Low SLCO4C1 expression serves as an independent prognostic predictor in lung cancer

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Low SLCO4C1 expression serves as an independent prognostic predictor in lung cancer

https://doi.org/10.21203/rs.3.rs-3900353/v1

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Lung cancer is a significant global health concern, accounting for 18.4 percent of all cancer-related deaths, imposing substantial social and economic burdens. We explored SLCO4C1 as a potential prognostic marker in lung cancer. Analyzing The Cancer Genome Atlas (TCGA) data, we assessed SLCO4C1 expression using boxplot analyses. Chi-square and Fisher's test evaluated associations with clinicopathological features, and diagnostic capacity was determined the receiver-operating characteristic (ROC) curve analysis. Kaplan-Meier survival curves assessed survival differences based on SLCO4C1 levels. Cox regression models and subgroup analyses examined prognostic factors. Our findings reveal significant downregulation of SLCO4C1 in lung cancer tissues, with correlations to patient gender, histological type, and T classification. ROC analysis indicated moderate diagnostic potential. Survival analysis demonstrated lower overall and relapse-free survival rates in patients with low SLCO4C1 expression. Univariate and multivariate Cox regression analyses suggested SLCO4C1 as an independent prognostic predictor for lung cancer. In conclusion, low SLCO4C1 expression serves as an independent prognostic biomarker in lung cancer, offering promise for prognostic and therapeutic applications.

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

SLCO4C1

Lung cancer

Prognosis

The Cancer Genome Atlas

Biomarker

It is important to note that lung cancer is a primary contributor to cancer-related mortality all over the world. It accounts for 18.4 percent of all cancer deaths and poses significant challenges to both society and the economy [1]. NSCLC, including Lung adenocarcinoma and lung squamous cell carcinoma, is primary type of this prevalent form of cancer that can be distinguished from SCLC based on the morphology of the cells [2, 3]. Lung cancer continues to be characterized by high mortality and poor prognosis [4, 5], despite the achievements that have been made in the treatment of the disease. Because of this circumstance, the identification of trustworthy prognostic biomarkers is of utmost importance because these biomarkers have the potential to significantly enhance the precision of clinical assessment systems involved in the management of lung cancer.

Transporters are a vast group of membrane proteins that are involved in communication of chemicals and signals both inside and outside of the biofilm, which is associated with development and progression in lung cancer. A significant correlation has been established between transporters and various aspects of lung cancer, including invasion, apoptosis, diagnosis, treatment, and prognosis [6–9]. This correlation has been established through empirical research. The Solute carrier organic anion transporter 4C1 (SLCO4C1) is a member of the family of organic anion transporting polypeptides that are specific to the human kidney. It plays a role in the transportation of conjugated steroids, bile acids, and a wide range of drugs. However, it is most commonly known for its role as a transporter for digoxin [10–12]. SLCO4C1 has been shown to have a close association with the development, diagnosis, treatment, and prognosis of multiple cancers, according to numerous studies [13–15]. SLCO4C1 is a tumor suppressor gene that helps prevent the development of head and neck cancer (HNC). This gene is frequently rendered inactive due to promoter methylation or somatic mutation [13]. When SLCO4C1 is disrupted in endometrial cancer (EC), it has an effect on cellular behaviors such as proliferation, apoptosis, and the EMT phenotype. This is primarily accomplished through the inactivation of the PI3K/Akt pathway [14]. SLCO4C1 promoter methylation has been identified as a potential stratification marker for prognostic assessment following radical prostatectomy [15]. This marker has been specifically identified in the context of prostate cancer (PC). However, the role of SLCO4C1 in lung cancer is still unknown, which calls for additional research to be conducted.

This research intends to evaluate the relevance of SLCO4C1 in lung cancer by examining its expression levels using TCGA data. Evaluation of expression variation is done using boxplot analyses. Associations between clinicopathological features are determined using chi-square and Fisher's tests. Diagnostic efficacy is evaluated using ROC curve analysis. Survival analysis and subgroup analyses are used to evaluate the prognostic value.

Data source

Patient clinical data and information regarding RNA sequencing were obtained from TCGA (https://cancergenome.nih.gov/). The inclusion and exclusion criteria established by the TCGA were followed in the selection of patients from the lung cancer cohort.

Statistical analysis

Utilizing R (version 3.6.1), statistical analyses were conducted[16]. These analyses included t-tests, Kruskal-Wallis tests, and Wilcoxon sum-rank tests. When drawing boxplots of SLCO4C1 expression according to clinical characteristics, the ggplot2 package [17] was utilized during the process. The chi-square test and Fisher's exact test were utilized to investigate correlation between SLCO4C1 expression and clinicopathological characteristics. In order to perform diagnostic evaluations using ROC curves, the pROC package [18] was utilized. Patients were classified into different groups according to the cut-off value that was determined by the left-top point of ROC curve when the evaluations were carried out. Plotting survival curves was accomplished by Survival package [19], with univariate cox regression being used to determine the variables that have an effect on survival and multivariate cox regression evaluating the independent factors that effect survival duration.

Characteristics of the Patient and Association Between SLCO4C1 Expression and Clinical Features

With 1,019 patients included, the clinical characteristics of those patients are presented in Table 1. The information consists of age, gender, radiation therapy, the presence of residual tumors, the histological type, the stage of the cancer, survival data, tumor classification, and SLCO4C1 expression levels.

Table 1

Characteristics of the Patient and Association Between SLCO4C1 Expression and Clinical Features.
Parameter	Parlab	N	SLCO4C1 expression				X²	P value
Parameter	Parlab	N	High	Hprop	Low	Lprop	X²	P value
Age	< 55	109	74	(10.05)	35	(13.78)	2.3078	0.1287
Age	≥ 55	881	662	(89.95)	219	(86.22)
Gender	FEMALE	407	311	(41.08)	96	(36.78)	1.3227	0.2501
Gender	MALE	611	446	(58.92)	165	(63.22)
Histological type	Lung adenocarcinoma	517	406	(53.63)	111	(42.37)	9.4386	0.0021
Histological type	Lung squamous cell carcinoma	502	351	(46.37)	151	(57.63)
Stage	I	521	404	(54.01)	117	(45.35)	6.3687	0.0870
	II	284	200	(26.74)	84	(32.56)
	III	168	122	(16.31)	46	(17.83)
	IV	33	22	(2.94)	11	(4.26)
T classification	T1	284	230	(30.38)	54	(20.69)	10.9002	0.0167
	T2	571	416	(54.95)	155	(59.39)
	T3	118	79	(10.44)	39	(14.94)
	T4	42	30	(3.96)	12	(4.60)
	TX	3	2	(0.26)	1	(0.38)
N classification	N0	652	492	(65.08)	160	(61.30)	2.1371	0.7489
	N1	227	162	(21.43)	65	(24.90)
	N2	114	83	(10.98)	31	(11.88)
	N3	7	6	(0.79)	1	(0.38)
	NX	17	13	(1.72)	4	(1.53)
M classification	M0	758	560	(74.77)	198	(75.86)	0.8712	0.6206
	M1	32	22	(2.94)	10	(3.83)
	MX	220	167	(22.3)	53	(20.31)
Radiation therapy	NO	782	590	(87.54)	192	(88.07)	0.0083	0.9276
Radiation therapy	YES	110	84	(12.46)	26	(11.93)
Residual tumor	R0	743	556	(91.15)	187	(87.38)	2.9141	0.3682
	R1	25	16	(2.62)	9	(4.21)
	R2	8	6	(0.98)	2	(0.93)
	RX	48	32	(5.25)	16	(7.48)
Vital status	DECEASED	404	283	(37.38)	121	(46.36)	6.1628	0.0130
Vital status	LIVING	614	474	(62.62)	140	(53.64)
Sample type	Primary Tumor	1017	755	(99.74)	262	(100)	0.0005	0.9816
Sample type	Recurrent Tumor	2	2	(0.26)	0	(0.00)

It was demonstrated by the findings that were presented in Table 1 that there are significant associations between the expression of SLCO4C1 and a variety of clinicopathological characteristics. Among these characteristics are the histological type (P < 0.01), the T classification (P < 0.05), and vital status (P < 0.05).

Differences in SLCO4C1 Expression among Subgroups of Lung Cancer Patients

Upon conducting a boxplot analysis, it was discovered SLCO4C1 expression was lower in lung cancer tissues compared to normal tissues (P < 2.2e-16; Fig. 1A). Furthermore, it was observed that there was a difference in expression between the subgroups, particularly with regard to gender (P = 0.022; Fig. 1C), histological type (P = 3.2e-09; Fig. 1D), and T classification (P = 0.033; Fig. 1F).

Diagnostic Ability of SLCO4C1

According to the results of the ROC curve analysis, SLCO4C1 has a diagnostic ability that is acceptable in the case of lung cancer (AUC = 0.897). When compared to normal lung tissues, this diagnostic capability performed consistently across all stages of lung cancer (stage I, area under the curve = 0.894; stage II, area under the curve = 0.904; stage III, area under the curve = 0.894; stage IV, area under the curve = 0.926; Fig. 2).

Influence of SLCO4C1 Expression on Overall Survival

According to the findings of the Kaplan-Meier comparison, there is a statistically significant connection between decreased SLCO4C1 expression and decreased overall survival rates (P = 0.00023; Fig. 3A). This association was consistently observed in subgroups, specifically among patients who were older (P = 0.00019; Fig. 3C), among females (P = 0.0083; Fig. 3D), among males (P = 0.017; Fig. 3E), among patients who had lung adenocarcinoma (P < 0.0001; Fig. 3F), and among those who had T2 (P = 0.018; Fig. 3O), N0 (P = 0.0027; Fig. 3R), M0 (P = 0.011; Fig. 3U), and R0 (P = 0.024; Fig. 3W) statuses.

Influence of SLCO4C1 Expression on Relapse-free Survival

According to the findings of the Kaplan-Meier analysis, patients who had a lower expression of SLCO4C1 had a lower chance of surviving without experiencing a relapse (P = 0.037; Fig. 4A). A similar pattern was observed in the subgroups, particularly in patients who had lung squamous cell carcinoma (LSCC) (P = 0.047; Fig. 4G) and those who had T2 (P = 0.018; Fig. 4O) and R0 (P = 0.024; Fig. 4W) statuses.

Cox Regression Model for Overall Survival

Cox regression model with a single variable for the purpose of further investigation, choose the potential variable. Multivariate Cox regression model found that low SLCO4C1 expression, stage, and T classification were all independent risk factors (HR = 1.37[1.10–1.7], P < 0.01). This was determined by comparing the hazard ratio to the confidence interval. The variables that were associated with overall survival were presented by the researchers in Fig. 5, which can be found here.

Cox Regression Model for Relapse-free Survival

Take the potential variable into consideration when conducting a Cox regression analysis with a single variable for the purpose of conducting further multivariate analysis. The variables that were associated with survival without relapse are presented in Fig. 6, which can be found here. Following the implementation of multivariate Cox regression, it was found that low SLCO4C1 expression, histological type, T classification, radiation therapy, and residual tumor status were independent factors that contributed to risk (HR = 1.35[1.01–1.80], P < 0.01).

The prognosis for lung carcinoma remains unfavorable, despite extensive research on innovative prognostic biomarkers across various cancer types [20–23]. The persistently poor outlook for lung cancer underscores the critical need to identify specific biomarkers for this particular disease.

Our investigation reveals a significant downregulation of SLCO4C1 in lung cancer, with its expression correlated to histological type and T classification. This suggests a potential association with the replication and proliferation of lung carcinoma cells, mirroring SLCO4C1's role as a tumor suppressor gene in head and neck cancer (HNC) through promoter methylation or somatic mutation [13]. Similar occurrences are noted in prostate cancer (PC), where SLCO4C1 promoter methylation serves as a prognostic marker post-radical prostatectomy [15], and in colorectal cancer, where methylation regulates mRNA expression for clinical stratification and prognosis [24].

In endometrial cancer (EC), SLCO4C1 interference primarily affects cellular behaviors through the inactivation of the PI3K/Akt pathway, influencing proliferation, apoptosis, and the epithelial-mesenchymal transition (EMT) phenotype [14]. Increased SLCO4C1 expression correlates with improved overall survival and relapse-free survival in lung cancer patients, suggesting potential mechanisms such as promoter methylation or PI3K/Akt pathway inactivation that could inhibit proliferation and metastasis. Further research is essential to uncover diverse mechanisms underlying SLCO4C1's anti-cancer effects across tumor types, positioning it as a potential predictive biomarker for lung cancer prognosis.

In the current investigation, ROC curve analysis demonstrates SLCO4C1's high effectiveness in diagnosing lung cancer, particularly in advanced stages. Comparable diagnostic markers, such as miR-520f and SHOX2 gene promoter hypermethylation, have shown significant AUCs in previous studies [25–27]. SLCO4C1, therefore, holds considerable potential as a diagnostic biomarker for lung cancer.

This study is groundbreaking in identifying SLCO4C1 as a novel signature for both the diagnosis and prognosis of lung carcinoma. However, limitations include reliance on a single database for data extraction, necessitating validation from diverse populations. Additional in vivo functional experiments and investigations into SLCO4C1's molecular mechanisms in lung cancer are crucial for a comprehensive understanding. The imperative for continued exploration and validation underscores the dynamic and interconnected aspects of cancer research, bringing us closer to more effective diagnostic and prognostic biomarkers in the fight against lung carcinoma.

In conclusion, it was discovered that the levels of SLCO4C1 expression were lower in lung cancer. This finding was found to be intimately connected with vital status, T classification, and histological type. A significant amount of diagnostic relevance for lung cancer has been demonstrated by SLCO4C1, which is an important finding. Furthermore, it has emerged as an independent prognostic factor, wherein a higher expression of SLCO4C1 correlates with improved overall survival and survival without relapse. Because of this, SLCO4C1 has the potential to become an exceptionally useful new biomarker for lung cancer.

Authors' contributions

Conceptualization: Lt W and Lx X.

Data collection and analysis: Qc L and Zm C.

Research process: Sn Z and J H.

Drafting the manuscript: Fy G.

Data statement

The datasets analyzed and/or used in this study are available from the corresponding author upon a reasonable request.

Competing interests

The authors declare no competing interests.

Funding

The study was partly supported by Guangdong Provincial Natural Scientific Foundation Committee (2023A1515012338).

Erratum: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2020 Jul;70(4):313. doi: 10.3322/caac.21609. Epub 2020 Apr 6. Erratum for: CA Cancer J Clin. 2018 Nov;68(6):394-424.doi: 10.3322/caac.21609.
Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature. 2018 Jan 24;553(7689):446-454. doi: 10.1038/nature25183.
Gridelli C, et al. Non-small cell lung cancer. Nat Rev Dis Primers. 2015; 1:15009.PMID:27188576.
Wong MCS, Lao XQ, Ho KF, Goggins WB, Tse SLA. Incidence and mortality of lung cancer: global trends and association with socioeconomic status. Sci Rep. 2017;7:14300. doi: 10.1038/s41598-017-14513-7.
Zaman A, Bivona TG. Emerging application of genomics-guided therapeutics in personalized lung cancer treatment. Ann Transl Med. 2018;6:160. doi: 10.21037/atm.2018.05.02.
Zhu Q, Liang X, Dai J, Guan X. Prostaglandin transporter, SLCO2A1, mediates the invasion and apoptosis of lung cancer cells via PI3K/AKT/mTOR pathway. Int J Clin Exp Pathol. 2015 Aug 1;8(8):9175-81. PMID: 26464663.
Nakanishi Y, et al. Correlations between class I glucose transporter expression patterns and clinical outcomes in non-small cell lung cancer. Thorac Cancer. 2023 Sep;14(27):2761-2769. doi: 10.1111/1759-7714.15060.
Kunii E, et al. Organic cation transporter OCT6 mediates cisplatin uptake and resistance to cisplatin in lung cancer. Cancer Chemother Pharmacol. 2015 May;75(5):985-91. doi: 10.1007/s00280-015-2723-x.
Scafoglio CR, et al. Sodium-glucose transporter 2 is a diagnostic and therapeutic target for early-stage lung adenocarcinoma. Sci Transl Med. 2018 Nov 14;10(467):eaat5933. doi: 10.1126/scitranslmed.aat5933.
Mikkaichi T, et al. Isolation and characterization of a digoxin transporter and its rat homologue expressed in the kidney. Proc Natl Acad Sci U S A. 2004 Mar 9;101(10):3569-74. doi: 11.1073/pnas.0304987101.
Mikkaichi T, Suzuki T, Tanemoto M, Ito S, Abe T. The organic anion transporter (OATP) family. Drug Metab Pharmacokinet. 2004 Jun;19(3):171-9. doi: 10.2133/dmpk.19.171.
Hagenbuch B, Meier PJ. Organic anion transporting polypeptides of the OATP/ SLC21 family: phylogenetic classification as OATP/ SLCO superfamily, new nomenclature and molecular/functional properties. Pflugers Arch. 2004 Feb;447(5):653-65. doi: 10.1007/s00424-003-1168-y.
Guerrero-Preston R, et al. Key tumor suppressor genes inactivated by "greater promoter" methylation and somatic mutations in head and neck cancer. Epigenetics. 2014 Jul;9(7):1031-46. doi: 10.4161/epi.29025.
Hu X, et al. Knockdown of SLCO4C1 inhibits cell proliferation and metastasis in endometrial cancer through inactivating the PI3K/Akt signaling pathway. Oncol Rep. 2020 Mar;43(3):919-929. doi: 10.3892/or.2020.7478.
Li X, et al. SLCO4C1 promoter methylation is a potential biomarker for prognosis associated with biochemical recurrence-free survival after radical prostatectomy. Clin Epigenetics. 2019 Jul 9;11(1):99. doi: 10.1186/s13148-019-0693-2.
Team RDCJC: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 14:12-21, 2009.
Wickham H: Ggplot2: Elegant graphics for data analysis. J R Stat Soc 174: 245-246, 2011.
Robin X, et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12: 77, 2011.
Therneau TM and Grambsch PM: Modeling survival data: Extending the Cox model. Springer, New York, 2000.
Ding F, Tang H, Nie D, Xia L. Long non-coding RNA Fer-1-like family member 4 is overexpressed in human glioblastoma and regulates the tumorigenicity of glioma cells. Oncol Lett. 2017 Aug;14(2):2379-2384. doi: 10.3892/ol.2017.6403.
Wang H, Li W, Wang G, Zhang S, Bie L. Overexpression of STMN1 is associated with the prognosis of meningioma patients. Neurosci Lett. 2017 Jul 27;654:1-5. doi: 10.1016/j.neulet.2017.06.020.
Jia W, Lu R, Jia G, Ni M, Xu Z. Expression of pituitary tumor transforming gene (PTTG) in human pituitary macroadenomas. Tumour Biol. 2013 Jun;34(3):1559-67. doi: 10.1007/s13277-013-0686-2.
Meng Y, et al. A comprehensive bioinformatic analysis of RanBP9 expression and its relation to prognosis in human breast cancer. Epigenomics. 2022 Jan;14(1):27-42. doi: 10.2217/epi-2021-0464.
Fu B, et al. Analysis of DNA methylation-driven genes for predicting the prognosis of patients with colorectal cancer. Aging (Albany NY). 2020 Nov 16;12(22):22814-22839. doi: 10.18632/aging.103949.
Zhou Y, Shen S. MiR-520f acts as a biomarker for the diagnosis of lung cancer. Medicine (Baltimore). 2019 Jul;98(30):e16546. doi: 10.1097/MD.0000000000016546.
Liu Q, Wang S, Pei G, Yang Y, Huang Y. [Diagnostic Efficacy of SHOX2 Gene Hypermethylation for Lung Cancer: A Meta-Analysis]. Zhongguo Fei Ai Za Zhi. 2021 Jul 20;24(7):490-496. Chinese. doi: 10.3779/j.issn.1009-3419.2021.101.27.
Li J, Cao C, Fang L, Yu W. Serum transfer RNA-derived fragment tRF-31-79MP9P9NH57SD acts as a novel diagnostic biomarker for non-small cell lung cancer. J Clin Lab Anal. 2022 Jul;36(7):10.1002/jcla.24492. doi: 10.1002/jcla.24492.

No competing interests reported.

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Low SLCO4C1 expression serves as an independent prognostic predictor in lung cancer

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Version 1

Abstract

Figures

Introduction

Materials and methods

Data source

Statistical analysis

Results

Characteristics of the Patient and Association Between SLCO4C1 Expression and Clinical Features

Differences in SLCO4C1 Expression among Subgroups of Lung Cancer Patients

Diagnostic Ability of SLCO4C1

Influence of SLCO4C1 Expression on Overall Survival

Influence of SLCO4C1 Expression on Relapse-free Survival

Cox Regression Model for Overall Survival

Cox Regression Model for Relapse-free Survival

Discussion

Conclusions

Declarations

References

Additional Declarations

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