53 Years Old is A Reasonable Cut-Off Value To Define Young And Old Patients In Clear Cell Renal Cell Carcinoma:A Study Based On TCGA And SEER Database

doi:10.21203/rs.2.12816/v1

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Research article

53 Years Old is A Reasonable Cut-Off Value To Define Young And Old Patients In Clear Cell Renal Cell Carcinoma:A Study Based On TCGA And SEER Database

https://doi.org/10.21203/rs.2.12816/v1

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published 29 May, 2021

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Background

The objectives of this study were to screen out cut-off age value and age-related differentially expressed genes (DEGs) in clear cell renal cell carcinoma (CCRCC) from Surveillance Epidemiology and End Results (SEER) database and The Cancer Genome Atlas (TCGA) database.

Methods

We selected 45,974 CCRCC patients from SEER and 530 RNA-seq data from TCGA database. The age cut-off value was defined using the X-tile program. Propensity score matching (PSM) was used to balance the differences between young and old groups. Hazard ratio (HR) was applied to evaluate prognostic risk of age in different subgroups. Age-related DEGs were identified via RNA-seq data. Survival analysis was used to assess the relationship between DEGs and prognosis.

Results

In this study, we divided the patients into young (n=14276) and old (n=31698) subgroups according to cut-off value (age=53). Age >53 years was indicated as independent risk factor for overall survival (OS) and cancer specific survival (CSS) of CCRCC before and after PSM. The prognosis of old group was worse than that in young group. 176 age-related DEGs were obtained by 530 CCRCC RNA-seq analysis result. The expression levels of KCNE5, CLDN6 and CACNG6 were related to prognosis of the elderly.

Conclusion

53 years old was cut-off value in CCRCC. The prognosis of the elderly was worse than young people. KCNE5, CLDN6 and CACNG6 were age-related differential genes which might play an important role in the poor prognosis of elderly CCRCC patients.

Cancer Biology

Oncology

Age

age-related genes

Clear cell renal cell carcinoma

The Cancer Genome Atlas

Surveillance Epidemiology and End Results

Over the past two decades, the incidence of renal cell carcinoma (RCC) at every stages was increased and this situation resulted in a steady increase in mortality per unit of population [1]. It is estimated that 65340 Americans will be diagnosed with RCC, and 14970 Americans will die of this cancer in 2018. RCC comprises about 3.8% of all new cancer. And the median age of RCC patient is 64 ages old. Clear cell renal cell carcinoma (CCRCC) is the most common subtype of RCC, it accounts for about 80% of RCC [2]. Age has prognostic significance in many solid cancers, and one of renal cancer known risk factors is age [3,4]. RCC shows a more favorable prognosis in young patiens, which may be due to the lower state of diagnosis [5]. In addition, age can influence the structural and molecular properties of the tumor vasculature in CCRCC by comparing the vascular properties of patients who over the age of 65 and under 65 years old [6]. Furthermore, expression levels of Piwil 1 mRNA in patients who under 64 years old are higher than that in older people (> 64 years old). But there still is no optimal age cut off value to define elderly and young people in CCRCC. Therefore, we determined the optimal cut-off value for age analyzing the clinical data SEER database, and explored differentially expressed genes (DEGs) between older and younger people of CCRCC by analyzing RNA-seq data from TCGA in present study.

Study population from SEER

SEER Stat software(version 8.3.5) was used to download CCRCC clinical data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. The downloaded data included: patient ID, the year and age at the time of diagnosis, sex, race, histological type, survival time, tumor size, marital status, Grade, SEER historic stage A, and cuase of death.

CCRCC patients were selected according to the following criteria: (1) site record International Classification of Diseases for Oncology, Third Revision (ICD-O-3) was C649; (2) histological type was 8310/3; (3) the year at time of diagnosis was 1988-2014. (4) CCRCC was primary tumor. The exclusion criteria were listed as following: (1) patients without race and gender information; (2) patients whose tumor size, survival time and other clinical information we need in this study were unknown.

Variable declaration

Race was defined as white, black and other. Marital was divided into Single/Other, and married. Tumor size was divided into less than 4 cm, 4 cm to 7 cm, and greater than 7 cm. Grade was grouped as I , II, III, IV. Laterality was divided into left and right. The SEER historic stage options included localized, regional and distant. And the chemotherapy, radiotherapy were divided into yes or no.

Cut off age in CCRCC

The “x tile plot” can provide a single, global assessment of every possible way by dividing a population into low- and high-level marker expression [7]. We used X-tile plots to assessed all possible age cutoff value, and the survival at every age cutoff value was computed by the log rank test. Then the most appropriate cut-off value was selected which had the highest X2 value.

RNA-seq Analysis of CCRCC from TCGA

The RNA sequencing and clinical information of CCRCC were download from TCGA database. We used these RNA-seq data for DEGs screening between younger and older group by edgeR package (adjusted p value <0.05 and | log2 fold change (FC) | ≥1). Then, we extracted clinical data from older adults (>53 years), including survival time and survival status. We selected the DEGs from the small to large false discovery rate (FDR). And the top 10 genes was for survival analysis. The differentially expression levels of these top 10 genes in these old patients were obtained. The median of gene expression was used to classify low and high group. Log rank test was used to compare statistically significant differences between high and low expression groups.

Statistical Analysis

We divided the patients into young and old groups according to the X-tile's best cut-off value. Chi-square test was used to compare the differences in the distribution of variables between younger and older group. We calculated the overall survival (OS) and cancer specific survival (CSS). In the CSS calculation, the cause of death for other reasons was defined as censorship. Propensity score matching (PSM) used logistic regression included relevant variables of Sex, race, marital status, size, grade, SEER historic stage A, radiation and chemotherapy to balance the baseline differences between the younger and older groups. The OS and CSS Survival curves were generated using the Kaplane-Meier method. And univariate and multivariate analysis Cox regression models were applied to adjust prognostic variables. The cases were stratified according to the relevant variables. Hazard Ratio (HR) of the CSS was calculated according to the age. When the 2-sided P value was < 0.05, the differences were considered statistically significant. The SPSS 24.0 and R 3.4.3 were used to conduct statistical analysis and DEGs screening.

53 was the age cut-off value and Baseline Characteristics

We obtained 45,974 CCRCC patients in totally. The median age of these patients was 60 years old (interquartile range: 51-69). At the same time, X tile result showed that 53 years old was defined as the best cut-off value for age (Figure 1). Then we divided the cohort into two groups: younger group (53 years or younger), older group (older than 53 years) according to the cut-off value. The detailed features of the patients between the two groups were presented in Table 1 (Table 1).

Survival Analysis

In the young group, 5- and 10-year OS rates were 86.4% and 78.2% respectively. In the old group, 5- and 10-year OS rates were 72.8% and 54.5 respectively (P <0.001; Figure 2 A). Univariate analysis results indicated that age, sex, race, marital status, size, grade, laterality, SEER historic stage A, radiation and chemotherapy,could predict patient suvival outcomes. Meanwhile, multivariate analysis showed that the age, sex, race, marital status, size, grade, laterality, SEER historic stage A, radiation and chemotherapy were independent prognostic factor for CCRCC OS(Table 2).

In the young group, 5- and 10-year CSS rates were 89.4% and 81.7% respectively. In the old group, 5- and 10-year CSS rates were 84.3% and 72.8% respectively (P <0.001; Figure 2 B).The results of univariate analysis showed that age, sex, marital status, size, grade, laterality, SEER historic stage A, radiation and chemotherapy were associated with patient's prognosis. Multivariate analysis showed that age, marital status, size, grade, SEER historic stage A, radiation and chemotherapy were independent prognostic factors for CCRCC CSS(Table 3).

Survival Analysis after PSM

The clinical characteristics of the patients between the younger and older groups had obvious differences. So PSM method was applied to balance the differences between the variables, and generated a new queue (All covariates were well balanced, P values > 0.05; Table 1). Univariate analysis results showed that HR for OS of the older patient were 2.056 (95% CI:1.948-2.170; P <0.001), HR for CSS were 1.496(95% CI, 1.399-1.600; P <0.001) when compared with the younger group. In the PSM queue, the younger people also had a higher survival rate than older people (Figure 2 C, D). Multivariate analysis results showed that compared with the younger group , HR for OS of the older patient were 2.128（95%CI:2.015-2.247; P <0.001）, HR for CSS were 1.573(95%CI:1.470-1.682; P <0.001). Other variable results were showed in Table 2, 3.

Subgroup analysis

We performed a subgroup analysis based on sex, race, marital status, size, grade, laterality, SEER historic stage A, radiation, and chemotherapy. In most subgroups, the older group had a worse prognosis than the younger group. However radiation, and chemotherapy and prognostic differences between young and old groups were not statistically significant (P >0.05) (Figure 3).

Three gene associated with poor prognosis in the elderly

We finally got 176 DEGs included 114 up-regulated genes and 62 down-regulated genes (Table S1). By ranking the FDR, we selected the top 10 gene(Table 4) for survival analysis, and investigated the correlation between the expression of these genes and prognosis of the elderly. The results showed that high expression of KCNE5，CLDN6 and CACNG6 were associated with poor prognosis in the elderly (Figure 4).

A total of 45,974 CCRCC patients were included in the SEER database, of which the 53-year-old cut-off value was used to divide the younger and older groups. Survival analysis results showed that younger age (under age 53) was an independent predictor of CCRCC. And we obtained some genes related to old patients with CCRCC by analyzing the RNA-seq data downloaded from TCGA.

Some studies reported that the 40 years old was suitable to act as the dividing line between young and old CCRCC patients. Xavier Taccoen et al. found that young (under 40 years of age) age was an independent prognostic factor for CCRCC, with a better prognosis [8]. Atiqullah Aziz et al found that young patients with RCC (age 40 or under) have a significantly lower all cause and disease specific killed [9]. Ho Won Kang et al. also found that young age was associated with favorable pathological features, although it is not survival independent prognostic factors in surgically treated RCC patients. But the result of the Kaplan-Meier analysis showed that the CCS rate was significantly better in the young age group than the other groups (middle age: ≥ 4 and < 60 years ; old age: ≥ 60 years) [10]. Analysis results between younger and older RCC patients (20-39 and 40-79) of Jeong Ho Kim et al found that younger RCC patients would have more favorable histological subtypes. And the 5-year CSS rates for young and older patients were 95.5% and 90.5% respectively. However, after PSM, the five-year CSS rate was 95.5% for the younger group and 94.7% for the older group, and the prognosis was not significantly different (log rank p = 0.184) [11]. In addition to the 40 years old, there were many age cut-off values, such as 45 and 55 years old. Yoshinobu Komai et al used the 45-year-old as a cut-off value for younger and older group. Compared with the older patients, the young patients with RCC had similar recurrence-free survival rates but better CSS rates [12]. Eun-Jung Jung et al believed that younger age was an independent predictor of prognosis through multivariate analysis. Whereas in their study, younger than 55 years of age was considered as young in CCRCC [13]. In this study, we used X-tile plots to assessed all possible age cuttoff value, and finally selected the age 53 as the cut off value for dividing younger and older group. And younger groups had better OS and CSS compared to older groups. What’s more, in the subgroups anaysis, The prognosis of old group was worse than that of young group in all subgroups of this study, especially in the Dmax <= 4cm subgroups (HR = 3.710(3.006-4.579), P < 0.001).

In recent years, with the development of cancer gene sequencing and targeted therapies, the research on gene expression of CCRCC had made some progress. In the CCRCC age-related studies, Xp11 translocation renal cell carcinoma was kind of RCC subtype, Malouf GG et al used the targeted therapy to treat patients, the objective responses was achieved and the patients got the better progression-free survival [14]. Mitchell TJ et al. analyzed the entire genome of CCRCC and found that 36% of patients experience 3p loss and 5q gain, which usually occured during childhood or adolescence. Meanwhile hotspots of point mutations in the 5' UTR of TERT, targeting a MYC-MAX-MAD1 repressor associated with telomere lengthening [15]. Malouf GG reported that ASPSCR1-TFE3 might be the most aggressive among the transcription factor E3 fusion genes in RCC patients [16].

In this study, we obtained some DEGs by comparing RNA-seq data from younger and older CCRCC patients. Then, the top 10 genes were used for survival analysis. As showed in the result (Figure 4), the expression of KCNE5(Potassium Voltage-Gated Channel Subfamily E Regulatory Subunit 5), CLDN6(Claudin 6) and CACNG6 (Calcium Voltage-Gated Channel Auxiliary Subunit Gamma 6) were related to the survival of the elderly.

KCNE5 is belongs to the human KCNE gene family. This KCNE gene mutations are related to various pathophysiological states, and most obvious KCNE gene mutation occurs in the arrhythmia [17]. However, its current research on cancer had not been reported. CLDN6 is a family member of CLDNs, which encodes a component of tight junction strands[18]. Some study had showed that CLDN6 expression was low in breast cancer cells and closely related to the proliferation and invasion of the cells[19, 20]. According to Gao F et al [21] there was a correlation between CLDN6 and gastric cancer. CLDN6 low expression has a certain negative impact on the prognosis of patients. Therefore, CLDN6 could be used as an important indicator for the prognosis of gastric cancer patients. But there was no research report on the role of CLDN6 in CCRCC. CACNG6 is a complete membrane protein. It stabilizes calcium channels during an inactive state [22]. CACNG6 polymorphism was associated with aspirin intolerance among asthma patients in the Korean population [23]. At the same time, our research also showed that KCNE5，CLDN6 and CACNG6 were not only age-related DEGs for the elderly, but their expression levels were related to the survival of the elderly, so we hypothesized that these genes might have clinical monitoring value in CCRCC which deserved for further research.

Our study had several potential limitations. The leading known risk factors for renal cancer were smoking, obesity and hypertension [24-29]. However, due to the lack of corresponding data in the SEER database, we were unable to study these factors. At the same time, retrospective analyses always carried the risk of various biases. We used the subgroup, PSM analysis and incorporate large amounts of patients in this study to minimize potential biases.

In conclusion, we proposed that 53-year-old was a reasonable cut-off value among CCRCC patients, and the elderly group had a worse prognosis than the younger group. At the same time, we screened three age-related DEGs (GRIK1, CILP2 and CACNG6), which might be associated with poor prognosis in the elderly, but the specific mechanism remained to be further studied.

CCRCC: clear cell renal cell carcinoma;

DEGs: Differentially expressed genes;

SEER: Surveillance Epidemiology and End Results;

TCGA: The Cancer Genome Atlas;

OS：overall survival;

CSS: cancer specific survival;

PSM: Propensity score matching;

HR: Hazard Ratio;

FC: fold change;

FDR：false discovery rate;

CPM：counts per million.

Funding

The present study was supported by research start-up fee for the eighth affiliated hospital, Sun Yat-sen University (grant no.zdbykyqdf005); 2018 Provincial College Students Innovation and Entrepreneurship Training Program Project (grant no. 201810570039).

Availability of data and materials

The datasets analyzed during the current study download from Surveillance Epidemiology and End Results database (https://seer.cancer.gov/) and The Cancer Genome Atlas (TCGA) database. (https://cancergenome.nih.gov/).

Authors’ contributions

Zhaohui He, Fucai Tang and Zechao Lu conceived the study; Zhaohui He and Fucai Tang conducted the work; Chengwu He, Hanbin Zhang and weijia Wu obtaining and analyzed the data; Fucai Tang and Zechao Lu wrote the manuscript, and Zhaohui He reviewed the manuscript. All the authors listed have read and approved the manuscript.

Ethics approval and consent to participate

The present study, the data download from Surveillance Epidemiology and End Results database and The Cancer Genome Atlas (TCGA) database, therefore, this article does not contain any studies with human participants or animals performed by any of the authors. Thus no ethical approval and patient consent are required. This article does not contain any studies with animals performed by any of the authors.

Conflicts of Interests

All authors declare that they have no conflict of interest to state.

Consent for publication

Not applicable.

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Table 1： The Clinical characteristic baseline of CCRC patients in SEER

Characteristic	Before PSM			After PSM
Characteristic	Younger (<=53 y)	Older (>53 y)	P	Younger (<=53 y)	Older (>53 y)	P
SEX			<0.001			0.613
male	9134	18958		9124	9165
female	5142	12740		5142	5101
Race			<0.001			0.322
Black	1020	1939		1014	1063
White	12199	27428		12195	12105
Other	1057	2331		1057	1098
Marital			<0.001			0.739
Single/Other	5124	10484		5116	5143
married	9152	21214		9150	9123
SIZE			<0.001			0.571
Dmax<=4cm	7058	12966		7052	6981
4cm < Dmax<=7cm	3771	10175		3771	3847
Dmax>7cm	3447	8557		3443	3438
GRADE			<0.001			0.566
Ⅰ	2204	4226		2199	2243
Ⅱ	7893	16362		7890	7873
Ⅲ	3376	8978		3375	3309
Ⅳ	803	2132		802	841
Laterality			0.228			0.840
Left	6949	15622		6946	6963
Right	7327	16076		7320	7303
SEER historic stage A			<0.001			0.754
Localized	11405	21988		11395	11345
Regional	1865	6576		1865	1892
Distant	1006	3134		1006	1029
Radiation			0.035			0.202
NO	13967	30909		13960	13928
YES	309	789		306	338
chemotherapy			<0.001			0.274
NO	13664	29986		13655	13617
YES	612	1712		611	649

Table 2：cox regression analysis of overall survival

Characteristic	Univariate cox regression before PSM		Multivariate cox regression before PSM		Multivariate cox regression after PSM
Characteristic
	95%CI	P	95%CI	P	95%CI	P

Age
young	Reference		Reference		Reference
old	2.338 (2.228-2.453)	<0.001	2.132(2.031-2.238)	<0.001	2.128（2.015-2.247）	<0.001
Sex
male	Reference		Reference		Reference
female	0.829(0.789-0.862)	<0.001	0.909（0.873-0.947）	<0.001	0.910（0.859-0.964）	0.001
Race
Black	Reference		Reference		Reference
White	0.895(0.831-0.964)	0.003	0.836（0.776-0.901）	<0.001	0.795（0.722-0.876）	<0.001
Other	0.798(0.721-0.884)	0<0.001	0.716（0.647-0.794）	<0.001	0.682（0.593-0.783）	<0.001
Marital
Single/Other	Reference		Reference		Reference
married	0.780(0.751-0.811)	<0.001	0.729（0.701-0.758）	<0.001	0.711（0.674-0.750）	<0.001
Size
Dmax<=4cm	Reference		Reference		Reference
4cm< Dmax<=7cm	1.967(1.868-2.070)	<0.001	1.403（1.330-1.480）	<0.001	1.382（1.284-1.488）	<0.001
Dmax>7cm	4.084(3.895-4.283)	<0.001	1.849（1.748-1.956）	<0.001	1.965（1.823-2.118）	<0.001
GRADE
Ⅰ	Reference		Reference		Reference
Ⅱ	1.151（1.081-1.225）	<0.001	0.966（0.907-1.029）	0.283	0.988（0.907-1.075）	0.773
Ⅲ	2.225（2.086-2.373）	<0.001	1.213（1.133-1.298）	<0.001	1.287（1.172-1.412）	<0.001
Ⅳ	5.296（4.911-5.712）	<0.001	1.823（1.681-1.978）	<0.001	1.856（1.661-2.075）	<0.001
Laterality		<0.001
Left	Reference		Reference		Reference
Right	0.936（0.902-0.971）	<0.001	0.953（0.918-0.989）	0.012	0.947（0.899-0.997）	0.038
SEER historic stage A
Localized	Reference		Reference		Reference
Regional	2.705（2.585-2.831）	<0.001	1.830(1.740-1.925)	<0.001	1.860（1.727-2.003）	<0.001
Distant	11.243（10.734-11.776）	<0.001	5.996(5.631-6.383)	<0.001	6.694（6.115-7.329）	<0.001
Radiation
NO	Reference		Reference		Reference
YES	6.150（5.725-6.607）	<0.001	1.340(1.239-1.450)	<0.001	1.319（1.187-1.465）	<0.001
chemotherapy
NO	Reference		Reference		Reference
YES	5.959（5.637-6.299）	<0.001	1.196(1.120-1.278)	<0.001	1.254（1.144-1.375）	<0.001

Table 3：cox regression analysis of cancer-specific survival

Characteristic	Univariate cox regression before PSM		Multivariate cox regression before PSM		Multivariate cox regression after PSM

	95%CL	P	95%CL	P	95%CL	P
age
young	Reference		Reference		Reference
old	1.834（1.731-1.942）	<0.001	1.549（1.462-1.642）	<0.001	1.573（1.470-1.682）	<0.001
SEX
male	Reference		Reference		Reference
female	0.781（0.743-0.821）	<0.001	1.008（0.957-1.061）	.769	1.023（0.950-1.102）	0.549
Race
Black	Reference		Reference		Reference
White	0.969（0.879-1.068）	0.525
Other	0.924（0.811-1.052）	0.233
Marital
Single/Other	Reference		Reference		Reference
married	0.911（0.866-0.957）	<0.001	0.843（0.801-.887）	<0.001	0.824（0.769-0.884）	<0.001
SIZE
Dmax<=4cm	Reference		Reference		Reference
4cm< Dmax<=7cm	3.886（3.566-4.233）	<0.001	2.324（2.127-2.540）	<0.001	2.433（2.153-2.750）	<0.001
Dmax>7cm	11.792（10.896-12.762）	<0.001	3.710（3.393-4.055）	<0.001	4.299（3.814-4.845）	<0.001
GRADE
Ⅰ	Reference		Reference		Reference
Ⅱ	1.420（1.287-1.567）	<0.001	1.028（0.931-1.135）	0.583	1.051（0.918-1.203）	0.472
Ⅲ	4.020（3.649-4.428）	<0.001	1.514（1.369-1.675）	<0.001	1.615（1.405-1.855）	<0.001
Ⅳ	10.814（9.738-12.009）	<0.001	2.229（1.995-2.490）	<0.001	2.264（1.944-2.636）	<0.001
Laterality
Left	Reference		Reference		Reference
Right	0.931（0.887-0.976）	0.003	0.954（0.910-1.001）	0.054	0.940（0.880-1.005）	0.07
SEER historic stage A
Localized	Reference		Reference		Reference
Regional	4.934（4.637-5.249）	<0.001	2.615（2.443-2.798）	<0.001	2.641（2.402-2.903）	<0.001
Distant	25.576（24.104-27. 138）	<0.001	9.900（9.172-10.685）	<0.001	10.134(9.108-11.276)	<0.001
Radiation
NO	Reference		Reference		Reference
YES	9.242（8.574-9.962）	<0.001	1.446（1.334-1.568）	<0.001	1.458(1.309-1.625)	<0.001
chemotherapy
NO	Reference		Reference		Reference
YES	8.887（8.369-9.438）	<0.001	1.179（1.100-1.263）	<0.001	1.268(1.152-1.396)	<0.001

Table 4： The top 10 age-related differentially expressed genes

Gene	logFC	logCPM	PValue	FDR
KCNE5	-3.871875697	-0.630531674	3.81E-101	6.88E-97
PMEL	-3.118739088	3.201080324	6.07E-93	5.48E-89
LPAR3	-3.721054563	-0.404306233	2.46E-62	1.48E-58
SPOCK3	-4.171219051	-0.519518204	1.49E-55	6.71E-52
PRSS38	-6.050624343	-1.293512652	9.29E-54	3.36E-50
PAX9	-2.898326355	0.312666906	8.33E-52	2.51E-48
LGI3	-3.236037193	0.023278558	2.20E-46	5.67E-43
CACNG6	-3.199514488	-2.392203888	4.55E-43	1.03E-39
CLDN6	-2.386529645	-2.048179057	1.76E-42	3.54E-39
ALB	5.560665626	6.78720187	1.14E-40	2.06E-37

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53 Years Old is A Reasonable Cut-Off Value To Define Young And Old Patients In Clear Cell Renal Cell Carcinoma:A Study Based On TCGA And SEER Database

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