Expression characteristics of long non-coding RNA in colon adenocarcinoma and its potential value for judging the survival and prognosis of patients: bioinformatics analysis based on The Cancer Genome Atlas database

Li, Ruofan; Gao, Xu; Sun, Haitao; Sun, Lixin; Hu, Xiaoming

doi:10.21037/jgo-22-384

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…In contrast, one report regarding AC132192.2 indicated its relevance in prostate cancer 62 . For the upregulated lncRNAs, AC010789.1, as previously stated, had a report regarding its function in colorectal cancer 55,63 . LINC00486, RF00019, LINC01115, and AC133530.1 all lack validation studies in PC, but other reports indicate involvement in several diseases, including cancer [64][65][66][67] .…”

Section: Discussionmentioning

confidence: 97%

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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Section: Discussionmentioning

confidence: 97%

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

Alsharoh

2023

Preprint

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“…Additionally, the hyper-methylated DMGs were found to be associated with biological processes related to neuron differentiation, cell fate commitment, and DNA-templated transcription, underscoring their relevance to the molecular mechanisms underlying CRC development and progression (Supplementary Figures S20b and S20c). Furthermore, the over-representation analysis of hypomethylated DMGs highlighted the identification of extracellular matrix components, such as the basement membrane and collagen-containing matrix, among the enriched cellular components, shedding light on their potential involvement in CRC invasion and metastasis (Li et al, 2022;Poursheikhani et al, 2020;Burtin et al, 1983). The TP53 pathway involves the genes CDKN2A and TP73.…”

Section: Colorectal Cancer Data Analysismentioning

confidence: 99%

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models*

Shokoohi

Khaniki

2023

Preprint

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Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

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“…The Cancer Microbiome Atlas (TCMA) was recently published and includes the curated tissue-endemic microbial profiles of 3689 unique samples from 1772 patients from five TCGA projects and 21 anatomical sites [16]. This may enable a multi-omics analysis with systematic microbe-host matching and has been actively used in studies on various cancers, such as gastric cancer, colon cancer, and HNSCC [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Potential Role of the Intratumoral Microbiota in Prognosis of Head and Neck Cancer

Hamada,

Inaba,

Nishiyama

et al. 2023

IJMS

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The tumor microbiome, a relatively new research field, affects tumor progression through several mechanisms. The Cancer Microbiome Atlas (TCMA) database was recently published. In the present study, we used TCMA and The Cancer Genome Atlas and examined microbiome profiling in head and neck squamous cell carcinoma (HNSCC), the role of the intratumoral microbiota in the prognosis of HNSCC patients, and differentially expressed genes in tumor cells in relation to specific bacterial infections. We investigated 18 microbes at the genus level that differed between solid normal tissue (n = 22) and primary tumors (n = 154). The tissue microbiome profiles of Actinomyces, Fusobacterium, and Rothia at the genus level differed between the solid normal tissue and primary tumors of HNSCC patients. When the prognosis of groups with rates over and under the median for each microbe at the genus level was examined, rates for Leptotrichia which were over the median correlated with significantly higher overall survival rates. We then extracted 35 differentially expressed genes between the over- and under-the-median-for-Leptotrichia groups based on the criteria of >1.5 fold and p < 0.05 in the Mann–Whitney U-test. A pathway analysis showed that these Leptotrichia-related genes were associated with the pathways of Alzheimer disease, neurodegeneration - multiple diseases, prion disease, MAPK signaling, and PI3K-Akt signaling, while protein–protein interaction analysis revealed that these genes formed a dense network. In conclusion, probiotics and specific antimicrobial therapy targeting Leptotrichia may have an impact on the prognosis of HNSCC.

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Expression characteristics of long non-coding RNA in colon adenocarcinoma and its potential value for judging the survival and prognosis of patients: bioinformatics analysis based on The Cancer Genome Atlas database

Cited by 8 publications

References 33 publications

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

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

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models*

Potential Role of the Intratumoral Microbiota in Prognosis of Head and Neck Cancer

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