Identification of Potential Prognostic Value of Clock-related LncRNA with Immune Implications in Non-small Cell Lung Cancer

doi:10.21203/rs.3.rs-2430614/v1

Download PDF

Research Article

Identification of Potential Prognostic Value of Clock-related LncRNA with Immune Implications in Non-small Cell Lung Cancer

https://doi.org/10.21203/rs.3.rs-2430614/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Objective

Our aim is to construct a prognostic model of clock-related lncRNAs for patients with non-small cell lung cancer (NSCLC) and evaluate its relationship with tumor immune microenvironment.

Methods

The data of 944 NSCLC cases were screened and obtained from the TCGA database. Circadian rhythm-related Gene Set (consisting of 70 clock-related genes) was identified using the GSEA website. Clock-related lncRNAs were obtained by co-expression analysis. The abundance of immune cell infiltrates in NSCLC patients was calculated using CIBERSORT. Clock-related lncRNAs risk scores were calculated via the Least Absolute shrinkage and Selection Operator (LASSO) penalized Cox regression analysis. Then we divided the 944 NSCLC cases into training set (473 cases) and validation set (471 cases) based on the median risk score. The independence risk factors for clinical pathological parameters and risk score were analyzed using the univariate and multivariate Cox regression analysis. We also explored the relationship between risk score and immune cell infiltration in NSCLC cases.

Results

A total of 12 clock-related lncRNAs were selected from the training set to calculate the risk score. Patients in the high-risk group had poor prognosis than patients in the low-risk group, which were both validated in the training set and validation set (P < 0.001, P < 0.05, respectively). The area under the curve (AUC) of risk score was 0.763 and 0.624, respectively. Multivariate Cox analysis showed that T stage, N stage, age, and clock-related lncRNAs risk score were independent risk factors for prognosis of patients with NSCLC. B Cell naive and Mast cells resting showed high invasion abundance in the low-risk groups (P < 0.01), while Macrophages M0, Macrophages M1 and Neutrophils cells showed high infiltration abundance in the high-risk groups (P < 0.01).

Conclusions

In conclusion, the risk score of 12 clock-related lncRNAs was identified as a prognostic biomarker for patients with NSCLC, and was significantly associated with the regulation of immune cell subtypes.

Non-small cell lung cancer

clock-related lncRNAs

immune microenvironment

prognosis

Lung cancer is one of the most common malignancies across the world. GLOBOCAN estimates approximately 2.2 million new cases of lung cancer, accounting for 11.4% of all new cancer cases worldwide in 2020, while 18.0% of cancer-related deaths were caused by lung cancer¹. Moreover, non-small cell lung cancer (NSCLC) is the most common primary lung cancer, accounting for up to 85%².

Circadian clock plays a great role in various processes, such as cell proliferation, metabolism, aging and DNA damage responses. Once the normal process is disrupted it might contribute to the development of cancer³. The change of sleep rhythm may cause the disorder of the core molecular clock, leading to the mutation of clock genes and abnormal expression of relevant genes^4,5, then perturbing the normal progress of endocrine and metabolism⁶, and finally inducing the occurrence of cancer. Circadian clock genes were reported that involved in the development of NSCLC. Abnormal expression of circadian clock genes might interfere with the microenvironment of lung cancer via regulating the infiltration of different immune cells^7,8. In addition, long non-coding RNA (lncRNA) participate in and promote the occurrence and development of various malignant tumors^{9,10,11,12,13,14}. However, most studies explored the association between lncRNA and NSCLC are limited to the role of lncRNA as a direct regulator for the NSCLC occurrence, such as proto-oncogenes or tumor suppressor genes¹⁵, there are few studies evaluated the role of clock-related lncRNA on NSCLC.

Therefore, our study attempted to screen out clock-related lncRNA by analyzing expression profile data related to NSCLC from TCGA database, establish a clock-related lncRNA prognostic model for NSCLC, and explore its relationship with tumor immune microenvironment.

2.1 Data collection

A total of 944 NSCLC cases with complete survival information and gene expression profiles were screened and obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Circadian clock-related gene sets (including a total of 70 genes) are available at Gene Set Enrichment Analysis (GSEA website: http://www.gsea-msigdb.org/gsea/index.jsp) based on the category of “REACTOME_CIRCADIAN_CLOCK”.

2.2 Identification Of Clock-related Lncrnas

To obtain the clock-related lncRNAs, we performed Pearson correlation analysis between the expression level of clock-related gene sets and lncRNAs in the samples. If the absolute value of correlation coefficient > 0.4 and P value < 0.001, the corresponding lncRNA was identified as clock-related lncRNA.

2.3 Construction Of The Risk Score

To screen the clock-related lncRNAs with significant prognostic value, we perform multivariate Cox regression analysis in 944 NSCLC cases using the “survival” packages in R (version 3.6.1). Then, the clock-related lncRNAs with P value < 0.05 were further analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression using the “glmnet ” packages in R. Moreover, the risk score of each case was calculated by the following formula: Risk score = Σcoefi* expgenei, where coefi represents the regression coefficient of lncRNA is extracted from the LASSO regression analysis, and expgenei represents the expression value of lncRNA. Based on the median value of risk score, the 944 NSCLC patients were divided into the low-risk groups and high-risk groups. In addition, Kaplan-Meier survival analysis was performed to evaluate the prognostic value of risk score using the "survival" and "survminer" packages in R. Log-rank P < 0.05 indicates significant difference.

2.4 Evaluation Of Prognostic Value For Risk Signature And Clinicopathological Features

To investigate the independent prognostic factors in NSCLC patients, we performed univariate and multivariate Cox regression analysis for risk score and clinicopathological features in 944 NSCLC patients. Variables with P < 0.05 in both univariate and multivariate Cox regression analyses were considered as independent prognostic factors.

2.5 Gene Function And Pathway Enrichment Analysis

To explore potential biological functions and significant signaling pathways of clock-related lncRNAs, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and Gene Ontology (GO) analysis were applied using the online DAVID tool with the cut-off of P value < 0.05, and the results were visualized using the “ClusterProfiler” package in R. GO terms were mainly classified into three types, including cellular component (CC), biological process (BP), and molecular function (MF), while the KEGG database gathers genomic, chemical and system function information. In addition, Gene aggregation enrichment analysis (GSEA) was also performed between the high- and low-risk group.

2.6 Immune Cell Infiltration And Risk Signature

Furthermore, we performed the CIBERSORT algorithm to obtain the immune cell infiltration matrix and quantify the immune cell subtypes enrichment for all included samples using the "E1071" package of R software (https://www.r-project.org/ R_3.6.3). Using Perl script, the samples with P < 0.05 were screened to obtain the new filtered immune cell infiltration matrix, and 5 kinds of immune cells subtypes relevant to NSCLC patients were obtained, including B cell naive, Neutrophils, Mast cells resting, Macrophages M0, and Macrophages M1. Next, we used t-test to analyze the infiltrating abundance of these 5 kinds of immune cells in low-risk and high-risk NSCLC patients, and visualized with the boxplots.

3.1 The clinical characteristics of lung cancer patients

After excluding the patients with missing survival data, a total of 944 patients (Lung adenocarcinoma: 477 cases; Lung squamous cell carcinoma: 467 cases) were eligible for our study. The detailed clinical characteristics of included cases, such as age, gender, and TNM grade, were presented at Table 1.

Table 1

Clinical characteristics of TCGA and GEO dataset
Variable	Lung adenocarcinoma (N=477)	Lung squamous cell carcinoma (N=467)
Age
< 67	242(50.7)	201(43.0)
>=67	235(49.3)	266(57.0)
Gender
Male	220(46.1)	346(74.1)
Female	257(53.9)	121(25.9)
T
T₁	159(33.3)	104(22.3)
T₂	254(53.2)	274(58.7)
T₃	43(9.0)	68(14.6)
T₄	18(3.8)	21(4.5)
Unknow	3(0.6)	0(0.0)
N
N₀	307(64.4)	295(63.2)
N₁	90(18.9)	124(26.6)
N₂	67(14.0)	39(8.4)
N3	2(0.4)	5(1.1)
Unknow	11(2.3)	4(0.9)
M
M₀	313(65.6)	387(82.9)
M₁	24(5.0)	6(1.3)
Unknow	140(29.4)	74(15.8)

3.2 Construction Of The Risk Model For Clock-related Lncrnas

Multivariable Cox regression analysis showed that 12 clock-related lncRNAs were significantly associated with OS of NSCLC patients on the training set, which AC022165.1, AC026310.2 and ITGA9-AS1 seem to serve as risk factors, and the remaining nine lncRNAs are protective factors. Then, Lasso penalized Cox regression analysis was performed, in which those 12 clock-related lncRNAs were identified. The

risk score for each sample was calculated by multiplying the corresponding coefficient of each lncRNA by the expression value. The results revealed that the risk score showed great prognostic value for NSCLC (Fig. 1). Subsequently, the training sets were stratified into low-risk and high-risk groups based on the median risk score of 0.990 (Fig. 2). The Kaplan-Meier survival curves indicated that patients in the low-risk group were significantly associated with better OS than patients in the high-risk group (P < 0.001) (Fig. 3). Moreover, the same results were identified in the validation set (P < 0.05). The area under the curve (AUC) of the training set and test set were 0.763 and 0.624, indicating that the risk characteristics predicted the prognosis of patients with NSCLC (Fig. 4).

3.3 Validation Of The Risk Signature

In order to validate the accuracy of prognostic value for risk score in the training, we also performed Kaplan-Meier survival curves for the validation set. Similarly, patients in the low-risk group were significantly associated with better OS than patients in the high-risk group.

3.4 Identification Of Independent Prognostic Factors

To identify independent prognostic factors for patients with lung cancer, univariate and multivariate Cox regression analysis was performed in the training set. The results showed that age, T stage, N stage, M stage and risk score were identified as independent risk factors for prognosis of NSCLC patients (Fig. 5). Similarly, those independent risk factors were also identified in the validation set, which verifies the accuracy of the results in the training set.

3.5 Functional Enrichment Analysis

To explore the biological information for the clock-related lncRNAs, GSEA analysis was performed between the high-risk and low-risk groups in the training set. The results found that some clock-related lncRNAs are not only involved in cancer development, but also involved in important pathways of immune regulation. The significant KEGG pathways of the clock-related lncRNAs were mainly enriched in herpes simplex virus 1 infection, the PI3K-Akt signaling pathway and ligand-receptor interactions in stimulating nerve tissue, with the highest Normalized Enrichment Score (NES). In addition, GO enrichment analysis, consisting of cellular components, molecular function, biological process, was performed through the online DAVID tool, and the clock-related lncRNAs were significantly enriched in the cell-cell junction, transcriptional coregulator activities, and regulation of cell morphogenesis (Fig. 6).

3.6 Relationship Between Immune Cell Infiltration And Risk Score

To understand the difference in the enrichment degree of immune cell subtypes between low- and high-risk groups, 22 kinds of immune cells subtypes were assessed using CIBERSORT. The results showed that top 5 types of immune cells were enriched in the NSCLC tissues, including B cell naive, Mast cells resting cells, Macrophages M0, Macrophages M1, and Neutrophils cells, which B cell naive and Mast cells resting cells were enriched in a higher proportion in the low-risk group (all P < 0.01), while Macrophages M0, Macrophages M1 and Neutrophils cells were enriched in a higher proportion in the high-risk group (all P < 0.01) (Fig. 7).

In present study, we divided the 944 cases with NSCLC into training set (473 cases) and validation set (471 cases) and obtained the gene set (REACTOME_CIRCADIAN_CLOCK) related to biological clock from Molecular Signatures Database. We established a prognostic model for clock-related lncRNAs in NSCLC cases, and explored its relationship with tumor immune microenvironment. The results indicated that patients in the low-risk group were significantly associated with better OS than patients in the high-risk group. In addition, the proportion of Neutrophils cells Macrophages M0, and Macrophages M1 in the high-risk group was significantly higher than that in the low-risk group.

Of these three risk factors of clock-related lncRNAs, 'ITGA9-AS1' is positively associated with the OS of patients with breast cancer, and ELOVL2-AS1 may be considered as a potential prognostic biomarker for patients with tamoxifen resistance¹⁶. Of the remaining nine protective factors, AC145124.1 showed valuable prognostic effects in BLCA patients¹⁷. MIR133A1HG is identified as a autophagy-related lncRNAs, and might be a novel biomarkers for predicting the survival of AML patients¹⁸. However, the importance of AC022165.1, AC026310.2 and other seven protective factors in NSCLC is rarely reported. Given the validated evidence of AC145124.1 and MIR133A1HG in previous studies, as well as our findings, these two lncRNAs might play important roles in NSCLC, which should be explored for their biological function in future research. In addition, our results found that the risk score of 12 clock-related lncRNAs showed great prognostic value in NSCLC cases, were presented at Table 2. Previous studies proposed that the risk score constructed based on four lncRNAs can effectively predict prognosis of glioblastoma¹⁹. Then, we performed

Table 2

Twelve prognostic clock-related lncRNAs identified from Pearson’s correlation analysis and multivariate Cox regression analysis.
id	coef	HR	HR.95L	HR.95H	pvalue
AL133445.2	-0.199	0.820	0.687	0.979	0.028
AC022165.1	0.328	1.388	1.204	1.600	6.091
AC037441.1	-0.077	0.926	0.836	1.026	0.141
AC026310.2	0.181	1.199	1.085	1.323	0.000
AC022540.1	-0.150	0.861	0.724	1.022	0.088
`ITGA9-AS1`	0.235	1.264	1.033	1.547	0.023
MIR133A1HG	-0.269	0.764	0.637	0.917	0.004
AC114980.1	-0.160	0.852	0.717	1.013	0.070
AL161757.5	-0.289	0.749	0.635	0.883	0.001
AC145124.1	-0.177	0.838	0.728	0.965	0.014
AC020765.2	-0.141	0.868	0.762	0.990	0.035
AC007663.4	-0.132	0.876	0.734	1.046	0.143

Kaplan-Meier survival curves analysis in the training set and validation set, and found that patients with high-risk scores were significantly related with worse OS than patients with low-risk scores (P < 0.001). The same results were obtained in the validation set (P < 0.05). ROC analysis indicated that the risk score clock-related lncRNAs showed great predictive value for prognosis of NSCLC cases. Moreover, the 1-year, 3-year and 5-year prognostic risk models constructed in other studies have shown that the predictive value of risk signature for 1-year OS for LUAD patients was better than that in the 3- and 5-year OS²⁰. Although our study did not discuss prognostic differences in overall survival of NSCLC, it has been proved that this model has good sensitivity and specificity, and can reflect the prognosis of NSCLC patients to a certain extent, providing a basis for clinical prognosis diagnosis and treatment of NSCLC patients.

In KEGG enrichment analysis, the signaling pathways, including PI3K − Akt signaling pathway and Herpes simplex virus 1 infection, were significantly upregulated in clock-related lncRNAs. Previous studies indicated that the activated T lymphocytes were sensitive to HSV1 infection. Once infected by HSV1, T cells might leads to rapid elimination of antiviral T cells, followed by immune dysfunction. Much evidence has shown that lncRNAs are involved in the development of NSCLC and play a variety of roles in the cell regulation process, such as promoting cell growth, proliferation, apoptosis, migration, invasion and inducing epithelial-mesenchymal transformation^21,22,23. The development of NSCLC involves in multiple signaling pathways, among which PI3K/protein kinase B, phosphatidylinositol-3 -kinase, PKB /Akt/mammalian target of rapamycin, which mTOR pathway is one of the most important regulatory pathways in NSCLC. It affects the development of NSCLC by inducing cell apoptosis, inhibiting cell proliferation, invasion, and migration, and regulating tumor angiogenesis^24,25,26. In the GO analysis, the clock-related lncRNAs were significantly enriched in the cell-cell junction, transcriptional coregulator activities, and regulation of cell morphogenesis.

In recent years, more and more studies have discussed the effects of tumor immune microenvironment on the prognosis of lung cancer, but no consensus has been reached. In this study, B cell naive and Mast cells were significantly enriched in the low-risk groups (P < 0.01), indicating that it has a better prognosis than non-small cell lung cancer. While Macrophages M0, Macrophages M1 and Neutrophils cells showed high infiltration abundance in the high-risk groups (P < 0.01), indicating that its increase was a poor prognostic factor for NSCLC.

To our knowledge, few studies explore the relationship between clock-related lncRNAs and prognostic of NSCLC, our results identified a new biomarker to predict the prognosis of NSCLC. However, there are some limitations in our study. On the one hand, the prognostic risk signature of clock-related lncRNAs was constructed based on the bioinformatics analysis of large dataset from TCGA database. Although our results showed good sensitivity and specificity, the predictive value still needs to be validated by large number of clinical research. On the other hand, the study was analyzed using publicly available datasets, while it's impossible to obtain all clinical data from each patient.

The change of sleep rhythm can cause the mutation of clock genes, affect the expression of related genes, and induce the occurrence of cancer. Based on the analysis of expression profile data of NSCLC from TCGA database, our study screened out the clock-related lncRNAs, and effectively constructed a prediction model for the risk score of NSCLC patients. It has been proved that this model has good sensitivity and specificity, which can reflect the prognosis of NSCLC patients to a certain extent, provide help for the clinical prognosis of NSCLC, and provide more alternative biomarkers for basic research.

NSCLC

Non-small cell lung cancer

lncRNA

Long non-coding RNA

TCGA

The cancer genome atlas

LASSO

Least absolute shrinkage and selection operator

KEGG

Kyoto encyclopedia of genes and genomes

Gene ontology

Cellular component

Biological process

Molecular function

GSEA

Gene aggregation enrichment analysis

AUC

Area under the curve

NES

Normalized enrichment score.

Acknowledgements

The authors gratefully acknowledge contributions from TCGA database and GSEA website.

Authors’ contributions

FH designed the study. JZ, XL and LH analyzed the data. YS, JZ, RL, HZ and FH contributed to interpretation of the results. JZ searched the dataset. YS, RL, XL, LH and HZ drafted the manuscript. FH contributed to critical revision of the manuscript for important intellectual content. FH and MK approved the fnal version of the manuscript. All authors read and approved the fnal manuscript.

Fundings

This study was supported by Fujian Provincial Health Research Talents Training Programme Medical Innovation Project [grant number 2019-CX-33], Joint Funds for the innovation of science and Technology, Fujian province [grant number 2019Y9022] and Central government-led local science and technology development special project [grant number 2020L3009]

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the TCGA database. The address of the TCGA database is: https：//portal.gdc.cancer.gov/. The data that support the findings of this study are openly available in [TCGA database].

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that all authors declare no competing interest

Author details

¹ Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China

² Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China

³ Fujian Provincial Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China

⁴ Fujian Digital Tumor Data Research Center, Fuzhou, China

⁵ Department of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital of Fujian Medical University, Respirology Medicine Centre of Fujian Province, Quanzhou, China.

Sung Hyuna,Ferlay Jacques,Siegel Rebecca L.,Laversanne Mathieu,Soerjomataram Isabelle,Jemal Ahmedin,Bray Freddie. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA: A Cancer Journal for Clinicians,2021,71(3)
Sung H, Ferlay J, Siegel RL et al. Global cancer statistics2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA Cancer JClin,2021 Feb 4.doi:10.3322/caac.21660.Online ahead of print.
YU, EA, WEAVER DR. Disrupting the circadian clock:genespecifc effects on aging, cancer, and other phenotypes[J].Aging,2011,3(5):479 493.
Jagannath A, Taylor L, Wakaf Z, Vasudevan SR, Foster RG. The genetics of circadian rhythms,sleep and health. Hum Mol Genet. 2017;26(R2):R128–38. 10.1093/hmg/ddx240.
Ashbrook LH, Krystal AD, Fu YH, Ptáček LJ. Genetics of the human circadian clock and sleep homeostat. Neuropsychopharmacology. 2020;45(1):45–54. 10.1038/s41386-019-0476-7.
Hastings M, O'Neill JS, Maywood ES. Circadian clocks: regulators of endocrine and metabolic rhythms. J Endocrinol. 2007;195(2):187–98. 10.1677/JOE-07-0378.
Bense RD, Sotiriou C, Piccart-Gebhart MJ, et al. Relevance of tumor-infiltrating immune cell composition and functionality for disease outcome in breast cancer. J Natl Cancer Inst. 2017;109:djw192.
Zheng X, Hu Y, Yao C. The paradoxical role of tumor-infiltrating immune cells in lung cancer. IRDR. 2017;6:234–41. 10.5582/irdr.2017.01059.
Liu Aihua and Liu Lihua. Long non-coding RNA ZEB2-AS1 promotes proliferation and inhibits apoptosis of colon cancer cells via miR-143/bcl-2 axis.[J]. Am J translational Res. 2019;11(8):5240–8.
Guo, Xu, et al. Effect and mechanism of long non-coding RNA ZEB2-AS1 in the occurrence and development of colon cancer.[J]. Math Biosci engineering: MBE. 2019;16(6):8109–20.
Xu, Chen, et al. Long non-coding RNA ZEB2-AS1 expression is associated with disease progression and predicts outcome in gastric cancer patients.[J]. J B U : official J Balkan Union Oncol. 2019;24(2):663–71.
Zhang G, et al. Long non-coding RNA ZEB2-AS1 promotes the proliferation, metastasis and epithelial mesenchymal transition in triple-negative breast cancer by epigenetically activating ZEB2.[J]. J Cell Mol Med. 2019;23(5):3271–9.
Wu, Fangxiong, et al. The lncRNA ZEB2-AS1 is upregulated in gastric cancer and affects cell proliferation and invasion via miR-143-5p/HIF-1α axis.[J]. OncoTargets and therapy. 2019;12(default):657–67.
Irina V, Novikova, Scott P, Hennelly, Karissa Y. Sanbonmatsu. Tackling Structures of Long Noncoding RNAs[J]. Int J Mol Sci. 2013;14(12):23672–84.
Kevin C, Wang, Howard Y, Chang. Molecular Mechanisms of Long Noncoding RNAs[J]. Mol Cell. 2011;43(6):904–14.
Zhang X, Gao S, Li Z, Wang W, Liu G. "Identification and Analysis of Estrogen Receptor α Promoting Tamoxifen Resistance-Related lncRNAs", BioMed Research International, vol. 2020, Article ID 9031723, 10 pages, 2020. https://doi.org/10.1155/2020/9031723
Li Rong-QuanHeZhi-GuangHuangTian-Yu. Yan-Ping Wei,Gang Chen,Xing-Gu Lin,Qiu-Yan Wang. RNA-Sequencing Data Reveal a Prognostic Four-lncRNA-Based Risk Score for Bladder Urothelial Carcinoma: An in Silico Update[J]. Cellular Physiology and Biochemistry,2018,50(4).
Zhao C. Wang Yulu,Tu Famei,Zhao Shuai,Ye Xiaoying,Liu Jing,Zhang Juan,Wang Zifeng. A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML) [J]. Frontiers in Genetics,2021,12.
Peng H, Qin K, Dai YH et al. Establishment of lnc RNA risk prediction model for glioblastoma based on TCGA database[J].Cancer Res Prev Treat,2019,46(5):417–420.
Donghui JinYuxuanSongYuanChenPeng, Zhang,David A, McClellan. Identification of a Seven-lncRNA Immune Risk Signature and Construction of a Predictive Nomogram for Lung Adenocarcinoma[J]. BioMed Research International,2020,2020
Yin D, Lu X, Su J, et al. Long noncoding ï¼²NA AFAP1-AS1 predicts a poor prognosis and regulates non-small cell lung cancer cell proliferation by epigenetically repressing p21 expression [. J] Mol Cancer. 2018;17(1):92.
Zhou H, Chen A, Shen J, et al. Long non-coding ï¼²NA LOC285194 functions as a tumor suppressor by targeting p53 in non-small cell lung cancer [J]. Oncol Rep. 2019;41(1):15–26.
Li K, Sun D, Gou Q, et al. Long non-coding ï¼²NA linc00460 promotes epithelial-mesenchymal transition and cell migration in lung cancer cells [J]. Cancer Lett. 2018;420:80–90.
Wang Q, Liu Z, Du K, et al. Babaodan inhibits cell growth by inducing autophagy through the PI3K/AKT /mTOï¼² pathway and enhances antitumor effects of cisplatin in NSCLC cells [J]. Am J Transl ï¼²es. 2019;11(8):5272–83.
Hu T, Shen H, Huang H et al. Cholesterol-lowering drug pitavastatin targets lung cancer and angiogenesis via suppressing prenylation-dependent ï¼²as/ï¼²af/MEK and PI3K/Akt/mTOï¼² signaling [J].Anticancer Drugs, 2020, 31(4) :377–384.
Chen M, Zhu LL, Su JL, et al. Prucalopride inhibits lung cancer cell proliferation, invasion, and migration through blocking of the PI3K/AKT /mTOï¼² signaling pathway [J]. Hum Exp Toxicol. 2020;39(2):173–81.
Raftery MJ, Behrens CK, ,Muller A, et al. Herpes Simplex Virus Type 1 Infection of Activated Cytotoxic T Cells[J]. J Exp Med. 1999;190(8):1103–14.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Identification of Potential Prognostic Value of Clock-related LncRNA with Immune Implications in Non-small Cell Lung Cancer

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

2.1 Data collection

2.2 Identification Of Clock-related Lncrnas

2.3 Construction Of The Risk Score

2.4 Evaluation Of Prognostic Value For Risk Signature And Clinicopathological Features

2.5 Gene Function And Pathway Enrichment Analysis

2.6 Immune Cell Infiltration And Risk Signature

Results

3.1 The clinical characteristics of lung cancer patients

3.2 Construction Of The Risk Model For Clock-related Lncrnas

3.3 Validation Of The Risk Signature

3.4 Identification Of Independent Prognostic Factors

3.5 Functional Enrichment Analysis

3.6 Relationship Between Immune Cell Infiltration And Risk Score

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1