Relationship between cancer stem cell-related SNPs and survival outcomes in patients with primary lung cancer

doi:10.21203/rs.3.rs-2718292/v1

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Research Article

Relationship between cancer stem cell-related SNPs and survival outcomes in patients with primary lung cancer

https://doi.org/10.21203/rs.3.rs-2718292/v1

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Journal Publication

published 11 Aug, 2023

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Background: Stem cells may be the source of cancer-causing mutant cells and are closely related to the prognosis of cancer. Our study aim to investigate the association between single nucleotide polymorphisms of cancer stem cell related genes and prognosis of lung cancer patients.

Methods: Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) was used to genotype SNPs loci. Log-rank test was used to analyze and compare the difference of total survival time among different demographic data, clinical data and genotypes. Cox regression model was used to analyze the correlation between survival time and quality of life of lung cancer under codominant, dominant, recessive and additive genetic models. Test levelα=0.05.

Results: Stratified analysis showed that rs3740535 had recessive AA genotype and additive GG genotype. Rs3130932 Dominant GT+GG genotype, additive TT genotype; Rs13409 additive TT genotype; Rs6815391 recessive CC genotype and additional TT genotype were associated with increased risk of lung cancer death. Rs3130932 recessive GG genotype was associated with reduced risk of lung cancer death.

Conclusion: Rs3130932, rs13409 and rs6815391 are associated with the prognosis of lung cancer patients, and can affect the prognostic survival time of lung cancer patients.

lung cancer stem cells

single nucleotide polymorphism

prognosis

gene

Primary lung cancer is one of the most common malignant tumors in the world. The incidence and mortality of lung cancer are on the rise all over the world. The mortality rate of lung cancer is increasing at the rate of 1%-5% annually, especially in China and other developing countries [1, 2]. According to global Cancer Epidemiology Statistics (GLOBOCAN2020), cancer statistics show that lung cancer is still the most deadly cancer in the world. It is estimated that there will be about 1.8 million deaths of lung cancer worldwide in 2020, ranking second only to female breast cancer [3]. China now has the largest number of lung cancer patients in the world. According to the global Cancer Report in 2020, lung cancer is still the most common cancer and the number of deaths in China [3]. Based on prognostic data from 61 countries worldwide, age-standardized 5-year net survival rates for lung cancer range from 10 to 20% [4]. Although the diagnosis and treatment of lung cancer in China has made great progress in the past decades, the current age-standardized 5-year survival rate is still less than 20.0% [5].

There is growing evidence that stem cells may be the source of mutated cells that drive cancer development. American scholar Claire Hahni Kim took stem cell related genes as research factors in her study on the association between smoking and lung cancer, and found that 25 SNPs were associated with lung cancer, with P value less than 0.1. There are dominant genes: RAN rs14035, TP53INP1 rs7760, TP53INP1 rs896849, EPCAM rs1126497, HEY1 rs1046472, HEY2 rs3734637, OCT4 RS13409 and WNT2 rs3729629 [6]. Most studies indicate that octamer-binding factor 4 (OCT4), which maintains the characteristics of lung cancer stem cells, is involved in the occurrence, development and metastasis of lung cancer, and is a potential important indicator for the diagnosis of lung cancer and the prognosis of lung cancer patients. It is also a new therapeutic target for lung cancer [7]. OCT4 knockdown can significantly increase the apoptosis of lung cancer cells, inhibit tumor growth and prolong the survival of mice, but it can not cure lung cancer [8, 9]. Chinese scholar Zeng Yuting and others found that suppressor of cytokine signaling 1 (SOCS1) is expressed by Rac Exchanger 1 (REX1), activation of JAK2/STAT3 signaling pathway in cervical cancer can promote the migration and invasion of cervical cancer cells in vitro and distant tumor metastasis in vivo [10]. T lymphoma invasion and metastasis induction gene 1 (RAC GTPase) is implicated in downstream regulation of OCT4 gene. The interaction between Tiam1 and C-terminal binding protein 2(CTBP2) promotes the proto-oncogenic function of CTBP2 and leads to cancer cell migration [11]. Therefore, this study intends to explore the association between SNPs of UPSTREAM REX1 of OCT4, downstream CTBP2 of OCT4 and OCT4 and the prognosis of lung cancer, hoping to improve the judgment ability of prognosis of lung cancer patients and guide the clinical development of more targeted treatment.

2.1 Object of study

The subjects were collected from the Department of thoracic Surgery of the First Affiliated Hospital of Fujian Medical University, Union Hospital of Fujian Medical University and Fuzhou General Hospital of Nanjing Military Command. Inclusion criteria were: ①New cases of primary lung cancer confirmed by bronchoscopy or surgical histopathology; ②The time of diagnosis was from January 2006 to December 2012; ③Have lived locally in Fujian for more than 10 years. The exclusion criteria were: patients diagnosed by pathological diagnosis as lung inflammation, benign lesions, secondary lung cancer, and patients in critical condition who could not clearly answer the questions. This study was approved by the Ethics Committee of Fujian Medical University, and all subjects signed informed consent during the investigation.

2.2 SNPs site selection and typing

2.2.1 Specimen collection and processing

5ml peripheral venous blood was collected from each primary lung cancer patient before treatment. DNA was extracted using whole blood genomic DNA extraction kit and dissolved with NaOH. NanoDropTM ND-1000 uv spectrophotometer was used to quantitatively determine the concentration and purity of extracted DNA products.

2.2.2 SNPs site selection

The downstream inflammatory signaling pathways of OCT4, REX1 and CTBP2 related to lung cancer were evaluated using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database (http://www.genome.jp/kegg/). And search for node genes in the pathway. Using NCBI gene functional annotation database (http://www.ncbi.nlm.nih.gov/gene/) for the sequences of genes and functional information.

The selection criteria for functional SNPs sites are as follows :(1) they are located in the coding region and may affect protein coding and properties; (2) located in the binding region of transcription factors, which may affect the transcriptional activity and thus affect the gene expression activity; (3) Located in the shearing site region, it may affect the selective shearing function of M RNA, and thus affect the protein function; (4) It is located in the 3'UTR region, namely the micro RNA binding region, which may affect the binding activity of micro RNA, and then affect the process of post-transcriptional modification of M RNA. According to the above criteria, we selected four functional SNP site rs13409, rs6815391, rs3740535 and rs3130932 in OCT4, REX1 and CTBP2 genes for subsequent analysis.

2.2.3 SNP genotyping

Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS)(Sequenom, USA) was used for genotyping of SNPs loci, and 5% of all samples were randomly selected for repeated detection by internal quality control to ensure the accuracy of genotyping.

2.3 Research Methods

2.3.1 Data collection and follow-up

According to the case data, gender, age, pathological type, stage, surgery, chemotherapy, radiotherapy, whether there was first diagnosed metastasis, pleural metastasis, lymphatic metastasis, hematogenous metastasis, Body Mass Index (BMI), maximum diameter of tumor, whether there was lung disease, and family history of tumor were recorded.

Follow-up investigation was conducted every six months after the case was confirmed, and the follow-up was completed on December 1, 2020. Follow-up included metastasis, recurrence, treatment and survival status (death, cause of death, date of death) after discharge.

2.3.2 Definition of variables

BMI = weight (kg)/[height (m)]². BMI is the Chinese standard [12], 18.5kg/m² ≤ BMI < 24.0kg/m² is considered normal, BMI < 18.5kg/m² is considered underweight and BMI ≥ 24.0kg/m² is considered overweight or obese. Smoking refers to cumulative smoking of more than 100 cigarettes in a lifetime. A family history of cancer refers to a history of malignancy in relatives such as parents, siblings, children, grandparents, maternal grandparents, uncles and aunts.

2.4 Statistical Treatment

SPSS 25.0 software was used to analyze the data and make statistics of general demographic characteristics. Log-rank test was used to analyze and compare the difference of total survival time among different demographic data, clinical data and genotypes. Cox regression model was used to analyze the correlation between survival time and quality of life of lung cancer under codominant, dominant, recessive and additive genetic models. Test level α = 0.05.

3.1 General information of the research object

The distribution of general demographic data and clinical information of 1059 lung cancer patients included in this study is shown in Table 1. The median survival time of all subjects was 24.33 months, the minimum age was 23 years, the maximum age was 86 years, and the mean age was 59.0 ± 10.7 years. There were 774 males (73.10%), 285 females (26.90%), and 671 smokers (63.90%). The pathological types were mainly adenocarcinoma, squamous cell carcinoma and small cell lung cancer. 572 cases (54.00%), 297 cases (28.00%) and 85 cases (8.00%), respectively. There were 457 patients (43.20%) in early stage, 602 patients (56.80%) in advanced stage, 552 patients (52.10%) in surgical treatment, 749 patients (70.70%) in chemotherapy, and 208 patients (19.60%) in radiotherapy. By October 11, 2020, there were 1045 cases of effective follow-up, with a follow-up rate of 98.7%, 14 cases of lost visitors, with a follow-up rate of 1.3%, 227 cases of survival (21.4%), and 818 cases of death (77.2%).

Table 1

Basic information distribution table of lung cancer patients
Variable	Variable	Number of cases(n = 1059)	Constituent ratio(%)
Sex	male	774	73.10
	female	285	26.90
Age	༜60	527	49.80
	≥ 60	532	50.20
Smoking	Yes	671	63.90
	No	379	36.10
Pathological pattern	Adenocarcinoma	572	54.00
	Squamous carcinoma	297	28.00
	Others	190	18.00
Clinical stages	Early stage	457	43.20
	Later stage	602	56.80
Operation	Yes	552	52.10
	No	507	47.90
Chemotherapy	Yes	749	70.70
	No	310	29.30
Radiation oncology	Yes	208	19.60
	No	851	80.40

3.2 Correlation between gene polymorphism and prognosis of lung cancer

Cox proportional risk model was used to explore the relationship between SNPs and overall survival of lung cancer. Considering that multiple factors may affect the prognosis of lung cancer patients, univariate analysis and multivariate analysis were conducted successively in this part. SNPs and overall survival time were analyzed.

The overall survival of lung cancer with different SNPs genotypes was shown in Table 2. Log-rank test showed that there was no significant difference in overall survival time between different SNPs genotypes (P > 0.05).

We further adopt Cox proportional hazards models to grouping children by age, gender, pathological type, with or without treatment, total stage of adjustment factors, under various genetic model for overall survival analysis of all SNPs, HR was calculated and the corresponding 95% CI, including codominant genetic model model, model of dominant and recessive models and additive model. Results of survival analysis were shown in Table 2. The correlation between SNPs loci and overall survival time in various genetic models was not found to be statistically significant.

Table 2

Results of overall survival analysis of SNPs lung cancer patients
Genetic locus	Genotype	Lung cancer
Genetic locus	Genotype	Total number/deaths	Log rank P	αHR(95%CI)^*
rs13409
Co-dominance	CC	396/507	0.965	1
	CT	292/379		0.948(0.815–1.104)
	TT	87/116		0.919(0.728–1.161)
Dominance	CC	396/507	0.831	1
	CT + TT	379/495		0.941(0.817–1.084)
Recessiveness	CC + CT	688/886	0.819	1
	TT	87/116		0.940(0.752–1.177)
Additivity			0.792	0.928(0.735–1.172)
rs6815391
Co-dominance	TT	365/473	0.541	1
	CT	333/433		1.075(0.926–1.249)
	CC	78/99		1.180(0.924–1.508)
Dominance	TT	365/473	0.347	1
	CC + CT	411/532		1.094(0.949–1.260)
Recessiveness	CT + TT	698/906	0.395	1
	CC	78/99		1.141(0.902–1.442)
Additivity			0.292	1.169(0.915–1.494)
rs3740535
Co-dominance	GG	467/587	0.667	1
	AG	275/366		0.948(0.816–1.101)
	AA	58/74		0.889(0.676–1.169)
Dominance	GG	467/587	0.371	1
	AA + AG	333/440		0.937(0.814–1.079)
Recessiveness	AG + GG	742/953	0.846	1
	AA	58/74		0.907(0.694–1.186)
Additivity			0.721	0.895(0.680–1.177)
rs3130932
Co-dominance	TT	350/442	0.497	1
	GT	101/451		0.959(0.827–1.113)
	GG	92/129		0.804(0.638–1.013)
Dominance	TT	350/442	0.299	1
	GG + GT	440/580		0.923(0.802–1.062)
Recessiveness	GT + TT	698/893	0.382	1
	GG	92/129		0.821(0.660–1.022)
Additivity			0.285	0.810(0.643–1.022)
Note: * Adjusting factors were age, sex, pathological type, presence or absence of treatment, and total stage

3.3 Stratified analysis of SNPs polymorphism and prognosis of lung cancer

SNPs were divided into three genetic models: dominant, recessive and additive genetic models. In order to explore the effects of SNPs polymorphism changes on patients with different demographic and clinical characteristics, stratified analysis was conducted based on the collected clinical data, as shown in Fig. 1–4.

The results showed that the risk effect of recessive and additional rs3740535 on prognosis was significant in patients with a family history of lung cancer. Among recessive genotypes, the risk of death in patients with rs3740535 AA genotype was 5.210 times higher than that in patients with AG + GG genotype (95%CI:1.273–21.324). Among the additional genotypes, the risk of death in patients with rs3740535 GG genotype was 30.583 times higher than that in patients with AA genotype (95%CI:1.879-497.832). Heterogeneity test showed that there was significant heterogeneity of this risk effect in both recessive and additive genotypes of rs3740535 with or without a family history of lung cancer (P_{heterogeneity} <0.05). (Fig. 1)

Rs3130932 has both dominant and additive effects on prognosis in patients with hematogenous metastasis. In dominant genotypes, patients with rs3130932 GT + GG genotype had 1.492 times higher risk of death than patients with TT genotype (95%CI:1.094–2.034). Among the additional genotypes, the risk of death in patients with blood type transfer was 1.856 times higher in patients with TT genotype than in patients with GG genotype (95%CI:1.097–3.143). Heterogeneity test showed that the risk effect of rs3130932 had significant heterogeneity between the dominant and additive genotypes with or without subgroups (all P_{heterogeneity} <0.05). Rs3130932 has a recessive protective effect on the prognosis of patients receiving radiotherapy. Patients with rs3130932 GG genotype had a significantly lower risk of lung cancer death than patients with GT + TT genotype (HR = 0.420, 95%CI:0.217–0.814). Heterogeneity test found that this protective effect was significantly different between radiotherapy subgroups (P_{heterogeneity} =0.009). (Fig. 2)

The risk effect of rs13409 on prognosis was significant in patients with a family history of lung cancer. The risk of death in patients with rs13409 TT genotype was 190,708.408 times higher than in patients with CC genotype (95%CI:220.699-164793486.900). Heterogeneity test showed that patients with a family history of lung cancer and carrying the rs13409 additional TT genotype had a higher risk of death than patients without a family history of lung cancer, and the risk was statistically significant (P_{heterogeneity} < 0.01). (Fig. 3)

In patients with pathological types other than adenocarcinoma and squamous cell carcinoma (HR = 1.590,95%CI:1.053–2.401), rs6815391 dominant CT + CC genotype carriers were associated with an increased risk of death, and this risk effect was significantly different between subgroups of pathological types (P_{heterogeneity} =0.037). Patients with a family history of lung cancer (HR = 0.358,95%CI:0.150–0.852) had a significantly reduced risk of death, and this protective effect was significantly different among the subgroups with and without a family history of lung cancer (P_{heterogeneity} =0.012). The recessive risk effect of rs6815391 on prognosis was more significant in patients who did not receive chemotherapy. The risk of death was 1.805 times higher in patients with CC genotype than in patients with CT + TT genotype (95%CI:1.115–2.922). Heterogeneity test found that this risk effect was significantly different between subgroups receiving chemotherapy or not (P_{heterogeneity} =0.027). In patients without chemotherapy (HR = 2.027,95%CI:1.186–3.466) and abnormal BMI (HR = 1.763,95%CI:1.110–2.800), rs6815391 patients with additional TT genotype had a higher risk of death than those with CC genotype. Heterogeneity test found significant heterogeneity between chemotherapy subgroups (P_{heterogeneity} =0.022) and statistically significant heterogeneity between normal BMI subgroups (P_{heterogeneity} =0.027) (Fig. 4).

Based on the stratified results, we further analyzed the interaction between different genotypes and factors with heterogeneity among sublayers of clinical variables (Table 3–5), and the results showed that, after adjusting for gender, age, initial diagnosis, pleural metastasis, lymphatic metastasis, hematologic metastasis, BMI, presence or absence of treatment, presence or absence of surgery, maximum tumor size, presence or absence of pulmonary disease, education, smoking history, total stage and other variables:

There were no statistically significant effects of rs3740535 recessive and additive models on the combined effect, multiplicative interaction and additive interaction of rs3740535 and family history of tumor. See Table 3 for details.

Table 3

Analysis of interaction between rs3740535 genotypes and family history of cancer
Implicit model			Additive model
Genotype	Family history of cancer	αHR(95%CI)^*	Genotype	Family history of cancer	αHR(95%CI)^*
AG + GG	No	1	AA	No	1
AG + GG	Other tumors	0.969(0.761–1.234)	AA	Other tumors	0.906(0.678–1.210)
AG + GG	Lung cancer	1.129(0.810–1.572)	AA	Lung cancer	1.179(0.752–1.847)
AA	No	0.949(0.669–1.346)	GG	No	1.009(0.706–1.443)
AA	Other tumors	0.677(0.347–1.321)	GG	Other tumors	0.704(0.358–1.385)
AA	Lung cancer	2.047(0.744–5.637)	GG	Lung cancer	2.130(0.758–5.981)
Multiplication interaction		1.035(0.912–1.175)	Multiplication interaction		1.019(0.873–1.190)
Relative excess risk(RERI)		0.125(-0.434-0.685)	Relative excess risk(RERI)		0.121(-0.434-0.675)
Attributable risk percent(ARP)		0.203(-0.594-1.001)	Attributable risk percent(ARP)		0.207(-0.692-1.106)
Synergic index(S)		0.754(0.172–3.307)	Synergic index(S)		0.775(0.205–2.931)
Note: * is adjusted for gender, age, first diagnosed metastasis, pleural metastasis, lymphatic metastasis, hematologic metastasis, BMI, whether there is treatment, whether there is surgery, maximum diameter of tumor, whether there is lung disease, education background, smoking history, and total stage (when adjusting factors are included, the corresponding combined items are not included in the adjustment).

In the dominant model, patients with rs3130932 GT + GG genotype had 1.362 times higher risk of death than patients with rs3130932 TT genotype with hematoascular metastasis (95%CI:1.021–1.818). In recessive model, the risk of death of lung cancer patients carrying rs3130932 GG genotype and receiving radiotherapy was 0.536 times higher than that of lung cancer patients carrying rs3130932 GT + TT genotype and receiving radiotherapy (95%CI:0.298–0.962). Rs3130932 recessive had a significant positive multiplicity interaction with radiotherapy. Patients with rs3130932 GG genotype had 0.650 times higher risk of death than patients with rs3130932 GT + TT genotype (95%CI:0.464–0.911). In the additive model, rs3130932 had no significant combined effect with blood metastasis and gender, but rs3130932 had significant positive multiplying interaction with gender. Rs3130932 polymorphism changes and blood group metastasis in the three models showed significant negative multiplicative interaction, but no significant additive interaction. Table 4 shows this in more detail [see Additional file 1].

In the dominant model of rs6815391, there was no significant association between rs6815391 and family history of tumor. Patients with rs6815391 TT genotype and pathological type other than adenocarcinoma and squamous cell carcinoma had a 0.707 times higher risk of death than patients with rs6815391 TT genotype and pathological type of adenocarcinoma (95%CI:0.512–0.978). Rs6815391 dominance had no significant multiplication and addition interaction with tumor family history and pathological type. In the recessive model, the risk of death in patients without chemotherapy carried by rs6815391 CC genotype was 1.859 times higher than that in patients receiving chemotherapy carried by rs6815391 CT + TT genotype (95%CI:1.165–2.965), but there was no significant interaction between multiplication and addition. In the additive model, the risk of death in patients without chemotherapy carrying rs6815391 TT genotype was 2.062 times higher than that in patients receiving chemotherapy carrying rs6815391 CC genotype (95%CI:1.263–3.366), while there was no significant combined effect between rs6815391 additive subgroup and BMI subgroup. There was no significant multiplicative and additive interaction between rs6815391 additive, chemotherapy and BMI subgroups. Table 5 fshows this in more detail [see Additional file 1].

There was no obvious combined effect between rs13409 and pulmonary disease, but there was negative multiplicative interaction and antagonistic additive interaction. In the additive model, there was no obvious combined and multiplied interaction between rs13409 and lung disease, and the risk of death of patients with rs13409 TT genotype and family history of lung cancer was 3.234 times higher than that of patients with rs13409 CC genotype and no family history of cancer (95%CI:1.138–9.196). There was no significant additive interaction between rs13409 and family history of lung disease and tumor. Table 6 shows this in more detail [see Additional file 1].

In this study, clinical data related to the prognosis of lung cancer were collected, and the relationship between the four SNPs in Wnt and Notch signaling pathways and the overall survival of lung cancer was analyzed, but the correlation between each SNPs locus and the overall survival time was not found to be statistically significant in various genetic models. Stratified analysis showed that rs3740535 recessive AA genotype and additive GG genotype; rs3130932 dominant GT + GG genotype and additive TT genotype; rs13409 additive TT genotype; rs6815391 recessive CC genotype and additional TT genotype were associated with increased risk of lung cancer death. Rs3130932 recessive GG genotype was associated with reduced risk of lung cancer death. Interaction based on stratified results showed that lung cancer patients with GT + GG genotype in the dominant model of rs3130932 had an increased risk of death after blood type metastasis, while patients with GG genotype in the recessive model had a reduced risk of death after radiotherapy or without blood type metastasis. Patients carrying TT genotype in the dominant model of rs6815391 with other pathological types had a reduced risk of death, while patients carrying CC genotype in the recessive model and TT genotype in the additional model had a significantly increased risk of death in patients not receiving chemotherapy. In the rs13409 additive model, TT genotype carriers with a family history of lung cancer had an increased risk of death.

In this study, we found that rs313932 in OCT4 gene, rs13409 in REX1 gene and rs6815391 in CTBP2 gene interact with some clinical features, suggesting that rs3130932, rs13409 and rs6815391 are not independent risk factors for primary lung cancer. Transcriptional factors in embryonic stem cells interact to form networks that jointly regulate the fate of embryonic stem cells. OCT4, embryonic Stem cell associated transcripts 4 (NANOG), Kruppel-like factor 4 (KLF4) Proto-oncogene proteins C-myc (C-MYC) and SRY related high mobility group Box2 (SOX2) are the basis for regulating stem cell pluripotency. There are scholars Son MY. REX1 is a necessary condition for pluripotency and reprogramming, can replace KLF4, and is a new central actor controlling pluripotency [13]. Moreover, the REX1 promoter contains binding sites of multiple core transcription factors, which have a bidirectional regulatory effect with OCT4 gene and have an effect on the pluripotency of stem cells. REX1 drives entry and exit pluripotency by lowering the reprogramming barrier (growth stagnation and apoptosis), facilitating mitochondrial fission, and transforming glycolytic metabolism from oxidative phosphorylation to glycolytic metabolism dependent on the cyclin B1/ B2-DRP1 pathway, altering cell cycle progression and metabolic states. Xu et al. [14] showed that REX1 could regulate F9 cell differentiation by inhibiting JAK/STAT(Janus kinase /signal and Activator of transcription) signal transduction pathway. This signaling pathway also plays an important role in controlling the self-renewal of ES cells.

Downstream of OCT4 regulation, it can also bind to related inhibitory complexes, the most prominent of which is found to be NuRD, or histone deacetylase complex [15].

Some foreign scholars have found that C-terminal binding protein (CTBP) is an important ligand for its transcriptional inhibition function, and NuRD binds to CTBP2 gene to make embryonic stem cells deacetyl and withdraw from pluripotency. CTBP2 also plays a role in NURD-related Sar-like4 (SALL4), By binding ubiquitin-specific protease 9X (USP9x) and CXXC Finger protein 5 (CXXC5) to TGF- B and Wnt signals, respectively, Ultimately, it amplifies the effect. CTBP expression was increased in malignant tissues compared with normal tissues. CTBP2 is highly expressed in prostate cancer, breast cancer, colon cancer, esophageal cancer, liver cancer and other common tumors. CTBP2 can increase the invasion and migration of cells, participate in some signaling pathways, affect cell cycle, and participate in apoptosis, and is closely related to some tumor suppressor genes.

In addition, some SNPS affect gene function by affecting the number or structure of gene expression products, which is related to patients' susceptibility to disease, chemotherapy sensitivity, adverse reactions and prognosis to a certain extent. Studies have shown that 0-6-methylguanine-DNA methyltransferase (O-6-methylguanine-DNA methyltransferase, MGMT gene, C2(ATP Binding cassette subfamily C member 2, ABCC2), ATP-binding cassette transporter family class C4(ABCC4), cytokines and inflammatory gene interleukin-6 (IL-6), Prostaglandin endoperoxide synthase 2 (PTGS2) and lymphotoxin-alpha (LTA) and multiple SNPs in the P38 mitogen Activated protein kinase (P38 MAPK) signaling pathway are associated with different domains of lung cancer prognosis, with some genotypes having low quality of life and poor treatment prognosis [16–19]. In addition, a study involving 400 lung cancer patients found that Glutathione Peroxidase 7 (GPX-7) gene and ABCC4 SNP were associated with peripheral neuropathy induced by chemotherapy [20], which proved the effect of SNP on gene expression.

In this study, stratified analysis and multi-factor analysis were adopted in data analysis to effectively control the interference of confounding factors. By further exploring the interaction between SNP and relevant influencing factors, the effects of multiple factors could be observed and abundant conclusions could be obtained. However, since prognosis is affected by a variety of environmental and genetic factors, the effect of single SNP on quality of life may be limited, and haplotype studies based on SNP may obtain more significant results. Although some progress has been made in lung cancer research at home and abroad, there are still many shortcomings in reducing the postoperative recurrence rate and five-year survival rate of lung cancer.

Rs3130932, rs13409 and rs6815391 are associated with the prognosis of lung cancer patients, and can affect the prognostic survival time of lung cancer patients. The results of this study lay the foundation for further research, but it still needs to be further verified by multi-center, large-sample study with rigorous design, and the specific mechanism needs to be further explored by relevant functional studies.

Conflict of Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Authors Contribution

Fei He designed the study. Xinying Xu performed the analysis. HuiYi Hu, JingShen Wang, YuXin Cai, and Jun Xie contributed to interpretation of the results. Yuhang Liu checked the data. Xinying Xu, HuiYi Hu, YuXin Cai and JingShen Wang drafted the manuscript. Mingqiang Kang and Fei He contributed to critical revision of the manuscript for important intellectual content. Fei He and Xinying Xu approved the final version of the manuscript. All authors approved the submitted version.

Funding declaration

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].

Data Availability Statement

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.The study has been approved by the Ethics Committee of Fujian Medical University ([2014] Fujian Medical University Ethics Review No. (98)). All participants signed informed consent forms. All methods were carried out in accordance with relevant guidelines and regulations.

Hirsch FR, Scagliotti GV, Mulshine JL, Kwon R, Curran WJ, Jr., Wu YL, et al. Lung cancer: current therapies and new targeted treatments. Lancet (London, England). 2017;389(10066):299-311.
Dela Cruz CS, Tanoue LT, Matthay RA. Lung cancer: epidemiology, etiology, and prevention. Clinics in chest medicine. 2011;32(4):605-44.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021;71(3):209-49.
Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet (London, England). 2018;391(10125):1023-75.
Zeng H, Chen W, Zheng R, Zhang S, Ji JS, Zou X, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. The Lancet Global health. 2018;6(5):e555-e67.
Kim CH. Environmental Tobacco Smoke, Genetic Susceptibility, and Lung Cancer among Never Smokers: UCLA; 2016.
Jen J, Tang YA, Lu YH, Lin CC, Lai WW, Wang YC. Oct4 transcriptionally regulates the expression of long non-coding RNAs NEAT1 and MALAT1 to promote lung cancer progression. Molecular cancer. 2017;16(1):104.
Chen YC, Hsu HS, Chen YW, Tsai TH, How CK, Wang CY, et al. Oct-4 expression maintained cancer stem-like properties in lung cancer-derived CD133-positive cells. PLoS One. 2008;3(7):e2637.
Hu T, Liu S, Breiter DR, Wang F, Tang Y, Sun S. Octamer 4 small interfering RNA results in cancer stem cell-like cell apoptosis. Cancer research. 2008;68(16):6533-40.
Zeng YT, Liu XF, Yang WT, Zheng PS. REX1 promotes EMT-induced cell metastasis by activating the JAK2/STAT3-signaling pathway by targeting SOCS1 in cervical cancer. Oncogene. 2019;38(43):6940-57.
Paliwal S, Ho N, Parker D, Grossman SR. CtBP2 Promotes Human Cancer Cell Migration by Transcriptional Activation of Tiam1. Genes & cancer. 2012;3(7-8):481-90.
Chen X, Cui J, Zhang Y, Peng W. The association between BMI and health-related physical fitness among Chinese college students: a cross-sectional study. BMC public health. 2020;20(1):444.
Son MY, Choi H, Han YM, Cho YS. Unveiling the critical role of REX1 in the regulation of human stem cell pluripotency. Stem cells (Dayton, Ohio). 2013;31(11):2374-87.
Xu J, Sylvester R, Tighe AP, Chen S, Gudas LJ. Transcriptional activation of the suppressor of cytokine signaling-3 (SOCS-3) gene via STAT3 is increased in F9 REX1 (ZFP-42) knockout teratocarcinoma stem cells relative to wild-type cells. Journal of molecular biology. 2008;377(1):28-46.
Pardo M, Lang B, Yu L, Prosser H, Bradley A, Babu MM, et al. An expanded Oct4 interaction network: implications for stem cell biology, development, and disease. Cell stem cell. 2010;6(4):382-95.
Sloan JA, de Andrade M, Decker P, Wampfler J, Oswold C, Clark M, et al. Genetic variations and patient-reported quality of life among patients with lung cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2012;30(14):1699-704.
Rausch SM, Clark MM, Patten C, Liu H, Felten S, Li Y, et al. Relationship between cytokine gene single nucleotide polymorphisms and symptom burden and quality of life in lung cancer survivors. Cancer. 2010;116(17):4103-13.
Rausch SM, Gonzalez BD, Clark MM, Patten C, Felten S, Liu H, et al. SNPs in PTGS2 and LTA predict pain and quality of life in long term lung cancer survivors. Lung cancer (Amsterdam, Netherlands). 2012;77(1):217-23.
Pierzynski JA, Ye Y, Lippman SM, Rodriguez MA, Wu X, Hildebrandt MAT. Socio-demographic, Clinical, and Genetic Determinants of Quality of Life in Lung Cancer Patients. Scientific reports. 2018;8(1):10640.
Johnson C, Pankratz VS, Velazquez AI, Aakre JA, Loprinzi CL, Staff NP, et al. Candidate pathway-based genetic association study of platinum and platinum-taxane related toxicity in a cohort of primary lung cancer patients. Journal of the neurological sciences. 2015;349(1-2):124-8.

No competing interests reported.

Additionalfile1.docx
ADDITIONAL FILE Additional file 1 Table 4 Analysis of interaction between rs3130932 genotype and hematogenous metastasis and radiotherapy Table 5 Analysis of interaction between rs6815391 genotypes and family history of tumor, chemotherapy and BMI Table 6 Analysis of interaction between rs13409 genotypes and family history of pulmonary disease and tumor

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published 11 Aug, 2023

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Editorial decision: Major revision
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Relationship between cancer stem cell-related SNPs and survival outcomes in patients with primary lung cancer

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Object of study

2.2 SNPs site selection and typing

2.2.1 Specimen collection and processing

2.2.2 SNPs site selection

2.2.3 SNP genotyping

2.3 Research Methods

2.3.1 Data collection and follow-up

2.3.2 Definition of variables

2.4 Statistical Treatment

3. Results

3.1 General information of the research object

3.2 Correlation between gene polymorphism and prognosis of lung cancer

3.3 Stratified analysis of SNPs polymorphism and prognosis of lung cancer

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1