Comparative Proteome Identifies Complement Component 3-Mediated Immune Response as Key Difference of Colon Adenocarcinoma and Rectal Adenocarcinoma

Identification of age-specific biomarkers of spinal cord injury: A bioinformatics analysis of young and aged mice models

BACKGROUND

Spinal cord injury (SCI) is a debilitating event that often results in long-term physical damage, disability, and a significant impact on a patient's quality of life. Past research has noted an age-dependent decline in regeneration post-SCI. This study seeks to identify potential biomarkers and enriched pathways in young and aged SCI mouse models.

METHODS

We retrieved the microarray data of spinal cord samples from SCI mice and control mice from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the R software and the Linear Models for Microarray Data (limma) package. The Gene Set Enrichment Analysis (GSEA) determined enrichment differences among gene sets. The Weighted Gene Co-expression Network Analysis (WGCNA) pinpointed clinically significant modules and hub genes in SCI. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was employed to uncover significantly related pathways of crucial genes in SCI.

RESULTS

We found 2560 DEGs in the young SCI group, comprised of 1733 upregulated RNAs and 827 downregulated RNAs. In the aged SCI group, 3048 DEGs were revealed including 1856 upregulated and 1192 downregulated genes. The GSEA revealed 12 enriched signaling pathways in the young SCI group, such as IL6/JAK/STAT3 signaling, interferon alpha response, and interferon gamma response, and 21 signaling pathways in the aged SCI group such as IL6/JAK/STAT3 signaling, E2F targets, and angiogenesis-related pathways. The WGCNA identified the turquoise module as significantly associated with the clinical traits of both young and aged SCI, and revealed 3181 hub genes. Ultimately, 1858 significant genes in SCI were found, with associated signaling pathways including epithelial-mesenchymal transition (EMT), interferon gamma response, and KARS signaling.

CONCLUSION

Our study explored the RNA expression patterns and enriched signaling pathways in young and aged SCI mice. These findings may provide potential biomarkers for targeted SCI therapy.

Vascular endothelial growth factor inhibitors promote antitumor responses via tumor microenvironment immunosuppression in advanced colorectal cancer

PURPOSE

This study aims to investigate changes in the tumor immune environment of patients who underwent therapy with a vascular endothelial growth factor (VEGF) inhibitor for advanced colorectal cancer.

METHODS

Patients (n = 135) with T3 or T4 colorectal cancer were included in this retrospective study. They were classified as follows: patients who had not received preoperative treatment (UPFRONT group, n = 54), who had received FOLFOX as preoperative chemotherapy (FOLFOX group, n = 55), and who had undergone resection after combination FOLFOX and bevacizumab as unresectable colorectal cancer (B-MAB group, n = 26). The number of cytotoxic T lymphocytes (CTLs), FOXP3+ lymphocytes (including regulatory T cells (Tregs)), CD163+ monocytes (including M2-type tumor-associated macrophages (TAM-M2 type)), and programmed cell death 1 (PD-1)+ lymphocytes was evaluated immunohistochemically in the cancer cell area (CC) and stromal cell area (ST) of surgical specimens, and compared among the three groups.

RESULTS

The CTL population did not differ among the three groups in both areas. In the B-MAB group, the numbers of PD-1+ cells in the ST, FOXP3+ lymphocytes in both areas, and CD163+monocytes in the ST was lower than that in the other two groups, and a correlation with the histological therapeutic effect was observed.

CONCLUSIONS

In advanced colorectal cancer, VEGF inhibitors may decrease the number of PD-1+ cells and inhibit the infiltration of FOXP3+ lymphocytes and CD163+monocytes into the tumor environment.

The genetic and epigenetic regulation of CD55 and its pathway analysis in colon cancer

Background

CD55 plays an important role in the development of colon cancer. This study aims to evaluate the expression of CD55 in colon cancer and discover how it is regulated by transcriptional factors and miRNA.

Methods

The expression of CD55 was explored by TIMER2.0, UALCAN, and Human Protein Atlas (HPA) databases. TRANSFAC and Contra v3 were used to predict the potential binding sites of transcription factors in the CD55 promoter. TargetScan and starBase v2.0 were used to predict the potential binding ability of miRNAs to the 3' untranslated region (3'UTR) of CD55. SurvivalMeth was used to explore the differentially methylated sites in the CD55 promoter. Western blotting was used to detect the expression of TFCP2 and CD55. Dual-luciferase reporter assay and chromatin immunoprecipitation (ChIP) assay were performed to determine the targeting relationship of TFCP2, NF-κB, or miR-27a-3p with CD55. CD55-related genes were explored by constructing a protein-protein interaction (PPI) network and performing pathway analysis by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).

Results

CD55 was highly expressed in colon cancer tissues. The mRNA and protein expression levels of TFCP2 were reduced by si-TFCP2. NF-κB mRNA was obviously reduced by NF-κB inhibitor and increased by NF-κB activator. CD55 protein was also inhibited by miR-27a-3p. Dual-luciferase reporter assays showed that after knocking down TFCP2 or inhibiting NF-κB, the promoter activity of CD55 was decreased by 21% and 70%, respectively; after activating NF-κB, the promoter activity of CD55 increased by 2.3 times. As TFCP2 or NF-κB binding site was mutated, the transcriptional activity of CD55 was significantly decreased. ChIP assay showed that TFCP2 and NF-κB combined to the promoter of CD55. The luciferase activity of CD55 3'UTR decreased after being co-transfected with miR-27a-3p mimics and increased by miR-27a-3p antagomir. As the miR-27a-3p binding site was mutated, we did not find any significant effect of miR-27a-3p on reporter activity. PPI network assay revealed a set of CD55-related genes, which included CFP, CFB, C4A, and C4B. GO and KEGG analyses revealed that the target genes occur more frequently in immune-related pathways.

Conclusion

Our results indicated that CD55 is regulated by TFCP2, NF-κB, miR-27a-3p, and several immune-related genes, which in turn affects colon cancer.

Developing an m5C regulator-mediated RNA methylation modification signature to predict prognosis and immunotherapy efficacy in rectal cancer

Background

Currently, a very small number of patients with colorectal cancer (CRC) respond to immune checkpoint inhibitor (ICI) treatment. Therefore, there is an urgent need to investigate effective biomarkers to determine the responsiveness to ICI treatment. Recently, aberrant 5-methylcytosine (m⁵C) RNA modification has emerged as a key player in the pathogenesis of cancer. Thus, we aimed to explore the predictive signature based on m⁵C regulator-related genes for characterizing the immune landscapes and predicting the prognosis and response to therapies.

Methods

The Cancer Genome Atlas (TCGA) cohort was used as the training set, while GEO data sets, real-time quantitative PCR (RT-qPCR) analysis from paired frozen tissues, and immunohistochemistry (IHC) data from tissue microarray (TMA) were used for validation. We constructed a novel signature based on three m⁵C regulator-related genes in patients with rectal adenocarcinoma (READ) using a least absolute shrinkage and selection operator (LASSO)-Cox regression and unsupervised consensus clustering analyses. Additionally, we correlated the three-gene signature risk model with the tumor immune microenvironment, immunotherapy efficiency, and potential applicable drugs.

Results

The m⁵C methylation-based signature was an independent prognostic factor, where low-risk patients showed a stronger immunoreactivity phenotype and a superior response to ICI therapy. Conversely, the high-risk patients had enriched pathways of cancer hallmarks and presented immune-suppressive state, which demonstrated that they are more insensitive to immunotherapy. Additionally, the signature markedly correlated with drug susceptibility.

Conclusions

We developed a reliable m⁵C regulator-based risk model to predict the prognosis, clarify the molecular and tumor microenvironment status, and identify patients who would benefit from immunotherapy or chemotherapy. Our study could provide vital guidance to improve prognostic stratification and optimize personalized therapeutic strategies for patients with rectal cancer.

MOPA: An integrative multi-omics pathway analysis method for measuring omics activity

Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks

Background

With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years.

Methods

In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models.

Results

All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types.

Conclusions

We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.

Analysis of Key Genes for Slow Transit Constipation Based on RNA Sequencing

Purpose

This study aims to identify key genes in slow transit constipation (STC). We also sought to explore the potential link between STC and colorectal cancer.

Patients and Methods

mRNA expression profiles were obtained by RNA sequencing, and differentially expressed genes were identified. Functional enrichment analysis and a protein–protein interaction (PPI) network was explored, and differentially expressed genes common to STC and colorectal cancer were examined. Analysis of the effect of constipation and colorectal cancer common genes on the overall survival of colorectal cancer patients based on GEPIA database.

Results

Functional enrichment showed that significantly different genes are related to lymphocyte chemotaxis, positive regulation of inflammatory response, cellular response to tumor necrosis factor, extracellular region, extracellular space and chemokine activity. The hub gene for STC was found in the PPI network. In addition, AQP8 and CFD were common differential genes for STC and colorectal cancer. AQP8 affects overall survival in patients with colorectal cancer.

Conclusion

Our findings will contribute to understanding the pathology of STC at the molecular level, with the first discovery that AQP8 may be a hub gene in the transition from STC to colorectal cancer.

Inside-Out of Complement in Cancer

The role of complement in cancer has received increasing attention over the last decade. Recent studies provide compelling evidence that complement accelerates cancer progression. Despite the pivotal role of complement in fighting microbes, complement seems to suppress antitumor immunity via regulation of host cell in the tumor microenvironment. Although most studies link complement in cancer to complement activation in the extracellular space, the discovery of intracellular activation of complement, raises the question: what is the relevance of this process for malignancy? Intracellular activation is pivotal for the survival of immune cells. Therefore, complement can be important for tumor cell survival and growth regardless of the role in immunosuppression. On the other hand, because intracellular complement (the complosome) is indispensable for activation of T cells, these functions will be essential for priming antitumor T cell responses. Here, we review functions of complement in cancer with the consideration of extra and intracellular pathways of complement activation and spatial distribution of complement proteins in tumors and periphery and provide our take on potential significance of complement as biomarker and target for cancer therapy.