Low CFB expression is independently associated with poor overall and disease-free survival in patients with lung adenocarcinoma.

Complement factor B (CFB) serves a pivotal role in the alternative signaling pathway of the complement system and exerts a key role in the labelling of target particles, resulting from effective clearance of the target. The present study aimed to investigate the association between low expression levels of CFB and the clinical features and survival status of patients with lung adenocarcinoma (LUAD). Patient data were based on RNA-sequencing and clinical data from The Cancer Genome Atlas database. All patients were divided into two groups based on the median expression of CFB. Kaplan-Meier curve and univariate Cox regression analyses were used to investigate the association between CFB and survival status. Gene set enrichment analysis was used to examine the effects of CFB expression on signaling pathway impairment. Furthermore, reverse transcription-quantitative PCR (RT-qPCR) and western blotting were used to verify the relative expression levels of CFB in LUAD tissues. The data revealed that residual tumor classification, Karnofsky performance score and cancer stage were associated with overall survival, and that Karnofsky performance score and stage were associated with disease-free survival. The results demonstrated that high expression levels of CFB were associated with increased patient overall and disease-free survival according to both continuous and categorical models. The results of multivariate analysis identified that high expression levels of CFB were associated with increased overall and disease-free survival according to both the continuous model [hazard ratio (HR), 0.48; 95% confidence interval (95% CI), 0.25-0.93; P=0.029 for overall survival; HR, 0.29; 95% CI, 0.15-0.59; P=0.001 for disease-free survival] and the categorical model (HR, 0.46; 95% CI, 0.22-0.93; P=0.031 for overall survival; HR, 0.25; 95% CI, 0.12-0.55; P=0.001 for disease-free survival) after adjusting for corresponding covariates (residual tumour classification, Karnofsky performance score and stage). Furthermore, the results of both RT-qPCR and western blotting indicated that the relative mRNA and protein expression levels of CFB in lung tumor tissues were downregulated compared with those in adjacent non-tumor tissues. Collectively, the present results suggested that CFB expression was an independent predictor of overall and disease-free survival in patients with LUAD. Copyright: © He et al.

Introduction

Lung cancer is the leading cause of cancer-associated death among men, with a 24% mortality rate, and women, with a 23% mortality rate, in the United States (1). Based on its histology, lung adenocarcinoma (LUAD) is classified into two main forms: Non-small cell lung carcinoma (NSCLC) and small cell lung carcinoma, which comprise ~85 and ~15% of all cases, respectively (2). NSCLC is further divided into three subtypes: Squamous-cell carcinoma, adenocarcinoma and large cell carcinoma. LUAD is the most common type of lung cancer, representing ~40% of all lung cancer types (3). LUAD may occur due to mutations in a variety of genes, including EGFR, ALK receptor tyrosine kinase, KRAS, ROS proto-oncogene 1, receptor tyrosine kinase, BRAF, erb-b2 receptor tyrosine kinase 2, MET proto-oncogene, receptor tyrosine kinase and ret proto-oncogene (4). At present, drugs targeting mutant genes, such as EGFR (5) and KRAS (6), associated with LUAD have been developed. However, LUAD is still one of the most aggressive and lethal tumor types, with a 5-year survival rate <5% (7). Therefore, it is important to identify novel target genes to improve the prognosis of patients with LUAD.

Complement factor B (CFB), localized to the major histocompatibility complex class III region on chromosome 6, serves a pivotal role in the alternative pathway of the complement system (8) and exerts a key role in labelling target particles, resulting from the effective clearance of the target (9). CFB is cleaved into two fragments, non-catalytic chain Ba and the catalytic subunit Bb, by complement factor D; the generated component 3b (C3b) binds Bb and properdin, resulting in the formation of the C3 convertase (C3bBb) during the activation process of the alternative pathway (10). By binding of additional C3b to the alternative pathway, C3bBb renders it able to cleave C5, and induces the amplification loop of the alternative pathway (8,10). It has been reported that low expression levels of CFB are associated with decreased expression levels of C3bBb and facilitate the degradation of the membrane attack complex (MAC), which leads to the inhibition of the alternative pathway of complement activation, thereby reducing the activation efficiency of the complement system in the whole body (8). During the regulation of the complex tumor microenvironment, complement proteins serve a dual role in the tumor microenvironment, which will eventually affect tumor progression (9). The dysregulation of complement activation pathways serves an important role in tumor progression (10–12).

A previous study reported that CFB is a candidate biomarker for pancreatic cancer diagnosis (13). Furthermore, it has been revealed that CFB expression is upregulated in patients with preeclampsia compared with in those with normotensive pregnancies (14). CFB has also been reported to serve a central role in mediating ultraviolet-induced immunosuppression (15). Previous data have indicated that CFB mRNA expression in the colonic mucosa is upregulated in the lower parts of the crypts compared with that observed on the luminal surface (16). Furthermore, a previous study has identified a strong association of CFB with molecular subtypes of breast cancer, including the luminal-A (LA), luminal-B, triple-negative and HER2-positive subtypes (17).

As a key immunomodulatory factor in the progression of lung cancer, complement activation induces escape from immunosurveillance via the C3/C5-dependent signaling pathway, which is activated by classical signaling pathways (10), thereby affecting the occurrence and development of lung cancer (18). Therefore, the present study aimed to determine the role of CFB in LUAD using data from The Cancer Genome Atlas (TCGA). The present analysis provided further evidence regarding the use of CFB as a potential biomarker for LUAD, and CFB may become an attractive candidate biomarker and potential prognostic target for LUAD.

Materials and methods

Data collection

RNA-sequencing (seq) expression (combining level-3 data from Illumina GA and Hi-Seq platforms) and clinical data for patients with LUAD were downloaded from TCGA data portal (http://cancergenome.nih.gov/). The TCGA-LUAD dataset contained data on 514 patients with LUAD for whom detailed clinical information was available. RNA-seq by Expectation Maximization expression values were used for statistical analysis.

Clinical feature analysis

LUAD samples were divided into two groups based on the median CFB expression value of 3.64. All statistical analyses were performed using R statistical software (version 3.4.1; http://www.r-project.org/). The means of the continuous variables in these two groups (high-CFB group and low-CFB group) were compared using an independent sample t-test, and the prevalence of categorical variables was compared using the χ2 test. Due to the small sample size of the groups for some variables, the comparisons were performed using Pearson's χ2 test with Yates' continuity correction and Fisher's exact test. Violin plots were used to visualize expression level differences for discrete variables, such as sex, smoking history, residual tumor and stage. Univariate logistic regression was used to investigate the association between CFB expression (categorical) and clinical features.

Survival analysis

Differences in overall survival and disease-free survival between the high-CFB group and the low-CFB group were compared using Kaplan-Meier curves, with P-values calculated via the log-rank test using the ‘survival’ package (https://cran.r-project.org/web/packages/survival/index.html; version 3.2–7) in R. Univariate Cox regression analysis was used to estimate the independent effects of CFB expression and other clinical features, including age, sex, ethnicity, smoking history, residual tumor classification, Eastern Cooperative Oncology Group (ECOG) score (19), Karnofsky performance score (20) and TNM stage (21), on overall survival and disease-free survival. The independent effect of CFB expression on overall survival and disease-free survival was evaluated via multivariate Cox analysis with adjusted covariates. For the sub-group analysis, patients were divided into three or four groups according to the tercile or quartile expression of CFB.

Gene set enrichment analysis (GSEA)

The JavaGSEA desktop application v3.0 was used to perform GSEA (22) of Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) pathways for the following comparisons: High-CFB vs. control, low-CFB vs. control and high-CFB vs. low-CFB. Gene sets with <10 genes or >500 genes were excluded. The t-statistic mean of the genes was computed for each metabolic pathway using a permutation test with 1,000 replications. Up- and downregulated metabolic pathways were defined as having a normalized enrichment score (NES) >0 or <0 for patients compared with controls, respectively. Enrichment analysis of Gene Ontology (https://www.ebi.ac.uk/QuickGO/) biological processes and hallmark pathways was also conducted. An absolute value of NES >1 and a false discovery rate-corrected P-value ≤0.05 were considered significant.

Patients and tissue samples

Written informed consent was obtained according to the guidelines of the Medical Ethics Committee of The First Affiliated Hospital of Kunming Medical University (Kunming, China). Between July and August 2020, a total of 3 patients who were pathologically diagnosed with NSCLC at The First Affiliated Hospital of Kunming Medical University (Kunming, China), including 2 male patients and 1 female patient (maximum age, 64 years; minimum age, 55 years; median age, 65 years), were recruited in the present study, and tumor types were confirmed by ≥2 experienced pathologists. The six paired samples collected were used to identify the expression levels of CFB in lung tumor tissues. The enrolled patients met the following criteria: i) No patients diagnosed as LUAD by pathology had received chemotherapy or radiotherapy; ii) the adjacent non-tumor lung tissues were collected ≥5 cm away from carcinoma tissues; iii) the lung tissue samples collected during surgery were frozen in liquid nitrogen (−196°C) within 30 min; and iv) tumor tissue samples contained ≥80% typical tumor cellularity, while the matched adjacent non-tumor lung tissues samples contained no cancer cells.

Reverse transcription-quantitative PCR (RT-qPCR)

Total RNA from tissues was isolated using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. cDNA was synthesized using 5X All-In-One MasterMix (cat. no. G492; Applied Biological Materials, Inc.). The mixture was incubated at 25°C for 10 min and then at 42°C for 15 min. SYBR-Green Master mix (cat. no. MasterMix-R; Applied Biological Materials, Inc.) was used to perform qPCR on a 7300 Real-Time PCR system (Applied Biosystems; Thermo Fisher Scientific, Inc.). Initially, the enzyme in the mixture was activated at 95°C for 10 min for 1 cycle, then the mixture was denaturalized at 95°C for 15 sec and annealed/extended at 60°C for 60 sec for 40 cycles. The relative mRNA expression in different samples was calculated using the 2-∆∆Cq normalization method (23). The following primers were used: CFB forward, 5′-AATCAAGGTCAGCGTAGGAGG-3′ and reverse, 5′-GGGAGACAAATGGGCCTGATA-3′. GAPDH was chosen as an internal control using the following primers: GAPDH forward, 5′-GCATCCTGGGCTACACTGAG-3′ and reverse, 5′-AAGTGGTCGTTGAGGGCAAT-3′.

Western blotting

Total protein from tissues was extracted using RIPA lysis buffer (Beijing Solarbio Science & Technology Co., Ltd.) containing protease inhibitor (EMD Millipore), and the protein concentration was determined using a BCA protein assay (Beijing Solarbio Science & Technology Co., Ltd.). A total 10–15 µg protein/lane was separated via 8–15% SDS-PAGE and transferred to PVDF membranes, which were incubated with specific primary antibodies overnight at 4°C after blocking with 5% non-fat milk at room temperature for 1 h. Primary antibodies used in the present study included anti-CFB (Abcam; cat. no. ab133765; 1:5,000) and anti-GAPDH (Cell Signaling Technology, Inc.; cat. no. 8884; 1:1,000), which were diluted in 1X TBS-Tween (TBST; containing 0.1% Tween-20). The membranes were washed with 1X TBST and incubated at room temperature for 1 h with HRP-conjugated anti-rabbit IgG secondary antibodies (Cell Signaling Technology, Inc.; cat. no. 7074; 1:10,000) diluted in 1X TBST. Finally, Protein bands were visualized using a High-Sig ECL Western Blotting kit (Tanon Science and Technology Co., Ltd.), and the band intensity value was subsequently measured using ImageJ software (v1.8.0; National Institutes of Health) with GAPDH serving as an internal control.

Statistical analysis

All the experiments, including RT-qPCR and western blotting, were repeated three times. All quantitative data were presented as the mean ± standard error and analyzed using SPSS software (version 22.0; IBM Corp) and GraphPad Prism 8 (GraphPad Software, Inc.). A paired-sample t-test was performed to compare the relative mRNA expression levels of the CFB gene between tumor and matched adjacent non-tumor tissues. A Wilcoxon rank sum test was used for comparisons of protein levels in two independent groups. P<0.05 was considered to indicate a statistically significant difference.

Results

Patient characteristics

The characteristics of patients with LUAD are presented in Table I. There was no significant difference in age, sex, ethnicity, residual tumor classification, ECOG score or Karnofsky performance score between the low-CFB and high-CFB groups. There was a higher percentage of former smokers and a lower percentage of non-smokers and current smokers in the low-CFB group compared with in the high-CFB group (P=0.021). Furthermore, patients in the low-CFB group had a higher stage (stage III and stage IV) compared with patients in the high-CFB group (P=0.010). Additionally, the violin plot indicated a negative association trend between CFB expression and stage (Fig. 1G). Furthermore, other clinical features showed no or an inconsistent association with CFB expression (Fig. 1A-F).

Table I.

Descriptive statistics stratified by CFB expression.

Variable[a]	CFB <3.64 (n=257)	CFB ≥3.64 (n=256)	P-value
Mean age ± SD, years	65.44±10.25	65.28±9.62	0.855
Sex, n (%)			0.143
Male	128 (49.81)	110 (42.97)
Female	129 (50.19)	146 (57.03)
Ethnicity, n (%)			0.568
Asian	2 (0.91)	5 (2.18)
White	191 (87.21)	196 (85.59)
Black or African American	25 (11.42)	28 (12.23)
American Indian or Alaska Native	1 (0.46)	0 (0.00)
Smoking history, n (%)			0.021
Non-smoker	30 (12.15)	45 (17.86)
Former smoker	166 (67.21)	139 (55.16)
Current smoker	51 (20.65)	68 (26.98)
Residual tumour classification, n (%)			0.716
R0	172 (96.09)	172 (94.51)
R1	5 (2.79)	8 (4.40)
R2	2 (1.12)	2 (1.10)
ECOG score (mean ± SD)	0.61±0.62	0.76±0.75	0.120
Karnofsky performance score (mean ± SD)	86.46±20.37	84.40±18.53	0.602
Stage, n (%)			0.010
Stage I	120 (48.00)	155 (60.78)
Stage II	62 (24.80)	59 (23.14)
Stage III	53 (21.20)	31 (12.16)
Stage IV	15 (6.00)	10 (3.92)

The continuous variables are presented as the mean ± SD, and the means for these variables in the two groups were compared using unpaired Students t-test. The categorical variables are presented as numbers (percentages of cases), and the prevalence of these variables was compared using Pearsons χ2 test. Due to the small sample size of the groups for some variables, the comparisons were performed using Pearsons χ2 test with Yates continuity correction and Fishers exact test. Some data for ethnicity, smoking history, residual tumour classification, ECOG score, Karnofsky performance score and stage were missing, so the numbers are less than the total number of samples. CFB, complement factor B; ECOG, Eastern Cooperative Oncology Group.

Figure 1.

CFB expression and its association with clinical features. Association between CFB expression and (A) age, (B) sex, (C) smoking history, (D) residual tumor classification, (E) ECOG score, (F) Karnofsky performance score, (G) stage, (H) overall survival and (I) disease-free survival. Low-CFB, CFB expression <3.64; high-CFB, CFB expression ≥3.64. CFB, complement factor B; ECOG, Eastern Cooperative Oncology Group.

CFB expression and clinical features

The associations between CFB expression and clinical features are shown in Table II. There were more former smokers in the low-CFB group compared with in the high-CFB group (P=0.026 for former smokers vs. non-smokers). Furthermore, there were more stage III patients in the low-CFB group compared with in the high-CFB group (P=0.002 for stage III vs. stage I). Other clinical features (age, sex, ethnicity, residual tumor classification, ECOG score and Karnofsky performance score) were not observed to be associated with CFB expression.

Table II.

Univariate logistic regression analysis of clinical features and CFB expression.

Variable	N1/N2[a]	OR (95% CI)	P-value
Age (years)		1.00 (0.98–1.02)	0.854
Sex
Male	128/110	Reference
Female	129/146	1.32 (0.93–1.86)	0.121
Ethnicity
Asian	2/5	Reference
White	191/196	–	–
Black or African American	25/28	–	–
American Indian or Alaska Native	1/0	–	–
Smoking history
Non-smoker	30/45	Reference
Former smoker	166/139	0.56 (0.33–0.93)	0.026
Current smoker	51/68	0.89 (0.49–1.60)	0.694
Residual tumour classification
R0	172/172	Reference
R1	5/8	1.60 (0.51–4.99)	0.418
R2	2/2	–	–
ECOG score		1.36 (0.92–2.01)	0.129
Karnofsky performance score		0.99 (0.97–1.02)	0.599
Stage
Stage I	120/155	Reference
Stage II	62/59	0.74 (0.48–1.13)	0.163
Stage III	53/31	0.45 (0.27–0.75)	0.002
Stage IV	15/10	0.52 (0.22–1.19)	0.120

Data are presented as the number of samples, and the regression results are presented as OR (95% CI) and P-values. Some data for ethnicity, smoking history, residual tumour classification, ECOG score, Karnofsky performance score and stage were missing, so the numbers are less than the total number of samples.

N1, number of samples in the CFB <3.64 group; N2, number of samples in the CFB ≥3.64 group. 95% CI, 95% confidence interval; CFB, complement factor B; ECOG, Eastern Cooperative Oncology Group; OR, odds ratio.

CFB expression, clinical features and patient survival

Data on CFB expression, clinical features and patient survival are presented in Tables III and IV. High CFB expression was significantly associated with overall survival and disease-free survival according to both the continuous (P=0.045 for overall survival; P=0.006 for disease-free survival) and categorical model (P=0.005 for overall survival; P=0.002 for disease-free survival). The Kaplan-Meier curves suggested that the low-CFB group had a significantly decreased overall survival (Fig. 1H) and disease-free survival (Figs. 1I and S1) compared with the high-CFB group. In the sub-group analysis of different smokers, there was no significant association between CFB expression and overall survival or disease-free survival (Fig. S2). Furthermore, the residual tumor classification was significantly associated with overall survival (P<0.001 for R1 vs. R0). The Karnofsky performance score was significantly associated with overall survival (P=0.031) and disease-free survival (P=0.013). Additionally, high stage was significantly associated with overall survival (P<0.001 for stage II vs. stage I; P<0.001 for stage III vs. stage I; P<0.001 for stage IV vs. stage I) and disease-free survival (P<0.001 for stage II vs. stage I; P<0.001 for stage III vs. stage I; P=0.034 for stage IV vs. stage I). These results indicated that the residual tumor classification, Karnofsky performance score and cancer stage were associated with overall survival, and that Karnofsky performance score and stage were associated with disease-free survival.

Table III.

Univariate Cox proportional hazards regression analysis of CFB expression, clinical features and overall survival.

Variable	N	Median survival time (Q1-Q3), months	HR (95% CI)	P-value
CFB (continuous)	504		0.77 (0.60–0.99)	0.045
CFB (categorical)
CFB <3.64	250	20.60 (11.55–35.12)	Reference
CFB ≥3.64	254	21.95 (14.67–38.68)	0.66 (0.49–0.88)	0.005
Age (years)	494		1.01 (0.99–1.02)	0.324
Sex
Male	235	20.66 (10.58–36.13)	Reference
Female	269	21.62 (14.72–37.12)	0.96 (0.72–1.29)	0.808
Ethnicity
Asian	7	18.66 (7.66–24.21)	Reference
White	387	20.83 (13.63–35.35)	NA	NA
Black or African American	53	22.01 (16.85–37.29)	NA	NA
American Indian or Alaska Native	1	15.34 (15.34–15.34)	NA	NA
Smoking history
Non-smoker	72	23.66 (13.84–36.09)	Reference
Former smoker	300	20.02 (13.44–35.26)	0.92 (0.60–1.41)	0.695
Current smoker	118	22.08 (14.60–36.73)	0.84 (0.51–1.37)	0.476
Residual tumour classification
R0	336	23.14 (14.59–39.77)	Reference
R1	13	21.98 (9.56–29.99)	3.33 (1.73–6.40)	<0.001
R2	3	8.02 (6.00–11.52)	NA	NA
ECOG score	213		1.33 (0.97–1.82)	0.076
Karnofsky performance score	97		0.99 (0.97–1.00)	0.031
Stage
Stage I	271	22.80 (15.64–41.85)	Reference
Stage II	119	22.24 (11.11–32.75)	2.53 (1.76–3.65)	<0.001
Stage III	81	15.37 (8.80–28.88)	3.61 (2.46–5.29)	<0.001
Stage IV	25	21.55 (15.01–31.11)	4.07 (2.33–7.11)	<0.001

Data are presented as the median (Q1-Q3 quantiles) survival time, and the regression results are presented as HR (95% CI) and P-values. Some data for ethnicity, smoking history, residual tumour classification, ECOG score, Karnofsky performance score and stage were missing, so the numbers are less than the total number of samples. N, number of samples. 95% CI, 95% confidence interval; CFB, complement factor B; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; NA, not available.

Table IV.

Univariate Cox proportional hazards regression analysis of CFB expression, clinical features and disease-free survival.

Variable	N	Median survival time (Q1-Q3), months	HR (95% CI)	P-value
CFB (continuous)	428		0.70 (0.55–0.90)	0.006
CFB (categorical)
CFB <3.64 FPKM	207	16.79 (8.15–27.76)	Reference
CFB ≥3.64 FPKM	221	19.74 (13.76–31.18)	0.62 (0.47–0.84)	0.002
Age (years)	418		1.01 (0.99–1.02)	0.317
Sex
Male	193	17.61 (8.54–29.01)	Reference
Female	235	18.17 (13.27–28.70)	0.97 (0.72–1.29)	0.822
Ethnicity
Asian	7	14.49 (6.00–22.88)	Reference
White	329	17.44 (10.09–27.23)	0.62 (0.23–1.69)	0.354
Black or African American	48	20.16 (15.66–33.78)	0.45 (0.15–1.35)	0.156
American Indian or Alaska Native	1	9.26 (9.26–9.26)	NA	NA
Smoking history
Non-smoker	60	17.92 (13.12–31.47)	Reference
Former smoker	259	16.92 (9.54–26.25)	1.07 (0.70–1.64)	0.758
Current smoker	97	20.70 (13.40–30.98)	0.74 (0.45–1.24)	0.253
Residual tumour classification
R0	282	18.99 (12.41–33.02)	Reference
R1	11	14.72 (5.18–18.84)	NA	NA
ECOG score	181		1.03 (0.73–1.45)	0.880
Karnofsky performance score	79		0.98 (0.97–1.00)	0.013
Stage
Stage I	246	19.22 (13.63–31.82)	Reference
Stage II	102	16.98 (8.21–26.88)	2.16 (1.54–3.02)	<0.001
Stage III	59	13.76 (7.23–24.95)	2.23 (1.47–3.37)	<0.001
Stage IV	14	19.88 (14.02–25.67)	2.22 (1.06–4.62)	0.034

Data are presented as the median (Q1-Q3 quantiles) survival time, and the regression results are presented as HR (95% CI) and P-values. Some data for ethnicity, smoking history, residual tumour classification, ECOG score, Karnofsky performance score and stage were missing, so the numbers are less than the total number of samples. N, number of samples. 95% CI, 95% confidence interval; CFB, complement factor B; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; NA, not available.

The results of the multivariate Cox regression analysis demonstrated that high CFB expression was associated with increased overall survival time (Table V) and disease-free survival time (Table VI), according to both the continuous model [hazard ratio (HR), 0.48; 95% confidence interval (95% CI), 0.25–0.93; P=0.029 for overall survival; HR, 0.29; 95% CI, 0.15–0.59; P=0.001 for disease-free survival] and the categorical model (HR, 0.46; 95% CI, 0.22–0.93; P=0.031 for overall survival; HR, 0.25; 95% CI, 0.12–0.55; P=0.001 for disease-free survival) after adjusting for corresponding covariates (residual tumour classification, Karnofsky performance score and stage).

Table V.

Multivariate Cox proportional hazards regression analysis of CFB expression and overall survival.

Variable	N	Median survival time (Q1-Q3), months	HR (95% CI)	P-value
CFB (continuous)	504		0.48 (0.25–0.93)	0.029
CFB (categorical)
CFB <3.64 FPKM	250	20.60 (11.55–35.12)	Reference
CFB ≥3.64 FPKM	254	21.95 (14.67–38.68)	0.46 (0.22–0.93)	0.031

Data are presented as the median (Q1-Q3 quantiles) survival time, and the regression results are presented as HR (95% CI) and adjusted P-values. N, number of samples. P-values were adjusted for residual tumour classification, Karnofsky performance score and stage. 95% CI, 95% confidence interval; CFB, complement factor B; HR, hazard ratio.

Table VI.

Multivariate Cox proportional hazards regression analysis of CFB expression and disease-free survival.

Variable	N	Median survival time (Q1-Q3), months	HR (95% CI)	P-value
CFB (continuous)	428		0.29 (0.15–0.59)	0.001
CFB (categorical)
CFB <3.64 FPKM	207	16.79 (8.15–27.76)	Reference
CFB ≥3.64 FPKM	221	19.74 (13.76–31.18)	0.25 (0.12–0.55)	0.001

Data are presented as the median (Q1-Q3 quantiles) survival time, and the regression results are presented as HR (95% CI) and adjusted P-values. N, number of samples. P-values were adjusted for Karnofsky performance score and stage. 95% CI, 95% confidence interval; CFB, complement factor B; HR, hazard ratio.

CFB expression and pathway impairment

Significantly enriched KEGG pathways of the three groups are presented in Fig. 2. There were four deregulated KEGG pathways for the high-CFB vs. control, including ‘steroid biosynthesis’, ‘nicotinate and nicotinamide metabolism’, ‘glutamatergic synapse’ and ‘autophagy’, whereas only ‘nicotinate and nicotinamide metabolism’ was upregulated in the low-CFB vs. control group. Furthermore, there were 17 downregulated pathways in the high-CFB vs. low-CFB group. These findings indicated that there was a marked difference in the pathway deregulation of individuals with different CFB expression states. Enrichment analysis of Gene Ontology biological processes (Table SI) and hallmark pathways (Table SII) was also conducted. The results demonstrated that CFB was associated with multiple signaling pathways related to cell proliferation and tumorigenesis, such as ‘positive regulation of G2/M transition of mitotic cell cycle’, ‘TNFA signaling via NF-κB’ and ‘Wnt/β-catenin signaling’.

Figure 2.

Gene set enrichment analysis results of Kyoto Encyclopedia of Genes and Genomes pathways in three groups. A red box indicates that the pathway is upregulated and a blue box indicates that the pathway is downregulated. A yellow circle indicates that the pathway is significantly enriched. CFB, complement factor B.

CFB expression in human lung tissues

It was identified that both the relative mRNA and protein expression levels of CFB were downregulated in tumor tissues compared with in matched adjacent non-tumor tissues (P<0.05; Fig. 3).

Figure 3.

Identification of downregulated CFB expression in 3 paired lung adenocarcinoma samples. (A) Relative mRNA expression levels of CFB were decreased in tumor tissue as detected by reverse transcription-quantitative PCR (n=6). (B) Relative downregulated protein expression levels of CFB in lung adenocarcinoma samples as detected by western blotting (n=6). (C) CFB protein expression was decreased in tumor tissues compared with in matched adjacent non-tumor tissues. Both the relative mRNA and protein expression levels of CFB in each patient's lung tumor and adjacent non-tumor tissue were normalized against GAPDH values as determined in three replicate experiments. *P<0.05; **P<0.01; ****P<0.0001. C, lung adenocarcinoma tissue; P, matched adjacent non-tumor tissue; CFB, complement factor B.

Discussion

Increasing attention has been paid to precise individualized medicine in cancer treatment, which is facilitated by the exploration and identification of biomarkers (24). The treatment of LUAD is associated with complex factors and comprises multiple targeted therapies. Therefore, a greater understanding of molecular biomarkers will help to improve the diagnosis and prognosis of human LUAD.

CFB is a key soluble component in the alternative pathway (8). CFB is cleaved by complement factor D into fragments Ba and Bb. The complement factor D and activated component Bb are serine proteinases. Furthermore, Bb remains attached to C3b and forms the alternative pathway convertase, C3bBb, which is a key enzyme involved in the activation of the alternative pathway (10). It has been demonstrated that low CFB expression is associated with decreased C3bBb expression and accelerates the degradation of the MAC, which leads to inhibition of the alternative pathway of complement activation, thereby significantly reducing the activation efficiency of the complement system in the whole body (8). During the regulation of the complex tumor microenvironment, complement proteins serve a dual role in the tumor microenvironment, which will eventually affect tumor progression (8,9). The present study demonstrated that there was no difference in age, sex, ethnicity, residual tumor classification, ECOG score or Karnofsky performance score between the low-CFB and high-CFB groups. Furthermore, there were more former smokers and stage III patients in the high-CFB group compared with in the low-CFB group. Notably, in both univariate and multivariate analysis, high CFB expression was associated with overall survival and disease-free survival, according to both the continuous and categorical models.

CFB is a serum protein that is not only produced by the liver (25–27) but can also be synthesized by the choroid, retinal pigment epithelial cells and neural retina (28). Furthermore, the CFB protein has been found in ocular drusen and Bruch's membrane (26), and was observed to be potently upregulated in patients with ulcerative colitis and Crohn's disease (29). Previous data have demonstrated that CFB may be highly expressed in association with inflammatory bowel disease, in addition to having a possible key role in systemic complement activation (30). A previous study has reported that polymorphisms in CFB are associated with the risk of age-related macular degeneration (31). Furthermore, the study of CFB non-synonymous variants may improve the understanding of chronic hepatitis B etiology (31). The present study first used online public data analysis to demonstrate that low CFB expression was associated with decreased overall and disease-free survival in patients with LUAD. Subsequently, the prognostic value of CFB expression in lung tumor tissues was further investigated.

Recent studies have reported that CFB is associated with the prognosis of different types of cancer (32–34). A previous study has demonstrated that CFB is not only identified in all crypts in the colonic mucosa, but is also expressed in adenomas and carcinomas (16). The expression levels of CFB have been revealed to be increased in the plasma of patients with pancreatic cancer, for which CFB may be a novel biomarker (13). A previous analysis of CFB genetic alterations and mRNA expression in breast cancer has been performed using public TCGA invasive breast carcinoma sample data (32). CFB exhibits a strong association with molecular subtypes of breast cancer, particularly the LA subtype (17). Furthermore, CFB is a potential biomarker in pancreatic ductal adenocarcinoma (13) and pancreatic cancer (32) with diagnostic significance, and is also associated with a high likelihood of relapse-free survival (17). To the best of our knowledge, there has been no study addressing the prognostic significance of CFB in patients with LUAD. In the present study, multivariate Cox regression analysis demonstrated that high CFB expression was associated with increased overall and disease-free survival according to a continuous model after adjusting for corresponding covariates. Consistent with these results, both RT-qPCR and western blotting indicated that the relative expression levels of the CFB gene in lung tumor tissues were decreased compared with those in adjacent non-tumor tissues.

There are several limitations to the present study. First, the association between CFB and the occurrence and progression of LUAD, as well as the specific pathogenesis, remains unknown. The current results can to some extent explain an association between CFB and LUAD; however, prospective randomized controlled studies are required to confirm these promising results. Second, the current data concerning drug therapy and prognosis of LUAD are not widely available. Given the limitations of the present study, further large-sample and in-depth studies are required to confirm these results.

In conclusion, the present study demonstrated that CFB expression was an independent predictor of overall and disease-free survival in patients with LUAD. The current results may therefore provide helpful information for the early diagnosis and drug development of LUAD.