About this Research Topic
Cancerous cell harbor genetic, epigenetic, and transcriptomic changes of multiple protein-coding and non-coding genes so that if an essential gene is shut down due to various reasons, including point mutation or swapping of DNA between chromosomes, then the cell can compensate by modulating the activities of other genes to survive. To address this, multiple large-scale studies are being engaged to catalogue genes that are essential for the survival and/or growth of cancer cells by carrying out small interfering RNA, short hairpin RNA or CRISPR based systematic loss of function screens for approximately 17000 protein-coding genes across nearly 800 cancer cell lines. Further efforts have been made to accurately identify driver genes linked with cancer vulnerability by conducting genome-wide CRISPR screens in 3D cancer-spheroid models as well. The screening data revealed hundreds of protein-coding genes upon which cancer cells are dependent for their growth and/or survival. However, there is a prominent gap in understanding the contribution of non-coding genes in constructing such a cancer dependency map.
Non-coding RNAs (ncRNAs), the RNA molecules that are not translated into proteins, constitute a large fraction of the transcriptome in human and other species. Evidence suggests that they play critical roles in cellular physiology and disease pathogenesis including cancer by regulating the expression of protein-coding genes at the transcriptional or post-transcriptional level. The ncRNAs include circular RNA, extracellular RNA, long non-coding RNA, microRNA, small nucleolar RNA, and several other varieties. Despite understanding which ncRNAs have a significant biological impact on modulating survival and growth of cancer cells and how they induce tumorigenesis, the use of this genetic information to determine cancer in precision medicine would be very limited. Similar to protein-coding genes, it could also be hypothesized that a subset of the ncRNA may introduce vulnerability across the cancer types. Investigation of such core dependencies may introduce new targets to combat against multiple cancer types.
Recent significant advancements of a dozen of high-throughput techniques that directly detect RNA duplexes in living cells and availability of multiple types of omics data for more than 30 cancer types have provided us with unprecedented opportunities to accurately identify regulatory mechanisms of ncRNAs. Therefore, robust computational methods or integrative bioinformatics approaches hold promise for decoding critical ncRNAs that are essential for cancer cell survival/growth and that contribute to completing the cancer dependency map.
We welcome Original Research articles as well as Review articles from the investigators in the field of ncRNAs and cancer. Potential topics include but are not limited to the following:
● Machine learning, computational methods and integrative bioinformatics approaches to identify cancer cell survival/growth modulating ncRNAs;
● Literature mining and/or meta-analyses of essential non-coding RNAs;
● Novel ncRNA-target prediction tools, except for microRNAs, utilizing machine learning approaches, robust mathematical, statistical approaches or high-throughput omics data to better understand how ncRNAs modulate cancer cell survival/growth;
● ncRNA-mRNA or ncRNA-ncRNA coessentiality networks;
● Databases or web-servers focusing on essential non-coding RNAs;
● Expression and impact of essential ncRNA in single cell resolution;
● Stresses, including drugs, involved in modulating the survival/growth linked ncRNAs.
Non-coding RNAs (ncRNAs), the RNA molecules that are not translated into proteins, constitute a large fraction of the transcriptome in human and other species. Evidence suggests that they play critical roles in cellular physiology and disease pathogenesis including cancer by regulating the expression of protein-coding genes at the transcriptional or post-transcriptional level. The ncRNAs include circular RNA, extracellular RNA, long non-coding RNA, microRNA, small nucleolar RNA, and several other varieties. Despite understanding which ncRNAs have a significant biological impact on modulating survival and growth of cancer cells and how they induce tumorigenesis, the use of this genetic information to determine cancer in precision medicine would be very limited. Similar to protein-coding genes, it could also be hypothesized that a subset of the ncRNA may introduce vulnerability across the cancer types. Investigation of such core dependencies may introduce new targets to combat against multiple cancer types.
Recent significant advancements of a dozen of high-throughput techniques that directly detect RNA duplexes in living cells and availability of multiple types of omics data for more than 30 cancer types have provided us with unprecedented opportunities to accurately identify regulatory mechanisms of ncRNAs. Therefore, robust computational methods or integrative bioinformatics approaches hold promise for decoding critical ncRNAs that are essential for cancer cell survival/growth and that contribute to completing the cancer dependency map.
We welcome Original Research articles as well as Review articles from the investigators in the field of ncRNAs and cancer. Potential topics include but are not limited to the following:
● Machine learning, computational methods and integrative bioinformatics approaches to identify cancer cell survival/growth modulating ncRNAs;
● Literature mining and/or meta-analyses of essential non-coding RNAs;
● Novel ncRNA-target prediction tools, except for microRNAs, utilizing machine learning approaches, robust mathematical, statistical approaches or high-throughput omics data to better understand how ncRNAs modulate cancer cell survival/growth;
● ncRNA-mRNA or ncRNA-ncRNA coessentiality networks;
● Databases or web-servers focusing on essential non-coding RNAs;
● Expression and impact of essential ncRNA in single cell resolution;
● Stresses, including drugs, involved in modulating the survival/growth linked ncRNAs.
Keywords: non-coding RNA, survival/growth, cancer dependency, integrative bioinformatics, machine learning
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