Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells
Background: As chemical signals of hormones, neuropeptides are essential to regulate cell growth by interacting with their receptors to achieve cell communications in cancer tissues. Previously, neuropeptide transcriptome analysis was limited to tissue-based bulk expression levels. The molecular mec...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/10/1/14 |
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| author | Yining Liu Min Zhao |
| author_facet | Yining Liu Min Zhao |
| author_sort | Yining Liu |
| collection | DOAJ |
| description | Background: As chemical signals of hormones, neuropeptides are essential to regulate cell growth by interacting with their receptors to achieve cell communications in cancer tissues. Previously, neuropeptide transcriptome analysis was limited to tissue-based bulk expression levels. The molecular mechanisms of neuropeptides and their receptors at the single-cell level remain unclear. We conducted a systematic single-cell transcriptome data integration analysis to clarify the similarities and variations of neuropeptide-mediated cell communication between various malignancies. Methods: Based on the single-cell expression information in 72 cancer datasets across 24 cancer types, we characterized actively expressed neuropeptides and receptors as having log values of the quantitative transcripts per million ≥ 1. Then, we created the putative cell-to-cell communication network for each dataset by using the known interaction of those actively expressed neuropeptides and receptors. To focus on the stable cell communication events, we identified neuropeptide and downstream receptors whose interactions were detected in more than half of all conceivable cell-cell interactions (square of the total cell population) in a dataset. Results: Focusing on those actively expressed neuropeptides and receptors, we built over 76 million cell-to-cell communications across 70 cancer datasets. Then the stable cell communication analyses were applied to each dataset, and about 14 million stable cell-to-cell communications could be detected based on 16 neuropeptides and 23 receptors. Further functional analysis indicates these 39 genes could regulate blood pressure and are significantly associated with patients’ survival among over ten thousand The Cancer Genome Atlas (TCGA)pan-cancer samples. By zooming in lung cancer-specific clinical features, we discovered the 39 genes appeared to be enriched in the patients with smoking. In skin cancer, they may differ in the patients with the distinct histological subtype and molecular drivers. Conclusions: At the single-cell level, stable cell communications across cancer types demonstrated some common and distinct neuropeptide-receptor patterns, which could be helpful in determining the status of neuropeptide-based cell communication and developing a peptide-based therapy strategy. |
| first_indexed | 2024-03-10T01:53:22Z |
| format | Article |
| id | doaj.art-d66bd8fa0016437dbd6d38fd1cf60cb0 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-10T01:53:22Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-d66bd8fa0016437dbd6d38fd1cf60cb02023-11-23T13:02:13ZengMDPI AGBiomedicines2227-90592021-12-011011410.3390/biomedicines10010014Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor CellsYining Liu0Min Zhao1The School of Public Health, Institute for Chemical Carcinogenesis, Guangzhou Medical University, Guangzhou 511436, ChinaSchool of Science, Technology and Engineering, University of the Sunshine Coast, Maroochydore Dc, QLD 4558, AustraliaBackground: As chemical signals of hormones, neuropeptides are essential to regulate cell growth by interacting with their receptors to achieve cell communications in cancer tissues. Previously, neuropeptide transcriptome analysis was limited to tissue-based bulk expression levels. The molecular mechanisms of neuropeptides and their receptors at the single-cell level remain unclear. We conducted a systematic single-cell transcriptome data integration analysis to clarify the similarities and variations of neuropeptide-mediated cell communication between various malignancies. Methods: Based on the single-cell expression information in 72 cancer datasets across 24 cancer types, we characterized actively expressed neuropeptides and receptors as having log values of the quantitative transcripts per million ≥ 1. Then, we created the putative cell-to-cell communication network for each dataset by using the known interaction of those actively expressed neuropeptides and receptors. To focus on the stable cell communication events, we identified neuropeptide and downstream receptors whose interactions were detected in more than half of all conceivable cell-cell interactions (square of the total cell population) in a dataset. Results: Focusing on those actively expressed neuropeptides and receptors, we built over 76 million cell-to-cell communications across 70 cancer datasets. Then the stable cell communication analyses were applied to each dataset, and about 14 million stable cell-to-cell communications could be detected based on 16 neuropeptides and 23 receptors. Further functional analysis indicates these 39 genes could regulate blood pressure and are significantly associated with patients’ survival among over ten thousand The Cancer Genome Atlas (TCGA)pan-cancer samples. By zooming in lung cancer-specific clinical features, we discovered the 39 genes appeared to be enriched in the patients with smoking. In skin cancer, they may differ in the patients with the distinct histological subtype and molecular drivers. Conclusions: At the single-cell level, stable cell communications across cancer types demonstrated some common and distinct neuropeptide-receptor patterns, which could be helpful in determining the status of neuropeptide-based cell communication and developing a peptide-based therapy strategy.https://www.mdpi.com/2227-9059/10/1/14single-cell RNAseqneuropeptide receptorcancer genomicsdata integration |
| spellingShingle | Yining Liu Min Zhao Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells Biomedicines single-cell RNAseq neuropeptide receptor cancer genomics data integration |
| title | Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells |
| title_full | Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells |
| title_fullStr | Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells |
| title_full_unstemmed | Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells |
| title_short | Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells |
| title_sort | decode the stable cell communications based on neuropeptide receptors network in 36746 tumor cells |
| topic | single-cell RNAseq neuropeptide receptor cancer genomics data integration |
| url | https://www.mdpi.com/2227-9059/10/1/14 |
| work_keys_str_mv | AT yiningliu decodethestablecellcommunicationsbasedonneuropeptidereceptorsnetworkin36746tumorcells AT minzhao decodethestablecellcommunicationsbasedonneuropeptidereceptorsnetworkin36746tumorcells |