Open Access

CFHR1 is a potentially downregulated gene in lung adenocarcinoma

  • Authors:
    • Geting Wu
    • Yuanliang Yan
    • Xiang Wang
    • Xinxin Ren
    • Xi Chen
    • Shuangshuang Zeng
    • Jie Wei
    • Long Qian
    • Xue Yang
    • Chunlin Ou
    • Wei Lin
    • Zhicheng Gong
    • Jianhua Zhou
    • Zhijie Xu
  • View Affiliations

  • Published online on: September 3, 2019     https://doi.org/10.3892/mmr.2019.10644
  • Pages: 3642-3648
  • Copyright: © Wu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

There is increasing evidence that human complement factor H‑related protein 1 (CFHR1) plays a crucial role in the development of malignant diseases. However, few studies have identified the roles of CFHR1 in the occurrence and prognosis of lung adenocarcinoma (LADC). In the present study, comprehensive bioinformatic analyses of data obtained from the Oncomine platform, UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA) demonstrated that CFHR1 expression is significantly reduced in both LADC tissues and cancer cells. The patients presenting with downregulation of CFHR1 had significantly lower overall survival (OS) and post progression survival (PPS) times. Through analysis of the datasets from Gene Expression Omnibus database, we found that the compound actinomycin D promoted CFHR1 expression, further displaying the cytotoxic effect in the LADC cell line A549. In addition, the expression level of CFHR1 in the cisplatin‑resistant LADC cell line CDDP‑R (derived from H460) was also significantly reduced. Our research demonstrated that low levels of CFHR1 are specifically found in LADC samples, and CFHR1 could serve as a potential therapeutic target for this subset of lung cancers. Determination of the detailed roles of CFHR1 in LADC biology could provide insightful information for further investigations.

Introduction

Lung cancer, as is well known, is the most common cause of cancer-related deaths all over the world. As a malignant tumor, lung cancer kills countless patients worldwide (1,2). Every year, 1.8 million individuals are diagnosed with lung cancer, and 1.6 million patients die as a result of the disease. In addition, the incidence rate of lung adenocarcinoma (LADC), the most common histologic subtype of lung cancer, has continued to increase in men and women (3). However, due to the delay in diagnosis, the treatment results for LADC remain unsatisfactory; 5-year survival rates vary from 4 to 17% depending on the stage and on regional differences (4). Moreover, the treatment and prognosis of LADC are still public health issues characterized by no progress in advanced diagnosis and treatment (1,5,6). Therefore, there is an urgent need to discover new molecular biomarkers for the early diagnosis and treatment of LADC.

The human complement factor H (CFH)-related protein (CFHR) family is composed of five members: CFHR1, CFHR2, CFHR3, CFHR4 and CFHR5, and each member of this group can bind to the central complement component C3b. Mutations, genetic deletions, duplications or rearrangements in the individual CFHR genes are associated with many diseases, including cancer (7,8). Recent research shows that CFH-related genes (CFHR1-5) are associated with age-related macular degeneration (AMD) (9). At the same time, large international genome-wide association studies have shown that deletion of CFHR1 is associated with a reduced risk of developing IgA nephropathy (10). Fratelli et al (11) found that the germinal homozygous deletion of the CFHR1 gene could act as a promising risk factor for acute myelogenous leukemia. Another report also indicated that CFHR1 was homozygously deleted in the cisplatin-resistance glioma cell lines U251 and CP2 (12). However, few studies have reported the relationship between the CFHR family and LADC, and the influence of CFHR1 on the pathological process of LADC remains unexplored.

The aim of the present study was to evaluate the function of CFHR1 and its relationship with clinical treatment and prognosis in human LADC. Our data indicate that CFHR1 functions as a potential tumor suppressor in LADC tissues and cell lines. Kaplan-Meier analysis suggests that CFHR1 is an independent prognostic factor for LADC patients.

Materials and methods

Data collection and reanalysis using different bioinformatic methods

The expression levels of CFHR1 in LADC tissues and cell lines are evidenced by a variety of bioinformatic network resources (Table S1).

Oncomine, a bioinformatics project that analyzes cancer transcriptome data to make it available to the biomedical research community, contains 65 gene expression datasets (13). UALCAN provides a silicon-based platform for validation of target genes and identification of tumor subpopulation-specific candidate biomarkers (14). GEPIA is a web server for gene expression profiling and interactive analyses in human cancers. It provides several key interactive customization features, such as differential expression analysis, patient survival analysis, and similarity gene detection (15). Through these public bioinformatics platforms, we clearly defined the expression profile of CFHR1 in human LADC tissues and cell lines.

Kaplan-Meier plotter is a web-based tool for assessing the impact of 54,675 genes on the survival of 10,461 cancer samples and quickly determining the prognosis of the diseases (16), such as overall survival (OS) and post progressive survival (PPS) (17).

We downloaded the therapeutic transcriptome microarray dataset from the Gene Expression Omnibus (GEO) database under the login numbers GSE6400 (18) and GSE21656 (19). Then, we re-analyzed the original data in these datasets to further understand the impact of CFHR1 expression on the chemotherapy response of LADC patients.

The web resource cBioPortal of cancer genomics provides a desired strategy for the exploration, visualization and analysis of multidimensional cancer genomics data (20). We used it to screen the coexpressed genes of CFHR1 in LADC tissues. Protein-protein interaction (PPI) networks of these coexpressed genes were constructed using the STRING database (21). Then, Cytoscape software (version 3.0) was used to perform detailed visualization (22).

Next, we used the web-based Gene SeT AnaLysis Toolkit (WebGestalt) to perform Gene Ontology (GO) enrichment analysis (23). Meanwhile, the Pathview algorithm was used to analyze the relevant KEGG pathways (24).

Statistical analyses

The Student's t-test and the statistical software package SPSS (SPSS12.0, IBM Analytics) were used to analyze the differentially expressed mRNAs between cancer tissues and noncancer tissues. Meanwhile, the relationship between CFHR1 expression and clinicopathological features of LADC patients was analyzed using a Chi-square test. The variants with statistical significance in single-factor analysis were further examined by multiple-factor analysis using the COX regression model. Pearson correlation coefficient was used to evaluate the correlation between genes. If P≤0.05, then the result was considered statistically significant.

Results

CFHR1 is downregulated in LADC tissues

To detect changes in CFHR1 expression between LADC and adjacent nontumor tissues, the expression profiles of CFHR1 were analyzed using three independent bioinformatic databases. First, as shown in Fig. 1A, it was found that CFHR1 transcription levels were significantly reduced in tumor tissues based on two microarray datasets from the Oncomine platform (25,26). Furthermore, the downregulation of CFHR1 transcription was confirmed in LADC tissues by using the UALCAN tool (Fig. 1B). Finally, to further confirm this result, the expression of CFHR1 was re-analyzed in the GEPIA database and the same above-mentioned trend was verified (Fig. 1C). This observation confirmed that CFHR1 is downregulated in LADC tissues.

CFHR1 as a presumed prognostic factor for adenocarcinoma

To date, almost no literature has reported the relationship between the expression of CFHR1 and the clinical prognosis of human LADC. Thus, we conducted a clinical follow-up survey with the most commonly used monitoring indicators, OS and PPS (17,27). By using the Kaplan-Meier plotter platform, it was found that patients with downregulated CFHR1 expression had a significantly shorter OS (P<0.01) (Fig. 2A). Moreover, patients with high levels of CFHR1 tended to have a longer PPS, although this difference was not significant (P>0.05) (Fig. 2B). The reasons may be due to the sample size, and future evaluations with larger datasets are warranted. Furthermore, associations between CFHR1 expression and KRAS mutation or T stage were observed to be statistically significant (P=0.013 and P=0.002, respectively) (Table I). No correlation was observed between CFHR1 expression and sex, age, race, EGFR mutation, EML4-ALK translocation, lymph node metastasis, distant metastasis, pathologic stage, smoking history and Karnofsky performance score (Table I). The multiple-factor analysis using the COX regression model indicated that pathological T stage was independently associated with CFHR1 transcription levels in LUAD samples (Table II). In summary, decreased CFHR1 expression in patients with LADC is likely to be a valuable prognostic factor.

Table I.

Single factor clinical data analysis related to CFHR1.

Table I.

Single factor clinical data analysis related to CFHR1.

SourceNo.Mean ± SDP-value
Sex 0.350
  Male2070.959±1.47
  Female2480.830±1.46
Kras_mutation 0.013
  No341.22±1.84
  Yes140.341±0.427
EGFR_mutation 0.301
  No1710.842±1.18
  Yes641.09±1.78
EML4_ALK_translocation 0.773
  No1830.888±1.38
  Yes230.978±1.72
Pathologic_T 0.002
  T1/T1a/T1b1400.979±1.66
  T2/T2a/T2b2540.814±1.28
  T3410.603±0.986
  T4181.12±1.47
  TX27.69±0.868
Race 0.802
  Caucasian3550.907±1.43
  Asian70.544±1.01
  Black or African-250.882±1.63
  American
Pathologic_N 0.801
  N02900.877±1.48
  N1830.838±1.16
  N2700.990±1.63
  N320
  NX91.22±2.23
Pathologic_M 0.560
  M03110.886±1.50
  M1/M1a/M1b220.936±1.61
  MX1180.915±1.38
Pathologic_stage 0.669
  Stage I/IA/IB2460.908±1.53
  Stage IIA/IIB1060.753±1.13
  Stage IIIA/IIIB791.02±1.63
  Stage IV230.895±1.58
Karnofsky performance score 0.394
  0–70130.698±0.692
  80191.10±1.67
  90190.395±0.835
  100290.637±1.41
Age (years) 0.471
  40–601160.858±1.35
  60–802920.911±1.50
  >80270.562±0.637

[i] CFHR1, human complement factor H-related protein 1. The clinical characteristics of the patients were downloaded from the dataset (TCGA Provisional) in cBioPortal and have not been published to date. P-values denoted in bold print are significant.

Table II.

Clinical multivariate data related to CFHR1.

Table II.

Clinical multivariate data related to CFHR1.

SourceType III sum of squaresdfMean squareFP-value
Kras_mutation_found   4.62714.6272.5130.120
Pathologic_T36.86749.2175.0050.002

[i] CFHR1, human complement factor H-related protein 1. P-value denoted in bold print is significant.

Role of CFHR1 in the treatment of adenocarcinoma

Next, from the GEO database, we screened two microarray datasets related to chemotherapy to further determine the effect of CFHR1 in the treatment of LADC patients. From the data of GSE6400 (18), it was found that treatment with the anticancer agent actinomycin D obviously upregulated the expression of CFHR1, further exerting this anti-proliferative activity in cultured A549 LADC cells (P=0.017) (Fig. 3A). Meanwhile, data from GSE21656 (19) indicated that the expression of CFHR1 in a cisplatin-resistant LADC cell line (CDDP-R) was significantly downregulated when compared with the parental cell line H460 (P=0.03) (Fig. 3B). These findings showed that changes in CFHR1 expression levels may be involved in the therapeutic response to cancer.

Network analysis of coexpressed genes of CFHR1

To further understand the biological function of CFHR1, we performed functional enrichment annotation analysis of its coexpressed genes. We downloaded the coexpressed genes of CFHR1 from the cBioPortal database and screened 243 coexpressed genes with criteria of P≤0.05 and |LogFC| ≥0.7 (Table SII). Then, the PPI network of 243 differentially coexpressed genes was performed by two frequently used algorithms, STRING and Cytoscape (Fig. 4A). At the same time, the Pathview database was used to analyze the KEGG pathways (Table SIII), and it was found that the most significant pathway was ribosome (Fig. 4B). Finally, to further illuminate the function among these 243 screened genes, WebGestalt was used to conduct the GO annotations and to identify the main molecular function (protein binding), biological process (response to stimulus and biological regulation) and cellular component (membrane) related to CFHR1 biology (Fig. 4C).

Discussion

The aim of the present study was to understand the potential of human complement factor H-related protein 1 (CFHR1) in the development and treatment of lung adenocarcinoma (LADC). The present study is the first to use multiple public datasets to analyze the expression of CFHR1 in LADC tissues. At the same time, the coexpressed genes of CFHR1 were analyzed and several possible signaling pathways were identified that could determine its biological significance in cancer development. Using the Oncomine, UALCAN and GEPIA datasets, it was demonstrated that CFHR1 is significantly downregulated in LADC tissues. Moreover, through statistical analysis of clinical data from TCGA, it was found that the expression level of CFHR1 was closely related to KRAS mutation and pathological T stage in LADC patients.

CFHR1 is a complement modulator that regulates complement by blocking complement C5 convertase activity and interfering with C5b surface binding (28). In autoimmune atypical hemolytic uremic syndrome (aHUS), CFH is blocked by FH autoantibodies, and 90% of patients carry homozygous deletions of CFHR1 (29). However, heterozygous CFHR1/CFH hybrid genes were also identified in 4.5% of patients with aHUS. These genomic rearrangements among CFH and CFHR have been proven to be associated with a high risk of posttransplant recurrence and poor clinical prognosis (30). In addition, Guo et al (31) found that abnormally expressed CFHR1 could act as a promising predictive biomarker for cervical squamous cell carcinoma. Using an off-site matrix-based electrochemistry platform, Arya and Estrela further investigated CFHR1 as a bladder cancer protein marker (32). CFHR1 gene polymorphisms also showed stronger associations with event-free survival in patients with follicular lymphoma (33). Although several studies have indicated the roles of CFHR1 in the pathological process of human diseases, including cancers, no studies have revealed the functions of CFHR1 in LADC. In the present study, we demonstrated that CFHR1 plays a potential role in tumor inhibition in LADC samples. In addition, it was also demonstrated that high expression of CFHR1 is significantly associated with prolonged clinical OS and PPS in LADC patients. This provides an idea for further comprehensive exploration of the molecular mechanism of CFHR1 as a promising therapeutic biomarker in LADC.

In the present study, the exact interaction between CFHR1 and its coexpressed genes was not found; however, the PPI that was constructed benefits the identification of the function of CFHR1 to some extent. Jullien et al (34) discovered that CFHR1 is connected with a decreased level of glomerular immune deposits. Moreover, interleukin-6 (IL-6), located in the PPI network (Fig. 4A), is considered as a cytokine that essentially functions in immunoregulation via a signal transducer and activator of transcription 3 (STAT3) -dependent manner (35). Therefore, a phenomenon may exist in which CFHR1 controls the secretion of IL-6 to influence the immunoregulation of human cancer cells. In addition, through the functional enrichment annotation analysis, the main functional pathway of the coexpressed genes of CFHR1 has been confirmed to be the ribosome pathway. Previous research has demonstrated that the ribosome signaling pathway is significantly related to the microenvironment and metabolic changes of cancer cells (36,37). However, according to the published literature, no relevant studies have illuminated the detailed function and mechanism of CFHR1 in the pathway modulation. Therefore, further studies are needed to clarify the roles of CFHR1 in these KEGG pathways.

Overall, our results suggest that CFHR1 is a candidate tumor suppressor in human LADC disease. The public database-based re-analysis methods also provide a novel research strategy for screening potential biomarkers related to the pathogenesis of malignant human diseases.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

The study was supported by the Natural Science Foundation of Hunan Province (grant no. 2019JJ50932), the National Natural Science Foundation of China (grant nos. 81803035, 81703036 and 81572946), the China Postdoctoral Science Foundation (grant no. 2017M610510), the Open-End Fund for the Valuable and Precision Instruments of Central South University (grant no. CSUZC201836), the Youth Fund of Xiangya Hospital (grant no. 2017Q17) and Postdoctoral Science Foundation of Central South University (grant no. 185702).

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Authors' contributions

GW, YY, JZ and ZX concieved and designed the study. GW, XW, XR, XC, SZ, JW, LQ, XY, CO, WL and ZG acquired and interpreted the data. GW, YY, JZ and ZX drafted the manuscript. YY and ZX revised the manuscript. All authors read and approved the final manuscript, and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author information

Dr Zhijie Xu is now a Postdoctoral Fellow at the Department of Pharmacy of Xiangya Hospital, Central South University, Changsha, Hunan.

References

1 

Hirsch FR, Scagliotti GV, Mulshine JL, Kwon R, Curran WJ Jr, Wu YL and Paz-Ares L: Lung cancer: Current therapies and new targeted treatments. Lancet. 389:299–311. 2017. View Article : Google Scholar : PubMed/NCBI

2 

Wei J, Yan Y, Chen X, Qian L, Zeng S, Li Z, Dai S, Gong Z and Xu Z: The roles of plant-derived Triptolide on non-small cell lung cancer. Oncol Res. 27:849–858. 2019. View Article : Google Scholar : PubMed/NCBI

3 

Ulahannan D, Khalifa J, Faivre-Finn C and Lee SM: Emerging treatment paradigms for brain metastasis in non-small-cell lung cancer: An overview of the current landscape and challenges ahead. Ann Oncol. 28:2923–2931. 2017. View Article : Google Scholar : PubMed/NCBI

4 

Zhou S, Yan Y, Chen X, Wang X, Zeng S, Qian L, Wei J, Yang X, Zhou Y, Gong Z and Xu Z: Roles of highly expressed PAICS in lung adenocarcinoma. Gene. 692:1–8. 2019. View Article : Google Scholar : PubMed/NCBI

5 

Zhang R, Sun S, Ji F, Liu C, Lin H, Xie L, Yang H, Tang W, Zhou Y, Xu J and Li P: CNTN-1 enhances chemoresistance in human lung adenocarcinoma through induction of epithelial-mesenchymal transition by targeting the PI3K/Akt pathway. Cell Physiol Biochem. 43:465–480. 2017. View Article : Google Scholar : PubMed/NCBI

6 

Yan Y, Su W, Zeng S, Qian L, Chen X, Wei J, Chen N, Gong Z and Xu Z: Effect and mechanism of tanshinone i on the radiosensitivity of lung cancer cells. Mol Pharm. 15:4843–4853. 2018. View Article : Google Scholar : PubMed/NCBI

7 

Skerka C, Chen Q, Fremeaux-Bacchi V and Roumenina LT: Complement factor H related proteins (CFHRs). Mol Immunol. 56:170–180. 2013. View Article : Google Scholar : PubMed/NCBI

8 

Rogers LM, Mott SL, Smith BJ, Link BK, Sahin D and Weiner GJ: Complement-regulatory proteins CFHR1 and CFHR3 and patient response to anti-CD20 monoclonal antibody therapy. Clin Cancer Res. 23:954–961. 2017. View Article : Google Scholar : PubMed/NCBI

9 

Hageman GS, Hancox LS, Taiber AJ, Gehrs KM, Anderson DH, Johnson LV, Radeke MJ, Kavanagh D, Richards A, Atkinson J, et al: Extended haplotypes in the complement factor H (CFH) and CFH-related (CFHR) family of genes protect against age-related macular degeneration: Characterization, ethnic distribution and evolutionary implications. Ann Med. 38:592–604. 2006. View Article : Google Scholar :

10 

Jullien P, Laurent B, Claisse G, Masson I, Dinic M, Thibaudin D, Berthoux F, Alamartine E, Mariat C and Maillard N: Deletion Variants of CFHR1 and CFHR3 associate with mesangial immune deposits but not with progression of IgA nephropathy. J Am Soc Nephrol. 29:661–669. 2018. View Article : Google Scholar : PubMed/NCBI

11 

Fratelli M, Bolis M, Kurosaki M, Dori M, Guarnaccia V, Spinelli O, Alberti M, Valoti E, Pileggi S, Noris M, et al: Association of CFHR1 homozygous deletion with acute myelogenous leukemia in the European population. Leuk Lymphoma. 57:1234–1237. 2016. View Article : Google Scholar : PubMed/NCBI

12 

Cui L, Fu J, Pang JC, Qiu ZK, Liu XM, Chen FR, Shi HL, Ng HK and Chen ZP: Overexpression of IL-7 enhances cisplatin resistance in glioma. Cancer Biol Ther. 13:496–503. 2012. View Article : Google Scholar : PubMed/NCBI

13 

Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, et al: Oncomine 3.0: Genes, pathways and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 9:166–180. 2007. View Article : Google Scholar : PubMed/NCBI

14 

Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI

15 

Tang Z, Li C, Kang B, Gao G, Li C and Zhang Z: GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 45:W98–W102. 2017. View Article : Google Scholar : PubMed/NCBI

16 

Lánczky A, Nagy Á, Bottai G, Munkácsy G, Szabó A, Santarpia L and Győrffy B: miRpower: A web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 160:439–446. 2016. View Article : Google Scholar : PubMed/NCBI

17 

Yan Y, Xu Z, Hu X, Qian L, Li Z, Zhou Y, Dai S, Zeng S and Gong Z: SNCA is a functionally low-expressed gene in lung adenocarcinoma. Genes (Basel). 9:E162018. View Article : Google Scholar : PubMed/NCBI

18 

Wang Z, Lecane PS, Thiemann P, Fan Q, Cortez C, Ma X, Tonev D, Miles D, Naumovski L, Miller RA, et al: Synthesis and biologic properties of hydrophilic sapphyrins, a new class of tumor-selective inhibitors of gene expression. Mol Cancer. 6:92007. View Article : Google Scholar : PubMed/NCBI

19 

Sun Y, Zheng S, Torossian A, Speirs CK, Schleicher S, Giacalone NJ, Carbone DP, Zhao Z and Lu B: Role of insulin-like growth factor-1 signaling pathway in cisplatin-resistant lung cancer cells. Int J Radiat Oncol Biol Phys. 82:e563–e572. 2012. View Article : Google Scholar : PubMed/NCBI

20 

Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 6:pl12013. View Article : Google Scholar : PubMed/NCBI

21 

Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45:D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI

22 

Su G, Morris JH, Demchak B and Bader GD: Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics. 47:8.13.1–24. 2014. View Article : Google Scholar

23 

Wang J, Vasaikar S, Shi Z, Greer M and Zhang B: WebGestalt 2017: A more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45:W130–W137. 2017. View Article : Google Scholar : PubMed/NCBI

24 

Luo W, Pant G, Bhavnasi YK, Blanchard SG Jr and Brouwer C: Pathview Web: User friendly pathway visualization and data integration. Nucleic Acids Res. 45:W501–W508. 2017. View Article : Google Scholar : PubMed/NCBI

25 

Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, et al: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 98:13790–13795. 2001. View Article : Google Scholar : PubMed/NCBI

26 

Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG, et al: Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 8:816–824. 2002. View Article : Google Scholar : PubMed/NCBI

27 

Zhou H, Vallieres M, Bai HX, Su C, Tang H, Oldridge D, Zhang Z, Xiao B, Liao W, Tao Y, et al: MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol. 19:862–870. 2017. View Article : Google Scholar : PubMed/NCBI

28 

Hannan JP, Laskowski J, Thurman JM, Hageman GS and Holers VM: Mapping the complement factor H-related protein 1 (CFHR1):C3b/C3d interactions. PLoS One. 11:e01662002016. View Article : Google Scholar : PubMed/NCBI

29 

Trojnar E, Józsi M, Uray K, Csuka D, Szilágyi Á, Milosevic D, Stojanović VD, Spasojević B, Rusai K, Müller T, et al: Analysis of linear antibody epitopes on factor H and CFHR1 using sera of patients with autoimmune atypical hemolytic uremic syndrome. Front Immunol. 8:3022017. View Article : Google Scholar : PubMed/NCBI

30 

Valoti E, Alberti M, Tortajada A, Garcia-Fernandez J, Gastoldi S, Besso L, Bresin E, Remuzzi G, Rodriguez de Cordoba S and Noris M: A novel atypical hemolytic uremic syndrome-associated hybrid CFHR1/CFH gene encoding a fusion protein that antagonizes factor H-dependent complement regulation. J Am Soc Nephrol. 26:209–219. 2015. View Article : Google Scholar : PubMed/NCBI

31 

Guo X, Hao Y, Kamilijiang M, Hasimu A, Yuan J, Wu G, Reyimu H, Kadeer N and Abudula A: Potential predictive plasma biomarkers for cervical cancer by 2D-DIGE proteomics and ingenuity pathway analysis. Tumour Biol. 36:1711–1720. 2015. View Article : Google Scholar : PubMed/NCBI

32 

Arya SK and Estrela P: Electrochemical ELISA-based platform for bladder cancer protein biomarker detection in urine. Biosens Bioelectron. 117:620–627. 2018. View Article : Google Scholar : PubMed/NCBI

33 

Charbonneau B, Maurer MJ, Fredericksen ZS, Zent CS, Link BK, Novak AJ, Ansell SM, Weiner GJ, Wang AH, Witzig TE, et al: Germline variation in complement genes and event-free survival in follicular and diffuse large B-cell lymphoma. Am J Hematol. 87:880–885. 2012. View Article : Google Scholar : PubMed/NCBI

34 

Jullien P, Laurent B, Claisse G, Masson I, Dinic M, Thibaudin D, Berthoux F, Alamartine E, Mariat C and Maillard N: Deletion variants of CFHR1 and CFHR3 associate with mesangial immune deposits but not with progression of IgA nephropathy. J Am Soc Nephrol. 29:661–669. 2018. View Article : Google Scholar : PubMed/NCBI

35 

Lamano JB, Lamano JB, Li YD, DiDomenico JD, Choy W, Veliceasa D, Oyon DE, Fakurnejad S, Ampie L, Kesavabhotla K, et al: Glioblastoma-derived IL6 induces immunosuppressive peripheral myeloid cell PD-L1 and promotes tumor growth. Clin Cancer Res. 25:3643–3657. 2019. View Article : Google Scholar : PubMed/NCBI

36 

Bustelo XR and Dosil M: Ribosome biogenesis and cancer: basic and translational challenges. Curr Opin Genet Dev. 48:22–29. 2018. View Article : Google Scholar : PubMed/NCBI

37 

de la Cruz J, Gómez-Herreros F, Rodríguez-Galán O, Begley V, de la Cruz Muñoz-Centeno M and Chávez S: Feedback regulation of ribosome assembly. Curr Genet. 64:393–404. 2018. View Article : Google Scholar : PubMed/NCBI

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October 2019
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APA
Wu, G., Yan, Y., Wang, X., Ren, X., Chen, X., Zeng, S. ... Xu, Z. (2019). CFHR1 is a potentially downregulated gene in lung adenocarcinoma. Molecular Medicine Reports, 20, 3642-3648. https://doi.org/10.3892/mmr.2019.10644
MLA
Wu, G., Yan, Y., Wang, X., Ren, X., Chen, X., Zeng, S., Wei, J., Qian, L., Yang, X., Ou, C., Lin, W., Gong, Z., Zhou, J., Xu, Z."CFHR1 is a potentially downregulated gene in lung adenocarcinoma". Molecular Medicine Reports 20.4 (2019): 3642-3648.
Chicago
Wu, G., Yan, Y., Wang, X., Ren, X., Chen, X., Zeng, S., Wei, J., Qian, L., Yang, X., Ou, C., Lin, W., Gong, Z., Zhou, J., Xu, Z."CFHR1 is a potentially downregulated gene in lung adenocarcinoma". Molecular Medicine Reports 20, no. 4 (2019): 3642-3648. https://doi.org/10.3892/mmr.2019.10644