Potential Non-coding RNAs from Microorganisms and their Therapeutic Use in the Treatment of Different Human Cancers

Biswas, Raju; Ghosh, Dipanjana; Dutta, Bhramar; Halder, Urmi; Goswami, Prittam; Bandopadhyay, Rajib

doi:10.2174/1566523220999201230204814

Cited by 8 publications

(4 citation statements)

References 76 publications

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“…New technologies such as high-throughput technologies (genomics, transcriptomics, etc.) have been used to better identify subsets of patients with sepsis, to identify patients at high risk of developing sepsis, and to provide the possibility for rapid and accurate diagnosis of infection [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Zhou

Dong²,

Wang

et al. 2022

Computational and Mathematical Methods in Medicine

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Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.

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Section: Introductionmentioning

confidence: 99%

Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Zhou

Dong²,

Wang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…CNL subgroup is composed of low-grade endometrioid tumors that are microsatellite-stable and have an intermediate prognosis. The prognostic value TCGA classification has repeatedly been confirmed, offering the possibility of drastically improving the patient management (12)(13)(14). However, this classification remains challenging in practice, mainly due to expensive and difficulties of multi-omics analyses.…”

Section: Introductionmentioning

confidence: 99%

Molecular classification grade 3 endometrial endometrioid carcinoma using a next-generation sequencing–based gene panel

Chen

Liu

et al. 2022

Front. Oncol.

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Over the past two decades, the incidence of endometrial cancer (EC) is increasing, and there is a need for molecular biomarkers to predict prognosis and guide treatment. A recent study from The Cancer Genome Atlas suggested to implement the EC analysis by molecular profile for improving diagnosis, prognosis, and therapeutic treatment. In this study, next-generation sequencing was performed on 70 cases of G3 endometrioid ECs (EECs) using an 11-gene panel (TP53, MLH1, MSH2, MSH6, PMS2, EPCAM, PIK3CA, CTNNB1, KRAS, PTEN, and POL) for molecular classification. The molecular classification based on the 11-gene NGS panel identified four molecular subgroups: POLE-ultramutated (n = 20, 28.6%), MSI-H (n = 27, 38.6%), NSMP (n = 13, 18.6%) and TP53mut (n = 10, 14.3%). The NGS method showed 98.6% (69 of 70 cases, kappa value 98%) in concordance with the cases assessed by immunohistochemistry (IHC). Among the seven dead cases, four were MSI-H tumors, two were TP53mut/p53abn tumors, and one was NSMP tumors with an average overall survival (OS) of 14.7 months. TP53mut subgroup showed that poor OS rates and POLE group have favorable prognosis. Our work suggested that the 11-gene panel is suitable for molecular classification in G3 EECs and for guiding prognosis and treatment decisions.

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“…Most patients are in the middle and late stages when diagnosed, which seriously threatens the survival and quality of life of patients ( Dekker et al, 2019 ; Cienfuegos-Jimenez et al, 2021 ; Peng et al, 2022 ). The 5-year relative survival ranges from more than 90% in stage I patients to slightly more than 10% in stage IV patients ( Brenner et al, 2014 ; Biswas et al, 2021 ). Due to the frequent recurrence and metastasis, the prognosis of CRC is yet to be improved, especially for those with unknown tissue origin ( He et al, 2020a , b ; Liu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

The microbiome types of colorectal tissue are potentially associated with the prognosis of patients with colorectal cancer

Zhao

et al. 2023

Front. Microbiol.

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As the second leading cause of cancer worldwide, colorectal cancer (CRC) is associated with a poor prognosis. Although recent studies have explored prognostic markers in patients with CRC, whether tissue microbes carry prognostic information remains unknown. Here, by assessing the colorectal tissue microbes of 533 CRC patients, we found that Proteobacteria (43.5%), Firmicutes (25.3%), and Actinobacteria (23.0%) dominated the colorectal tissue microbiota, which was different from the gut microbiota. Moreover, two clear clusters were obtained by clustering based on the tissue microbes across all samples. By comparison, the relative abundances of Proteobacteria and Bacteroidetes in cluster 1 were significantly higher than those in cluster 2; while compared with cluster 1, Firmicutes and Actinobacteria were more abundant in cluster 2. In addition, the Firmicutes/Bacteroidetes ratios in cluster 1 were significantly lower than those in cluster 2. Further, compared with cluster 2, patients in cluster 1 had relatively poor survival (Log-rank test, p = 0.0067). By correlating tissue microbes with patient survival, we found that the relative abundance of dominant phyla, including Proteobacteria, Firmicutes, and Bacteroidetes, was significantly associated with survival in CRC patients. Besides, the co-occurrence network of tissue microbes at the phylum level of cluster 2 was more complicated than that of cluster 1. Lastly, we detected some pathogenic bacteria enriched in cluster 1 that promote the development of CRC, thus leading to poor survival. In contrast, cluster 2 showed significant increases in the abundance of some probiotics and genera that resist cancer development. Altogether, this study provides the first evidence that the tissue microbiome of CRC patients carries prognostic information and can help design approaches for clinically evaluating the survival of CRC patients.

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Potential Non-coding RNAs from Microorganisms and their Therapeutic Use in the Treatment of Different Human Cancers

Cited by 8 publications

References 76 publications

Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Molecular classification grade 3 endometrial endometrioid carcinoma using a next-generation sequencing–based gene panel

The microbiome types of colorectal tissue are potentially associated with the prognosis of patients with colorectal cancer

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