Identification of genes encoding secreted proteins that are preferentially expressed in normal adult mouse and human lung. Proteins secreted from the cells (the secretome) regulate cell behavior and are clinically relevant in cancer, because they are a rich source of biomarkers and are targets of approved cancer treatments (41–43). Therefore, we focused on genes encoding secreted proteins, which we refer to as sGenes, to identify those preferentially expressed in the normal adult lungs of mice and humans (referred to hereafter as sLungGenes). We collected and curated 2591 orthologous human and mouse sGenes from 2 representative secreted protein databases: the secretome from the Human Protein Atlas (HPA) (46) and the Metazoa (Human/Animal) Secretome and Subcellular Proteome KnowledgeBase (MetazSecKB) (47). For these 2591 sGenes, we performed gene expression analyses using 3 available data sources: HPA database with expression data from 37 adult human tissue types, Genotype-Tissue Expression (GTEx) with expression data from 30 adult human tissue types (48), and Encyclopedia of DNA Elements Consortium (ENCODE) with expression data from 12 tissue types in 10-week-old C57BL/6J mice (49).

For each of the 2591 orthologous human and mouse sGenes, the lung-preferential expression scores (hP Lung_HPA , hP Lung_GTEx , and mP Lung ) across the available tissue types from the 3 databases were independently calculated. The preferential expression scores (hP Lung_HPA , hP Lung_GTEx , and mP Lung ) were converted to Z scores (Z-hP Lung_HPA , Z-hP Lung_GTEx , and Z-mP Lung ) (see Methods and Supplemental Table 1; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.129344DS1). We selected the top-ranking sLungGenes by setting the threshold of Z-hP Lung (Z-hP Lung_HPA and Z-hP Lung_GTEx ) and Z-mP Lung values as 4.0 and 2.0, respectively (Figure 1). We selected the top-ranking sLungGenes overlapped by at least 2 tissue-specific gene expression resources to ensure the reliability of their preferential expression in the lung (Figure 1). For these high-confidence sLungGenes, we performed a literature query in PubMed and found most had literature evidence supporting relevance to lung cancer (29/31) or indicating that their activity restricts cancer cell growth (25/31) (Figure 1 and Supplemental Table 2). These results suggested that the sGenes preferentially expressed in the lung likely contribute to the tissue specificity of lung cancer. We followed up on the one gene that had not been previously reported as relevant to lung cancer, NDNF.

Figure 1 Identification of genes encoding secreted proteins preferentially expressed in both mouse and human lung. (A) 3D scatter plot of the lung-preferential expression Z score of 2591 orthologous sGenes in human and mouse. Each dot indicates a cross-species conserved sGene (see Supplemental Table 1). Top-ranking sLungGenes, selected by setting the threshold of Z-hP Lung (Z-hP Lung_HPA and Z-hP Lung_GTEx ) as 4.0 and Z-mP Lung values as 2.0, were highlighted with a color reflecting their amount of overlap among all 3 data sets as shown in B. (B) Summary of the top-ranking sLungGenes overlapped by at least 2 tissue-specific gene expression resources. sGene, genes encoding secreted proteins; sLungGenes, genes encoding secreted proteins that are preferentially expressed in the adult lung; z-P Lung , lung-preferential expression Z score.

Verification of lung-specific expression of NDNF in normal adult mouse and human lung. NDNF, also known as A930038C07Rik, C4orf31, Epidermacan, and NORD, is the one top-ranking sLungGene without known functions in lung cancer (Figure 1 and Supplemental Table 2). We investigated its expression and potential function in lung cancer.

Previously, mouse Ndnf has been reportedly expressed in the developing and adult central nervous system (50–54), as well as in ischemic skeletal muscle and the heart upon myocardial infarction (55, 56). Human NDNF expression was reported in neocortex, umbilical cord blood, and bone marrow multipotent mesenchymal stromal cells (57, 58). Our analysis of the human and mouse tissue databases indicated preferential expression of Ndnf in the normal adult lung in humans (Figure 1 and Supplemental Figures 1 and 2) and mice (Figure 1 and Supplemental Figure 3), which we validated using several approaches. We measured Ndnf transcripts by quantitative real-time reverse transcriptase PCR (RT-qPCR), which showed that Ndnf was preferentially expressed in the lung compared with other mouse tissues (Figure 2A). Along with the differential expression of Ndnf mRNA, we detected Ndnf proteins by Western blotting predominantly in the adult mouse lung with much lower or barely detectable amounts in the other examined tissues (Figure 2B and Supplemental Figure 4). Furthermore, we detected NDNF mRNA by in situ hybridization in both the normal adult mouse and human lung (Figure 2, C and D, Supplemental Figure 5). NDNF mRNA was not detected in several other human tissues including breast, liver, and spleen (Supplemental Figure 6). Consistent with the information from the HPA and MetazSecKB resources, as well as the previous reports (50, 55, 59), we confirmed that both mouse Ndnf and human NDNF were secreted when ectopically expressed in HEK293T cells (Supplemental Figure 7). Together, we experimentally confirmed that NDNF/Ndnf (human/mouse) encode secreted proteins and are preferentially expressed in the normal adult mouse and human lung.

Figure 2 NDNF is detected predominantly in normal mouse and human lung. (A) RT-qPCR analysis of Ndnf mRNA levels in the lung and other indicated tissues of 10-week-old C57BL/6N mice (n = 3). (B) Western blot analysis of Ndnf protein levels in the lung and other tissues collected from 10-week-old C57BL/6N mice. A representative image is shown for 1 mouse with quantitative data from n = 3 mice shown below. Band intensity was quantified using ImageJ (NIH), and Ndnf abundance in each tissue was normalized to GAPDH. The relative Ndnf abundances were plotted. Box plots show 25th to 75th percentile; whiskers extend to the minimum and maximum values. One-way ANOVA was used for statistical analysis. ****P < 0.0001. (C and D) RNAscope in situ hybridization detection of NDNF/Ndnf mRNA (red) expression in the normal adult mouse (C) and human (D) lung. Cell nuclei are counterstained with hematoxylin (blue). Note that NDNF/Ndnf mRNA is mainly detected in the alveolar epithelium, whereas much less is found in the bronchial epithelium. Sense probes that served as negative controls are shown in Supplemental Figure 5. Scale bar: 100 μm.

Decreased NDNF expression in lung adenocarcinoma and association with better clinical outcome of patients. The alveolar epithelium contains lung stem or progenitor cells and constitutes the major cell type responsible for lung adenocarcinoma, which is the most prevalent form of lung cancer and arises from the alveoli throughout the lungs (60–62). Intriguingly, in situ hybridization in the normal human and mouse lung revealed that NDNF/Ndnf were expressed mainly by the alveolar epithelial cells (Figure 2, C and D). We thus evaluated whether NDNF has clinical relevance in lung adenocarcinoma. We examined the expression of NDNF in tumor and normal samples using the lung adenocarcinoma data set in TCGA. When compared with histologically normal lung tissues adjacent to the tumor, expression of NDNF was significantly reduced in lung adenocarcinoma (Figure 3A). Expression of Ndnf in lung adenocarcinoma was also examined using the K-rasLA1 mouse model. The K-rasLA1 mice develop lung adenocarcinoma through somatic activation of a K-ras allele carrying an activating mutation in codon 12 (G12D) (9). Both RT-qPCR analysis and Western blots showed that Ndnf mRNA and protein were decreased in K-rasG12D–induced adenocarcinoma when compared with paired surrounding nontumor lung tissues (Figure 3, B and C). Thus, we found that NDNF was downregulated in human lung adenocarcinoma and in a murine model of this cancer.

Figure 3 NDNF expression is decreased in lung adenocarcinoma and associated with better clinical outcome of patients. (A) NDNF mRNA in tumor (n = 517) and normal lung tissues (n = 59) from TCGA lung adenocarcinoma database. Box plots show 25th to 75th percentile; whiskers extend to the minimum and maximum values. The 2-tailed Mann-Whitney U test was used for statistical analysis. ****P < 0.0001. (B) Ndnf mRNA in tumor and matched normal lung tissue adjacent to the tumor (NAT) from K-rasLA1 mice (n = 10). Transcript abundance was determined by RT-qPCR analysis. The 2-tailed paired t test was used for statistical analysis. ****P < 0.0001. (C) Western blot analysis of Ndnf in tumor (T) and matched normal lung tissue adjacent to the tumor (NAT) from K-rasLA1 mice (n = 8). (D–F) Kaplan-Meier curves showing the correlation between NDNF expression and clinical outcome, as analyzed for overall survival (OS), progression-free survival (PFS), and postprogression survival (PPS) of lung adenocarcinoma patients. (G–I) Kaplan-Meier curves showing the correlation between NDNF expression and OS of lung adenocarcinoma patients at indicated stages. Number of samples in the high- and low-NDNF groups and a corresponding log-rank P value are indicated on each graph. Kaplan-Meier curves were created using the Kaplan-Meier Plotter (www.kmplot.com) with lung adenocarcinoma patients grouped according to the median expression value of NDNF. Information of lung adenocarcinoma patients involved in the survival analysis is in Supplemental Table 3.

We evaluated whether the expression level of NDNF has any predictive value for survival of lung adenocarcinoma patients by performing Kaplan-Meier analyses (63). We found patients with higher NDNF expression had better overall survival than those with lower expression (Figure 3D, Supplemental Figure 8, and Supplemental Tables 3 and 4). Lung adenocarcinoma patients with high NDNF also showed significantly better progression-free survival and postprogression survival compared with those with low levels of NDNF (Figure 3, E and F). In addition, the higher expression of NDNF was better associated with overall survival of patients with stage I, when compared with stages II and III, lung adenocarcinoma (Figure 3, G–I). Therefore, the aberrant expression of NDNF/Ndnf in human and mouse lung adenocarcinoma, as well as the association of NDNF mRNA level with patient survival, indicated that NDNF may serve as a prognostic biomarker for early-stage lung adenocarcinoma.

Negative correlation between NDNF expression and progression of human lung adenocarcinoma. To further investigate the relevance of NDNF expression level with human lung adenocarcinoma, we performed a detailed analysis of RNA-Seq data from TCGA. In addition to a significantly lower amount of NDNF in tumor tissues compared with matched normal surrounding tissues (Figure 4A), we found that patients with larger tumors (T3–T4) had substantially less NDNF expression compared with patients with small tumors (T1–T2) (Figure 4B). We observed a similar relationship between NDNF expression and TNM stages: samples at advanced stage (II and III) had lower NDNF expression than samples at early stage (I) (Figure 4C). Moreover, lung adenocarcinoma patients with lymph node metastasis expressed less NDNF than those without lymph node metastasis (Figure 4D).

Figure 4 NDNF transcript abundance negatively correlates with progression of human lung adenocarcinoma. (A) NDNF mRNA in matched normal lung tissue adjacent to the tumor (NAT) and tumor tissues (n = 57). (B–D) NDNF mRNA in normal adjacent lung tissues and tumor tissues with different primary tumor size and extent (B), different tumor-node-metastasis (TNM) stages (C), and different lymph node metastasis status (D). In A–D, analysis was performed with data in TCGA for lung adenocarcinoma. Box plots show 25th to 75th percentile; whiskers extend to the minimum and maximum values. (E–J) NDNF mRNA expression from human lung adenocarcinoma microarrays. Representative images of NDNF mRNA (red) detected by RNAscope ISH in matched normal adjacent lung tissues (NAT) and tumor tissues with different primary tumor size and extent (E), different TNM stages (G) and different lymph node metastasis status (I). Scale bar, 100 μm. Quantitative analysis of NDNF expression detected by RNAscope ISH (NDNF ISH score; see Methods) from lung adenocarcinoma with different primary tumor size and extent (F), different TNM stages (H), and different lymph node metastasis status (J). Data are shown as mean ± SD. The 2-tailed paired t test (A) or 1-way ANOVA followed by Holm-Šídák multiple-comparisons test (B–D, F, H, and J) was used for statistical analysis. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

We evaluated this negative correlation between NDNF mRNA level and lung adenocarcinoma progression by in situ hybridization using tissue microarrays from 4 independent human lung adenocarcinoma cohorts consisting of 30 normal lung tissue and 207 adenocarcinoma samples covering distinct pathological stages (Supplemental Table 5). Consistent with the results from TCGA database analysis, we observed that NDNF mRNA detected by in situ hybridization (ISH) was markedly lower (for ISH scores, see Methods) in lung adenocarcinoma tumor tissues than in the matched normal adjacent samples (Figure 4, E–J). Specifically, lower levels of NDNF were associated with larger tumor size (Figure 4, E and F), more advanced tumor stage (Figure 4, G and H), and further cancer progression (Figure 4, I and J). We applied χ2 testing, which established the significance of the finding that reduced NDNF levels correlated with the higher clinical grade and TNM classifications in patients with lung adenocarcinoma (Supplemental Table 5). Together, these data demonstrated that decreased NDNF expression occurred in patients with lung adenocarcinoma and was significantly correlated with tumor progression and poor prognosis.

Tumor-suppressive properties of NDNF in mouse and human lung cancer cell lines. Little is known about the functional role of NDNF in cancer. We explored the potential role of NDNF in tumorigenesis by exposing A549 and LLC1, lung cancer cell lines from human and mouse, respectively, to purified Ndnf protein. The purified Ndnf protein inhibited growth of A549 and LLC1, as indicated by a reduced rate of growth assessed using a Cell Counting Kit-8 (CCK-8) assay (Figure 5A). Likewise, ectopic expression of NDNF/Ndnf caused similar growth inhibition in A549/LLC1 lung cancer cells (Supplemental Figure 9). Exogenous Ndnf also impaired anchorage-dependent and anchorage-independent growth of both cell lines (Figure 5, B and C). We also assessed the effects of reduced NDNF expression in the growth of A549 and LLC1 lung cancer cells. Knockdown of human NDNF in A549 cells with 2 shRNAs, 1 targeting the coding sequence and the other targeting the 3′-UTR of NDNF, increased cell growth compared with that of control cells expressing scrambled shRNA (Figure 5, D–F, and Supplemental Figure 10). Knockdown with 2 shRNAs targeting the mouse Ndnf gene, but not a scrambled sequence, produced similar results in LLC1 cells (Figure 5, D–F, and Supplemental Figure 10). In addition, purified Ndnf protein attenuated the increased growth of A549 and LLC1 cells resulting from NDNF (or Ndnf) knockdown (Figure 5, D–F, and Supplemental Figure 11). Collectively, these results indicated that NDNF plays an important role in limiting the growth and tumorigenic properties of both mouse and human lung cancer cells.

Figure 5 Tumor-suppressive properties of NDNF in mouse and human lung cancer cell lines. (A–C) Effect of purified Ndnf protein on the growth of human and mouse lung cancer cell lines. Quantitative analysis of cell viability using the CCK-8 assay (A), of colony formation (B), and of growth in soft agar (C) of the A549 and LLC1 cells with or without purified Ndnf (200 ng/mL). Representative images from the colony formation assay and soft agar assay are shown in B and C. (D–F) Effect of shRNA-based knockdown of NDNF on the growth of human A549 cells and knockdown of Ndnf on LLC1 cells. Knockdown efficiency of shRNA targeting human NDNF and mouse Ndnf is shown in Supplemental Figure 10. Quantitative analysis of cell viability using the CCK-8 assay (D), of colony formation (E), and of growth in soft agar (F) of the indicated cells stably expressing shRNA targeting NDNF or Ndnf as appropriate or control scrambled shRNA of cells with or without purified Ndnf (200 ng/mL). In A and D, data are shown as mean ± SD of n > 3 replicates of a single experiment. Data are representative of n ≥ 3 experiments. In B, C, E, and F, data are shown as box-and-whiskers plots of n ≥ 6 replicates from n ≥ 3 experiments. Box plots show 25th to 75th percentile; whiskers extend to the minimum and maximum values. The 2-tailed Mann-Whitney U test (A–C) or 1-way ANOVA followed by Holm-Šídák multiple-comparisons test (D–F) was used for statistical analysis. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Representative images of the colony formation assay (E) and soft agar assay (F) are shown in Supplemental Figure 11.

Tumor-suppressive properties of NDNF in the xenograft model. To assess the relevance of NDNF in vivo, we generated subcutaneous xenograft tumors using the human A549 cells or mouse LLC1 cells in immune-compromised mice. We found that downregulation of NDNF expression significantly promoted the growth of A549 tumors (Figure 6A). Not only were the tumor sizes of A549-NDNF-shRNA–derived xenografts notably larger than those of xenografts originating from A549-scramble-shRNA cells at day 32 after injection (Figure 6B), but the tumor weights of A549-NDNF-shRNA–derived xenografts were also much higher than those of xenografts originating from A549-scramble-shRNA cells at day 32 (Figure 6B). Moreover, the percentage of cells positive for Ki67, a marker of proliferating cells, was greater in the NDNF-shRNA–derived xenografts compared with the percentage in the scramble-shRNA–derived xenografts (Figure 6C). NDNF knockdown had similar effects on LLC1-derived tumors (Figure 6, D–F). These results are consistent with the observation that NDNF expression levels negatively correlated with Ki67 expression (Spearman’s r = –0.3915) and with the abundance of transcripts of other proliferation-related genes PCNA, CDC6, CDC45, and CDT1 (Supplemental Figure 12). Collectively, our in vitro and in vivo experiments support a tumor-suppressive role for NDNF in lung cancer.

Figure 6 Tumor-suppressive properties of NDNF in the xenograft model. Human lung cancer A549 cells (A–C) or mouse lung cancer LLC1 cells (D–F) stably expressing shRNA targeting NDNF gene or control scrambled shRNA were injected into nude mice. (A and D) Tumor volume was measured at the indicated time points. Data are shown as mean ± SD for tumors from n = 6 mice at each time point. (B and E) Mice injected with A549 cells were sacrificed on day 32 after injection and those injected with LLC1 cells on day 23 after injection. Tumors were removed and photographed. Tumor volumes and weights were measured. (C and F) Proliferating cells were detected by Ki67 staining in tumors at day 32 (A549 cell tumors) or day 23 (LLC1 cell tumors). Representative images and quantitative analyses are shown. Scale bar: 20 μm. Data are shown as box-and-whiskers plots of tumors from 5 mice for each cell line from 20 images per tumor. Box plots show 25th to 75th percentile; whiskers extend to the minimum and maximum values. The 2-tailed Mann-Whitney U test was used for statistical analysis. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Epigenetic silencing of NDNF in lung adenocarcinoma. Tumor suppressor genes are often rendered dysfunctional through multiple mechanisms during the oncogenic processes. Such processes include mutation, deletion, genetic rearrangement, and epigenetic silencing of transcription (64–66). Using data from cBioPortal (67, 68), we determined that reduced NDNF genomic DNA copy number or mutation of the NDNF gene did not frequently occur in patients with lung adenocarcinoma. Thus, we explored other mechanisms of lung cancer–associated downregulation of NDNF.

Epigenetic gene silencing associated with DNA methylation primarily in the promoter region is an important mechanism of gene inactivation in cancer (69). In lung cancer, hypermethylation inactivation of tumor suppressor genes is common (70, 71). We investigated whether NDNF is epigenetically silenced through promoter methylation in lung adenocarcinoma. We analyzed 422 lung adenocarcinoma tissue samples with both gene expression data from RNA-Seq and matched DNA methylation data from Infinium HumanMethylation450 microarray in TCGA database. Paired normal tissue was available for 21 of these samples. We compared the methylation level across the 16 CpG sites in the NDNF promoter region (Figure 7A). DNA methylation levels at 14 of the 16 CpG sites were significantly higher in tumor tissues than in normal tissues (Figure 7B). Furthermore, an inverse correlation (Spearman’s r = –0.6971) between NDNF expression and the average methylation level across all the 16 CpG sites in the NDNF promoter region was observed (Figure 7C). We also examined the magnitude of differential methylation across the 16 CpG sites. As reported previously (72), we found that the most dramatic DNA methylation alterations (ΔM) in lung adenocarcinoma preferentially occurred at CpG island shores, which are the relatively low CpG density regions flanking the traditional CpG island (Supplemental Figure 13). Consistently, the correlation between NDNF expression level and methylation level at individual CpG sites located on each shore was greater than that within the traditional CpG islands (Supplemental Figure 14 and Supplemental Table 6). These data indicated that downregulation of NDNF can occur by epigenetic silencing in lung adenocarcinoma. Together, our results suggested that DNA methylation at the CpG island shores in the predicted promoter region contributes to NDNF silencing in lung adenocarcinoma.

Figure 7 Cancer-related DNA methylation sites correspond to those associated with tissue-specific expression of NDNF. (A) Schematic diagram of the human NDNF locus. The 16 available CpG sites in the NDNF promoter region from Infinium HumanMethylation450 microarray are indicated as vertical lines. Black boxes, exons; blue box, CpG island; green text, CpG island shore methylation sites. (B) Methylation levels at the 16 CpG sites in tumor (red) compared with that in normal lung tissue adjacent to the tumor (black) using lung adenocarcinoma data from TCGA database. (C) Scatter plot and correlation between NDNF mRNA abundance and the average methylation level across 16 CpG sites in the NDNF promoter region in lung adenocarcinoma samples (n = 422, red dots) and normal lung tissue adjacent to the tumor (n = 21, black dots) from TCGA database. (D) Methylation level averaged across the 16 CpG sites in normal tissue from bladder, breast, kidney, and prostate (blue) compared with that in normal lung tissue (black). Data are from samples adjacent to tumor tissue from TCGA database (Supplemental Table 8). (E) Scatter plot and correlation between NDNF mRNA abundance and the average methylation level across all the 16 CpG sites in the NDNF promoter region in samples from normal lung, bladder, breast, kidney, and prostate tissues adjacent to tumor from TCGA. Lung samples shown as black dots, all others as blue dots. (F) Scatter plot and correlation between alterations in tissue-specific DNA methylation and alterations in cancer-specific methylation at each of the 16 CpG sites in the NDNF promoter region. Each circle represents a CpG site: green circle, CpG island shore; black circle, CpG island. Tissue-specific DNA methylation alterations were calculated as the difference in DNA methylation (ΔM) between the average methylation for 4 other tissues (bladder, breast, kidney, and prostate) and lung (from the averages in D); cancer-specific methylation alterations were calculated as the difference in DNA methylation between the average for lung adenocarcinoma tissues and the average for normal lung from tissue adjacent to tumors (from averages in B; Supplemental Figure 13). Spearman’s r and P values are indicated. Box plots show 25th to 75th percentile; whiskers extend to the minimum and maximum values. The 2-tailed Mann-Whitney U test was used for statistical analysis. n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001; and ****P < 0.0001.

Association between cancer-related DNA methylation sites and those for tissue-specific expression of NDNF. DNA methylation is also a key epigenetic mechanism underlying tissue-specific gene expression (73, 74). Many of the locations of cancer-related methylation changes overlap with those that distinguish gene expression in normal tissues (72, 75–78). Thus, we analyzed histologically normal tissue samples adjacent to tumors across 5 tissue types — lung, bladder, breast, kidney, and prostate — with DNA methylation data in TCGA database. We found that methylation levels at most of the 16 CpG sites in the NDNF promoter region were significantly lower in the lung when compared with the 4 other tissues in combination (Figure 7D) or individually (Supplemental Figure 15). Additionally, we observed an inverse correlation (Spearman’s r = –0.6363) between NDNF mRNA expression and the average methylation level across the 16 CpG sites in the samples from the 5 tissues with both gene expression data from RNA-Seq and matched DNA methylation data in TCGA database (Figure 7E). The greatest differences in tissue-specific DNA methylation (ΔM), as well as stronger correlations between NDNF expression level and the methylation level at individual CpG sites, occurred at CpG island shores (Supplemental Figures 16 and 17 and Supplemental Table 7). The ΔM values between either the combination or each of the other 4 tissues individually and lung were highly correlated with that between lung adenocarcinoma tissues and normal lung tissue across the 16 CpG sites in the NDNF promoter region (Figure 7F and Supplemental Figure 18). Taken together, we demonstrated that lung cancer–related DNA methylation sites, predominantly occurring at CpG island shores, correspond to those associated with tissue-specific expression of NDNF and contribute to NDNF silencing in lung cancer.