Abstract
Adenocarcinoma (AD) and squamous cell carcinoma (SCC) are both classified as major forms of non-small cell lung cancer, but differences in clinical prognoses and molecular mechanisms are remarkable. Recent studies have supported the importance of understanding immune status in that it influences clinical outcomes of cancer, and immunotherapies based on the theory of “immune editing” have had notable clinical success. Our study aimed to identify specific long non-coding (lnc) RNAs that control key immune-related genes and to use them to construct risk models for AD and SCC. Risk scores were used to separate patients into high- and low-risk groups, and we validated the prognostic significance of both risk scores with our own cohorts. A Gene Set Enrichment Analysis suggested that the immune responses of patients in the AD high-risk group and the SCC low-risk group tended to be weakened. Evaluation of immune infiltration revealed that the degree of infiltration of dendritic cells is of particular importance in AD. In addition, prediction of responses to immune checkpoint inhibitor (ICI) treatments, based on the T Cell Immune Dysfunction and Exclusion and immunophenoscore models, indicated that deterioration of the immune microenvironment is due mainly to T cell exclusion in AD patients and T cell dysfunction in SCC patients and that high-risk patients with SCC might benefit from ICI treatment. The prediction of downstream targets via The Cancer Proteome Atlas and RNA-seq analyses of a transfected lung cancer cell line indicated that the lncRNA LINC00996 is a potential therapeutic target in AD.
Highlights
Lung cancer ranks first in mortality among malignant tumors [1]
In referring to the gene sets called “immune response” and “immune system progress” from GSEA, we searched for associated Long non-coding RNA (lncRNA) with Pearson correlation coefficients of at least 0.4 and values of p less than 0.05 in AD and squamous cell carcinoma (SCC) patients upon comparison of cancerous tissues to non-cancerous tissues
The risk score for AD patients is defined according to the following equation, where relative lncRNA expression is noted with the relevant gene symbol: risk score (AD) = 0.3292 × AC245595.1 − 0.7741 × LINC00996 + 0.2592 × VIM − AS1 + 0.1354 × SFTA1P + 0.4720 × MSC − AS1 + 0.5041 * TMPO − AS1 + 0.4536 × ABALON + 0.36102 × AL606489.1 − 0.5293 × AC025048.4 + 0.5714 × LINC01138 − 0.2651 × IPO5P1 − 0.3861 × AC008763.1 − 0.3246 × AC026355.1 − 0.5747 × AC123595.1
Summary
Lung cancer ranks first in mortality among malignant tumors [1]. There are important differences between AD and SCC [1, 6]. As compared with AD, the degree of malignancy of SCC is lower, and prognoses of patients tend to be better [1, 9,10,11]. With the rise of individualized treatment, more and more research has focused on the differences between AD and SCC [6, 12, 13]. Lai-Goldman and colleagues have noted significant differences in tumor immunity among subtypes of AD and SCC [6]. It can be seen that research on both AD and SCC is critical to the development of effective therapies for lung cancer
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
