Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomization Article Swipe
Youpeng Chen
,
Enzhong Li
,
Zhenglin Chang
,
Tingting Zhang
,
Zhenfeng Song
,
Haojie Wu
,
Zhangkai J. Cheng
,
Baoqing Sun
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fphar.2024.1433147
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fphar.2024.1433147
Our research aimed to identify new therapeutic targets for Lung adenocarcinoma (LUAD), a major subtype of non-small cell lung cancer known for its low 5-year survival rate of 22%. By employing a comprehensive methodological approach, we analyzed bulk RNA sequencing data from 513 LUAD and 59 non-tumorous tissues, identifying 2,688 differentially expressed genes. Using Mendelian randomization (MR), we identified 74 genes with strong evidence for a causal effect on risk of LUAD. Survival analysis on these genes revealed significant differences in survival rates for 13 of them. Our pathway enrichment analysis highlighted their roles in immune response and cell communication, deepening our understanding. We also utilized single-cell RNA sequencing (scRNA-seq) to uncover cell type-specific gene expression patterns within LUAD, emphasizing the tumor microenvironment’s heterogeneity. Pseudotime analysis further assisted in assessing the heterogeneity of tumor cell populations. Additionally, protein-protein interaction (PPI) network analysis was conducted to evaluate the potential druggability of these identified genes. The culmination of our efforts led to the identification of five genes (tier 1) with the most compelling evidence, including SECISBP2L , PRCD , SMAD9 , C2orf91 , and HSD17B13 , and eight genes (tier 2) with convincing evidence for their potential as therapeutic targets.
Related Topics
Concepts
Mendelian randomization
Biology
Gene
Druggability
Computational biology
Lung cancer
Identification (biology)
Adenocarcinoma
Genetics
Bioinformatics
Cancer
Medicine
Oncology
Genetic variants
Genotype
Botany
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fphar.2024.1433147
- https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2024.1433147/pdf
- OA Status
- gold
- Cited By
- 6
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400792218
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400792218Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fphar.2024.1433147Digital Object Identifier
- Title
-
Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomizationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-18Full publication date if available
- Authors
-
Youpeng Chen, Enzhong Li, Zhenglin Chang, Tingting Zhang, Zhenfeng Song, Haojie Wu, Zhangkai J. Cheng, Baoqing SunList of authors in order
- Landing page
-
https://doi.org/10.3389/fphar.2024.1433147Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2024.1433147/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2024.1433147/pdfDirect OA link when available
- Concepts
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Mendelian randomization, Biology, Gene, Druggability, Computational biology, Lung cancer, Identification (biology), Adenocarcinoma, Genetics, Bioinformatics, Cancer, Medicine, Oncology, Genetic variants, Genotype, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6Per-year citation counts (last 5 years)
- References (count)
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55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.heterogeneity | 131 |
| abstract_inverted_index.randomization | 55 |
| abstract_inverted_index.type-specific | 113 |
| abstract_inverted_index.adenocarcinoma | 10 |
| abstract_inverted_index.communication, | 99 |
| abstract_inverted_index.differentially | 50 |
| abstract_inverted_index.heterogeneity. | 123 |
| abstract_inverted_index.identification | 161 |
| abstract_inverted_index.methodological | 33 |
| abstract_inverted_index.understanding. | 102 |
| abstract_inverted_index.protein-protein | 137 |
| abstract_inverted_index.microenvironment’s | 122 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5016606637, https://openalex.org/A5021129101 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I4210098361, https://openalex.org/I4210154954, https://openalex.org/I92039509 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.92779761 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |