics, and sex-specific CRC prognostic biomarkers including ESM1, GUCA2A, and VWA2 for males and CLDN1 and FUT1 for female CRC patients were proposed [ 53 ]. One possible mechanism to explain this phenomenon was the interaction of sex hormones and their regulators in CRC.…”

Section: Discussionmentioning

confidence: 99%

The Impact of Matrix Metalloproteinase-11 Polymorphisms on Colorectal Cancer Progression and Clinicopathological Characteristics

Huang

Shiu

et al. 2022

Diagnostics

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Colorectal cancer (CRC) is the third most common cause of cancer mortality worldwide and the most prevalent cancer in Taiwan. The matrix metalloproteinase (MMP)-11 is a proteolytic enzyme of the MMP family which is involved in extracellular matrix degradation and tissue remodeling. In this study, we focused on the associations of MMP-11 single-nucleotide polymorphisms (SNPs) with CRC susceptibility and clinicopathological characteristics. The MMP-11 SNPs rs131451, rs738791, rs2267029, rs738792, and rs28382575 in 479 controls and 479 patients with CRC were analyzed with real-time polymerase chain reaction. We found that the MMP-11 SNP rs738792 “TC + CC” genotype was significantly associated with perineural invasion in colon cancer patients after controlling for clinical parameters [OR (95% CI) = 1.783 (1.074–2.960); p = 0.025]. The MMP-11 rs131451 “TC + CC” genotypic variants were correlated with greater tumor T status [OR (95% CI):1.254 (1.025–1.534); p = 0.028] and perineural invasion [OR (95% CI):1.773 (1.027–3.062); p = 0.040) in male CRC patients. Furthermore, analyses of The Cancer Genome Atlas (TCGA) revealed that MMP-11 levels were upregulated in colorectal carcinoma tissue compared with normal tissues and were correlated with advanced stage, larger tumor sizes, and lymph node metastasis. Moreover, the data from the Genotype-Tissue Expression (GTEx) database exhibited that the MMP-11 rs738792 “CC” and “CT” genotypic variants have higher MMP-11 expression than the “TT” genotype. In conclusion, our results have demonstrated that the MMP-11 SNPs rs738792 and rs131451 may have potential to provide biomarkers to evaluate CRC disease progression, and the MMP-11 rs131451 polymorphism may shed light on sex discrepancy in CRC development and prognosis.

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“…Very recently, several techniques have been proposed based on supervised learning in cancer classifications [17,[45][46][47][48][49][50][51][52][53]. From the literature, most of the research is focusing on feature selection as well as cancer classification.…”

Section: Cancer Classification With Machine Learning Methodsmentioning

confidence: 99%

“…Recent work has applied feature selection techniques and machine learning to identify the CRC biomarkers. The Cancer Genome Atlas (TCGA) data are used to perform computational analysis to identify sex-specific biomarkers [46]. On the other hand, several machine learning techniques such as SVM, Random Forest, k-nearest neighbors, and naïve Bayesian tools have been used for the classification and identification of diagnostic markers for major depressive disorder (MDD) [48].…”

Section: Cancer Classification With Machine Learning Methodsmentioning

confidence: 99%

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Mazlan

Sahabudin²,

Remli³

et al. 2021

Processes

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Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

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The Importance of Sex in the Discovery of Colorectal Cancer Prognostic Biomarkers

Cited by 21 publications

References 35 publications

Estrogen Receptor Beta Influences the Inflammatory p65 Cistrome in Colon Cancer Cells

Estrogen Receptor Beta Influences the Inflammatory p65 Cistrome in Colon Cancer Cells

The Impact of Matrix Metalloproteinase-11 Polymorphisms on Colorectal Cancer Progression and Clinicopathological Characteristics

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

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