Prospective proteomics for discovering biomarkers in lung adenocarcinoma: a literature review

Introduction

Lung adenocarcinoma (LUAD) is one of the most common types of non-small cell lung cancer (NSCLC), accounting for approximately 40% of all lung cancer cases (1). It poses substantial clinical challenges due to its molecular heterogeneity and frequent late-stage diagnosis. While smoking remains a primary risk factor, a number of LUAD cases in never-smokers can be found, suggesting distinct etiological mechanisms (2). With an improved understanding of the mechanisms underlying LUAD, the discovery of novel tumor markers, and the continuous development of new drugs and treatment modalities, a reduction in lung cancer mortality is anticipated (3). However, LUAD remains one of the most invasive and lethal tumor types, often diagnosed at advanced stages involving metastatic tumors. This is primarily due to the challenge of distinguishing benign from malignant solid lung nodules at an early stage, resulting in a 5-year survival rate of less than 20% (4-6). The identification of molecular markers could contribute significantly to the development of more precise diagnostic tools for lung cancer and is essential in cancer diagnosis and treatment (7,8). Current imaging modalities and tissue biopsies exhibit limited sensitivity for early detection, while invasive procedures carry risks of complications. Liquid biopsy-based molecular markers could address these limitations by enabling noninvasive monitoring and risk stratification. However, clinically validated biomarkers for early LUAD detection remain scarce, underscoring the urgent need for robust diagnostic tools.

Proteomic technologies have emerged as powerful platforms for biomarker discovery, offering comprehensive insights into disease-associated protein alterations across tissues and biofluids (9,10). Modern mass spectrometry (MS)-based workflows achieve high sensitivity in quantifying >5,000 proteins, while high-specificity aptamer-based platforms like SomaScan enable high-throughput analysis of low-abundance targets (11,12). Proteomics directly characterizes functional effectors of cellular processes, including post-translational modifications (PTMs) and protein-protein interactions critical to tumor progression (13-15). The clinical relevance of proteomic biomarkers stems from their detectability in readily accessible specimens-serum proteomic signatures correlate strongly with tissue-derived profiles, while urine exosomes contain tumor-specific protein cargo (16,17). Systematic integration of multi-specimen proteomic data could reveal organ-specific metastatic patterns and therapy resistance mechanisms (18).

This review critically evaluates proteomics advancements in LUAD biomarkers research, comparing the technical and clinical merits of major proteomic platforms across various specimens. It systematically catalogs experimentally validated LUAD-associated proteins demonstrating diagnostic/prognostic potential, and discusses challenges in clinical translation, such as optimizing biomarker specificity and meeting regulatory requirements. The integration of proteomics with other omics modalities will further establish a robust framework for identifying precision biomarkers. The goal is to guide future biomarker development for precision LUAD management (Figure 1). We present this article in accordance with the Narrative Review reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1092/rc).

Figure 1 Flowchart for screening and identification of lung adenocarcinoma biomarkers using proteomic technologies. BALF, bronchoalveolar lavage fluid; DEPs, differentially expressed proteins; MS, mass spectrometry; PEA, proximity extension assay.

Methods

We systematically searched PubMed and Web of Science databases from 2002 to 2025 to identify studies investigating biomarkers for the diagnosis, treatment, and prognosis of LUAD. Secondary references from retrieved articles were additionally screened. The search strategy summary for this study is detailed in Table 1. Inclusion criteria encompassed: (I) proteomics studies on LUAD; and (II) biomarkers validated for LUAD diagnosis, treatment response assessment, or prognosis prediction (recurrence, metastasis, or survival). We excluded non-proteomic biomarkers along with those lacking diagnostic or therapeutic utility. Moreover, non-original research (e.g., case reports, letters, short communications, editorials), conference abstracts without full-text, and non-English publications were excluded to ensure translational accuracy and data homogeneity. Following screening, 133 studies met eligibility criteria and were included in the final analysis (Figure S1).

Development of proteomics techniques

MS-based proteomics techniques

MS ionizes biological samples and separates resulting ions by their mass-to-charge ratio (m/z), enabling ultrahigh analytical sensitivity (amol-level detection limit) in protein detection and quantification in complex matrices. MS facilitates high-resolution analysis of PTMs such as phosphorylation and glycosylation; analyses of accessible biofluids (serum, plasma, and urine) have proven particularly valuable for non-invasive cancer monitoring (19), valuable for non-invasive cancer monitoring, revealing dynamic biomarker expression patterns (20-22). While two-dimensional gel electrophoresis (2-DE) remains a foundational separation method, its integration with MS (2-DE-MS) enhances proteome coverage, facilitating a deeper understanding of protein profiles in biological samples (23). In a clinical study conducted by Mao et al., proteomic analysis of LUAD cells and exosomes overexpressing BCAR1 (BCAR1-OE) by MS revealed the involvement of BCAR1 in the formation of invasive circulating tumor cells (CTCs) and immune evasion, potentially making it a novel therapeutic target in LUAD; the analytical sensitivity reached 10⁻¹⁸ mol, yet the clinical sensitivity requires large-sample validation (24). Technical limitations persist in analyzing intricate samples like tissue lysates, where dynamic range constraints and ionization suppression effects reduce low-abundance protein detection. Ongoing advancements in high-field asymmetric waveform ion mobility spectrometry (FAIMS) and trapped ion mobility spectrometry (TIMS) aim to improve resolution and targeting capabilities, enabling better detection and analysis of proteins (25). Yet the reliability of MS-based biomarker discovery is critically modulated by pre-analytical variables: Hemolysis in blood samples elevates hemoglobin-derived peptides, like HBA1, confounding true tumor-associated signals (26). Despite these challenges, MS continues to serve as the gold-standard platform for biomarker discovery, though its high analytical sensitivity does not ensure clinical utility, with optimized workflows achieving highly sensitivity in controlled studies.

Proximity extension assay (PEA) technique

The PEA, an exclusive high-throughput, high-specificity, high-analytical-sensitivity, and wide-dynamic-range targeted proteomics technology developed by Olink Proteomics, combines real-time quantitative polymerase chain reaction (qPCR) with multiplex immunoassays (27). PEA harnesses dual recognition of target biomolecules using a pair of unique DNA oligonucleotide-labeled matching antibodies. Demonstrating exceptional high sensitivity in targeted detection, PEA typically outperforms liquid chromatography (LC)-MS methods, offering a broader dynamic range, superior precision, and reproducibility within the pg/mL range (28). The origins of PEA technology as an innovator in the field of proteomics can be traced back to 2002, when Professor Ulf Landegren’s team at Uppsala University in Sweden first proposed the proximity ligation assay (PLA) (29). By 2008, PLA technology had found widespread application in medicine, including the detection of multiple biomarkers in plasma samples (27). In 2011, Olink Bioscience further refined PLA, introducing the PEA technique, which resulted in enhanced detection efficiency and sensitivity (30). However, PEA’s superior analytical performance remains vulnerable to hemolyzed plasma (H-index >0.05), which generates false-positive signals for heme-binding proteins (31). In 2013, the team led by Stine Buch Thorsen leveraged PEA to screen 74 different biomarkers in the plasma of colorectal cancer (CRC) patients and healthy individuals. They reported a combination of carcinoembryonic antigen (CEA), transferrin receptor-1 (TFRC), macrophage migration inhibitory factor (MIF), osteopontin (OPN/SPP1), and cancer antigen 242 (CA242) proteins as serological biomarkers for detecting CRC, with a clinical sensitivity of 56% and clinical specificity of 90%, advocating the advantages and potential of this biomarker panel. Clinical sensitivity reflects diagnostic performance, distinct from analytical detection capability. While PEA technology has facilitated the identification of highly diagnostic protein biomarkers in several diseases, its limitation lies in the restricted number of proteins detected in each analysis and its ability only to identify known proteins, hindering comprehensive screening and detection of functionally significant yet unknown proteins.

Spatial proteomics techniques

Spatial proteomics technology aims to explore the spatial distribution characteristics of proteins within cells, tissues, or organs, leveraging high analytical sensitivity (µm-scale resolution) to associate protein expression with their specific spatial locations. While traditional proteomics techniques primarily focus on the types and abundance of proteins, spatial proteomics explores the positional information of proteins within the tissue microenvironment (32,33). Mechanistically, this technology involves the use of precise laser capture microdissection (LCM) to extract targeted tissue regions or cells, followed by optimized non-destructive extraction and enzymatic digestion of proteins into peptides and subsequent high-sensitivity MS analysis to determine the expression patterns of proteins across different spatial locations (34,35). Modern sequencing-based spatial proteomics techniques offer a unique “spatial targeting” detection advantage, exhibiting significant practical value in tumor immune microenvironment research and biomarker screening (36,37). McNamara et al. conducted spatial proteomics analysis of tissues from human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients (n=28), which revealed that proteomic alterations after a single cycle of HER2-targeted therapy could facilitate the identification of tumors that ultimately achieved pathological complete response, with performance superior to pre-treatment measures or transcriptomic changes. These findings offer new avenues for personalized treatment of early HER2-positive breast cancer. This study also indicates the potential of spatial proteomics analysis of tumor immune microenvironments during therapy to circumvent the limitations of dissociation techniques, thereby enhancing our ability to reveal features associated with targeted therapeutic responses in cancer (38). Notably, in spatial proteomics, pre-analytical imperatives critically dictate data reliability: repeated freeze-thaw cycles (FTCs), for instance, destabilize antibody-DNA conjugates and double the limit of detection (LOD) for cytokines, while prolonged pre-fixation ischemia abolishes phosphorylation gradients in pathways such as epidermal growth factor receptor (EGFR) (39). While spatial proteomics technology enables the localization and quantitative analysis of proteins at the tissue and cellular levels, with remarkable analytical sensitivity, the technical and data analysis complexities necessitate further exploration and innovation.

SomaScan proteomics technology

SomaScan is a highly multiplexed platform offering broad analytical sensitivity utilized for quantitative measurement of proteins in complex matrices such as plasma or serum, wherein protein concentration features are converted into corresponding DNA features and then quantified on a commercial DNA microarray platform (40). In 2007, SomaLogic leveraged specially processed nucleic acid aptamers to measure protein levels in blood or tissue, encompassing half of the human proteome. This technology has found widespread applications in biomarker discovery, cohort studies, and drug development endeavors (41). Mehan et al. utilized SomaScan technology to generate blood-based proteomic profiles from NSCLC cases (n=94) and controls (n=269), including long-term smokers and individuals with benign pulmonary nodules. A total of 1,033 proteins were identified, ultimately yielding a 7-marker panel, which achieved an area under the curve (AUC) of 0.85 corresponding to high clinical sensitivity for nodule malignancy classification across all cases. This non-invasive assay could enhance the positive predictive value of CT screening, mitigating the need for invasive procedures in cases of benign pulmonary nodules (42). Preanalytical variation can compromise protein abundance measurements, manifesting either as in vitro or spurious hemolysis, or alternatively as repeated FTCs (43). Currently, the integration of SomaScan with immunoassays for validation and identification of proteins is being explored to explore potential candidate biomarkers in various diseases. However, SomaScan technology also presents certain limitations: while providing high analytical sensitivity, its clinical sensitivity remains constrained by biological heterogeneity, similar to PEA technology, it can only detect known proteins and is unable to identify unknown proteins. Besides, the technology is associated with substantial costs and requires specialized equipment and skilled personnel for operation.

In summary, MS-based, PEA, spatial proteomics, and SomaScan technologies each have unique strengths and limitations in proteomic research, differing in dynamic range, detection limits, input needs, throughput, and cost per sample. MS-based methods are best for unbiased discovery and PTM analysis, PEA and SomaScan excel in high throughput and sensitivity for targeted protein quantification, and spatial proteomics offers crucial positional context for tissue microenvironments. Choosing the right platform depends on research goals, with Table 2 aiding in decision-making by comparing key performance metrics.

LUAD protein biomarkers

The discovery of novel biomarkers offers new avenues for the early diagnosis and treatment of LUAD. In recent years, researchers have utilized proteomic technologies to identify potential protein biomarkers from various biological samples, including tissues, blood, urine, and sputum, which hold promise for diagnostic and prognostic applications (Table 3).

Biomarkers in tissues

Tissue-derived biomarkers provide direct molecular insights into tumor pathophysiology (34). Over the past few decades, researchers have developed methods to extract proteins from formalin-fixed paraffin-embedded (FFPE) and fresh tissues, enabling their analysis using cutting-edge techniques like MS and protein arrays (84). For instance, enzymes like tyrosyl-tRNA synthetase (TyrRSH), microtubule-actin crosslinking factor 1 (MACF-1) (15), thymosin β4 (TMSB4), ubiquitin, cytochrome C (85), among others have been identified as candidate diagnostic biomarkers for lung cancer. Notably, several candidate biomarkers (e.g., TyrRSH/MACF-1) were identified in single-center studies with limited cohort sizes and lack independent validation, potentially compromising their diagnostic reliability due to insufficient statistical power. Protein biomarkers derived from cancer tissues may exhibit higher sensitivity and clinical applicability for pathological diagnosis and prognostic evaluation of LUAD. However, tissue sample acquisition involves invasive procedures, limiting its applicability for early disease screening and diagnosis. Yet, certain LUAD-specific proteins, due to cellular disruption in tissues, can also be detected in the blood. For example, CEA, a broad-spectrum tumor marker, demonstrated elevated levels in various cancers, including LUAD, and is detectable in both tissues and blood, highlighting its value for monitoring disease progression (86). Cytokeratin 19 fragments (CYFRA21-1) are predominantly found in lung alveolar epithelium and typically released into the bloodstream upon cellular disruption in LUAD, serving as an adjunct diagnostic marker in both tissue pathology and blood-based assays (87). Şenbabaoğlu et al. further leveraged spatial omics through the MOSBY model to identify two clinically significant colocalization signatures: an endoplasmic reticulum (ER) stress-associated feature predicting chemotherapy resistance in LUAD, and a T-effector/cysteine signature indicating poor prognosis across cancers (67). Critically, MOSBY-derived spatial biomarkers demonstrated survival prediction power independent of transcriptomic data (concordance indices competitive with multimodal networks), supporting their clinical utility for treatment stratification. Hence, for early screening, diagnosis, disease monitoring, and treatment evaluation in LUAD, non-invasive and easily accessible biological samples are crucial for obtaining more efficient biomarkers.

Biomarkers in blood

The circulatory system distributes blood to every part of the body, carrying proteins released by cells and tissues. Therefore, while the most prominent molecular alterations typically occur at the primary tumor site and surrounding tissues, these changes may also be detectable in the blood. Whole blood comprises serum, plasma, red blood cells (RBCs), white blood cells (WBCs), and clotting factors. Among these components, serum and plasma are the most commonly used components in routine blood tests due to their rich abundance of proteins, which are synthesized, secreted, shed, or lost from cells and tissues throughout the body (88-90). Biomarkers detected in the blood can be utilized for the diagnosis, risk assessment, prognosis monitoring, and evaluation of treatment effectiveness in LUAD (91). Gasparri et al. utilized proteomics to analyze the protein expression profiles in the serum of lung cancer patients (n=46) and high-risk non-cancer participants (n=41). Arylsulfatase A (ARSA) was identified as a promising candidate biomarker for early lung cancer diagnosis, capable of distinguishing early lung cancer patients from high-risk healthy participants with high specificity and selectivity (71). The promising diagnostic performance of ARSA reported by Gasparri et al. requires cautious interpretation, as the modest cohort size (n=87) may compromise statistical power for reliable validation. While demonstrating high specificity in the discovery phase, the biomarker’s generalizability warrants confirmation in larger, multi-center cohorts to mitigate potential overfitting risks. Patz et al. analyzed protein expression data from serum samples of 100 lung cancer patients as a training set and ultimately identified a panel of proteins, including CEA, retinol-binding protein (RBP), squamous cell carcinoma (SCC), and alpha-1 antitrypsin (A1AT), which demonstrated optimal accuracy in the detection and diagnosis of lung cancer (92). Furthermore, proteomic analysis based on exosomes revealed various tumor-related proteins originating from LUAD cells and detectable in patient blood, further supporting their potential as liquid biopsy markers (93). Thus, blood serves as a primary source for identifying protein biomarkers in LUAD. However, blood biomarkers still exhibit certain limitations in disease diagnosis. After specific proteins are released into the blood, they may become significantly diluted due to the large blood volume, making it challenging to detect target proteins. This results in insufficient sensitivity and specificity for early disease detection. Besides, the presence of high-abundance proteins in the blood reduces the detection rate of low-abundance, yet crucial proteins.

Biomarkers in urine

Compared to blood, urine sampling entails minimal protein degradation, exhibits greater post-collection stability, and is non-invasive and easily accessible. Therefore, screening and identifying tumor-specific protein biomarkers from urine is a common approach (94). Wang et al. employed matrix-assisted laser desorption/ionization time-of-flight MS (MALDI-TOF-MS) combined with weak cation exchange magnetic bead analysis to identify seven protein fragments with excellent diagnostic performance for LUAD in urine collected from LUAD patients and healthy controls. Subsequent validation in LUAD tissues revealed that levels of IGKC, AAT, SH3BGRL3, OPN/SPP1, and fibrinogen were higher in cancerous tissues compared to adjacent tissues, indicating a close association with LUAD development. These findings indicate that urine protein fragment assessment may offer a novel, non-invasive, and reproducible method for detecting and monitoring LUAD (75). Although this study included both tissue validation and a urine cohort, the absence of multi-center external validation constrains broader applicability of these findings. MS-based research by Jin et al. revealed differential expression of lymphocyte migration-regulating proteins in exosomes from the urine of lung cancer patients. WASL, STK10, and WNK1 were identified as potential biomarkers for lung cancer diagnosis (73). Hence, urine exosomal proteins also hold promise as non-invasive and convenient biomarkers for early detection of lung cancer. Despite the identification of potential urine protein biomarkers for LUAD, they may lack sufficient specificity and sensitivity given that their levels fluctuate in various disease states other than LUAD, making it challenging to differentiate changes attributable to LUAD from those associated with other urological or systemic diseases. Besides, these changes may be subtle in the early stages of the disease, leading to potential false-negative results.

Biomarkers in other samples

In addition to tissues, blood, and urine, other biological samples, such as particles in exhaled breath, have been investigated as potential biomarkers for LUAD. Phospholipid transfer protein (PLTP) and hepatocyte growth factor receptor exhibited significant elevations in the breath samples of LUAD patients, suggesting their utility as early diagnostic biomarkers for LUAD. Interestingly, the levels of these markers decreased following tumor resection, potentially indicating successful removal of the tumor. Therefore, PLTP and mesenchymal-epithelial transition (MET) have huge potential as biomarkers for LUAD diagnosis and assessment of successful tumor removal (82). However, this study was limited by a small sample size (n=35) and a lack of independent cohort validation, underscoring the preliminary nature of PLTP and MET as candidate biomarkers. Sputum, bronchoalveolar lavage fluid (BALF), aspirate samples, exhaled breath, nasal lavage fluid, or airway epithelial sampling are unique sources specific to lung cancer and other respiratory tract cancers and represent potential alternatives for biomarker selection (95). These samples may provide information about molecular changes that are anatomically closer to tumor cells and their microenvironment, making them potentially more relevant for clinical decisions in early disease screening. BALF and saliva, which are rich in protein content, have also been investigated as potential sources for biomarkers (89). The research team led by Ana Sofia Carvalho analyzed the protein composition of BALF samples from lung cancer patients and found a high abundance of EVE protein in the BALF samples, indicating that diagnosis based on BALF proteomics could improve the sensitivity of lung cancer detection (78). The aforementioned studies highlight promising protein biomarkers for LUAD across diverse biospecimens, but face significant methodological issues. Small sample sizes (e.g., n=28 for breath-based PLTP/MET, n=87 for serum ARSA) limit statistical power and risk false associations, especially in complex proteomic analyses. Lack of independent validation, particularly in multi-center cohorts, hinders generalizability, as markers like urinary WASL/STK10/WNK1 and others remain unverified beyond initial studies. Overfitting is also a concern with limited samples and high-dimensional data, potentially leading to misleadingly high diagnostic performance that doesn’t hold in larger populations. Collectively, these biases hinder the reliable progression of most candidates beyond preliminary stages, emphasizing the need for future research to prioritize larger, diverse cohorts, rigorous multi-center validation, and statistical approaches (e.g., cross-validation, penalized regression) to prevent overfitting, thereby accelerating the clinical translation of LUAD protein biomarkers.

LUAD biomarkers based on proteomics combined with other omics technologies

A single proteomics technique offers a limited understanding of LUAD at the protein level. To gather more comprehensive cellular information for more efficient and precise biomarkers, integrating genomics, transcriptomics, metabolomics, and other omics technologies is essential (Figure 2). With advancements in big data processing and analytical capabilities, multi-omics technologies have made progress in the identification of biomarkers for LUAD (96,97).

Figure 2 Flowchart of multi-omics data integration workflow for clinical translational research. GNN, graph neural network; LDA, linear discriminant analysis; mPCA, multivariate principal component analysis; MOFA, multi-omics factor analysis; PPI, protein-protein interaction; SNF, similarity network fusion; t-SNE, t-distributed stochastic neighbor embedding; UMAP, Uniform Manifold Approximation and Projection; WGCNA, weighted gene co-expression network analysis.

Integration of proteomics and genomics

It is now understood that genomic analysis can reveal the sequence, copy number, and expression changes in thousands of genes, aiding in the discovery of tumor-specific transcripts (98). Through high-throughput genomic sequencing technologies, many gene mutations have been identified in LUAD, such as KEAP1, BRAF, and K-RAS mutations (99-101). Some of these mutations or other molecular alterations (such as chromosomal rearrangements) represent therapeutic targets that can predict the response and outcomes of specific lung cancer treatments (102,103). By integrating genomic genetic information with proteomic expression data, researchers can more comprehensively depict disease biological characteristics. For instance, specific gene mutations can lead to the abnormal expression of corresponding proteins, which can serve as potential biomarkers. Moreover, genomic information can contribute to the identification of key signaling pathways associated with LUAD, thus providing guidance for proteomics research (104). Xu et al. substantiated that plasma levels of heat shock protein 90 (HSP90) could serve as a prognostic biomarker for LUAD through a multi-omics strategy comprising proteomic analysis using label-free methods on matched tumor and adjacent tissue samples from LUAD, phosphoproteomic analysis on matched samples using TiO₂ enrichment, genomic sequencing, and messenger RNA (mRNA) sequencing (105). Besides, after analyzing tissues from patients with pre-invasive LUAD (n=98) and invasive LUAD (n=99), Zhang et al. delineated a proteogenomic landscape of LUAD and revealed that reduced expression levels of the SPATA18 protein correlated with increased tumor cell proliferation and poor prognosis. This finding provides valuable insights into the initiation and progression of LUAD, demonstrating the importance of multi-omics integrated analysis in understanding the processes of disease occurrence and development (106). Therefore, the combined analysis of genomic and proteomic data enables more precise identification of LUAD-related biomarkers and potential therapeutic targets.

Integration of proteomics and transcriptomics

Transcriptomics focuses on RNA-level gene expression and provides genome-wide information about the structure and function of genes involved in specific biological processes. By quantifying gene expression levels, transcriptomics, when integrated with proteomics, enables a more comprehensive exploration of the regulatory processes from gene transcription to protein expression (105,107). In LUAD, specific co-expression patterns of transcripts and proteins may be associated with disease progression and prognosis, providing novel insights for the discovery of biomarkers. Dantas et al. integrated bulk RNA sequencing and single-cell RNA sequencing technologies with lung cancer proteomic datasets to screen and identify tissues and blood samples from lung cancer patients (n=122) and healthy individuals (n=39). They found that TIMP1 mRNA and protein expression levels were higher in the cancer group than in the healthy group, with TIMP1 yielding an AUC of 0.90, further confirming the association between high tumor expression of TIMP1 and poor prognosis in lung cancer patients (108). The combined analysis of transcriptomics and proteomics, by integrating gene expression and protein expression data, provides a novel approach for a comprehensive understanding of the molecular mechanisms of the organism, revealing potential biomarkers or therapeutic targets and advancing the development of personalized medicine. However, the integration of both technologies still faces some challenges: the dynamic range of protein expression levels in proteomics is substantially broader than that of RNA expression levels in transcriptomics, which may result in certain protein alterations not being reflected at the transcriptional level. Besides, gene expression and protein translation exhibit temporal and spatial heterogeneity within cells, and combined analyses may not accurately reflect this intricacy. Future research is essential to further explore the interaction mechanisms between transcriptomics and proteomics, providing new targets for the treatment of LUAD.

Integration of proteomics and metabolomics

Metabolomics quantifies intracellular metabolites, providing deeper insights into biomarker research for LUAD. Advances in MS technology have enabled comprehensive analysis of metabolites such as lipids, carbohydrates, amino acids, organic acids, and nucleotides, allowing for a global assessment of metabolic changes in various biological matrices (109,110). Lipid spectrometry-based research by Chen et al. revealed glycerophosphoryl-N-arachidonoyl ethanolamine (GpAEA) and sphinganin as potential sensitive and specific tools for the diagnosis and prognosis of lung cancer, where high levels of GpAEA and low levels of sphinganin indicated increased lung cancer risk (111). Albeit these biomarkers show promise for lung cancer development, further research involving larger populations is essential (112). Indeed, while such studies reflect the overall state of cellular metabolism, they may not analyze specific interactions between molecules, which limits the understanding of disease mechanisms and the identification of therapeutic targets (113). Metabolomics reveals metabolic changes in cells under specific physiological conditions, while proteomics characterizes the proteins involved in biological processes (114). The integrated analysis of these two disciplines elucidates the relationship between metabolism and protein functionality during the occurrence and progression of LUAD (115). For instance, the abnormal accumulation of certain metabolites may be related to changes in the activity of specific proteins, offering potential biomarkers for disease diagnosis and monitoring. Fahrmann et al. integrated proteomic and metabolomic data from early LUAD tissues, revealing changes in glycosylation, glutamine breakdown, increased Nrf2 activation, enhanced NAD salvage pathways, and escalated polyamine biosynthesis were related to differential regulation in one-carbon metabolism, all critical factors in early carcinogenesis (116). Qian et al.’s research indicated that the potential biomarkers CRP, LBP, and CD14 in the serum of LUAD patients mediate IL-8 production, showing a negative correlation with neuroacids and all-trans retinoic acid (113), suggesting that understanding their relationships facilitates exploration of the mechanisms associated with LUAD development and provides potential therapeutic strategies for lung cancer immunotherapy. Hence, through such multi-omics integration, researchers can not only identify new biomarkers but also develop more effective treatment strategies to address complex disease states.

Conclusions

The development of highly sensitive proteomics technologies has facilitated the exploration of lung cancer biomarkers across different biological sample sources, with many protein molecules being identified as potential lung cancer biomarkers in laboratory settings. However, no single molecular biomarker for lung cancer has been integrated into routine clinical practice, largely due to formidable barriers to clinical translation. These barriers include regulatory challenges like meeting Clinical Laboratory Improvement Amendments (CLIA) and In Vitro Diagnostic Regulation (IVDR) standards, which require extensive validation, consuming significant resources and time. Reproducibility issues, such as high inter-laboratory variations due to inconsistent workflows, also affect biomarker reliability. Additionally, proteomic biomarker platforms face cost-effectiveness challenges, with higher operational costs compared to established tools like ctDNA assays, limiting their clinical scalability.

Biomarkers to distinguish between benign and malignant lung nodules, as well as prognostic molecules for early-stage lung cancer patients undergoing screening, are still lacking. Modern multi-omics integration technologies have enhanced the discovery rate of biomarkers and the effectiveness of clinical applications, with several promising LUAD biomarkers already identified. Moreover, the integrated and synergistic application of multi-omics technologies not only provides foundational data for widespread application use in lung cancer research but also offers new perspectives for exploring underlying mechanisms associated with lung cancer. The synergistic integration of proteomics with genomics, transcriptomics, and metabolomics provides novel methodologies and expands the scope of investigation in life science. This has the potential to enhance the identification, detection, and validation levels of biomarkers, aiding in expanding our understanding of lung cancer. Current proteomics research still faces numerous challenges, including sample heterogeneity, insufficient technical standardization, and complexity in data analysis. These factors may lead to significant differences between research results, impacting the clinical translation of biomarkers. To overcome the “Valley of Death”, future research should focus on prospective cohort studies with nested case-control arms to assess biomarker performance against National Lung Screening Trial (NLST) benchmarks for early detection. Technological innovation and standardization, such as harmonized protocols, are essential for ensuring cross-study comparability and reliability. Key biomarkers involved in LUAD initiation and progression should be targeted to develop effective treatments, and addressing translational barriers is crucial for realizing their clinical potential.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1092/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1092/prf

Funding: This work was supported by the Zhejiang Medical and Health Research Projects (No. 2025KY1841).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1092/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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