Abstract
Lung cancer is responsible for 2.21 million annual cancer cases and is the leading worldwide cause of cancer-related deaths. Specifically, lung adenocarcinoma (LUAD) is the most prevalent lung cancer subtype resulting from genetic causes; LUAD has a 15% patient survival rate due to it commonly being detected in its advanced stages. This study aimed to identify a novel biomarker signature of early-stage LUAD utilizing gene expression analysis of human lung tissue samples. Using 22 pairs of LUAD and matched normal lung microarrays, 229 differentially expressed genes were identified. These genes were networked for their protein–protein interactions, and 44 hub genes were determined from protein essentiality. Survival analysis of 478 LUAD patient samples identified four statistically significant candidates. These candidate genes’ expression profiles were validated from GTEx and TCGA (347 normal, 483 LUAD samples); immunohistochemistry validated the subsequent protein presence. Through intensive bioinformatic identification and multiple validations of the four-biomarker gene signature, AGER, MGP, and PECAM1 were identified as downregulated in LUAD; SLC2A1 was identified as upregulated in LUAD. These four biologically significant genes are involved in tumorigenesis and poor LUAD prognosis, meriting their use as a clinical biomarker signature and therapeutic targets for early-stage LUAD.
1. Introduction
Cancers are a modern threat to society and are classified by the uncontrolled growth and spread of abnormal cells throughout the body that can result in death, either with or without treatment [1]. Lung cancer, responsible for 2.21 million annual cases, is the leading worldwide cause of cancer-related deaths, with prognosis tightly linked to stage at diagnosis [2,3]. Lung cancers can be classified into two main subgroups: non-small-cell lung cancer (NSCLC), responsible for 85% of all lung cancer cases, and small-cell lung cancer, responsible for 15% of all lung cancer cases [2]. Specifically, lung adenocarcinoma (LUAD) is responsible for ten percent of all NSCLC cases in patients with no smoking history, suggesting that the incidence of LUAD is related to genetic variations and susceptibility [4]. In normal lung cells, proto-oncogenes are regulators of cell growth. However, modifications can occur in these genes, forming oncogenes and leading to carcinogenesis. These oncogenes can form through changes such as chromosomal translocation (e.g., chromosomal translocation of the MYC oncogene in Burkitt’s lymphoma), point mutation (e.g., point mutation at codon 12 of the RAS oncogene), and gene amplification (e.g., amplification of c-MYC in neuroblastoma) [5]. A second pivotal gene type that is involved in the pathogenesis of cancer is tumor suppressor genes. In normal cells, these genes regulate cell growth and block cancer; mutations that reduce the presence or activation of these genes lead to cancer. For example, P53 is a tumor suppressor gene that is a tetramer and, when mutated, can lead to significant effects as the tetramer will always contain at least one mutant P53 protein [5]. In LUAD, genetic abnormalities occur in normal alveolar and bronchial epithelial cells, promoting precancerous lesions such as atypical adenomatous hyperplasia (AAH). Epidermal growth factor receptor or anaplastic lymphoma kinase mutations within these lesions lead to elevated cell proliferation that allows these cells to survive and accumulate mutations [6]. Further mutations then cause these AAH lesions to progress into adenocarcinoma in situ (AIS), a non-invasive stage characterized by cellular atypia and architectural distortion [7]. With the AIS lesions acquiring more genetic instability, the lesions become invasive adenocarcinoma, penetrating the basement membrane and spreading to surrounding tissues [8].
The identification of markers in LUAD is critical as it can allow for diagnosing LUAD in its earlier stages and may enable timely intervention [9]. Low-dose CT (LDCT) screening can improve early detection and reduce mortality by approximately 20% in high-risk smokers. However, it suffers from frequent false positives and overdiagnosis [3,10]. These issues underscore a specific need for biomarkers to refine the screening and diagnosis of LUAD. Furthermore, utilizing a biomarker signature, a composite of multiple biomarkers can enhance diagnostic accuracy by integrating numerous molecular insights and minimizing variability [11]. Previous biomarker identification studies have been performed using bioinformatic analysis of gene data or laboratory tests and have discovered many biomarkers for various diseases [12,13,14,15,16]. Despite this, very few clinically recognized biomarkers for early-stage LUAD [12,13] are currently available. Identifying biomarkers for early-stage LUAD diagnosis is essential, as LUAD is a prominent cancer usually detected in its advanced stages [13,17]. At these advanced stages, there is a 15% survival rate. Early detection of LUAD through an accurate biomarker could allow for treatments to be conducted on a less advanced tumor, leading to an improved patient outcome [12,13,17].
This research aimed to identify a predictive biomarker signature of unique cancer-related genes that can be utilized for the early-stage diagnosis of LUAD to reduce disease mortality. In this investigation, we identified a novel biomarker signature to aid in predicting poor LUAD prognosis via early-stage samples.
Currently, many clinically recognized biomarkers of LUAD, such as carcinoembryonic antigen and CYFRA21-1, have limited sensitivity and specificity considering an application in early-stage disease [18]. Our research identified a biomarker signature comprising AGER, MGP, PECAM1, and SLC2A1, which uniquely integrates various relevant pathways such as cell adhesion, angiogenesis, and metabolic reprogramming, which improves the overall specificity and diagnostic confidence as opposed to single-marker approaches [19]. The signature utilizes the novel markers AGER and MGP; the downregulation of these markers has rarely been explored in LUAD [20,21]. In summary, the utilization of a biomarker signature with relevant markers increases diagnostic accuracy beyond single-marker approaches [10].
2. Materials and Methods
2.1. Data Acquisition
The transcriptomics dataset GSE32863 was obtained from the Gene Expression Omnibus (GEO), and the dataset was analyzed using the GEO2R tool. The dataset comprises gene expression profiles from 58 primary LUAD tumor tissues and 58 matched adjacent normal lung tissues [13]. The analysis was focused on analyzing 22 tumors classified as stages I-II and the 22 associated matched normal samples. All microarray data were derived from a high-density oligonucleotide platform that covers approximately 21,000 genes [13]. Each sample was assigned labels via the define feature, and the grouping was verified before proceeding with the analysis.
2.2. Differential Gene Expression Analysis
To accurately identify differentially expressed genes (DEGs), the Benjamini–Hochberg adjustment method was applied to the GEO2R analysis tool. Additionally, a highly stringent adjusted p-value (adj. p) cutoff of adj. p ≤ 0.01 was utilized to ensure statistical significance. Furthermore, a Log2 Fold Change (Log2FC) cutoff of | Log2FC | ≥ 2 was used to determine up- and downregulated DEGs [22]. Utilizing R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria) data quality and normalization were verified by evaluating median-centered expression distributions for all selected samples in numerous comparative boxplots. The combination of cutoffs and validation of median-centered ensured that only the most significant and biologically relevant changes were selected for further downstream analysis.
2.3. Protein–Protein Interaction Networks and Hub Gene Identification
Once all genes were categorized as upregulated or downregulated, the entire set was uploaded into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING v11.5; https://string-db.org, ELIXIR Core Data Resource, EMBL, Heidelberg, Germany) to construct a protein–protein interaction (PPI) network. The network was then exported into Cytoscape v3.26.0 (The Cytoscape Consortium, San Diego, CA, USA; https://cytoscape.org), where the cytoHubba plugin v0.1 (developed by Institute of Systems Biology, National Tsing Hua University, Hsinchu, Taiwan) was used to identify hub genes. Using the two most robust centrality algorithms, Maximal Clique Centrality (MCC) and Density of Maximum Neighborhood Component (DMNC), both algorithms computed the shortest path to identify hub genes. In constructing the protein–protein interaction network, interactions with a confidence score of ≥0.4 were considered significant participants in the network [23]. Using the two topological algorithms, MCC and DMNC, nodes were ranked by centrality; a combination of both computations was used to determine the final hub gene set for further analysis. This approach considered overlapping high-centrality nodes from both methods, which elevated the confidence that the hub genes were pivotal in the LUAD network.
2.4. Kaplan–Meier Survival Analysis
Hub genes were exported into the Gene Expression Profiling Interactive Analysis 2 (GEPIA-2, v2.0; http://gepia2.cancer-pku.cn, Peking University, Beijing, China) platform to generate Kaplan–Meier survival curves, which visualize the correlation between gene expression levels with the overall survival (OS) of LUAD patients. The LUAD patient data were imported from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Genes that were observed to have significant survival correlation were retained, thus highlighting their prognostic potential in earlier-stage LUAD. Genes with log-rank p-values < 0.05 in the Kaplan–Meier analysis were deemed to be associated with overall survival. The default median expression cutoff in GEPIA-2 allowed for the high and low expression groups to be dichotomized [24]. The genes that met the criterion were retained as candidates for prognostic potential, allowing the subsequent analysis to focus on the most robust survival correlations.
2.5. Transcriptomic and Proteomic Validation
After OS analysis, the smaller set of key genes were validated for their overall differential expression using the “Expression DIY” tool in GEPIA-2, which accessed TCGA and GTEx for LUAD and healthy tissue expression data. Due to the high specificity of antigen–antibody binding reactions, immunohistochemistry (IHC) stains from the Human Protein Atlas (HPA) (https://www.proteinatlas.org/, Science for Life Laboratory, Uppsala University, Uppsala, Sweden) were used to validate the presence of proteins encoded by the biomarker genes. For the immunohistochemical validation, lung tissue staining was retrieved for each available candidate gene from the HPA [25]. The HPA provided results from three independent normal lung tissue subjects and five to six independent LUAD tumor tissue subjects for each gene, enabling a comparison between protein presence in normal and cancerous tissues. The staining level and intensity strength were reported.
3. Results
3.1. Identification of DEGs
The GSE32863 expression profile was downloaded from GEO and included LUAD tumor and normal lung samples for 22 unique patients, which assayed 21,044 distinct genes per sample as described by Selamat et al. [13]. The boxplot, shown in Figure 1a, was also analyzed and displayed a precise median centering. GEO2R generated a volcano plot of every gene demonstrated in Figure 1b and predicted the Log2FC and p-value for each DEG shown in Table A1. Using |Log2FC| ≥ 2 as a differential expression cutoff, 229 DEGs were identified, of which 80 were upregulated in LUAD and 149 were downregulated in LUAD (Table 1).
Figure 1.
Boxplot of sample expression and volcano plot of DEGs from GSE32863. (a) Boxplot of selected samples: median centering shows the statistical correction to be accurate and the samples ready for further analysis. (b) Volcano plot of DEGs: each dot represents a gene regulation and significance prediction.
Table 1.
Set of upregulated and downregulated genes from GEO2R.
3.2. PPI Analysis of DEGs
Using the STRING database, 229 DEGs were used to construct a PPI network to show interacting proteins, resulting in 227 interacting nodes and 445 edges (Figure A1). The network was exported to Cytoscape for analysis, where cytoHubba was used to screen for hub genes. These are represented by their high connectivity and interactions with other genes in LUAD, as shown by the proteins or nodes with more interactions or edges (Figure 2). CytoHubba’s most robust algorithms, MCC and DMNC, produced two separate PPIs of the hub genes shown in Figure 2. From the two PPIs with some overlapping identifications, 44 total hub genes were identified (Table 2).
Figure 2.
PPI network of hub gene interconnectedness from Cytoscape. All edges (lines) represent protein interactions between specific nodes (proteins). (a) DMNC algorithm hub gene predictions; (b) MCC algorithm hub gene predictions.
Table 2.
Hub genes from MCC and DMNC algorithms based on PPI network.
3.3. Survival Analysis of Hub Genes
Using GEPIA-2, the correlation between hub gene expression and LUAD patient survival could be made using 478 patients accessed from TCGA and GTEx. OS analysis indicated that the underexpression of AGER (p = 0.002), MGP (p = 0.00093), and PECAM1 (p = 0.0035) correlated with lower survival. Conversely, OS indicated that the overexpression of SLC2A1 (p = 2.4 × 10−5) leads to lower survival (Figure 3). From the analysis of all 44 hub genes, AGER, MGP, PECAM1, and SLC2A1 can be significantly correlated with the prognosis of LUAD, meaning these genes are candidate biomarkers for the early detection of LUAD.
Figure 3.
Kaplan–Meier survival analysis of candidate markers. (a) AGER survival analysis. (b) MGP survival analysis. (c) PECAM1 survival analysis. (d) SLC2A1 survival analysis.
3.4. Transcriptomic Verification of Key Genes Using LUAD Samples from TCGA and GTEx
GEPIA-2 was used to generate differential expression boxplots to validate the candidate biomarker genes for their mRNA differential expression between LUAD and normal lung tissues. GEPIA-2 accessed LUAD and normal lung gene expression data from TCGA and GTEx databases, and the data were used as a test dataset; 483 patients were accessed for tumor tissue gene expression data, and 347 patients were accessed for normal tissue gene expression data. GEPIA-2 produced differential expression boxplots and evaluated for statistical significance to show that AGER, MGP, and PECAM1 were downregulated in LUAD compared to normal tissues (Figure 4). GEPIA-2 verified that SLC2A1 was upregulated in LUAD compared to normal tissues (Figure 4). This verification revealed that AGER, MGP, and PECAM1 are significantly downregulated in LUAD and that SLC2A1 is significantly upregulated in LUAD.
Figure 4.
Validation of gene regulation with TCGA and GTEx databases. (a) AGER downregulation validation. (b) MGP downregulation validation. (c) PECAM1 downregulation validation. (d) SLC2A1 upregulation validation. * p < 0.01.
3.5. Proteomic Validation of Key Genes Using Immunohistochemistry
Using the HPA, AGER, MGP, PECAM1, and SLC2A1, normal lung and LUAD tumor IHC staining was accessed. The protein presence was represented by the amount of IHC staining within the normal human lung tissue and LUAD tumor tissue sample. Intensity and level of staining was observed across the biopsy samples. Figure 5 is representative of the overall findings. The IHC showed that AGER and PECAM1 have high intensity and strong staining in all normal lung samples (3/3 subjects), whereas in LUAD tumor tissues, these proteins were absent (0/5–6 showed any positive staining). SLC2A1 had no staining in the normal lung samples (0/3) but had strong expression in the 4/6 LUAD tumor cases. Matching normal lung tissue and LUAD tumor tissue IHC staining for MGP presence was unavailable. According to the extensive bioinformatic analysis, MGP is a highly accurate candidate for its function as a gene biomarker. Overall, AGER, PECAM1, and SLC2A1 were validated for their expression in normal and LUAD tumor IHC samples, showing that the candidate gene biomarkers can function as biologically significant biomarkers to aid in the detection of early-stage LUAD. The robustness of these tissue biomarkers is reinforced by transcriptomic analyses, underscoring their prognostic potential.
Figure 5.
Representative normal lung and tumor cell IHC staining from HPA.
4. Discussion
This study aimed to identify and validate a novel biomarker signature for early-stage LUAD. We successfully determined a biomarker signature composed of AGER, MGP, PECAM1, and SLC2A1 that can be utilized to accurately diagnose early-stage LUAD. Using GEO2R, 229 DEGs were identified and networked by the STRING database, and cytoHubba was used to screen 44 hub genes using the MCC and DMNC algorithms. Overall survival (OS) analysis was conducted to verify the correlation of each hub gene’s expression with LUAD prognosis using a TCGA and GTEx test dataset of 478 patient survival data points. AGER, MGP, PECAM1, and SLC2A1 were the key genes significantly correlated with poorer LUAD prognosis. GEPIA-2 was used to validate the genes’ expression in LUAD by accessing 483 LUAD tumor tissue expression patients and 347 normal lung tissue patients from TCGA and GTEx to act as a test dataset. Lastly, HPA was used to validate the downstream protein expression of each gene using IHC stains of both normal and LUAD tumor tissue. The cumulative approach of identifying genetic biomarkers using GEO, TCGA, GTEx, and HPA allows for the usage of a biologically significant biomarker signature for early-stage LUAD.
There have been several recent studies that identify early diagnostic markers or gene signatures for LUAD that each highlight varying aspects of tumor biology. Li et al. in 2023 used a proteomic profile to propose a panel of secreted proteins that included midkine, WFDC2, and CXCL14 as candidates for early-stage markers for LUAD [9]. Chen et al. performed an integrated analysis of various transcriptomic datasets that elucidated cell cycle-related genes, e.g., ASPM and CCNB2; these genes were upregulated and associated with a poorer LUAD prognosis [26]. These studies highlight that early-stage LUAD is characterized by diverse molecular changes and provides valuable context for interpreting our biomarker signature.
Superior to other panels, the four-gene signature identified in this study does not overlap with the above candidates, highlighting its novelty [18]. This signature is critically unique because it emphasizes the loss of certain tumor-suppressive factors alongside a known oncogenic driver. AGER, MGP, and PECAM1 are significantly downregulated. Downregulating AGER and PECAM1 is highly notable because of their involvement in maintaining normal alveolar cell adhesion and vascular integrity in the lung. The loss of these two genes could potentially facilitate the disorganized growth and angiogenesis that characterizes incipient tumors [20,27]. The upregulation of SLC2A1 in the signature shows a development in the Warburg effect that can even occur in earlier tumor stages, which is consistent with alternate reports that link SLC2A1 to aggressive lung cancer behavior [28]. The integration of gene expression, patient survival data, and proteomic validation showed a comprehensive validation of these markers’ clinical relevance. Compared to the existing literature that relied on mRNA data alone, these results demonstrate their significance through rigorous cross-validation.
We list our top four biomarkers as follows: AGER, MGP, PECAM1 (downregulated), and SLC2A1 (upregulated). Advanced Glycosylation End-Product Specific Receptor (AGER) is a transmembrane multi-ligand receptor in the immunoglobulin superfamily. AGER is involved in almost every cell type; AGER acts as an adhesion molecule in lung epithelium, creating contact between alveolar type I cells and their substrate [20]. This study showed AGER is underexpressed in LUAD compared with normal lung tissues; underexpression was correlated with a poorer cancer prognosis (Figure 3). IHC analysis also revealed that AGER had no staining in the LUAD tumor and had high staining in the normal lung, validating the downregulation of the gene in LUAD (Figure 5). AGER downregulation has been linked to the loss of cell differentiation and epithelial structure organization simultaneously with oncogenic transformation [20]. Furthermore, existing studies link AGER to a poorer prognosis of LUAD, highlighting its biomarker potential [29].
Matrix Gla Protein (MGP) is a protein in the extracellular matrix near vascular tissues. MGP is a calcification inhibitor that maintains normal vascular function [30]. This study showed that MGP is underexpressed in LUAD compared to normal lung tissues; underexpression was correlated with a poorer cancer prognosis (Figure 3). MGP downregulation has been linked to elevated bone morphogenic protein signaling, which is connected to arterial–venous malformations and elevated angiogenesis. MGP has also been shown to become upregulated in later-stage cancers to increase tumor stabilization and perfusion [30,31].
Platelet And Endothelial Cell Adhesion Molecule 1 (PECAM1) is a transmembrane protein, part of the immunoglobulin superfamily [32,33]. PECAM1 connects adjacent endothelial cells and regulates inflammation, leukocyte migration, and vascular responses during sepsis [32]. This study showed PECAM1 is underexpressed in LUAD compared to normal lung tissues, and the underexpression was linked to poorer cancer prognosis (Figure 3). IHC analysis also revealed that PECAM1 had no staining in the LUAD tumor and had high staining in the normal lung, validating the downregulation of the gene in LUAD (Figure 5). PECAM1 downregulation has been shown to mediate the secretion of metallopeptidase inhibitor 1, a protein linked to tumor cell proliferation [27].
Solute carrier family 2-facilitated glucose transporter member 1 (SLC2A1) is a glucose transporter highly concentrated in tissue endothelium and epithelium. SLC2A1 is involved in glucose uptake and allows for aerobic glycolysis [34]. This study showed that SLC2A1 is overexpressed in LUAD compared to normal lung tissues, and this overexpression was linked to a poorer cancer prognosis (Figure 3). IHC analysis also revealed that SLC2A1 had high staining in the LUAD tumor and had no staining in the normal lung, validating the upregulation of the gene in LUAD (Figure 5). SLC2A1 overexpression has been linked to transporting more glucose across the cell membrane and allowing for the continuation of aerobic glycolysis and the cell cycle, aiding tumor cell proliferation [34].
Circulating cell-free DNA (cfDNA) is being increasingly utilized in cancer diagnosis, including LUAD. The biomarker signature identified in this study, more specifically AGER and MGP, has unique methylation patterns in tumor versus normal tissues [13,21]. These differences in methylation are detectable in cfDNA and can provide a minimally invasive diagnostic potential to complement imaging techniques such as LDCT screening effectively [35]. Future research may investigate the early-detection performance of these biomarkers in patient plasma cfDNA samples.
Therapeutic strategies targeting the signature could aim to restore the downregulated tumor suppressors or potentially inhibit the upregulated oncogenic pathways in LUAD. Low-dose DNA methyltransferase inhibitors, such as 5-azacytidine, can reverse the gene silencing in lung tumors; analogs of retinoic acid have been shown to halt proliferation mediated by AGER and MGP [36,37]. The metabolic vulnerability highlighted by the upregulation of SLC2A1 can be targeted through inhibitors such as WZB117, which impair glucose uptake and could reduce LUAD cell proliferation [38]. The downregulation of PECAM1 is indicative of an enhanced angiogenesis, which can be mitigated by the multi-kinase inhibitor nintedanib [39]. Targeted small-molecule antagonists of AGER show promise in disrupting tumor-promoting AGER-mediated signaling [40]. Taken together, these strategies for modulating significant mediators of LUAD prognosis can support the translational potential of this biomarker signature.
The presented biomarker signature shows significant potential as a diagnostic tool in clinical practice. A combined immunohistochemical assay that incorporates AGER, MGP, PECAM1, and SLC2A1 could significantly aid pathology-based distinction between malignant and benign lesions in lung biopsy. Moreover, loss of AGER and PECAM1, coupled with an elevated SLC2A1 in staining, could identify these malignancies effectively in earlier stages of LUAD [29,41].
Additionally, blood-based detection assays that identify altered methylation in AGER and MGP, as well as circulating soluble proteins such as soluble AGER (sRAGE) and MGP, offer minimally invasive options. sRAGE shows potential in allowing for differentiation between LUAD and healthy individuals due to its consistently observed reduction in malignancy [42]. To reduce the prevalence of false positives, these minimally invasive tests could be used as a complement to clinical imaging.
5. Conclusions
This study identified and validated a biomarker signature of AGER, MGP, PECAM1, and SCL2A1 as key genes linked to tumorigenesis and poorer LUAD prognosis. The correlation of these genes to LUAD suggests their clinical usage as biomarkers in detecting early-stage LUAD and are possible targets for therapy. In vivo studies can be used to determine the specific role of these markers in tumor growth, invasion, and response to therapy to further validate these markers.
Author Contributions
S.S.S. was primarily responsible for conceptualization, conducting the research, analyzing the results, and writing the manuscript. C.M.C. reviewed the text and results. J.T.R. reviewed the datasets, methodology, and results. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The datasets analyzed during the current study are available in the Gene Expression Omnibus under accession number GSE32863 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32863, accessed on 22 September 2024) and are published by Selamat et al. (DOI: 10.1101/gr.132662.111).
Acknowledgments
The authors kindly acknowledge Roderick V. Jensen for his guidance in developing the methodology and Maurizio Fava at Massachusetts General Hospital and Harvard Medical School for their gracious support.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| NSCLC | Non-Small-Cell Lung Cancer |
| LUAD | Lung Adenocarcinoma |
| AAH | Atypical Adenomatous Hyperplasia |
| AIS | Adenocarcinoma In Situ |
| LDCT | Low-Dose CT |
| GEO | Gene Expression Omnibus |
| DEG | Differentially Expressed Gene |
| Log2FC | Log2 Fold Change |
| STRING | Search Tool for the Retrieval of Interacting Genes/Proteins |
| PPI | Protein–Protein Interaction |
| MCC | Maximal Clique Centrality |
| DMNC | Density of Maximum Neighborhood Component |
| GEPIA-2 | Gene Expression Profiling Interactive Analysis-2 |
| OS | Overall Survival |
| TCGA | The Cancer Genome Atlas |
| GTEx | Genotype-Tissue Expression Database |
| IHC | Immunohistochemistry |
| HPA | Human Protein Atlas |
| AGER | Advanced Glycosylation End-Product Specific Receptor |
| MGP | Matrix Gla Protein |
| PECAM1 | Platelet and Endothelial Cell Adhesion Molecule 1 |
| SLC2A1 | Solute Carrier Family 2-Facilitated Glucose Transporter Member 1 |
| cfDNA | Circulating Cell-Free DNA |
| sRAGE | Soluble Receptor for Advanced Glycation End-Products |
Appendix A
Table A1.
GEO2R annotation of all significant DEGs.
Table A1.
GEO2R annotation of all significant DEGs.
| Gene Symbol | Gene Title | p-Value | Log2FC |
|---|---|---|---|
| GCNT3 | Glucosaminyl (N-acetyl) transferase 3, mucin type | 6.25 × 10−10 | 3.47 |
| CEACAM5 | Carcinoembryonic antigen-related cell adhesion molecule 5 | 2.55 × 10−7 | 3.45 |
| SPP1 | Secreted phosphoprotein 1 | 2.61 × 10−8 | 3.38 |
| CST1 | Cystatin SN | 4.11 × 10−6 | 2.9 |
| SPINK1 | Serine peptidase inhibitor, Kazal type 1 | 9.49 × 10−5 | 2.85 |
| CRABP2 | Cellular retinoic acid-binding protein 2 | 7.23 × 10−9 | 2.75 |
| MMP11 | Matrix metallopeptidase 11 | 1.30 × 10−7 | 2.72 |
| XRCC2 | X-ray repair cross-complementing 2 | 1.65 × 10−7 | 2.62 |
| COMP | Cartilage oligomeric matrix protein | 3.81 × 10−6 | 2.59 |
| SEMA3E | Semaphorin 3E | 1.83 × 10−7 | 2.57 |
| FKBP14 | FK506-binding protein 14 | 1.88 × 10−7 | 2.54 |
| CHRNA5 | Cholinergic receptor nicotinic alpha 5 subunit | 7.94 × 10−8 | 2.51 |
| SHROOM4 | Shroom family member 4 | 7.24 × 10−8 | 2.44 |
| SFN | Stratifin | 1.64 × 10−11 | 2.43 |
| NLRP8 | NLR family pyrin domain-containing 8 | 1.41 × 10−7 | 2.43 |
| LMOD3 | Leiomodin 3 | 6.95 × 10−7 | 2.43 |
| LAD1 | Ladinin 1 | 2.39 × 10−13 | 2.42 |
| PROM2 | Prominin 2 | 6.75 × 10−12 | 2.42 |
| GSDMB | Gasdermin B | 9.54 × 10−12 | 2.42 |
| EEF1A2 | Eukaryotic translation elongation factor 1 alpha 2 | 7.99 × 10−5 | 2.42 |
| SERINC2 | Serine incorporator 2 | 5.37 × 10−10 | 2.4 |
| TDP1 | Tyrosyl-DNA phosphodiesterase 1 | 2.74 × 10−7 | 2.39 |
| MBTD1 | mbt domain-containing 1 | 2.95 × 10−7 | 2.39 |
| ITCH-IT1 | ITCH intronic transcript 1 | 2.07 × 10−6 | 2.34 |
| FCGBP | Fc fragment of IgG-binding protein | 5.37 × 10−7 | 2.33 |
| FMC1 | Formation of mitochondrial complex V assembly factor 1 homolog | 5.55 × 10−7 | 2.33 |
| DTWD2 | DTW domain-containing 2 | 5.68 × 10−7 | 2.33 |
| IL17RD | Interleukin 17 receptor D | 1.28 × 10−6 | 2.31 |
| HYPK | Huntingtin-interacting protein K | 8.37 × 10−9 | 2.3 |
| USP49 | Ubiquitin-specific peptidase 49 | 3.78 × 10−7 | 2.3 |
| MMP9 | Matrix metallopeptidase 9 | 3.94 × 10−6 | 2.3 |
| SEZ6L2 | Seizure-related 6 homolog-like 2 | 1.18 × 10−8 | 2.29 |
| APOPT1 | Apoptogenic 1, mitochondrial | 1.38 × 10−7 | 2.27 |
| ZNF682 | Zinc finger protein 682 | 2.15 × 10−6 | 2.27 |
| HSD17B7 | Hydroxysteroid 17-beta dehydrogenase 7 | 1.54 × 10−7 | 2.26 |
| ZNF483 | Zinc finger protein 483 | 9.67 × 10−7 | 2.26 |
| ZNF69 | Zinc finger protein 69 | 3.20 × 10−8 | 2.24 |
| COL1A1 | Collagen type I alpha 1 chain | 8.75 × 10−7 | 2.24 |
| EXO5 | Exonuclease 5 | 1.05 × 10−6 | 2.24 |
| MAGT1 | Magnesium transporter 1 | 1.23 × 10−7 | 2.23 |
| HNRNPU | Heterogeneous nuclear ribonucleoprotein U | 2.04 × 10−6 | 2.23 |
| GPR1 | G protein-coupled receptor 1 | 6.78 × 10−7 | 2.22 |
| TM4SF4 | Transmembrane 4 L six family member 4 | 1.28 × 10−3 | 2.21 |
| SLC2A1 | Solute carrier family 2 member 1 | 2.91 × 10−8 | 2.2 |
| ZNF14 | Zinc finger protein 14 | 7.02 × 10−8 | 2.2 |
| METTL21A | Methyltransferase-like 21A | 1.17 × 10−7 | 2.2 |
| ALPP | Alkaline phosphatase, placental | 1.65 × 10−7 | 2.2 |
| DENR | Density regulated re-initiation and release factor | 8.76 × 10−7 | 2.2 |
| ZNF394 | Zinc finger protein 394 | 2.44 × 10−7 | 2.19 |
| BLZF1 | Basic leucine zipper nuclear factor 1 | 3.08 × 10−7 | 2.19 |
| SPDEF | SAM pointed domain-containing ETS transcription factor | 4.41 × 10−8 | 2.18 |
| DMC1 | DNA meiotic recombinase 1 | 2.22 × 10−7 | 2.16 |
| LRRFIP1 | LRR-binding FLII-interacting protein 1 | 2.72 × 10−7 | 2.16 |
| MIGA1 | Mitoguardin 1 | 4.61 × 10−6 | 2.14 |
| MDK | Midkine (neurite growth-promoting factor 2) | 3.50 × 10−11 | 2.13 |
| AOC4P | Amine oxidase, copper-containing 4, pseudogene | 8.05 × 10−7 | 2.13 |
| CEACAM1 | Carcinoembryonic antigen-related cell adhesion molecule 1 | 9.58 × 10−11 | 2.12 |
| SSTR2 | Somatostatin receptor 2 | 2.99 × 10−6 | 2.12 |
| TMEM17 | Transmembrane protein 17 | 1.99 × 10−7 | 2.11 |
| SLC35E1 | Solute carrier family 35 member E1 | 3.62 × 10−7 | 2.11 |
| ZNF577 | Zinc finger protein 577 | 2.81 × 10−7 | 2.11 |
| SGPP2 | Sphingosine-1-phosphate phosphatase 2 | 8.66 × 10−15 | 2.1 |
| CAPN8 | Calpain 8 | 6.39 × 10−7 | 2.1 |
| CEP19 | Centrosomal protein 19 | 6.05 × 10−8 | 2.09 |
| DDX51 | DEAD-box helicase 51 | 5.58 × 10−8 | 2.07 |
| MCMDC2 | Minichromosome maintenance domain-containing 2 | 3.28 × 10−7 | 2.07 |
| PYCR1 | Pyrroline-5-carboxylate reductase 1 | 3.82 × 10−15 | 2.06 |
| YRDC | yrdC N6-threonylcarbamoyltransferase domain-containing | 9.04 × 10−7 | 2.06 |
| WDR74 | WD repeat domain 74 | 3.59 × 10−6 | 2.06 |
| OCIAD1 | OCIA domain-containing 1 | 9.42 × 10−8 | 2.05 |
| CCBE1 | Collagen- and calcium-binding EGF domains 1 | 4.29 × 10−6 | 2.05 |
| N4BP2 | NEDD4-binding protein 2 | 1.46 × 10−6 | 2.04 |
| PTGR2 | Prostaglandin reductase 2 | 1.18 × 10−5 | 2.04 |
| DUSP19 | Dual-specificity phosphatase 19 | 1.19 × 10−7 | 2.03 |
| TOP2A | Topoisomerase (DNA) II alpha | 3.55 × 10−9 | 2.02 |
| EID2B | EP300-interacting inhibitor of differentiation 2B | 1.33 × 10−7 | 2.01 |
| TRIM13 | Tripartite motif-containing 13 | 1.96 × 10−7 | 2.01 |
| TNFSF15 | Tumor necrosis factor superfamily member 15 | 8.00 × 10−6 | 2.01 |
| POFUT1 | Protein O-fucosyltransferase 1 | 9.23 × 10−8 | 2 |
| PODXL2 | Podocalyxin-like 2 | 3.46 × 10−6 | 2 |
| ENPP2 | Ectonucleotide pyrophosphatase/phosphodiesterase 2 | 2.81 × 10−15 | −2 |
| C11orf96 | Chromosome 11 open reading frame 96 | 1.56 × 10−12 | −2 |
| GYPC | Glycophorin C (Gerbich blood group) | 2.70 × 10−19 | −2 |
| WNT3A | Wnt family member 3A | 2.40 × 10−15 | −2 |
| MS4A7 | Membrane spanning 4-domains A7 | 1.07 × 10−12 | −2 |
| CALCRL | Calcitonin receptor-like receptor | 1.11 × 10−10 | −2 |
| PLPP3 | Phospholipid phosphatase 3 | 3.14 × 10−10 | −2 |
| SOSTDC1 | Sclerostin domain-containing 1 | 3.51 × 10−12 | −2 |
| SFTPD | Surfactant protein D | 1.97 × 10−10 | −2 |
| JAM2 | Junctional adhesion molecule 2 | 3.65 × 10−9 | −2 |
| RASL12 | RAS-like family 12 | 4.78 × 10−5 | −2 |
| TAGLN | Transgelin | 8.07 × 10−9 | −2.1 |
| CD34 | CD34 molecule | 1.78 × 10−7 | −2.1 |
| GRK5 | G protein-coupled receptor kinase 5 | 3.86 × 10−7 | −2.1 |
| STOM | Stomatin | 1.07 × 10−10 | −2.1 |
| ABI3BP | ABI family member 3-binding protein | 3.73 × 10−11 | −2.1 |
| CD52 | CD52 molecule | 4.08 × 10−7 | −2.1 |
| BCHE | Butyrylcholinesterase | 4.72 × 10−11 | −2.1 |
| SMAD6 | SMAD family member 6 | 1.69 × 10−7 | −2.1 |
| HYAL1 | Hyaluronoglucosaminidase 1 | 2.47 × 10−9 | −2.1 |
| SLPI | Secretory leukocyte peptidase inhibitor | 9.70 × 10−9 | −2.1 |
| TPSAB1 | Tryptase alpha/beta 1 | 3.32 × 10−8 | −2.1 |
| FBLN1 | Fibulin 1 | 1.06 × 10−7 | −2.1 |
| DPT | Dermatopontin | 2.72 × 10−11 | −2.1 |
| FCN1 | Ficolin 1 | 3.36 × 10−11 | −2.1 |
| KLF4 | Kruppel-like factor 4 | 4.62 × 10−11 | −2.1 |
| SOCS2 | Suppressor of cytokine signaling 2 | 1.39 × 10−9 | −2.1 |
| PPP1R14A | Protein phosphatase 1 regulatory inhibitor subunit 14A | 8.61 × 10−13 | −2.1 |
| SDCBP | Syndecan-binding protein | 7.32 × 10−9 | −2.1 |
| PTGDS | Prostaglandin D2 synthase | 3.03 × 10−12 | −2.1 |
| FPR1 | Formyl peptide receptor 1 | 5.96 × 10−7 | −2.2 |
| VSIG4 | V-set and immunoglobulin domain-containing 4 | 5.97 × 10−5 | −2.2 |
| ADAMTS1 | ADAM metallopeptidase with thrombospondin type 1 motif 1 | 3.52 × 10−15 | −2.2 |
| CLIC5 | Chloride intracellular channel 5 | 2.13 × 10−12 | −2.2 |
| CYYR1 | Cysteine and tyrosine rich 1 | 1.30 × 10−8 | −2.2 |
| VWF | von Willebrand factor | 6.63 × 10−7 | −2.2 |
| STX11 | Syntaxin 11 | 6.98 × 10−9 | −2.2 |
| RASIP1 | Ras-interacting protein 1 | 4.49 × 10−7 | −2.2 |
| CA2 | Carbonic anhydrase 2 | 3.23 × 10−12 | −2.2 |
| LAMP3 | Lysosomal-associated membrane protein 3 | 1.65 × 10−11 | −2.2 |
| FEZ1 | Fasciculation and elongation protein zeta 1 | 5.42 × 10−11 | −2.2 |
| MGP | Matrix Gla protein | 1.87 × 10−10 | −2.2 |
| RAMP3 | Receptor (G protein-coupled) activity-modifying protein 3 | 2.51 × 10−8 | −2.3 |
| PDK4 | Pyruvate dehydrogenase kinase 4 | 4.03 × 10−5 | −2.3 |
| PLA2G1B | Phospholipase A2 group IB | 2.12 × 10−12 | −2.3 |
| HOXA5 | Homeobox A5 | 6.17 × 10−7 | −2.3 |
| EPAS1 | Endothelial PAS domain protein 1 | 2.81 × 10−14 | −2.3 |
| PI16 | Peptidase inhibitor 16 | 6.75 × 10−12 | −2.3 |
| S100A4 | S100 calcium-binding protein A4 | 1.70 × 10−11 | −2.3 |
| CXCL12 | C-X-C motif chemokine ligand 12 | 5.52 × 10−11 | −2.3 |
| GAS1 | Growth arrest-specific 1 | 3.34 × 10−7 | −2.3 |
| GPC3 | Glypican 3 | 2.41 × 10−17 | −2.3 |
| MAL | mal, T-cell differentiation protein | 1.85 × 10−12 | −2.3 |
| CLEC14A | C-type lectin domain family 14 member A | 2.38 × 10−11 | −2.3 |
| CES1 | Carboxylesterase 1 | 3.23 × 10−10 | −2.3 |
| FABP5 | Fatty acid-binding protein 5 | 9.86 × 10−10 | −2.3 |
| MME | Membrane metallo-endopeptidase | 2.38 × 10−8 | −2.3 |
| IL33 | Interleukin 33 | 5.05 × 10−4 | −2.3 |
| ANOS1 | Anosmin 1 | 5.92 × 10−15 | −2.3 |
| C7 | Complement component 7 | 2.52 × 10−12 | −2.3 |
| ITLN1 | Intelectin 1 | 4.14 × 10−10 | −2.3 |
| DNASE1L3 | Deoxyribonuclease 1-like 3 | 6.60 × 10−14 | −2.4 |
| SCGB3A2 | Secretoglobin family 3A member 2 | 1.08 × 10−10 | −2.4 |
| PECAM1 | Platelet and endothelial cell adhesion molecule 1 | 1.42 × 10−4 | −2.4 |
| FHL1 | Four-and-a-half LIM domains 1 | 2.20 × 10−4 | −2.4 |
| ITM2A | Integral membrane protein 2A | 8.81 × 10−16 | −2.4 |
| EDNRB | Endothelin receptor type B | 1.64 × 10−11 | −2.4 |
| FAM110D | Family with sequence similarity 110 member D | 6.27 × 10−10 | −2.4 |
| ID3 | Inhibitor of DNA-binding 3, HLH protein | 6.42 × 10−13 | −2.4 |
| MAMDC2 | MAM domain-containing 2 | 1.10 × 10−4 | −2.4 |
| S100A8 | S100 calcium-binding protein A8 | 2.93 × 10−10 | −2.4 |
| SVEP1 | Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing 1 | 2.13 × 10−8 | −2.4 |
| C9orf24 | Chromosome 9 open reading frame 24 | 1.42 × 10−6 | −2.4 |
| SRPX | Sushi repeat-containing protein, X-linked | 8.31 × 10−17 | −2.5 |
| ACTG2 | Actin, gamma 2, smooth muscle, enteric | 2.37 × 10−12 | −2.5 |
| AQP4 | Aquaporin 4 | 5.42 × 10−11 | −2.5 |
| TSC22D1 | TSC22 domain family member 1 | 1.33 × 10−10 | −2.5 |
| CRYAB | Crystallin alpha B | 3.23 × 10−12 | −2.5 |
| MMRN1 | Multimerin 1 | 8.74 × 10−17 | −2.5 |
| CD300LG | CD300 molecule-like family member g | 7.31 × 10−19 | −2.5 |
| PCOLCE2 | Procollagen C-endopeptidase enhancer 2 | 2.97 × 10−17 | −2.5 |
| TSPAN7 | Tetraspanin 7 | 1.53 × 10−14 | −2.5 |
| COX7A1 | Cytochrome c oxidase subunit 7A1 | 1.68 × 10−13 | −2.5 |
| ABCA8 | ATP-binding cassette subfamily A member 8 | 3.43 × 10−10 | −2.5 |
| CDH5 | Cadherin 5 | 2.81 × 10−14 | −2.5 |
| PRG4 | Proteoglycan 4 | 2.90 × 10−12 | −2.6 |
| SOX18 | SRY-box 18 | 2.85 × 10−10 | −2.6 |
| CD93 | CD93 molecule | 1.95 × 10−8 | −2.6 |
| PGM5 | Phosphoglucomutase 5 | 1.33 × 10−7 | −2.6 |
| SRGN | Serglycin | 2.14 × 10−10 | −2.6 |
| CYP4B1 | Cytochrome P450 family 4 subfamily B member 1 | 5.78 × 10−18 | −2.6 |
| MARCO | Macrophage receptor with collagenous structure | 4.90 × 10−9 | −2.6 |
| ADH1B | Alcohol dehydrogenase 1B (class I), beta polypeptide | 5.60 × 10−15 | −2.6 |
| WIF1 | WNT inhibitory factor 1 | 1.29 × 10−8 | −2.6 |
| SDPR | Serum deprivation response | 3.88 × 10−16 | −2.6 |
| EFEMP1 | EGF-containing fibulin-like extracellular matrix protein 1 | 5.36 × 10−14 | −2.6 |
| HIGD1B | HIG1 hypoxia-inducible domain family member 1B | 2.13 × 10−13 | −2.6 |
| CPB2 | Carboxypeptidase B2 | 2.12 × 10−10 | −2.6 |
| C2orf40 | Chromosome 2 open reading frame 40 | 8.89 × 10−15 | −2.6 |
| TNNC1 | Troponin C1, slow skeletal and cardiac type | 1.59 × 10−14 | −2.7 |
| CD36 | CD36 molecule | 1.85 × 10−12 | −2.7 |
| RGCC | Regulator of cell cycle | 5.90 × 10−12 | −2.7 |
| CAV2 | Caveolin 2 | 2.43 × 10−11 | −2.7 |
| LDB2 | LIM domain-binding 2 | 2.12 × 10−12 | −2.7 |
| TIMP3 | TIMP metallopeptidase inhibitor 3 | 1.06 × 10−12 | −2.7 |
| CLDN5 | Claudin 5 | 8.58 × 10−12 | −2.7 |
| SEPP1 | Selenoprotein P, plasma, 1 | 6.48 × 10−18 | −2.7 |
| SPARCL1 | SPARC-like 1 | 2.92 × 10−14 | −2.7 |
| CPA3 | Carboxypeptidase A3 | 2.36 × 10−11 | −2.8 |
| TEK | TEK receptor tyrosine kinase | 4.42 × 10−15 | −2.8 |
| PEBP4 | Phosphatidylethanolamine-binding protein 4 | 4.29 × 10−11 | −2.8 |
| CFD | Complement factor D | 1.26 × 10−16 | −2.9 |
| FMO2 | Flavin-containing monooxygenase 2 | 1.74 × 10−15 | −2.9 |
| IL6 | Interleukin 6 | 1.28 × 10−11 | −2.9 |
| SPOCK2 | Sparc/osteonectin-, cwcv-, and kazal-like domains proteoglycan (testican) 2 | 1.89 × 10−12 | −2.9 |
| CRTAC1 | Cartilage acidic protein 1 | 4.74 × 10−5 | −2.9 |
| SFTPC | Surfactant protein C | 5.93 × 10−17 | −2.9 |
| AGER | Advanced glycosylation end-product-specific receptor | 9.41 × 10−18 | −3 |
| DCN | Decorin | 2.18 × 10−15 | −3 |
| LYVE1 | Lymphatic vessel endothelial hyaluronan receptor 1 | 2.02 × 10−9 | −3 |
| GPIHBP1 | Glycosylphosphatidylinositol-anchored high-density lipoprotein-binding protein 1 | 7.41 × 10−7 | −3 |
| MT1M | Metallothionein 1M | 5.58 × 10−12 | −3 |
| CLDN18 | Claudin 18 | 1.46 × 10−5 | −3 |
| PGC | Progastricsin | 1.85 × 10−12 | −3 |
| TMEM100 | Transmembrane protein 100 | 8.56 × 10−14 | −3 |
| MFAP4 | Microfibrillar-associated protein 4 | 2.52 × 10−12 | −3.1 |
| ADIRF | Adipogenesis regulatory factor | 5.46 × 10−12 | −3.1 |
| ACKR1 | Atypical chemokine receptor 1 (Duffy blood group) | 5.58 × 10−12 | −3.1 |
| INMT | Indolethylamine N-methyltransferase | 2.78 × 10−14 | −3.1 |
| GNG11 | G protein subunit gamma 11 | 3.44 × 10−6 | −3.2 |
| FOSB | FosB proto-oncogene, AP-1 transcription factor subunit | 1.51 × 10−17 | −3.2 |
| SCGB1A1 | Secretoglobin family 1A member 1 | 3.02 × 10−17 | −3.2 |
| CCL14 | C-C motif chemokine ligand 14 | 1.79 × 10−6 | −3.2 |
| TCF21 | Transcription factor 21 | 1.65 × 10−8 | −3.4 |
| PLAC9 | Placenta-specific 9 | 6.10 × 10−14 | −3.5 |
| GKN2 | Gastrokine 2 | 3.61 × 10−11 | −3.7 |
| CAV1 | Caveolin 1 | 1.85 × 10−10 | −3.7 |
| SFTPA1 | Surfactant protein A1 | 5.78 × 10−18 | −3.7 |
| HBA1 | Hemoglobin subunit alpha 1 | 8.81 × 10−16 | −3.7 |
| ADH1A | Alcohol dehydrogenase 1A (class I), alpha polypeptide | 3.11 × 10−21 | −3.7 |
| CLEC3B | C-type lectin domain family 3 member B | 2.41 × 10−17 | −3.7 |
| FABP4 | Fatty acid-binding protein 4 | 9.37 × 10−12 | −3.8 |
| HBA2 | Hemoglobin subunit alpha 2 | 5.69 × 10−14 | −3.8 |
| FCN3 | Ficolin 3 | 3.10 × 10−19 | −3.8 |
| FAM107A | Family with sequence similarity 107 member A | 3.11 × 10−21 | −4.1 |
| ITLN2 | Intelectin 2 | 4.32 × 10−16 | −4.1 |
| MCEMP1 | Mast cell-expressed membrane protein 1 | 1.75 × 10−13 | −4.2 |
| CA4 | Carbonic anhydrase 4 | 2.41 × 10−10 | −4.2 |
| HBB | Hemoglobin subunit beta | 5.78 × 10−18 | −4.4 |
Appendix B
Figure A1.
All edges (lines) represent protein interactions between specific nodes (proteins).
References
- Siegel, R.L.; Giaquinto, A.N.; Jemal, A. Cancer statistics, 2024. CA Cancer J. Clin. 2024, 74, 12–49. [Google Scholar] [CrossRef] [PubMed]
- Li, C.; Lei, S.; Ding, L.; Xu, Y.; Wu, X.; Wang, H.; Zhang, Z.; Gao, T.; Zhang, Y.; Li, L. Global burden and trends of lung cancer incidence and mortality. Chin. Med. J. 2023, 136, 1583–1590. [Google Scholar] [CrossRef]
- Freitas, C.; Sousa, C.; Machado, F.; Serino, M.; Santos, V.; Cruz-Martins, N.; Teixeira, A.; Cunha, A.; Pereira, T.; Oliveira, H.P.; et al. The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer. Front. Oncol. 2021, 11, 634316. [Google Scholar] [CrossRef]
- Dubin, S.; Griffin, D. Lung Cancer in Non-Smokers. Mo. Med. 2020, 117, 375–379. [Google Scholar]
- Kontomanolis, E.N.; Koutras, A.; Syllaios, A.; Schizas, D.; Mastoraki, A.; Garmpis, N.; Diakosavvas, M.; Angelou, K.; Tsatsaris, G.; Pagkalos, A.; et al. Role of Oncogenes and Tumor-suppressor Genes in Carcinogenesis: A Review. Anticancer Res. 2020, 40, 6009–6015. [Google Scholar] [CrossRef] [PubMed]
- Travis, W.D.; Brambilla, E.; Nicholson, A.G.; Yatabe, Y.; Austin, J.H.M.; Beasley, M.B.; Chirieac, L.R.; Dacic, S.; Duhig, E.; Flieder, D.B.; et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J. Thorac. Oncol. 2015, 10, 1243–1260. [Google Scholar] [CrossRef]
- Liang, P.; Peng, M.; Tao, J.; Wang, B.; Wei, J.; Lin, L.; Cheng, B.; Xiong, S.; Li, J.; Li, C.; et al. Development of a genome atlas for discriminating benign, preinvasive, and invasive lung nodules. MedComm (2020) 2024, 5, e644. [Google Scholar] [CrossRef] [PubMed]
- Gong, J.; Yu, D. Mapping the immune terrain in lung adenocarcinoma progression: Tfh-like cells in tertiary lymphoid structures. Cell Oncol. 2024, 47, 1493–1496. [Google Scholar] [CrossRef]
- Li, J.; Li, J.; Hao, H.; Lu, F.; Wang, J.; Ma, M.; Jia, B.; Zhuo, M.; Wang, J.; Chi, Y.; et al. Secreted proteins MDK, WFDC2, and CXCL14 as candidate biomarkers for early diagnosis of lung adenocarcinoma. BMC Cancer 2023, 23, 110. [Google Scholar] [CrossRef]
- Boutsikou, E.; Hardavella, G.; Fili, E.; Bakiri, A.; Gaitanakis, S.; Kote, A.; Samitas, K.; Gkiozos, I. The Role of Biomarkers in Lung Cancer Screening. Cancers 2024, 16, 1980. [Google Scholar] [CrossRef]
- Huang, Y.; Ma, S.; Xu, J.Y.; Qian, K.; Wang, Y.; Zhang, Y.; Tan, M.; Xiao, T. Prognostic biomarker discovery based on proteome landscape of Chinese lung adenocarcinoma. Clin. Proteom. 2024, 21, 2. [Google Scholar] [CrossRef] [PubMed]
- Mo, L.; Wei, B.; Liang, R.; Yang, Z.; Xie, S.; Wu, S.; You, Y. Exploring potential biomarkers for lung adenocarcinoma using LC-MS/MS metabolomics. J. Int. Med. Res. 2020, 48, 300060519897215. [Google Scholar] [CrossRef] [PubMed]
- Selamat, S.A.; Chung, B.S.; Girard, L.; Zhang, W.; Zhang, Y.; Campan, M.; Siegmund, K.D.; Koss, M.N.; Hagen, J.A.; Lam, W.L.; et al. Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression. Genome Res. 2012, 22, 1197–1211. [Google Scholar] [CrossRef]
- Shen, Y.; Dong, S.; Liu, J.; Zhang, L.; Zhang, J.; Zhou, H.; Dong, W. Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets. Biomed. Res. Int. 2020, 2020, 9710421. [Google Scholar] [CrossRef]
- Skanland, S.S.; Cremaschi, A.; Bendiksen, H.; Hermansen, J.U.; Thimiri Govinda Raj, D.B.; Munthe, L.A.; Tjonnfjord, G.E.; Tasken, K. An in vitro assay for biomarker discovery and dose prediction applied to ibrutinib plus venetoclax treatment of CLL. Leukemia 2020, 34, 478–487. [Google Scholar] [CrossRef]
- Zhang, Y.; Zheng, Y.; Fu, Y.; Wang, C. Identification of biomarkers, pathways and potential therapeutic agents for white adipocyte insulin resistance using bioinformatics analysis. Adipocyte 2019, 8, 318–329. [Google Scholar] [CrossRef] [PubMed]
- Ding, L.; Getz, G.; Wheeler, D.A.; Mardis, E.R.; McLellan, M.D.; Cibulskis, K.; Sougnez, C.; Greulich, H.; Muzny, D.M.; Morgan, M.B.; et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 2008, 455, 1069–1075. [Google Scholar] [CrossRef]
- Hamad, W.; Grigore, B.; Walford, H.; Peters, J.; Alexandris, P.; Bonfield, S.; Standen, L.; Boscott, R.; Behiyat, D.; Kuhn, I.; et al. Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review. Cancer Epidemiol. Biomarkers Prev. 2025, 34, 19–34. [Google Scholar] [CrossRef]
- Cao, W.; Tang, Q.; Zeng, J.; Jin, X.; Zu, L.; Xu, S. A Review of Biomarkers and Their Clinical Impact in Resected Early-Stage Non-Small-Cell Lung Cancer. Cancers 2023, 15, 4561. [Google Scholar] [CrossRef]
- Marinakis, E.; Bagkos, G.; Piperi, C.; Roussou, P.; Diamanti-Kandarakis, E. Critical role of RAGE in lung physiology and tumorigenesis: A potential target of therapeutic intervention? Clin. Chem. Lab. Med. 2014, 52, 189–200. [Google Scholar] [CrossRef]
- Caiado, H.; Cancela, M.L.; Conceicao, N. Assessment of MGP gene expression in cancer and contribution to prognosis. Biochimie 2023, 214, 49–60. [Google Scholar] [CrossRef] [PubMed]
- Cahill, C.M.; Sarang, S.S.; Bakshi, R.; Xia, N.; Lahiri, D.K.; Rogers, J.T. Neuroprotective Strategies and Cell-Based Biomarkers for Manganese-Induced Toxicity in Human Neuroblastoma (SH-SY5Y) Cells. Biomolecules 2024, 14, 647. [Google Scholar] [CrossRef] [PubMed]
- Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef]
- Tang, Z.; Li, C.; Kang, B.; Gao, G.; Li, C.; Zhang, Z. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017, 45, W98–W102. [Google Scholar] [CrossRef]
- Thul, P.J.; Lindskog, C. The human protein atlas: A spatial map of the human proteome. Protein Sci. 2018, 27, 233–244. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Jin, L.; Jiang, Z.; Liu, S.; Feng, W. Identifying and Validating Potential Biomarkers of Early Stage Lung Adenocarcinoma Diagnosis and Prognosis. Front. Oncol. 2021, 11, 644426. [Google Scholar] [CrossRef]
- Abraham, V.; Cao, G.; Parambath, A.; Lawal, F.; Handumrongkul, C.; Debs, R.; DeLisser, H.M. Involvement of TIMP-1 in PECAM-1-mediated tumor dissemination. Int. J. Oncol. 2018, 53, 488–502. [Google Scholar] [CrossRef]
- Mathew, M.; Nguyen, N.T.; Bhutia, Y.D.; Sivaprakasam, S.; Ganapathy, V. Metabolic Signature of Warburg Effect in Cancer: An Effective and Obligatory Interplay between Nutrient Transporters and Catabolic/Anabolic Pathways to Promote Tumor Growth. Cancers 2024, 16, 504. [Google Scholar] [CrossRef]
- Wang, Q.; Zhu, W.; Xiao, G.; Ding, M.; Chang, J.; Liao, H. Effect of AGER on the biological behavior of non-small cell lung cancer H1299 cells. Mol. Med. Rep. 2020, 22, 810–818. [Google Scholar] [CrossRef]
- Sharma, B.; Albig, A.R. Matrix Gla protein reinforces angiogenic resolution. Microvasc Res. 2013, 85, 24–33. [Google Scholar] [CrossRef]
- Jaminon, A.M.G.; Dai, L.; Qureshi, A.R.; Evenepoel, P.; Ripsweden, J.; Soderberg, M.; Witasp, A.; Olauson, H.; Schurgers, L.J.; Stenvinkel, P. Matrix Gla protein is an independent predictor of both intimal and medial vascular calcification in chronic kidney disease. Sci. Rep. 2020, 10, 6586. [Google Scholar] [CrossRef] [PubMed]
- Villar, J.; Zhang, H.; Slutsky, A.S. Lung Repair and Regeneration in ARDS: Role of PECAM1 and Wnt Signaling. Chest 2019, 155, 587–594. [Google Scholar] [CrossRef]
- Woodfin, A.; Voisin, M.B.; Nourshargh, S. PECAM-1: A multi-functional molecule in inflammation and vascular biology. Arterioscler. Thromb. Vasc. Biol. 2007, 27, 2514–2523. [Google Scholar] [CrossRef]
- Wang, Y.; Shi, S.; Ding, Y.; Wang, Z.; Liu, S.; Yang, J.; Xu, T. Metabolic reprogramming induced by inhibition of SLC2A1 suppresses tumor progression in lung adenocarcinoma. Int. J. Clin. Exp. Pathol. 2017, 10, 10759–10769. [Google Scholar]
- Jin, Y.; Lu, R.; Liu, F.; Jiang, G.; Wang, R.; Zheng, M. DNA methylation analysis in plasma for early diagnosis in lung adenocarcinoma. Medicine 2024, 103, e38867. [Google Scholar] [CrossRef] [PubMed]
- Mahesh, S.; Saxena, A.; Qiu, X.; Perez-Soler, R.; Zou, Y. Intratracheally administered 5-azacytidine is effective against orthotopic human lung cancer xenograft models and devoid of important systemic toxicity. Clin. Lung Cancer 2010, 11, 405–411. [Google Scholar] [CrossRef]
- Zito, G.; Naselli, F.; Saieva, L.; Raimondo, S.; Calabrese, G.; Guzzardo, C.; Forte, S.; Rolfo, C.; Parenti, R.; Alessandro, R. Retinoic Acid affects Lung Adenocarcinoma growth by inducing differentiation via GATA6 activation and EGFR and Wnt inhibition. Sci. Rep. 2017, 7, 4770. [Google Scholar] [CrossRef]
- Liu, Y.; Cao, Y.; Zhang, W.; Bergmeier, S.; Qian, Y.; Akbar, H.; Colvin, R.; Ding, J.; Tong, L.; Wu, S.; et al. A small-molecule inhibitor of glucose transporter 1 downregulates glycolysis, induces cell-cycle arrest, and inhibits cancer cell growth in vitro and in vivo. Mol. Cancer Ther. 2012, 11, 1672–1682. [Google Scholar] [CrossRef] [PubMed]
- Reck, M.; Kaiser, R.; Mellemgaard, A.; Douillard, J.Y.; Orlov, S.; Krzakowski, M.; von Pawel, J.; Gottfried, M.; Bondarenko, I.; Liao, M.; et al. Docetaxel plus nintedanib versus docetaxel plus placebo in patients with previously treated non-small-cell lung cancer (LUME-Lung 1): A phase 3, double-blind, randomised controlled trial. Lancet Oncol. 2014, 15, 143–155. [Google Scholar] [CrossRef]
- Manigrasso, M.B.; Pan, J.; Rai, V.; Zhang, J.; Reverdatto, S.; Quadri, N.; DeVita, R.J.; Ramasamy, R.; Shekhtman, A.; Schmidt, A.M. Small Molecule Inhibition of Ligand-Stimulated RAGE-DIAPH1 Signal Transduction. Sci. Rep. 2016, 6, 22450. [Google Scholar] [CrossRef]
- Tan, Z.; Yang, C.; Zhang, X.; Zheng, P.; Shen, W. Expression of glucose transporter 1 and prognosis in non-small cell lung cancer: A pooled analysis of 1665 patients. Oncotarget 2017, 8, 60954–60961. [Google Scholar] [CrossRef] [PubMed]
- Jing, R.; Cui, M.; Wang, J.; Wang, H. Receptor for advanced glycation end products (RAGE) soluble form (sRAGE): A new biomarker for lung cancer. Neoplasma 2010, 57, 55–61. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).