This preprint is under consideration at World Journal of Surgical Oncology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Identification of molecular markers associated with the progression and prognosis of lung adenocarcinoma:a bioinformatic study
Kexin Du
Beijing University of Chinese Medicine Affiliated Dongzhimen Hospital
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Objective: For the identification of genes of prognostic significance related to tumor microenvironment (TME) in lung adenocarcinoma.
Methods and Materials: Transcriptome data and clinical data of lung adenocarcinoma originated from the Cancer Genome Atlas (TCGA) database. Immune scores and stromal scores were calculated by “Estimation of Stromal and Immune cells in Malignant Tumors using Expression data” algorithm. Based on these calculated scores, the samples were classified as high and low score groups. The average score of respective gene in different groups was calculated. A heat map was created to screen out genes exhibiting differential expressions. The interaction of up-regulated differentially expressed genes (DEGs) and down-regulated DEGs was harvested from a Venn diagram and then covered in the overlapping genes. The core genes influencing prognosis of lung adenocarcinoma were screened out by function enrichment analysis, protein-protein interaction network analysis and Kaplan-Meier (K-M) method on the overlapping genes.
Results: A total of 515 samples of lung adenocarcinoma were harvested from TCGA database. As revealed from the results, a high immune score was related to a high survival rate, while the matrix did not show significant relationships to survival rate. A total of 775 DEGs and 367 overlapping genes were harvested. The functions of these overlapping genes were tightly correlated with DEGs and immune response and were noticeably improved in cytokine-cytokine receptor interaction and chemokine signaling pathway. Eight genes, namely, CCR5, CCR2, CCL14, GNRH2, PKHD1L1, MS4A1, FCER2 and FDCSP, were correlated with prognosis of lung adenocarcinoma.
Conclusion: The genes and pathways affecting prognosis of lung adenocarcinoma were screened out, which offer ideas for subsequent study on lung adenocarcinoma.
Keywords
lung adenocarcinoma,pathway,sex,CCL14,GNRH2,CCR5,CCR2
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This preprint is under consideration at World Journal of Surgical Oncology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Identification of molecular markers associated with the progression and prognosis of lung adenocarcinoma:a bioinformatic study
Kexin Du
Beijing University of Chinese Medicine Affiliated Dongzhimen Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 21 Dec, 2020
No community comments so far
Reviewer #1 agreed
On 17 Jan, 2021
Reviewers invited
Invitations sent on 22 Dec, 2020
Editor assigned
On 16 Dec, 2020
Submission checks complete
On 16 Dec, 2020
Editor invited
On 16 Dec, 2020
First submitted
On 14 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Objective: For the identification of genes of prognostic significance related to tumor microenvironment (TME) in lung adenocarcinoma.
Methods and Materials: Transcriptome data and clinical data of lung adenocarcinoma originated from the Cancer Genome Atlas (TCGA) database. Immune scores and stromal scores were calculated by “Estimation of Stromal and Immune cells in Malignant Tumors using Expression data” algorithm. Based on these calculated scores, the samples were classified as high and low score groups. The average score of respective gene in different groups was calculated. A heat map was created to screen out genes exhibiting differential expressions. The interaction of up-regulated differentially expressed genes (DEGs) and down-regulated DEGs was harvested from a Venn diagram and then covered in the overlapping genes. The core genes influencing prognosis of lung adenocarcinoma were screened out by function enrichment analysis, protein-protein interaction network analysis and Kaplan-Meier (K-M) method on the overlapping genes.
Results: A total of 515 samples of lung adenocarcinoma were harvested from TCGA database. As revealed from the results, a high immune score was related to a high survival rate, while the matrix did not show significant relationships to survival rate. A total of 775 DEGs and 367 overlapping genes were harvested. The functions of these overlapping genes were tightly correlated with DEGs and immune response and were noticeably improved in cytokine-cytokine receptor interaction and chemokine signaling pathway. Eight genes, namely, CCR5, CCR2, CCL14, GNRH2, PKHD1L1, MS4A1, FCER2 and FDCSP, were correlated with prognosis of lung adenocarcinoma.
Conclusion: The genes and pathways affecting prognosis of lung adenocarcinoma were screened out, which offer ideas for subsequent study on lung adenocarcinoma.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8