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BMC Cancer

, 19:67 | Cite as

Identifying primary site of lung-limited Cancer of unknown primary based on relative gene expression orderings

  • Mengyao Li
  • Hongdong LiEmail author
  • Guini Hong
  • Zhongjie Tang
  • Guanghao Liu
  • Xiaofang Lin
  • Mingzhang Lin
  • Lishuang Qi
  • Zheng GuoEmail author
Open Access
Research article
  • 205 Downloads
Part of the following topical collections:
  1. Genetics, genomics and epigenetics

Abstract

Background

Precise diagnosis of the tissue origin for metastatic cancer of unknown primary (CUP) is essential for deciding the treatment scheme to improve patients’ prognoses, since the treatment for the metastases is the same as their primary counterparts. The purpose of this study is to identify a robust gene signature that can predict the origin for CUPs.

Methods

The within-sample relative gene expression orderings (REOs) of gene pairs within individual samples, which are insensitive to experimental batch effects and data normalizations, were exploited for identifying the prediction signature.

Results

Using gene expression profiles of the lung-limited metastatic colorectal cancer (LmCRC), we firstly showed that the within-sample REOs in lung metastases of colorectal cancer (CRC) samples were concordant with the REOs in primary CRC samples rather than with the REOs in primary lung cancer. Based on this phenomenon, we selected five gene pairs with consistent REOs in 498 primary CRC and reversely consistent REOs in 509 lung cancer samples, which were used as a signature for predicting primary sites of metastatic CRC based on the majority voting rule. Applying the signature to 654 primary CRC and 204 primary lung cancer samples collected from multiple datasets, the prediction accuracy reached 99.36%. This signature was also applied to 24 LmCRC samples collected from three datasets produced by different laboratories and the accuracy reached 100%, suggesting that the within-sample REOs in the primary site could reveal the original tissue of metastatic cancers.

Conclusions

The result demonstrated that the signature based on within-sample REOs of five gene pairs could exactly and robustly identify the primary sites of CUPs.

Keywords

Cancer of unknown primary Relative gene expression orderings Metastasis Lung cancer Colorectal cancer 

Abbreviation

CRC

Colorectal cancer

CUP

Cancer of unknown primary

DEGs

Differentially expressed genes

FDR

False discovery rate

GEO

Gene Expression Omnibus

LmCRC

Lung-limited metastatic colorectal cancer

MTP

Molecular tumor profiling

PPI

Protein-protein interaction

REOs

Relative gene expression orderings

RMA

Robust multi-array average

Background

Despite the recent advances in pathology investigations and imaging technology, the primary site remains unknown for about 3% of all the malignancies [1, 2, 3]. By definition, the cancer of unknown primary (CUP) is metastatic at diagnosis with unknown primary site, which indicates a high malignant degree with poor prognosis [4]. In clinical, the therapeutic strategy for CUPs often needs the recognition of primary sites, as the current clinical guidelines recommend the same or similar treatment scheme for metastases as their primary counterparts [5, 6].

Some investigators have tried to use gene expression profiling to predict the primary tumor sites for CUPs [7, 8]. For example, Greco et al. made use of a 92-gene molecular tumor profiling (MTP) assay to predict the primary tissues of CUPs, which showed an accuracy of 75% [8]. However, risk scores of such signatures rely on the absolute expression levels of genes, which could be affected by experimental batch effects [9]. Thus, such signatures often fail in independent samples [10, 11, 12]. It has been reported that the within-sample relative gene expression orderings (REOs) of gene pairs within individual samples are insensitive to experimental batch effects [10, 11, 12], invariant to monotonic data transformation [13, 14], and robust against partial RNA degradation [15] as well as sampling site uncertainty within a tumor tissue [16]. Based on these unique advantages, some classifiers based on REO signatures, such as TSP [10] and K-TSP [11], were proposed to identify transcriptional signatures for discriminating cancer subtypes [17, 18, 19], which obviate the need of data normalization for the discovery and validation datasets and thus can be applied to the individual level [17, 20, 21].

Colorectal cancer (CRC) is the third most frequent cancer worldwide, which accounts for approximately 10% of the global cancer burden [22, 23]. About 25% of the CRC patients present with metastases at diagnosis [24], of which liver and lung are the most frequent metastasis sites [25]. It has been reported that the lung metastases of CRC share high genomic concordance with the primary CRC [23]. Thus, we could hypothesize that the gene expression patterns of lung metastases of CRC would be more similar to the primary tumor on the susceptible primary organ than the metastatic organ. Here, using samples from the lung-limited metastatic colorectal cancer (LmCRC), we validated this hypothesis by comparing the stable REOs in LmCRC with the stable within-sample REOs in primary CRC and primary lung cancer, respectively. Then, we extracted a signature consisting of five gene pairs from primary CRC and lung cancer, and showed that this signature could predict all the LmCRC samples into the CRC-like group.

Methods

Data source and data preprocessing

The gene expression data analyzed in this study were downloaded from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) [26]. Detailed information for each dataset was described in Table 1. Set1~Set3 denoted the gene expression profiles for primary CRC from three datasets respectively. Totally, there were 498 primary CRC samples. Set4~Set6 denoted the gene expression profiles for primary lung cancer from three datasets respectively. Totally, there were 509 primary lung cancer samples. The profiles were used to select the primary CRC and lung cancer characteristic REOs. Set7 denoted the gene expression profiles for LmCRC samples.
Table 1

The datasets analyzed in this study

Label

Dataset

Platform

Sample size

Ref (PMID)

Training sets

Primary CRC

 Set1

GSE21510

GPL570

123

21270110

 Set2

GSE14095

GPL570

189

21680303

 Set3

GSE41258

GPL96

186

19359472

Primary lung cancer

 Set4

GSE31210

GPL570

226

22080568

 Set5

GSE14814

GPL96

133

20823422

 Set6

GSE43580

GPL570

150

23966112

Validation sets

Primary CRC

 

GSE2138

GPL96

20

16247484

 

GSE7208

GPL96

59

17638901

 

GSE39582

GPL570

566

23700391

 

GSE5364

GPL96

9

18636107

 

GSE19249

GPL571

15

20522636

Primary lung cancer

 

GSE19804

GPL570

60

20802022

 

GSE33532

GPL570

80

Michael Meister, et al.

 

GSE18842

GPL570

46

20878980

 

GSE5364

GPL96

18

18636107

 

GSE19249

GPL571

7

20522636

Lung metastases of CRC

 Set7

GSE41258

GPL96

20

19359472

 

GSE5851

GPL571

3

17664471

 

GSE28702

GPL570

1

22095227

The raw data (.CEL files) for each dataset were downloaded from GEO and normalized by the robust multi-array average method (RMA) in the Bioconductor package [27, 28, 29] except GSE14095. Because the raw data were not provided, the normalized data provided by the authors were downloaded for GSE14095. The original platform annotation files obtained from GEO for each dataset were used to annotate the CloneIDs to GeneIDs.

Detection of stable REO gene pairs

For a dataset, if gene A had a higher expression level than gene B in more than 95% samples, the gene pair (A, B) was defined as a stable REO gene pair. A concordance score was used to evaluate the reproducibility of stable REO gene pairs identified from two independent datasets. If two lists of stable REO gene pairs overlapped k gene pairs, of which s gene pairs had the same REO patterns, the concordance score will be calculated as s/k × 100%. The cumulative binomial distribution model was used to evaluate the probability of observing a concordance score of s/k × 100% by chance as follows:
$$ P=1-\sum \limits_{i=0}^{s-1}\left({}_i^k\right){\left({P}_e\right)}^i{\left(1-{P}_e\right)}^{k-1} $$
(1)

where Pe was the probability of a gene pair having the concordant relationship in the two datasets by chance (here, Pe = 0.5).

Selection of predictive gene pairs as a candidate signature

If a stable REO gene pair (A, B) in class1 showed the reverse REO pattern (B, A) in class2, it was defined as a reversed gene pair between these two classes. Theoretically, according to the REOs of the reversed gene pairs, we could classify the samples in these two classes. The procedure for predictive signature selection was as follows:

Firstly, calculated the appearance frequency of each gene in reversed gene pairs.

Secondly, calculated the average rank difference score ΔavgR for each reversed gene pair, as described in formula (2):
$$ \Delta {avgR}_{ij}=\frac{\sum \limits_{n=1}^{N1}\left|{R}_{n,i}-{R}_{n,j}\right|+\sum \limits_{m=1}^{N2}\left|{R}_{m,i}-{R}_{m,j}\right|}{N1+N2} $$
(2)

Here N1 and N2 represented the number of profiles in class1 and class2, respectively. Rn,i, Rn,j, Rm,i, and Rm,j represented the rank of gene i or j in the n-th and m-th profile of class1 and class2 respectively.

Thirdly, sorted the genes according to the appearance frequencies in the reverse order and selected one reversed pair with the maximum ΔavgR score for each gene.

At last, the top n gene pairs were selected as the candidate predictive signature.

Anti-lung cancer drugs and protein-protein interaction data

The data of antitumor drugs and their target genes were collected from the DrugBank database (http://www.drugbank.ca/) [30], which contains 174 kinds of anticancer drugs approved by the U.S. Food and Drug Administration and 570 corresponding target genes. A total of 10 anti-lung cancer drugs and their 11 corresponding target genes were used in this study (Table 2).
Table 2

Anti-lung cancer drugs and their target genes

Drug ID

Drug

FDA

Target genes

DB00317

Gefitinib

approved

EGFR

DB00361

Vinorelbine

approved

TUBB

DB00642

Pemetrexed

approved

TYMS, ATIC, DHFR, GART

DB05390

INS 316

investigational

P2RY2

DB08865

Crizotinib

approved

ALK, MET

DB08916

Afatinib

approved

EGFR, ERBB2, ERBB4

DB09063

Ceritinib

approved

ALK

DB09330

Osimertinib

approved

EGFR

DB09559

Necitumumab

approved

EGFR

DB11363

Alectinib

approved

ALK

The human protein-protein interaction (PPI) data were constructed as previously described [31]. The PPI data were downloaded from Human Protein Reference Database (HPRD) [32] in November 2016.

Results

High REO concordance between lung metastases of CRC and primary CRC

To evaluate whether the REO patterns of lung metastases of CRC were similar to the primary CRC or lung cancer, a total of 498 primary CRC samples and 509 primary lung cancer samples were used (training set, Table 1).

First, gene pairs with stable REOs in more than 95% samples were identified and referred to as stable REO gene pairs. In the primary CRC Set1, Set2 and Set3, 156,893,727, 120,114,768 and 60,208,179 stable gene pairs were identified, respectively. Each two of the three lists of stable REO gene pairs showed significantly high concordances (Table 3), with a concordance score ranged from 91.3% (P <  2.20 × 10− 16) to 99.0% (P <  2.20 × 10− 16). There was a total of 35,220,621 stable REO gene pairs overlapped among these three datasets, which was denoted as primary CRC characteristic stable REO gene pairs. In primary lung cancer Set4, Set5 and Set6, 154,434,794, 53,985,252 and 140,533,599 stable REO gene pairs were identified, respectively (Table 3). A total of 31,739,263 stable REO gene pairs overlapped in all of these three datasets were denoted as primary lung cancer characteristic stable gene pairs.
Table 3

Concordance scores of stable REO gene pairs of primary CRC and primary lung cancer datasets

Dataset

Number of stable gene pairs

Number of overlaps

Concordance score

p-value

Primary CRC

 GSE21510

156,893,727

108,393,508

99.00%

<  2.20 × 10−16

 GSE14095

120,114,768

 GSE21510

156,893,727

43,803,419

91.20%

<  2.20 × 10−16

 GSE41258

60,208,179

 GSE14095

120,114,768

38,324,773

94.10%

<  2.20 × 10− 16

 GSE41258

60,208,179

Primary lung cancer

 GSE31210

154,434,794

35,809,034

86.00%

<  2.20 × 10−16

 GSE14814

53,985,252

 GSE43580

140,533,599

128,549,112

99.60%

<  2.20 × 10−16

 GSE31210

154,434,794

 GSE14814

53,985,252

33,941,889

88.10%

<  2.20 × 10−16

 GSE43580

140,533,599

Then, the characteristic stable REO gene pairs for primary CRC and lung cancer were compared to REO gene pairs identified for LmCRC samples. Between primary CRC and lung cancer, 6599 characteristic stable REO gene pairs showed the reverse REO patterns. These 6599 gene pairs (involving 4802 genes) were examined in each of the 20 LmCRC samples in Set7. In these 20 LmCRC samples, the REOs of the 6599 gene pairs were highly concordant with the primary CRC rather than with the primary lung cancer, which varied from the lowest concordance score of 88.36% to the highest concordance score of 99.89% (Fig. 1).
Fig. 1

REO patterns of the 6599 gene pairs for each lung metastasis of CRC samples. The green bar stood for the proportion of REO pattern the same as that in primary CRC. The yellow bar stood for the proportion of REO pattern the same as that in primary lung cancer

Collectively, these results indicated that, though with some characteristics of lung cancer, the lung metastases of CRC samples were more similar to the primary CRC. Therefore, the characteristic stable gene pairs of primary tumors could be applied to predict the primary tumor site of CUP.

A robust signature for discriminating lung metastases of CRC from lung cancer

The 6599 gene pairs with reversal REO patterns between the primary CRC and primary lung cancer samples were used in developing the signature for discriminating the primary CRC and lung cancer. The candidate predictive signature was selected based on the appearance frequencies of genes in the reversed gene pairs between primary CRC and lung cancer and the average rank difference score ΔavgR of each gene pair, as described in Methods. Then, sequentially took odd numbered gene pairs (i.e. 1, 3, 5, ... gene pairs) from the candidate gene pair list to classify primary CRC and lung cancer samples by the majority voting rules: if more than half of the REOs of the gene pairs in a sample were consistent with the candidate signature gene pairs, the sample would be predicted into CRC-like group, otherwise, the sample would be predicted into the lung cancer-like group. The classification accuracy was 99.36% when five gene pairs were taken, and kept unchanged at 99.36% from five gene pairs to 43 gene pairs. Interestingly, the gene SLC34A2 was included in all the 5 gene pairs (Table 4). This gene was reported to play an essential role in the tumorigenesis and progression of non-small cell lung cancer [33] and other pneumonosis such as pulmonary alveolar microlithiasis [34].
Table 4

Top five genes with highest appearance frequencies and their gene pairs with maximum ΔavgR

Gene Symbol

Appearance Frequency

Gene Pair Symbola

ΔavgR

GUCY2C

1969

GUCY2C, SLC34A2

10,298.34705

CDH17

1322

CDH17, SLC34A2

10,273.16384

FABP1

485

FABP1, SLC34A2

10,001.53088

SLC34A2

474

KRT20, SLC34A2

10,657.31487

USH1C

270

USH1C, SLC34A2

9020.681651

aThe former genes had higher expression levels than the latter genes in the CRC

The prediction capacity of the signature was further tested in an independent dataset comprising 654 primary CRC and 204 primary lung cancer samples collected from seven datasets (Table 1). The result showed that 99.54% of the 654 primary CRC samples were correctly predicted into the CRC group and 99.51% of the 204 primary lung cancer samples were correctly predicted, reaching an average prediction accuracy of 99.53%. This result suggested that the signature had a robust discriminating capability to distinguish the primary CRC and lung cancer samples.

Using the signature to predict the 20 LmCRC samples collected from 3 datasets (Table 1), the result showed that all these samples were correctly classified to the CRC group. The accurate prediction indicated that the REO patterns in primary CRC and primary lung cancer could be applied to identify the tissue of origin for pulmonary tumor. There were another three lung metastases of CRC samples in GSE5851 and one in GSE28702, which were also predicted to the CRC group, with the prediction accuracy of 100%.

Lung cancer characteristics of lung metastases of CRC

A total of 2034 differentially expressed genes (DEGs) were distinguished between the primary CRC and lung metastases of CRC in GSE41258 by the Student’s t-test with false discovery rate (FDR) less than 1%. In the PPI network, 90.91% (10) of the 11 anti-lung cancer drugs target genes had direct PPI links with at least 119 DEGs (Fig. 2). Especially, three anti-lung cancer drugs target genes (DHFR, GART and ALK) also presented in the DEGs. The DEGs centrally had direct interaction with EGFR, ERBB2, ERBB4, MET and TUBB, which suggested that the divergence between the primary CRC and the metastases of CRC was related to lung cancer. Furthermore, the direct interaction with anti-lung cancer drug target genes indicated these target genes could also be regarded as the CRC lung metastases treatment target genes, and their corresponding drugs, including Osimertinib, Necitumumab, Gefitinib, Afatinib, Osimertinib, Necitumumab, Crizotinib and Vinorelbine could be considered to be included in the LmCRC regimen.
Fig. 2

The PPI links between DEGs and anti-lung cancer drug target genes

Discussion

Up to now, there are massive data of primary tumors in the public databases, whereas the data for metastases are rare. As the REO-based signature is insensitive to experimental batch effects, we could obviate the need for data normalization for the discovery and validation datasets. Especially, because the REO-based signature obviates the need for data normalization, it can be applied at the individual level [17, 20, 21].

The linear progression model, where the accumulated genetic alterations in the primary tumor could lead to metastases, is generally accepted in researches on cancer progression [23]. In the linear progression model, the metastases acquire most traits of the primary site. Accordingly, the treatment for the metastases is the same or similar to their primary counterparts. Therefore, to precisely identify whether a lesion is the primary tumor or metastasis from other cancerous organs is essential for tailoring the regimen. The liver- and lung-limited metastases are the most frequent migrating targets of CRC [25]. In this study, taking the LmCRC as an example, we showed a high concordance rate between the REOs within lung metastases and their primary CRC tissues other than the primary lung cancer tissues, which provided a direct evidence for the linear progression model.

On the other hand, there might be some differences between the lung metastases and primary CRC. By using the Student’s t-test with FDR < 0.01, 2034 DEGs were detected between the primary CRC and lung metastases samples in GSE41258, among which 274 genes were also detected as DEGs between the primary CRC and primary lung cancer samples in GSE19249. Then, we made use of the normal colorectal tissue and lung tissue samples to further explore whether these 274 DEGs might exhibit lung tissue-specific characteristics. The result showed that 52 of the 274 DEGs were DEGs between the normal colorectal tissue and lung tissue samples (Student’s t-test with FDR < 0.01). These results indicated that the lung metastases of CRC might possess some characteristics of the host organ, which needs to be further confirmed by analyzing microdissected samples of lung metastases of CRC to eliminate the possible confounding influence of residual lung tissues in the LmCRC samples. Finally, a PPI network analysis was conducted for DEGs between the primary CRC and their lung counterparts. As shown in Fig. 2, three DEGs (DHFR, GART and ALK) also played roles as anti-lung cancer drugs target genes. As the treatment for lung-limited metastases of CRC was curative resection accompanied with the regimen for CRC [35, 36], the analysis indicated that some lung cancer drugs could be recommended for LmCRC patients, which deserves further study for tailoring the treatment regimen for the LmCRC patients.

Conclusions

The REOs-based signature could identify the primary tissue of LmCRC with an accuracy of 100%. The within-sample REOs in primary sites could be a powerful approach for predicting the origin tissues of CUPs.

Notes

Acknowledgements

Not applicable.

Funding

This work was supported in part by the National Natural Science Foundation of China (grant numbers 81501215, 81501829, 81372213, 81572935, 61601151 and 21534008) and Natural Science Foundation of Fujian Province, China (grant number 2016 J01706) and Joint Fund for Program of Science and Technology innovation of Fujian Province, China (grant number 2016Y9102, 2016Y9044). The funding bodies had no role in the design of the study, collection, analysis, and interpretation of data and in writing of the manuscript.

Availability of data and materials

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

Authors’ contributions

MYL, HL and ZG conceived and designed the study; ZT contributed to the data collection and pre-processing; GL, XL and ML contributed to the data collection, literature review, result discussion and code testing; MYL conducted the signature identification and wrote the manuscript; HL, GH, LQ and ZG edited the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Ethics approval and participant consent was not necessary as this study involved the use of a previously-published de-identified database GEO.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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© The Author(s). 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Mengyao Li
    • 1
  • Hongdong Li
    • 2
    Email author
  • Guini Hong
    • 1
  • Zhongjie Tang
    • 1
  • Guanghao Liu
    • 1
  • Xiaofang Lin
    • 1
  • Mingzhang Lin
    • 1
  • Lishuang Qi
    • 3
  • Zheng Guo
    • 1
    • 4
    Email author
  1. 1.Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of BioinformaticsFujian Medical UniversityFuzhouChina
  2. 2.Department of BioinformaticsGannan Medical UniversityGanzhouChina
  3. 3.College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbinChina
  4. 4.Fujian Key Laboratory of Tumor MicrobiologyFujian Medical UniversityFuzhouChina

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