Radiogenomic Analysis of F-18-Fluorodeoxyglucose Positron Emission Tomography and Gene Expression Data Elucidates the Epidemiological Complexity of Colorectal Cancer Landscape

Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review

The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

INTRODUCTION

Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels.

METHODS

In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression.

RESULTS

Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways.

CONCLUSIONS

This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.

CEBPB-mediated upregulation of SERPINA1 promotes colorectal cancer progression by enhancing STAT3 signaling

Colorectal cancer (CRC) is a highly malignant carcinoma associated with poor prognosis, and metastasis is one of the most common causes of death in CRC. Serpin Family A Member 1 (SERPINA1) is a serine protease inhibitor from the Serpin family. Till now, the function and mechanism of SERPINA1 in CRC progression have not been fully illustrated. We established highly metastatic colorectal cancer cells named as RKO-H and Caco2-H by mice liver metastasis model. By integrative bioinformatic approaches, we analyzed the prognostic value and clinical significance of SERPINA1 in CRC, and predicted potential transcription factors. Colony formation, EDU, MTS, Transwell and wound healing assay were performed to evaluate the biological functions of SERPINA1 in CRC in vitro. Experiments in vivo were conducted to explore the effects of SERPINA1 on liver metastasis of CRC. ChIP and luciferase reporter gene assays were performed to identify the transcriptional regulatory mechanism of SERPINA1 by CEBPB. Our results show that SERPINA1 is highly expressed in CRC and correlated with poor clinical outcomes. SERPINA1 promotes the proliferation, migration by activating STAT3 pathway. Mechanistically, CEBPB binds SERPINA1 gene promoter sequence and promotes the transcription of SERPINA1. SERPINA1 drives CEBPB-induced tumor cell growth and migration via augmenting STAT3 signaling. Our results suggest that SERPINA1 is a potential prognostic marker and may serve as a novel treatment target for CRC.

Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53- / - p73+ / + and p53- / - p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

Preclinical Repurposing of Sitagliptin as a Drug Candidate for Colorectal Cancer by Targeting CD24/CTNNB1/SOX4-Centered Signaling Hub

Despite significant advances in treatment modalities, colorectal cancer (CRC) remains a poorly understood and highly lethal malignancy worldwide. Cancer stem cells (CSCs) and the tumor microenvironment (TME) have been shown to play critical roles in initiating and promoting CRC progression, metastasis, and treatment resistance. Therefore, a better understanding of the underlying mechanisms contributing to the generation and maintenance of CSCs is crucial to developing CSC-specific therapeutics and improving the current standard of care for CRC patients. To this end, we used a bioinformatics approach to identify increased CD24/SOX4 expression in CRC samples associated with poor prognosis. We also discovered a novel population of tumor-infiltrating CD24+ cancer-associated fibroblasts (CAFs), suggesting that the CD24/SOX4-centered signaling hub could be a potential therapeutic target. Pathway networking analysis revealed a connection between the CD24/SOX4-centered signaling, β-catenin, and DPP4. Emerging evidence indicates that DPP4 plays a role in CRC initiation and progression, implicating its involvement in generating CSCs. Based on these bioinformatics data, we investigated whether sitagliptin, a DPP4 inhibitor and diabetic drug, could be repurposed to inhibit colon CSCs. Using a molecular docking approach, we demonstrated that sitagliptin targeted CD24/SOX4-centered signaling molecules with high affinity. In vitro experimental data showed that sitagliptin treatment suppressed CRC tumorigenic properties and worked in synergy with 5FU and this study thus provided preclinical evidence to support the alternative use of sitagliptin for treating CRC.

Identification of Hub genes with prognostic values in colorectal cancer by integrated bioinformatics analysis

BACKGROUND

Our study aimed to investigate the Hub genes and their prognostic value in colorectal cancer (CRC) via bioinformatics analysis.

METHODS

The data set of colorectal cancer was downloaded from the GEO database (GSE21510, GSE110224 and GSE74602) for differential expression analysis using the GEO2R tool. Hub genes were screened by protein-protein interaction (PPI) comprehensive analysis. GEPIA was used to verify the expression of Hub genes and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations, and the effects of mutation on the patients' prognosis. The TIMER database was used to study the correlation between Hub genes and immune infiltration in CRC. Gene set enrichment analysis (GSEA) was used to explore the biological function and signal pathway of the Hub genes and corresponding co-expressed genes.

RESULTS

We identified 346 differentially expressed genes (DEGs), including 117 upregulated and 229 downregulated. Four Hub genes (AURKA, CCNB1, EXO1 and CCNA2) were selected by survival analysis and differential expression validation. The protein and mRNA expression levels of AURKA, CCNB1, EXO1 and CCNA2 were higher in CRC tissues than in adjacent tissues. There were varying degrees of immune cell infiltration and gene mutation of Hub genes, especially B cells and CD8+ T cells. The results of GSEA showed that Hub genes and their co-expressed genes mainly participated in chromosome segregation, DNA replication, translational elongation and cell cycle.

CONCLUSION

Overexpression of AURKA, CCNB1, CCNA2 and EXO1 had a better prognosis for CRC and this effect was correlation with gene mutation and infiltration of immune cells.

Investigating the Diagnostic and Therapeutic Potential of SREBF2-Related Lipid Metabolism Genes in Colon Cancer

Purpose

Colon cancer is one of the leading causes of death worldwide, and screening of effective molecular markers for the diagnosis is prioritised for prevention and treatment. This study aimed to investigate the diagnostic and predictive potential of genes related to the lipid metabolism pathway, regulated by a protein called sterol-regulatory element-binding transcription Factor 2 (SREBF2), for colon cancer and patient outcomes.

Methods

We used machine-learning algorithms to identify key genes associated with SREBF2 in colon cancer based on a public database. A nomogram was created to assess the diagnostic value of these genes and validated in the Cancer Genome Atlas. We also analysed the relationship between these genes and the immune microenvironment of colon tumours, as well as the correlation between gene expression and clinicopathological characteristics and prognosis in the China Medical University (CMU) clinical cohort.

Results

Three genes, 7-dehydrocholesterol reductase (DHCR7), hydroxysteroid 11-beta dehydrogenase 2 (HSD11B2), and Ral guanine nucleotide dissociation stimulator-like 1 (RGL1), were identified as hub genes related to SREBF2 and colon cancer. Using the TCGA dataset, receiver operating characteristic curve analysis showed the area under the curve values of 0.943, 0.976, and 0.868 for DHCR7, HSD11B2, and RGL1, respectively. In the CMU cohort, SREBF2 and DHCR7 expression levels were correlated with TNM stage and tumour invasion depth (P < 0.05), and high DHCR7 expression was related to poor prognosis of colon cancer (P < 0.05). Furthermore, DHCR7 gene expression was positively correlated with the abundance of M0 and M1 macrophages and inversely correlated with the abundance of M2 macrophages, suggesting that the immune microenvironment may play a role in colon cancer surveillance. There was a correlation between SREBF2 and DHCR7 expression across cancers in the TCGA database.

Conclusion

This study highlights the potential of DHCR7 as a diagnostic marker and therapeutic target for colon cancer.

Phenomic Imaging

Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

Chromatin accessibility dynamics in colorectal cancer liver metastasis: Uncovering the liver tropism at single cell resolution

Tumor metastasis causes over 90% of cancer related death and no currently available therapies target it. However, there is limited understanding regarding the epigenetic regulation of genes during this complex process. Here by integrating single-cell ATAC-seq (scATAC-seq), single-cell RNA-seq (scRNA-seq), microarray, bulk RNA-seq, immunohistochemistry (IHC) staining, as well as proteomics datasets from paired primary and liver metastatic colorectal cancer (CRC) patient-derived xenograft (PDX) model and patients, we discovered that liver metastatic CRC cells lose their colon-specific chromatin accessible sites yet gain liver-specific ones. Importantly, we observed elevated accessibility of HNF4A, a liver-specific transcription factor, in liver metastatic CRC cells. Subsequently, we performed clustering analysis of liver metastatic CRC cells together with cells involved in liver development, revealing significant heterogeneity among the liver metastatic CRC cells. Over 50% of the liver metastatic CRC cells exhibited characteristics similar to those of erythroid progenitors and hepatocytes, showing increased expression of genes involved in oxidative phosphorylation and glycolysis. Moreover, our discovery further revealed that the MHC and IFN response genes in these cells exhibit moderate epigenetic activity, which is significantly associated with the low objective response rates in checkpoint blockade immunotherapy. Our findings uncovered the critical roles of HNF4A and the cell populations within liver metastatic CRC cells might serve as crucial therapeutic targets for addressing liver metastasis and improving the immunotherapy response in patients with CRC.

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.

Cell cycle protein BORA is associated with colorectal cancer progression by AURORA-PLK1 cascades: a bioinformatics analysis

Colorectal cancer (CRC) is the third most diagnosed cancer in the world. A better understanding of the molecular mechanism of CRC is essential for making novel strategies for the CRC management and its prevention. The present study aims to explore the molecular mechanism through integrated bioinformatics analysis by analyzing genes and their co-expression pattern in normal and CRC states. GSE110223, GSE110224 and GSE113513 gene expression profiles were analyzed in this study. The co-expression networks for normal and tumor samples were constructed separately and analyzed to identify the modules, sub-networks and key genes. Gene regulatory network analysis was done to understand the regulatory mechanism of selected genes. Survival analysis was performed for the identified sub-networks and key genes to understand their role in CRC progression. A total of seven modules were detected and the KEGG pathway analysis revealed these modules were mainly enriched with cell cycle, metabolism and signaling-related pathways. E2F6 and ETV4 transcription factors regulating the activity of multiple genes of identified modules were found to be up-regulated in CRC. Six Sub-networks and seven key genes, BORA, CCT7, DTL, RUVBL1, RUVBL2, THEM6 and TMEM97 associated with the CRC progression were identified. Disease-gene association analysis identified a novel association of the BORA gene with CRC that activates and regulates the AURORA-PLK1 cascades in the cell cycle. Survival analysis indicates that the overexpressed BORA is associated with unfavourable overall survival in CRC. The mechanistic role of BORA in the regulation of cell cycle progression suggests that BORA might act as a potential therapeutic target for CRC.

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.

Introduction to radiomics for a clinical audience

Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.

Preclinical Evaluation of a Novel Small Molecule LCC-21 to Suppress Colorectal Cancer Malignancy by Inhibiting Angiogenic and Metastatic Signatures

Colorectal cancer (CRC) is one of the most common cancers, and it frequently metastasizes to the liver and lymph nodes. Despite major advances in treatment modalities, CRC remains a poorly characterized biological malignancy, with high reported cases of deaths globally. Moreover, cancer stem cells (CSCs) and their microenvironment have been widely shown to promote colon cancer development, progression, and metastasis. Therefore, an understanding of the underlying mechanisms that contribute to the maintenance of CSCs and their markers in CRC is crucial in efforts to treat cancer metastasis and develop specific therapeutic targets for augmenting current standard treatments. Herein, we applied computational simulations using bioinformatics to identify potential theranostic markers for CRC. We identified the overexpression of vascular endothelial growth factor-α (VEGFA)/β-catenin/matrix metalloproteinase (MMP)-7/Cluster of Differentiation 44 (CD44) in CRC to be associated with cancer progression, stemness, resistance to therapy, metastasis, and poor clinical outcomes. To further investigate, we explored in silico molecular docking, which revealed potential inhibitory activities of LCC-21 as a potential multitarget small molecule for VEGF-A/CTNNB1/MMP7/CD44 oncogenic signatures, with the highest binding affinities displayed. We validated these finding in vitro and demonstrated that LCC-21 inhibited colony and sphere formation, migration, and invasion, and these results were further confirmed by a Western blot analysis in HCT116 and DLD-1 cells. Thus, the inhibitory effects of LCC-21 on these angiogenic and onco-immunogenic signatures could be of translational relevance as potential CRC biomarkers for early diagnosis.

Integrated Analysis of Gene Expression and Metabolite Data Reveals Candidate Molecular Markers in Colorectal Carcinoma

Background: This study investigated potential gene targets and metabolite markers associated with colorectal carcinoma (CRC). Materials & Methods: Gene expression data (GSE110224) related with CRC were obtained from Gene Expression Omnibus, including 17 tumor tissues and 17 normal colon ones. The gene differential analysis, functional analysis, protein-protein interaction (PPI) analysis, and metabolite network construction were performed to identify key genes related to CRC. Moreover, an external dataset was used to validate genes of interest in CRC, and corresponding survival analysis was also conducted. Results: The authors extracted 197 differentially expressed genes (75 upregulated and 122 downregulated genes). Moreover, upregulated genes were closely associated with rheumatoid arthritis and amoebiasis pathways. The downregulated genes were mainly related to bile secretion and proximal tubule bicarbonate reclamation pathway. Combined with PPI network and metabolite prediction, the overlapped nine genes (CXCL1, CXCL8, CXCL10, HDS1782, IL18, PCK1, PTGS2, SERPINB2, TMP1) were found to be critical in CRC. Similar gene expression profiles of nine critical genes were validated by an external dataset, except for SERPINB2. In addition, the expressions of TIMP1, IL1B, and PTGS2 were closely related with prognosis. Finally, the metabolite network analysis revealed that there were close associations between prostaglandin E2 and three pathways (rheumatoid arthritis, amoebiasis, and leishmaniasis). Conclusion: CXCL1/CXCL8/IL1B/PTGS2-prostaglandin E2 axes were the potential signatures involved in CRC progression, which could provide new insights to understand the molecular mechanisms of CRC.

A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression

Background

Gene expression analysis can provide useful information for analyzing complex biological mechanisms. However, many reported findings are unrepeatable due to small sample sizes relative to a large number of genes and the low signal-to-noise ratios of most gene expression datasets.

Results

Meta-analysis of multi-data sets is an efficient method for tackling the above problem. To improve the performance of meta-analysis, we propose a novel meta-analysis framework. It consists of two parts: (1) a novel data augmentation strategy. Various cross-platform normalization methods exist, which can preserve original biological information of gene expression datasets from different angles and add different “perturbations” to the dataset. Using such perturbation, we provide a feasible means for gene expression data augmentation; (2) elastic data shared lasso (DSL-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\varvec{L}}}_{\mathbf{2}}$$\end{document}L2). The DSL-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{L}}_{\mathbf{2}}$$\end{document}L2 method spans the continuum between individual models for each dataset and one model for all datasets. It also overcomes the shortcomings of the data shared lasso method when dealing with highly correlated features. Comprehensive simulation experiment results show that the proposed method has high prediction and gene selection performance. We then apply the proposed method to non-small cell lung cancer (NSCLC) blood gene expression data in order to identify key tumor-related genes. The outcomes of our experiment indicate that the method could be used for identifying a set of robust disease-related gene signatures that may be used for NSCLC early diagnosis or prognosis or even targeting.

Conclusion

We propose a novel and effective meta-analysis method for biological research, extrapolating and integrating information from multiple gene expression datasets.

Identification of Hub Genes for Early Diagnosis and Predicting Prognosis in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is among the most common digestive system malignancies worldwide, and its pathogenesis and gene signatures remain unclear. This study explored the genetic characteristics and molecular mechanisms underlying colon cancer development. Three gene expression data sets were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was used to determine differentially expressed genes (DEGs) between COAD and normal tissues. Then, the intersection of the data sets was obtained. Metascape was used to perform the functional enrichment analyses. Next, STRING was used to build protein-protein interaction (PPI) networks. Hub genes were identified and analysed using Cytoscape. Next, survival analysis and expression analysis of the hub genes were performed. ROC curve analysis was performed for further test of the diagnostic efficacy. Finally, alterations in the hub genes were predicted and analysed by cBioPortal. Altogether, 436 DEGs were detected. The DEGs were mainly enriched in cell cycle phase transition, nuclear division, meiotic nuclear division, and cytokinesis. Based on PPI networks, 20 hub genes were selected. Among them, 6 hub genes (CCNB1, CCNA2, AURKA, NCAPG, DLGAP5, and CENPE) showed significant prognostic value in colon cancer (P < 0.05), while 5 hub genes (CDK1, CCNB1, CCNA2, MAD2L1, and DLGAP5) were associated with early colon cancer diagnosis and ROC curve analysis showed good diagnostic accuracy. In conclusion, integrated bioinformatics analysis was used to identify hub genes that reveal the potential mechanism of carcinogenesis and progression of colon cancer. The hub genes might be novel biomarkers for early diagnosis, treatment, and prognosis of colon cancer.

A Three-Genes Signature Predicting Colorectal Cancer Relapse Reveals LEMD1 Promoting CRC Cells Migration by RhoA/ROCK1 Signaling Pathway

Objective

Colorectal cancer (CRC) patients that experience early relapse consistently exhibit poor survival. However, no effective approach has been developed for the diagnosis and prognosis prediction of postoperative relapsed CRC.

Methods

Multiple datasets from the GEO database and TCGA database were utilized for bioinformatics analysis. WGCNA analyses and RRA analysis were performed to identify key genes. The COX/Lasso regression model was used to construct the recurrence model. Subsequent in vitro experiments further validated the potential role of the hub genes in CRC.

Results

A comprehensive analysis was performed on multiple CRC datasets and a CRC recurrence model was constructed containing LEMD1, SERPINE1, and SIAE. After further validation in two independent databases, we selected LEMD1 for in vitro experiments and found that LEMD1 could regulate CRC cell proliferation, migration, invasion, and promote EMT transition. The Rho-GTPase pulldown experiments further indicated that LEMD1 could affect RhoA activity and regulate cytoskeletal dynamics. Finally, we demonstrated that LEMD1 promoted CRC cell migration through the RhoA/ROCK1 signaling pathway.

Conclusions

In this study, a CRC relapse model consisting of LEMD1, SERPINE1, and SIAE was constructed by comprehensive analysis of multiple CRC datasets. LEMD1 could promote CRC cell migration through the RhoA/ROCK signaling pathway.

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.

Loss of Hyaluronan and Proteoglycan Link Protein-1 Induces Tumorigenesis in Colorectal Cancer

Colorectal cancer (CRC) is the third most common diagnosed cancer worldwide, but there are no effective cures for it. Hyaluronan and proteoglycan link protein-1 (HAPLN1) is a component of the extracellular matrix (ECM) proteins and involved in the tumor environment in the colon. Transforming growth factor (TGF)-β is a key cytokine that regulates the deposition of ECM proteins in CRC. However, the role of HAPLN1 in TGF-β contributions to CRC remains unknown. We found that the mRNA expression of HAPLN1 was decreased in tumors from CRC patients compared with healthy controls and normal tissue adjacent to the tumor using two existing microarray datasets. This was validated at the protein level by tissue array from CRC patients (n = 59). HAPLN1 protein levels were also reduced in human CRC epithelial cells after 24 h of TGF-β stimulation, and its protein expression correlated with type I collagen alpha-1 (COL1A1) in CRC. Transfection of HAPLN1 overexpression plasmids into these cells increased protein levels but reduced COL1A1 protein, tumor growth, and cancer cell migration. TGF-β stimulation increased Smad2/3, p-Smad2/3, Smad4, and E-adhesion proteins; however, HAPLN1 overexpression restored these proteins to baseline levels in CRC epithelial cells after TGF-β stimulation. These findings suggest that HAPLN1 regulates the TGF-β signaling pathway to control collagen deposition via the TGF-β signaling pathway and mediates E-adhesion to control tumor growth. Thus, treatments that increase HAPLN1 levels may be a novel therapeutic option for CRC.

Prognostic Prediction, Immune Microenvironment, and Drug Resistance Value of Collagen Type I Alpha 1 Chain: From Gastrointestinal Cancers to Pan-Cancer Analysis

Background: Gastrointestinal cancers patients might experience multiple primary tumors in the digestive tract. Therefore, identifying potential biomarkers can help us better understand the underlying mechanism. From the GEO database, four profiles of gastrointestinal cancers were gathered for the screening process, and six hub genes were found by bioinformatics analysis. Collagen type I alpha 1 chain (COL1A1), one of the hub genes, is a component of the extracellular matrix and is critical for tumor microenvironment. However, the expression level, signaling pathway, prognostic prediction, and immunological value of COL1A1 in different cancers remain unclear.

Methods: We comprehensively analyzed gene expression and genetic alteration patterns of COL1A1 among 33 types of malignancies from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression projects. Besides, we explored the correlation of COL1A1 with cancer prognosis, immune infiltrates, PD-L1, tumor mutational burden (TMB)/microsatellite instability status (MSI), and the pathway and drug sensitivity of co-expressed genes.

Results: The results showed that COL1A1 was highly expressed and associated with poor prognosis in the majority of cancers. The most common alteration type of COL1A1 was missense mutation, and COL1A1 was associated with poor prognosis in KIRP, LGG, MESO, SKCM, and STAD. For the immunologic significance, COL1A1 expression was closely related to high TMB in THYM, LAML, ACC, KICH, PRAD, and LGG, and high MSI in TGCT, MESO, PRAD, COAD, SARC, and CESC. In addition, COL1A1 was positively correlated with the abundance of CAFs, macrophages, and tumor-infiltrating lymphocytes. However, it was negatively correlated with CD8⁺ T cells mainly in CESC, HNSC-HPV+, and SKCM. Besides, as a component of the extracellular matrix, COL1A1 was involved in the activation of epithelial-mesenchymal transition (EMT), and high expression of HTRA1 was resistant to multiple drugs.

Conclusion:COL1A1 can serve as a prognostic and immunological biomarker in different cancers.

ASPM is a Novel Candidate Gene Associated with Colorectal Cancer Cell Growth

Colorectal cancer (CRC) is one of the most prevalent diseases worldwide; however, the molecular mechanisms involved in CRC remain unclear. Thus, we aimed to explore a novel biomarker for CRC. In this study, we screened 361 differentially expressed genes; 152 downregulated genes; and 209 upregulated genes) through analysis of the GSE44861, GSE110223, GSE110224, and GSE113513 CRC datasets. Next, ASPM, CCNA2, CCNB1, CEP55, KIF20A, MAD2L1, MELK, RRM2, TOP2A, TPX2, TRIP13, and TTK were identified as hub genes associated with the cell cycle in CRC through comprehensive bioinformatics analysis using the Cytoscape and Metascape software, the Database for Annotation, Visualization, and Integrated Discovery (DAVID), and the Oncomine and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases. Furthermore, ASPM mRNA expression in CRC tissues was verified in Oncomine, The Cancer Genome Atlas and our data, and ASPM was found to be significantly upregulated in CRC tissues compared with that in the noncancer colon tissues. Functionally, we showed that overexpression of ASPM significantly promoted the proliferation and inhibited apoptosis; silencing of ASPM suppressed the proliferation of CRC cells by affecting the cell cycle G1/S transition by reducing cyclin E1 expression, and inducing apoptosis. Overall, our findings indicated that ASPM plays a crucial role in the regulation of CRC cell proliferation, and ASPM is a potential candidate diagnostic tool and therapeutic target for CRC.

Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach

Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.

Identification of genes associated with clinicopathological features of colorectal cancer

Objective

To identify genes associated with the clinicopathological features of colorectal cancer (CRC).

Methods

Gene expression profiles were downloaded and preprocessed by GEOquery and affy R packages, respectively. The limma package was applied to identify the differentially expressed genes (DEGs) in CRC. Gene Ontology and Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analyses for the DEGs were carried out using the clusterProfiler package. Protein–protein interaction (PPI) and weighted gene co-expression (WGC) networks were constructed using the STRING database and WGCNA package, respectively.

Results

A total of 523 DEGs (283 downregulated and 240 upregulated genes) in CRC tissues were identified. These DEGs were mainly enriched in 111 biological processes, 16 cellular components and 40 molecular functions, such as proteinaceous extracellular matrix, extracellular structure organization and chemokine-mediated signalling pathway. PPI and WGC networks showed that four upregulated genes (KIF2C, CDC45, CEP55 and DTL) were key genes. Subgroup analysis based on individual cancer stages and histological subtypes indicated that the expression of these key genes was upregulated in CRC stages I–IV, adenocarcinoma and mucinous adenocarcinoma.

Conclusions

The study provides new insights into understanding the pathogenesis of CRC. These identified genes may act as potential targets for CRC diagnosis and treatment.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

REG4 is a Potential Biomarker for Radiochemotherapy Sensitivity in Colorectal Cancer

Purpose

Colorectal cancer (CRC) is one of the most common types of malignancies, and radiochemotherapy (RCT) followed by surgery is the recommended approach for CRC treatment. However, some cases do not respond to first-line conventional chemotherapy or even progress further after treatment. Moreover, there is a risk of severe side effects, such as radiodermatitis. Therefore, identifying predictors for RCT sensitivity is an essential step toward predicting and eventually overcoming resistance.

Materials and Methods

We used integrative bioinformatics analysis and experimental validation to show that regenerating family member 4 (REG4) may be a potential biomarker for RCT sensitivity in CRC.

Results

REG4, whose expression is upregulated in some CRC tissues and downregulated in RCT-sensitive CRC cells, was identified as a potential genetic marker for RCT sensitivity in CRC. Immunohistochemistry-based tissue microarray of human CRC was used to experimentally validate REG4 data obtained from the bioinformatics analysis.

Conclusion

Collectively, these results indicate that REG4 may be a potential biomarker for RCT sensitivity in CRC.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

Identification of Potential Diagnostic and Prognostic Biomarkers for Colorectal Cancer Based on GEO and TCGA Databases

Colorectal cancer (CRC) is one of the most common neoplastic diseases worldwide. With a high recurrence rate among all cancers, treatment of CRC only improved a little over the last two decades. The mortality and morbidity rates can be significantly lessened by earlier diagnosis and prompt treatment. Available biomarkers are not sensitive enough for the diagnosis of CRC, whereas the standard diagnostic method, endoscopy, is an invasive test and expensive. Hence, seeking the diagnostic and prognostic biomarkers of CRC is urgent and challenging. With that order, we screened the overlapped differentially expressed genes (DEGs) of GEO (GSE110223, GSE110224, GSE113513) and TCGA datasets. Subsequent protein-protein interaction network analysis recognized the hub genes among these DEGs. Further functional analyses including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in CRC. Kaplan-Meier analysis and Cox hazard ratio analysis were carried out to clarify the diagnostic and prognostic role of these genes. In conclusion, our present study demonstrated that CCNA2, MAD2L1, DLGAP5, AURKA, and RRM2 are all potential diagnostic biomarkers for CRC and may also be potential treatment targets for clinical implication in the future.

Data mining combined with experiments to validate CEP55 as a prognostic biomarker in colorectal cancer

INTRODUCTION

Colorectal cancer (CRC) is a common tumor with high morbidity and mortality. Current specific diagnosis regarding CRC remains complicated and costly, and specific diagnostic biomarkers are lacking.

METHODS

To find potential diagnostic and prognostic biomarkers for CRC, we screened and analyzed many CRC sequencing data by The Cancer Genome Atlas Program and Gene Expression Omnibus, and validated that CEP55 may be a potential diagnostic biomarker for CRC by molecular cytological experiments and immunohistochemistry, among others.

RESULTS

We found that CEP55 is upregulated in CRC tissues and tumor cells and can promote CRC proliferation and metastasis by activating the p53/p21 axis and that CEP55 mutations in tumor patients result in worse overall survival and disease-free survival time. Besides, we also found that genes, such as CDK1, CCNB1, NEK2, KIF14, CDCA5, and RFC3 were upregulated in tumors, and their mutations would affect the prognosis of CRC patients, but these results await for more experimental evidence.

CONCLUSION

Our study validates CEP55 as a potential diagnostic and prognostic biomarker for CRC, and we also provide multiple genes and potential molecular mechanisms that may serve as diagnostic and prognostic markers for CRC.

Bioinformatic Identification of Hub Genes and Analysis of Prognostic Values in Colorectal Cancer

The purpose of this study is to discover novel hub genes which are helpful for diagnosis, prognosis, and targeted therapy in colorectal cancer (CRC) by using bioinformatics analysis. GSE74602, GSE110225, and GSE113513 were extracted from the gene expression omnibus (GEO). Differentially expressed genes (DEGs) in expression profiles were identified by GEO2R. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses of the DEGs were carried out in the Database for Annotation, Visualization, and Integrated Discovery (DAVID). String database and cytoscape were used for building protein-protein interaction (PPI) network and module analysis. The UALCAN was used for in-depth analysis of data of CRC patients from The Cancer Genome Atlas (TCGA) to identify expression levels and overall survival rates of hub genes. The DEGs included 107 up-regulation genes and 232 down-regulation genes. Twenty-nine (29) hub genes and two significant modules were screened from PPI network. The expression levels of hub genes in TCGA were verified. Survival analysis curve indicated high expression of CCNA2, CCNB1, DLGAP5, were related to high survival rates, and low expression of TIMP1 were associated with high survival rates. These results suggest that DEGs may be the hub genes of CRC, and CCNA2, CCNB1, DLGAP5, TIMP1 may be the potential prognostic markers of CRC.

Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.

Comprehensive analysis of location-specific hub genes related to the pathogenesis of colon cancer

The molecular mechanisms underlying colon cancer lesions at different sites are not entirely clear. Herein, we aimed to explore location-specific gene profiles related to the pathogenesis of colon cancer and to identify their function. The robust rank aggregation (RRA) method was used to integrate colon cancer microarray datasets and screen differentially expressed gene (DEG) profiles between left- and right-sided colon cancers. Then, weighted gene co-expression network analysis (WGCNA) was performed to cluster the DEGs into modules and identify hub genes. The selected hub genes were validated using The Cancer Genome Atlas dataset and clinical tissues. We assessed the association of selected hub genes with the methylation status in immune cells. In total, 905 DEGs were identified by RRA; five gene modules and 18 hub genes were related to the clinical traits of colon cancer by WGCNA. Four hub genes were selected and shown to be associated with colon cancers on different sides and distant metastasis in the validation analysis. The four hub genes showed a low methylation status, and their expression was significantly associated with methylation status. Positive correlations were observed between the four hub genes and tumor purity and among the four types of immune cells. Gene set enrichment analysis revealed that the four hub genes were mainly involved in two cancer-related pathways. In conclusion, this study identified a set of location-specific genes related to the pathogenesis of colon cancer. These four hub genes may act as novel candidate targets for the treatment of colon cancer.

A Comprehensive Repertoire of Transfer RNA-Derived Fragments and Their Regulatory Networks in Colorectal Cancer

To provide systematic insight into the composition and expression of transfer RNA (tRNA) derivatives transcriptome in colorectal cancer (CRC). tRNA derivatives expression profiles in three pairs of CRC and adjacent normal colon tissues were performed by tRNA-derived small RNA fragments (tRFs) and tRNA halves (tiRNA) sequencing, and microarray data of transcriptomes from CRC and paired controls were retrieved from Gene Expression Omnibus database. The differentially expressed tRFs and tiRNAs and differentially expressed genes between CRC and paired normal samples were screened. The functional regulations between tRF and tiRNA and gene were identified. A total of 60 upregulated and 48 downregulated tRNA derivatives and 7373 upregulated and 12,138 downregulated messenger RNA (mRNA) were identified. The tRF and tiRNA-gene regulatory modules were constructed by analyzing computational tRF and tiRNA-target predictions and inverse expression relationships between tRF and tiRNAs and mRNA. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway annotation showed that the function of targets of tiRNA-Tyr-GTA was mainly enriched in negative regulation of epithelial cell apoptotic process and peroxisome proliferator activated-receptors (PPAR) signaling pathway. Cellular response to monoamine stimulus and inflammatory bowel disease was enriched in function of tiRNA-Val-CAC. Two functions, including negative regulation of c-Jun N-terminal kinase (JNK) cascade and choline metabolism in cancer, were enriched in tRF-Gln-CTG. The function of mesenchymal to epithelial transition was enriched in tRF-Leu-TAG. For the first time to our knowledge, our study provided a landscape of tRNA derivatives expression profiles in CRC. Further tRF and tiRNA-gene regulatory modules construction explored the potential functions related to these tRNA derivatives in the pathogenesis of CRC.

Radiogenomics Based on PET Imaging

Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.

Identification and Verification of Core Genes in Colorectal Cancer

Colorectal cancer, a malignant neoplasm that occurs in the colorectal mucosa, is one of the most common types of gastrointestinal cancer. Colorectal cancer has been studied extensively, but the molecular mechanisms of this malignancy have not been characterized. This study identified and verified core genes associated with colorectal cancer using integrated bioinformatics analysis. Three gene expression profiles (GSE15781, GSE110223, and GSE110224) were downloaded from the Gene Expression Omnibus (GEO) databases. A total of 87 common differentially expressed genes (DEGs) among GSE15781, GSE110223, and GSE110224 were identified, including 19 upregulated genes and 68 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for common DEGs using clusterProfiler. These common DEGs were significantly involved in cancer-associated functions and signaling pathways. Then, we constructed protein-protein interaction networks of these common DEGs using Cytoscape software, which resulted in the identification of the following 10 core genes: SST, PYY, CXCL1, CXCL8, CXCL3, ZG16, AQP8, CLCA4, MS4A12, and GUCA2A. Analysis using qRT-PCR has shown that SST, CXCL8, and MS4A12 were significant differentially expressed between colorectal cancer tissues and normal colorectal tissues (P < 0.05). Gene Expression Profiling Interactive Analysis (GEPIA) overall survival (OS) has shown that low expressions of AQP8, ZG16, CXCL3, and CXCL8 may predict poor survival outcome in colorectal cancer. In conclusion, the core genes identified in this study contributed to the understanding of the molecular mechanisms involved in colorectal cancer development and may be targets for early diagnosis, prevention, and treatment of colorectal cancer.

Identification of Hub Genes Related to Carcinogenesis and Prognosis in Colorectal Cancer Based on Integrated Bioinformatics

The high mortality of colorectal cancer (CRC) patients and the limitations of conventional tumor-node-metastasis (TNM) stage emphasized the necessity of exploring hub genes closely related to carcinogenesis and prognosis in CRC. The study is aimed at identifying hub genes associated with carcinogenesis and prognosis for CRC. We identified and validated 212 differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets and the Cancer Genome Atlas (TCGA) database. We investigated functional enrichment analysis for DEGs. The protein-protein interaction (PPI) network was constructed, and hub modules and genes in CRC carcinogenesis were extracted. A prognostic signature was developed and validated based on Cox proportional hazards regression analysis. The DEGs mainly regulated biological processes covering response to stimulus, metabolic process, and affected molecular functions containing protein binding and catalytic activity. The DEGs played important roles in CRC-related pathways involving in preneoplastic lesions, carcinogenesis, metastasis, and poor prognosis. Hub genes closely related to CRC carcinogenesis were extracted including six genes in model 1 (CXCL1, CXCL3, CXCL8, CXCL11, NMU, and PPBP) and two genes and Metallothioneins (MTs) in model 2 (SLC26A3 and SLC30A10). Among them, CXCL8 was also related to prognosis. An eight-gene signature was proposed comprising AMH, WBSCR28, SFTA2, MYH2, POU4F1, SIX4, PGPEP1L, and PAX5. The study identified hub genes in CRC carcinogenesis and proposed an eight-gene signature with good reproducibility and robustness at the molecular level for CRC, which might provide directive significance for treatment selection and survival prediction.

Elastin is a key factor of tumor development in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the most common cancer and a leading cause of death worldwide. Extracellular matrix (ECM) proteins regulate tumor growth and development in CRC. Elastin (ELN) is a component of ECM proteins involved in the tumor microenvironment. However, the role of ELN in CRC remains unclear.

METHODS

In this study, we analyzed ELN gene expression in tumors from CRC patients and adjacent non-tumor colon tissues and healthy controls from two existing microarray datasets. ELN protein was measured in human normal colon cells and colon cancer epithelial cells and tumor development was assessed in colon epithelial cells cultured in medium with or without ELN peptide on plates coated with ELN recombinant protein. Control plates were coated with PBS only.

RESULTS

We found ELN gene expression was increased in tumors from CRC patients compared to adjacent non-tumor tissues and healthy controls. ELN protein was increased in cancer cells compared to normal colon epithelial cells. Transforming growth factor beta (TGF-β) was a key cytokine to induce production of ECM proteins, but it did not induce ELN expression in colon cancer cells. Matrix metalloproteinase 9 (MMP9) gene expression was increased, but that of MMP12 (elastase) did not change between CRC patients and control. Tissue inhibitor of metalloproteinases 3 (TIMP3) gene expression was decreased in colon tissues from CRC patients compared to healthy controls. However, MMP9, MMP12 and TIMP3 proteins were increased in colon cancer cells. ELN recombinant protein increased proliferation and wound healing in colon cancer epithelial cells. This had further increased in cancer cells incubated in plates coated with recombinant ELN coated plate and in culture media containing ELN peptide. A potential mechanism was that ELN induced epithelial mesenchymal transition with increased alpha-smooth muscle actin and vimentin proteins but decreased E-cadherin protein. Tumor necrosis factor alpha (TNF) mRNA was also increased in CRC patients compared to controls. ELN recombinant protein induced further increases in TNF protein in mouse bone marrow derived macrophages after lipopolysaccharide stimulation.

CONCLUSIONS

These data suggest ELN regulates tumor development and the microenvironment in CRC.

A comparison of curated gene sets versus transcriptomics-derived gene signatures for detecting pathway activation in immune cells

BACKGROUND

Despite the significant contribution of transcriptomics to the fields of biological and biomedical research, interpreting long lists of significantly differentially expressed genes remains a challenging step in the analysis process. Gene set enrichment analysis is a standard approach for summarizing differentially expressed genes into pathways or other gene groupings. Here, we explore an alternative approach to utilizing gene sets from curated databases. We examine the method of deriving custom gene sets which may be relevant to a given experiment using reference data sets from previous transcriptomics studies. We call these data-derived gene sets, "gene signatures" for the biological process tested in the previous study. We focus on the feasibility of this approach in analyzing immune-related processes, which are complicated in their nature but play an important role in the medical research.

RESULTS

We evaluate several statistical approaches to detecting the activity of a gene signature in a target data set. We compare the performance of the data-derived gene signature approach with comparable GO term gene sets across all of the statistical tests. A total of 61 differential expression comparisons generated from 26 transcriptome experiments were included in the analysis. These experiments covered eight immunological processes in eight types of leukocytes. The data-derived signatures were used to detect the presence of immunological processes in the test data with modest accuracy (AUC = 0.67). The performance for GO and literature based gene sets was worse (AUC = 0.59). Both approaches were plagued by poor specificity.

CONCLUSIONS

When investigators seek to test specific hypotheses, the data-derived signature approach can perform as well, if not better than standard gene-set based approaches for immunological signatures. Furthermore, the data-derived signatures can be generated in the cases that well-defined gene sets are lacking from pathway databases and also offer the opportunity for defining signatures in a cell-type specific manner. However, neither the data-derived signatures nor standard gene-sets can be demonstrated to reliably provide negative predictions for negative cases. We conclude that the data-derived signature approach is a useful and sometimes necessary tool, but analysts should be weary of false positives.

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.