Comprehensive analysis of alfa defensin expression and prognosis in human colorectal cancer

Xi-nan, Zhao; Lu, Mingyu; Liu, Zhigang; Zhang, Mingming; Yuan, Hongmei; Dan, Zhaoqiang; Wang, Dai-Hua; Ma, Bin; Yang, Yue; Yang, Feifei; Sun, Ruifang; Li, Lin; Dang, Chengxue

doi:10.3389/fonc.2022.974654

Cited by 6 publications

(5 citation statements)

References 48 publications

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“…Proteinase-3 (PRTN3), a neutrophil-derived and serine protease, is associated with poor prognosis in some carcinomas by regulating neutrophil differentiation and inflammation . α-Defensin (DEFA1), which promotes tumoral proliferation and invasion, is positively correlated with poor progression-free survival of colorectal cancer patients . As shown in Figure E,G, by comparison of the different stages, PRTN3 and DEFA1 were significantly increased in late-stage RC patients and therefore could be potential indicators of tumoral progression.…”

Section: Resultsmentioning

confidence: 95%

“…42 α-Defensin (DEFA1), which promotes tumoral proliferation and invasion, is positively correlated with poor progression-free survival of colorectal cancer patients. 43 As shown in Figure 4E,G, by comparison of the different stages, PRTN3 and DEFA1 were significantly increased in late-stage RC patients and therefore could be potential indicators of tumoral progression. In addition, myeloperoxidase (MPO) is a local mediator of inflammation in tissue damage.…”

Section: Profiling Of Dysregulated Proteins and Related Pathways In R...mentioning

confidence: 83%

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Spatial Proteomic Profiling of Colorectal Cancer Revealed Its Tumor Microenvironment Heterogeneity

Xie,

Kong,

et al. 2024

J. Proteome Res.

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Colorectal cancer is a predominant malignancy with a second mortality worldwide. Despite its prevalence, therapeutic options remain constrained and surgical operation is still the most useful therapy. In this regard, a comprehensive spatially resolved quantitative proteome atlas was constructed to explore the functional proteomic landscape of colorectal cancer. This strategy integrates histopathological analysis, laser capture microdissection, and proteomics. Spatial proteome profiling of 200 tissue section samples facilitated by the fully integrated sample preparation technology SISPROT enabled the identification of more than 4000 proteins on the Orbitrap Exploris 240 from 2 mm2 × 10 μm tissue sections. Compared with normal adjacent tissues, we identified a spectrum of cancer-associated proteins and dysregulated pathways across various regions of colorectal cancer including ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. Additionally, we conducted proteomic analysis on tumoral epithelial cells and paracancerous epithelium from early to advanced stages in hallmark rectum cancer and sigmoid colon cancer. Bioinformatics analysis revealed functional proteins and cell-type signatures associated with different regions of colorectal tumors, suggesting potential clinical implications. Overall, this study provides a comprehensive spatially resolved functional proteome landscape of colorectal cancer, serving as a valuable resource for exploring potential biomarkers and therapeutic targets.

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Section: Resultsmentioning

confidence: 95%

Section: Profiling Of Dysregulated Proteins and Related Pathways In R...mentioning

confidence: 83%

Spatial Proteomic Profiling of Colorectal Cancer Revealed Its Tumor Microenvironment Heterogeneity

Xie,

Kong,

et al. 2024

J. Proteome Res.

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“…For the upregulated DETs, PAX7 was previously reported to have some relationship with cancers, yet studies regarding this specific gene transcript are lacking 52 . For DEFA6 and DEFA5, a report suggested a link 31 between them and clinical outcomes in colorectal cancer 53 . While there were no studies regarding AC010789.1 and TMPRSS15 in PC, some studies linked the potential implications of these transcripts with other cancers 54,55 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Alsharoh

2023

Preprint

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Pancreatic cancer (PC) is associated with high mortality overall. Recent literature has focused on investigating long noncoding RNAs (lncRNAs) in several cancers, but studies on their functions in PC are lacking. The purpose of this study was to identify novel lncRNAs and utilize machine learning to techniques to predict metastatic cases of PC using the identified lncRNAs. To identify significantly altered expression of lncRNA in PC, data was collected from The Cancer Genome Atlas (TCGA) and RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas were extracted for differential gene expression analysis. To assess the contribution of these lncRNAs to metastatic progression, different ML algorithms were used, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC) and eXtreme Gradient Boosting Classifier (XGBC). To improve the predictive accuracy of these models, hyperparameter tuning was performed, in addition to reducing bias through the synthetic minority oversampling technique. Out of 60,660 gene transcripts shared between 151 PC patients, 38 lncRNAs that were significantly differentially expressed were identified. To further investigate the functions of the novel lncRNAs, gene set enrichment analysis (GSEA) was performed on the population lncRNA panel. GSEA results revealed enrichment of several terms implicated in proliferation. Moreover, using the 4 ML algorithms to predict metastatic progression returned 76% accuracy for both SVM and RFC, explicitly based on the novel lncRNA panel. To the best of my knowledge, this is the first study of its kind to identify this lncRNA panel to differentiate between non-metastatic PC and metastatic PC, with many novel lncRNAs previously unmapped to PC. The ML accuracy score reveals important involvement of the detected RNAs. Based on these findings, I suggest further investigations of this lncRNA panelin vitroandin vivo, as they could be targeted for improved outcomes in PC patients, as well as assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors.

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“…Within this pathway, DEFA5 and DEFA6 exhibited downregulation in late stages compared to the early stage. DEFA5 is closely associated with colorectal adenocarcinoma, and its high expression correlates with a more favorable prognosis [37]. DEFA6 has been demonstrated to support proliferation, migration, invasion, and colony formation of CRC cell lines in vitro [38].…”

Section: Non-preserved Modules Are Functionally Distinct Between the ...mentioning

confidence: 99%

Single-cell transcriptomics reveals stage- and side-specificity of gene modules in colorectal cancer

Rahiminejad,

Mukund,

Maurya

et al. 2024

Preprint

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BACKGROUND: An understanding of mechanisms underlying colorectal cancer (CRC) development and progression is yet to be fully elucidated. This study aims to employ network theoretic approaches to analyse single cell transcriptomic data from CRC to better characterize its progression and sided-ness. METHODS: We utilized a recently published single-cell RNA sequencing data (GEO-GSE178341) and parsed the cell X gene data by stage and side (right and left colon). Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified gene modules with varying preservation levels (weak or strong) of network topology between early (pT1) and late stages (pT234), and between right and left colons. Spearman’s rank correlation (ρ) was used to assess the similarity or dissimilarity in gene connectivity. RESULTS: Equalizing cell counts across different stages, we detected 13 modules for the early stage, two of which were non-preserved in late stages. Both non-preserved modules displayed distinct gene connectivity patterns between the early and late stages, characterized by low ρ values. One module predominately dealt with myeloid cells, with genes mostly enriched for cytokine-cytokine receptor interaction potentiallystimulating myeloid cells to participate in angiogenesis. The second module, representing a subset of epithelial cells, was mainly enriched for carbohydrate digestion and absorption, influencing the gut microenvironment through the breakdown of carbohydrates. In the comparison of left vs. right colons, two of 12 modules identified in the right colon were non-preserved in the left colon. One captured a small fraction of epithelial cells and was enriched for transcriptional misregulation in cancer, potentially impacting communication between epithelial cells and the tumor microenvironment. The other predominantly contained B cells with a crucial role in maintaining human gastrointestinal health and was enriched for B-cell receptor signalling pathway. CONCLUSIONS: We identified modules with topological and functional differences specific to cell types between the early and late stages, and between the right and left colons. This study enhances the understanding of roles played by different cell types at different stages and sides, providing valuable insights for future studies focused on the diagnosis and treatment of CRC.

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Comprehensive analysis of alfa defensin expression and prognosis in human colorectal cancer

Cited by 6 publications

References 48 publications

Spatial Proteomic Profiling of Colorectal Cancer Revealed Its Tumor Microenvironment Heterogeneity

Spatial Proteomic Profiling of Colorectal Cancer Revealed Its Tumor Microenvironment Heterogeneity

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Single-cell transcriptomics reveals stage- and side-specificity of gene modules in colorectal cancer

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