Significance of Parkinson Family Genes in the Prognosis and Treatment Outcome Prediction for Lung Adenocarcinoma

Li, Yanqi; Xiao, Lü; Zhang, Jiao; Liu, Quan‐Xing; Zhou, Dong; Deng, Xu; Qiu, Yuan; Chen, Qian; Li, Man-Yuan; Yang, Guixue; Zheng, Hong; Dai, Ji‐Gang

doi:10.3389/fmolb.2021.735263

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…In this strategy, we introduced the representation of the gene expression profile using pathway scores from gene set enrichment analysis to test the ability to predict disease outcome as a domain-agnostic predictive method. Other published works proposed methods that are highly domain-dependent and require human intervention for curation [43], this method also uses GSEA but only as a post processing analysis tool. Other methods also used network-based approaches for outcome prediction with gene set enrichment [44, 45] but their focus was using the gene set enrichment as a post processing strategy to validate the findings across other biological data sources.…”

Section: Resultsmentioning

confidence: 99%

Multi-task analysis of gene expression data on cancer public datasets

Martins

2023

Preprint

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BackgroundThere is an availability of omics and often multi-omics cancer datasets on public databases such as Gene Expression Omnibus (GEO), International Cancer Genome Consortium and The Cancer Genome Atlas Program. Most of these databases provide at least the gene expression data for the samples contained in the project. Multi-omics has been an advantageous strategy to leverage personalized medicine, but few works explore strategies to extract knowledge relying only on gene expression level for decisions on tasks such as disease outcome prediction and drug response simulation. The models and information acquired on projects based only on expression data could provide decision making background for future projects that have other level of omics data such as DNA methylation or miRNAs.ResultsWe extended previous methodologies to predict disease outcome from the combination of protein interaction networks and gene expression profiling by proposing an automated pipeline to perform the graph feature encoding and further patient networks outcome classification derived from RNA-Seq. We integrated biological networks from protein interactions and gene expression profiling to assess patient specificity combining the treatment/control ratio with the patient normalized counts of the deferentially expressed genes. We also tackled the disease outcome prediction from the gene set enrichment perspective, combining gene expression with pathway gene sets information as features source for this task. We also explored the drug response outcome perspective of the cancer disease still evaluating the relationship among gene expression profiling with single sample gene set enrichment analysis (ssGSEA), proposing a workflow to perform drug response screening according to the patient enriched pathways.ConclusionWe showed the importance of the patient network modeling for the clinical task of disease outcome prediction using graph kernel matrices strategy and showed how ssGSEA improved the prediction only using transcriptomic data combined with pathway scores. We also demonstrated a detailed screening analysis showing the impact of pathway-based gene sets and normalization types for the drug response simulation. We deployed two fully automatized Screening workflows following the FAIR principles for the disease outcome prediction and drug response simulation tasks.AvailabilityThe ScreenDOP code is available athttps://github.com/yascoma/screendopwhile the DReCaS is available athttps://github.com/YasCoMa/caliscoma_pipeline/

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Section: Resultsmentioning

confidence: 99%

Multi-task analysis of gene expression data on cancer public datasets

Martins

2023

Preprint

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Induro-RT mediated circRNA-sequencing (IMCR-seq) enables comprehensive profiling of full-length and long circular RNAs from low input total RNA

Unlu,

Maguire,

Guan

et al. 2024

Nucleic Acids Research

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Circular RNA (circRNA) has recently gained attention for its emerging biological activities, relevance to disease, potential as biomarkers, and promising an alternative modality for RNA vaccines. Nevertheless, sequencing circRNAs has presented challenges. In this context, we introduce a novel circRNA sequencing method called Induro-RT mediated circRNA-sequencing (IMCR-seq), which relies on a group II intron reverse transcriptase with robust rolling circle reverse transcription activity. The IMCR-seq protocol eliminates the need for conventional circRNA enrichment methods such as rRNA depletion and RNaseR digestion yet achieved the highest circRNA enrichment and detected 6–1000 times more circRNAs for the benchmarked human samples compared to other methods. IMCR-seq is applicable to any organism, capable of detecting circRNAs of longer than 7000 nucleotides, and is effective on samples as small as 10 ng of total RNA. These enhancements render IMCR-seq suitable for clinical samples, including disease tissues and liquid biopsies. We demonstrated the clinical relevance of IMCR-seq by detecting cancer-specific circRNAs as potential biomarkers from IMCR-seq results on lung tumor tissues together with blood plasma samples from both a healthy individual and a lung cancer patient. In summary, IMCR-seq presents an efficient and versatile circRNA sequencing method with high potential for research and clinical applications.

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Significance of Parkinson Family Genes in the Prognosis and Treatment Outcome Prediction for Lung Adenocarcinoma

Cited by 2 publications

References 52 publications

Multi-task analysis of gene expression data on cancer public datasets

Multi-task analysis of gene expression data on cancer public datasets

Induro-RT mediated circRNA-sequencing (IMCR-seq) enables comprehensive profiling of full-length and long circular RNAs from low input total RNA

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