Bibliometrics and potential gene analysis of post-translational modifications in lung cancer

Introduction

According to the 2022 global cancer statistics, lung cancer cases account for 12.4% of total cases, making it the most common cancer in the world. Lung cancer is the leading cause of cancer death (accounting for 18.7% of total cancer deaths) in very high human development index (HDI) countries, high HDI countries, such as China, and medium HDI countries (1). According to histological classification, lung cancer is mainly divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). SCLC is characterized by its highest malignancy and mortality, accounting for 15% of all lung cancers (2). NSCLC is the main type of lung cancer, accounting for more than 85% of all lung cancer patients (3).

The pathogenesis of lung cancer is complex and diverse, involving multiple factors such as gene mutations, epigenetic changes, immune escape, and tumor microenvironment. The results of a tumor tissue mutation sequencing study of Chinese NSCLC patients showed that among 656 mutations, TP53-p.Glu358Val is a driver mutation of lung cancer, which can activate mitochondrial autophagy to maintain cancer cell growth (4). Epigenetics is a discipline that studies the mechanism of gene expression regulation, focusing mainly on the relationship between gene expression and genetic information, without involving changes in DNA sequence (5). As one of the mechanisms of epigenetics, post-translational modification (PTM) has attracted increasing attention in lung cancer (6). For example, the E3 ubiquitin protein ligase tripartite motif containing 3 (TRIM3) directly interacts with solute carrier family 7 member 11 (SLC7A11) through its NCL-1, HT2A, and LIN-41 (NHL) domain, resulting in K11-linked ubiquitination of SLC7A11 K37 site, thereby promoting the proteasome-mediated degradation of SLC7A11. Downregulation of TRIM3 in lung adenocarcinoma and squamous cell carcinoma of the lung promotes the development of cancer (7). In addition, depletion of the deubiquitinase ubiquitin specific peptidase 9 X-linked protein disrupts lysine demethylase 4C in lung cancer cells, inhibits transforming growth factor-β2 (TGF-β2)/Smad signaling pathway and produces radio resistance, reducing the effect of radiotherapy (8).

Although there are many studies on PTM, the overall trend and specific conclusions of the research on lung cancer are still unclear. Based on this, we conducted this research. First, we collected literature related to lung cancer PTM from the past 25 years and studied its development trend and potential functions through traditional bibliometrics. Secondly, we searched for existing genes related to PTM through text mining and conducted in-depth mining. Finally, in order to further explore the current role of lung cancer PTM, we used public sequencing data to find key genes in the future. We present this article in accordance with the BIBLIO and STREGA reporting checklists (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1262/rc).

Methods

Data source and retrieval

We retrieved publication data from the Web of Science Core Collection (WOSCC) database for the period from January 1, 2000, to March 24, 2025. Only records classified as “articles” were included. The collected information covered publication details, authors, countries, institutions, journals, keywords, and citation counts. The following is the search strategy:

(TS=(“lung cancer” OR “lung carcinoma” OR “lung neoplasms” OR “pulmonary neoplasm” OR “cancer of lung”)) AND TS=(“Posttranslational Modifications” OR “Post-Translational Modifications” OR “Post Translational Modifications” OR “Protein Modification, Post-Translational” OR “Modification, Post-Translational Protein” OR “Modifications, Post-Translational Protein” OR “Post-Translational Protein Modifications” OR “Protein Modification, Post Translational” OR “Protein Modifications, Post-Translational” OR “Amino Acid Modification, Post-Translational” OR “Amino Acid Modification, Post Translational” OR “Post-Translational Amino Acid Modification” OR “Post Translational Amino Acid Modification” OR “Posttranslational Amino Acid Modification” OR “Amino Acid Modification Posttranslational” OR “Post-Translational Protein Modification” OR “Post Translational Protein Modification” OR “Post-Translational Modification” OR “Modification, Post-Translational” OR “Modifications, Post-Translational” OR “Post Translational Modification” OR “Posttranslational Modification” OR “Modification, Posttranslational” OR “Modifications, Posttranslational” OR “Phosphorylation” OR “Phosphorylations” OR “Acetylation” OR “Acetylations” OR “ubiquitylation” OR “Ubiquitylation” OR “methylation” OR “Methylations” OR “glycosylation” OR “Protein Glycosylation” OR “Glycosylation Protein” OR “sumoylation” OR “Sumoylations” OR “SUMO-Conjugation” OR “SUMO Conjugation” OR “SUMO-Conjugations” OR “myristoylation” OR “Lipoylation” OR “Palmitoylation” OR “Prenylation” OR “Isoprenylation” OR “Farnesylation” OR “Geranylgeranylation” OR “sulfation”).

Bibliometric analysis

Articles were analyzed based on annual output, country, institution, journal, author, keywords, and citation metrics to identify key characteristics and present descriptive findings. Bibliometric analysis and data visualization were performed using the Bibliometrix package in R (v.4.3.1), while VOSviewer (v.1.6.19) was employed to visualize networks of authors, countries, and keywords.

Data collection

The collected data were first organized into table format, after which the abstracts were systematically reviewed to extract the genes and PTMs mentioned in each study. Based on existing literature, the identified PTMs were categorized into seven main types: acetylation, glycosylation, methylation, phosphorylation, ubiquitination, sulfation, and lipidation. PTMs falling outside these categories were classified as “Others”. Studies mentioning two or more PTM types were categorized as “Various Modes”.

We used TCGA RNA-seq data to analyze the differential expression and prognostic status of PTM-related genes in lung adenocarcinoma. GSE253204 is a dataset that includes treatment and non-treatment data with the targeted drug palbociclib in the H358 cell line. Using this dataset, we conducted differential expression analysis to explore the relationship between PTM genes and immunotherapy.

Functional enrichment analysis and protein-protein interaction (PPI) network construction

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Cluster Profiler package, with a stringent statistical threshold of P<0.05 applied to ensure significance. To further elucidate the protein interaction landscape, we constructed a PPI network leveraging data from the STRING database (version 11.5), employing an interaction score threshold of 0.9 and 0.7 to filter high-confidence interactions. The resulting network was visualized and analyzed using Cytoscape v3.8.2, with key protein expression subnetworks identified through the Molecular Complex Detection (MCODE) plugin to highlight the most significant functional modules.

Prognostic and differences analysis of unstudied genes

We retrieved PTM gene sets associated with lung cancer from the PTM database (https://ptmd.biocuckoo.cn/), and identified additional genes through literature review that had not been extensively studied. Utilizing the RNA expression data from the TCGA Lung Adenocarcinoma dataset, differential expression analysis was performed using the limma package. The analysis identified differentially expressed genes (DEGs) based on stringent thresholds of absolute log-fold change >1 and false discovery rate (FDR) <0.05. Subsequently, we conducted survival analysis using the “survival” and “survminer” packages to evaluate the association between gene expression and patient survival outcomes. To determine the optimal cutoff point for gene expression levels, the “survi_cutpoint” function from the “survminer” package was employed, with the minimum proportion of samples in each group (minprop) set to 0.3 and a significance threshold of P<0.05. This approach ensured robust statistical analysis while maintaining sufficient sample representation in each subgroup.

Statistical analysis

All statistical analyses were performed using R software (v.4.3.1). DEGs were identified using the limma package with thresholds of absolute log₂ fold change (log₂FC) >1 and FDR <0.05. Kaplan-Meier survival analysis was conducted to assess associations between gene expression and patient outcomes, with optimal cutoff values determined using the “survminer” package (P<0.05). Functional enrichment analysis including GO and KEGG pathway analysis was performed using Cluster Profiler (P<0.05). PPI networks were constructed from the STRING database (v.11.5) and visualized using Cytoscape v3.8.2 with the MCODE plugin for module identification.

Gene expression matrices were log₂-transformed and normalized. Correlation analysis used Pearson correlation coefficients with |R²|>0.3 and P<0.05 as significance thresholds. Categorical variables were compared using Chi-squared tests, while continuous variables were analyzed using independent samples t-tests or Mann-Whitney U tests. Multiple hypothesis testing was corrected using the Benjamini-Hochberg method. Univariate and multivariate Cox proportional hazards regression models assessed prognostic value, with hazard ratios (HRs) and 95% confidence intervals (CIs) reported. All P values were two-tailed with P<0.05 considered statistically significant.

Results

Volume and countries/regions of publication

Figure 1 showed the trend of annual publications, with the total number of articles surged from 23 in 2000 to 314 in 2024. Since 2000, the number of related studies has been increased year by year, reached a peak of 337 in 2019. China produced the most related articles, with 2,394 articles. It is followed by the United States, Korea, and Japan, with 201–1,400 articles published. Germany, Italy and Spain have published between 101 and 200 articles, ranking in the third tier of the world’s publication volume (Figure 2A and Table 1). Whether it is single country publication or multiple country publication, China is the most productive country, with the United States and South Korea ranking second and third. In addition, most of countries published papers by one single country (Figure 2B). The most cited article in the field of post-translational modification in lung cancer is by Fabbri and Muller, published in PNAS in 2007, titled “MicroRNA-29 Family Reverts Aberrant Methylation in Lung Cancer by Targeting DNA Methyltransferases 3A and 3B” (Table 2).

Figure 1 Annual number of published articles.

Figure 2 Volume and countries/regions of publication. (A) Map of national publication volume. (B) Top 10 countries with the most publications. MCP, multiple country publications; NA, not applicable; SCP, single country publications.

Collaborative network of country, institution and author

The network of the cooperation distribution of articles in various countries and regions showed that China is the largest initiator of transnational cooperation, and maintains important international cooperation relations with Pakistan, India, Saudi Arabia and other countries in this field. The second is the United States, and the most cooperation is with Japan and Taiwan. The frequency of cooperation between Japan, Taiwan and South Korea and other countries or regions is second only to the United States. This showed that with the opening up of the world, scientific research exchanges and cooperation between countries are becoming more and more frequent. However, countries such as Sudan and Türkiye have less cooperation with other countries (Figure 3A). Analysis of the cooperation network of organizations around the world showed that the most frequent cooperation is between universities. China Medical University and Shanghai Jiao Tong University have the most cooperation with other institutions, and the cooperation partners are basically other local universities or hospitals. In addition, there is also a lot of cooperation between research institutes, such as German Cancer Res CTR and German CTR Lung Res DZL. These results showed that the scientific research resources of universities and hospitals are relatively rich, which promotes exchanges and cooperation between them (Figure 3B). According to the statistics of the authors, the top four authors are Wei Li (29 articles), Jing Wang (26 articles), Yang Liu (25 articles), and Wei Wang (25 articles), while Ignacio I. Wistuba and John K. Field have a higher H-index (Figure 3C). The active cooperation between researchers is also worth noting, especially the close collaboration between Xiupeng Zhang, Yuan Miao, Enhua Wang, and Lin Cai (Figure 3D). Overall, the academic cohesion in the field of PTM of lung cancer research is strong and the cooperation network is relatively close. This efficient scientific research collaboration model provides important support for promoting the in-depth development of research in this field.

Figure 3 Collaborative network of country, institution and author. (A) Collaborative network of countries/regions. (B) Collaborative network of institutions. (C) The top 10 authors with the most published articles. (D) Collaborative network of authors.

Autophagy and methylation are hotpot keywords of PTMs in lung cancer

This study generated a visualization network of PTM in lung cancer keywords through VOSViwer. The size of each node in the graph corresponds to the frequency of the keyword, and the circle gradually expands as the frequency increases. The results showed that “lung cancer” is the most frequent keyword, followed by two subtypes of lung cancer, “small cell lung cancer” and “non-small cell lung cancer”. “DNA methylation”, “autophagy”, “cell cycle” and other cell biological processes related to the tumorigenesis mechanism are also research hotspots. In addition, the network connection lines show that genes such as STAT3, p21, and p16 are often associated with PTM in lung cancer (Figure 4A). After removing the character “tumor”, “autophagy” jumped to become the most popular keyword, followed by “methylation” and “DNA methylation” (Figure 4B). The above results showed that people’s attention to autophagy and PTMs has increased significantly.

Figure 4 Autophagy and methylation are hotpot keywords of PTMs in lung cancer. (A) Top 100 keywords network. (B) Top 100 keyword frequencies (remove tumor characters). PTMs, post-translational modifications.

Tyrosine kinase inhibitors and hypermethylation are research hotspots on phosphorylation and post-translational modifications in lung cancer

This study extracted genes and specific PTM methods (acetylation, glycosylation, methylation, phosphorylation, ubiquitination, sulfation, and lipidation) from articles related to PTM in lung cancer. Between 2000 and 2025 March, the number of publications on PTM in lung cancer increased steadily, with phosphorylation being the most studied modification, followed by methylation and other PTMs. Ubiquitination was the least researched (Figure 5A). We constructed keyword networks for phosphorylation and methylation, the two most frequently studied modifications. In the phosphorylation network, “tyrosine kinase” and “gefitinib” were the most common keywords (Figure 5B). In the methylation network, “hypermethylation” and “promoter hypermethylation” were predominant (Figure 5C). Consistent with the trends in PTM studies, phosphorylation genes, followed by other genes and methylation genes, represented the top three categories of genes modified by PTMs (Figure 5D). Intersection analysis of different PTMs revealed that a higher number of modification types corresponded with fewer intersection genes. The largest intersection genes were between phosphorylation and other PTMs, followed by phosphorylation and methylation (Figure 5E).

Figure 5 Tyrosine kinase inhibitors and hypermethylation are research hotspots on phosphorylation and post-translational modifications in lung cancer. (A) Statistics of annual PTM. (B) Network of phosphorylation keywords. (C) Network of methylation keywords. (D) Annual number of genes with different PTMs. (E) PTM gene intersection. PTM, post-translational modification.

STAT3 is the key gene of PTM in lung cancer

Publication analysis showed that the number of PTM genes that have not been studied is much more than the number of PTM genes that have been studied, indicating that there is huge untapped potential in the field of PTM research (Figure 6A). Among the top 30 PTM-related genes, STAT3 had the highest frequency of occurrence (228 times) (Figure 6B), and methylation was the most common PTM in the PPI network analysis. The results indicate that when PPI network thresholds were set to 0.9 and 0.7, the differences between the resulting networks were relatively small (Figure 6C and Figure S1). GO enrichment analysis revealed that the most associated biological process was gland development (123 genes), while the most prominent cellular component was focal adhesion (70 genes), and the most frequent molecular function was DNA transcription factor binding (128 genes). KEGG analysis showed that the majority of genes were enriched in the FOXO signaling pathway (Figure S2). MCODE analysis of the most three common PTMs indicated that genes in the “other” category had high scores and dense network connections, with BUB1B showing the highest score. In contrast, phosphorylation-related genes generally had lower scores (Figure 6D), suggesting that different PTMs collaborate closely to drive specific biological processes.

Figure 6 STAT3 is the key gene of PTM in lung cancer. (A) The annual ratio of publications containing genes to those not containing genes. (B) Top 30 gene frequencies. (C) PPI networks of all genes. (D) Gene networks with the highest MCODE scores. MCODE, Molecular Complex Detection; PPI, protein-protein interaction; PTM, post-translational modification.

Phosphorylation and mitosis are the main PTM and enriched pathways of unstudied genes

In previous studies, we performed bibliometric analysis based on published literature. However, function of PTM in lung cancer remains insufficiently explored. Therefore, we used existing tools to predict potential novel PTM-related genes.

Based on a PTM database, we predicted genes of which its PTMs have not been investigated in lung cancer. Differential expression analysis and prognostic analysis were conducted on these genes to identify those with significant expression differences and potential functional relevance. Specifically, 7,523 PTM-related genes that had not been studied in lung cancer were identified by subtracting PTM genes reported in the literature from those listed in the PTM database and PhosphoSitePlus database (Figure 7A). As shown in Figure 7B,7C, differential expression and Kaplan-Meier survival analyses were performed on these unstudied genes. The intersection of DEGs and prognostic genes yielded a set of 134 candidate genes (Figure 7D). Among these, phosphorylation was found to be the predominant PTM type in 113 genes (Figure 7E). GO and KEGG pathway enrichment analyses revealed that these genes were significantly associated with nuclear division, chromosome segregation, and mitosis (Figure 7F and Figure S3), indicating their potential involvement in key regulatory mechanisms during mitosis and their critical roles in cell cycle control. Cell mitosis and targeted therapy have important impacts on lung cancer. To explore the relationship between 113 genes and cell division and targeted therapy, we used the GSE253204 dataset to analyze whether these genes are associated with targeted therapy. After analysis, we found that 18 genes are related to palbociclib targeted therapy (Figure 8A). The 18 DEGs were subjected to correlation analysis with 86 mitotic genes from the REACTOME_REGULATION_OF_MITOTIC_CELL_CYCLE.v2025.1.Hs dataset in the Gene Set Enrichment Analysis (GSEA) database using The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) data. Based on Pearson correlation thresholds of |R²| greater than 0.3 and a P value less than 0.05, 13 significantly correlated genes were selected. Figure 8B shows the correlation between these 13 genes and the 86 mitotic genes.

Figure 7 Phosphorylation and mitosis are the main PTM and enriched pathways of unstudied genes. (A) Literature search for genes that intersect with PTM database genes. (B) Volcano plot of difference analysis of unstudied gene. (C) Volcano plot of prognostic analysis of unstudied genes. (D) Intersection between differentially expressed genes and prognostic genes. (E) PTM patterns of intersection genes. (F) GO enrichment of intersection genes. DEGs, differentially expressed genes; FC, fold change; GO, Gene Ontology; HR, hazard ratio; KM, Kaplan-Meier; PTM, post-translational modification; PTMD, post translational modification database.

Figure 8 Identification of palbociclib-responsive genes and their correlation with mitotic cell cycle regulators. (A) Volcano plot displaying differentially expressed genes from the GSE253204 dataset associated with palbociclib targeted therapy response. The x-axis represents log₂ fold change and the y-axis represents −log₁₀(P value). (B) Heatmap illustrating the correlation between the 13 most significantly correlated palbociclib-responsive genes and 86 mitotic cell cycle regulatory genes from the REACTOME_REGULATION_OF_MITOTIC_CELL_CYCLE.v2025.1.Hs dataset. Red indicates a positive correlation, and blue indicates a negative correlation. *, P<0.05.

Discussion

According to the latest statistics, lung cancer has become the leading cancer globally, ranking second in diagnosis rates among men in 33 countries and first in mortality rates among men in 89 countries (1). Given the serious threat posed by lung cancer, we conducted a bibliometric analysis using publication data from the Web of Science (WOS) to examine current research trends and prominent focus areas in the field. From 2000 to March 2025, the number of lung cancer-related publications has steadily increased, reflecting growing interest among researchers worldwide. However, the annual publication volume gradually decreased after 2019. On the one hand, it may be caused by the coronavirus disease 2019 (COVID-19) pandemic. On the other hand, it may also be because researchers have a clearer understanding of this research field.

In terms of publication output by country or region, the top four contributors are all classified as very high or high HDI countries. This may correlate with higher lung cancer mortality rates in these regions, and also reflects the role of strong economic development and higher quality of life in supporting scientific research. Notably, while China has the highest number of publications, its H-index is not the highest. The United States ranks second in publication volume but holds the highest H-index. Similarly, Japan’s H-index surpasses that of South Korea, despite having fewer publications. These discrepancies indicate that publication volume does not directly equate to scientific impact or productivity. Although a large population may provide ample human resources for research, the quality and influence of the output can vary significantly. In terms of collaboration networks, China plays a central role across countries, institutions, and author collaborations—likely linked to its high publication volume. China maintains active partnerships with countries such as India, Iran, and Pakistan, while the United States collaborates more frequently with Japan, Taiwan, and Thailand. These patterns suggest that geopolitical and regional relationships can significantly influence international research collaboration. Additionally, institutions such as Chinese Medical University and Shanghai Jiao Tong University demonstrate strong domestic cooperation, highlighting active scientific exchange and resource sharing. However, most institutional collaborations remain domestic, with limited cross-border partnerships. This indicates a need to enhance international institutional collaboration to overcome technical barriers and foster innovative thinking. In terms of individual author productivity, Wei Li, Jing Wang, Yang Liu, and Wei Wang have published the most articles in the field of lung cancer. However, similar to national-level trends, publication count does not always correspond to scientific influence. For example, although Ignacio I. Wistuba and John K. Field have published fewer articles, they maintain high H-index values. Ignacio I. Wistuba, a professor at the University of Texas MD Anderson Cancer Center, specializes in molecular biology and has published extensively in top-tier journals such as Nature, Cell, and Cancer Cell. His review article, “PD-L1 as a biomarker of response to immune-checkpoint inhibitors,” published in Nature Reviews Clinical Oncology, has been cited 1,143 times (9), underscoring the high impact of his research contributions.

Keyword network analysis revealed the current research hotspots in the field of lung cancer. Whether or not the term “tumor” was excluded, “autophagy”, “methylation”, and “DNA methylation” consistently rank as top keywords, followed by “EGFR” and “metastasis”. Among them, autophagy—as a critical cellular physiological process—has gained increasing attention in lung cancer research. Key pathways and factors studied in NSCLC autophagy include genomic alterations, mammalian target of rapamycin (mTOR) signaling, and endoplasmic reticulum stress (10). Recent findings suggest that circRNA (circPLCE1) enhances ubiquitination of heat shock protein 70, thereby activating autophagy related 5 (ATG5)-dependent macro-autophagy and ultimately suppressing lung cancer progression (11). Methylation, a common chemical modification in epigenetics, involves the addition of a methyl group (-CH₃) to DNA bases (12). It plays a significant role in lung cancer, with abnormal methylation emerging as a promising diagnostic biomarker (13-15). A study found that gene methylation levels were significantly lower in lung cancer patients with a history of smoking than in non-smokers (16). Based on this, multi-gene methylation assays have been developed for early lung cancer diagnosis, targeting genes such as ras-association domain family protein isoform 1A (RASSF1A), short stature homeobox 2 (SHOX2), and retinoic acid receptor beta 2 (RARB2) (17-20). These findings underscore the critical role of methylation in lung cancer and explain its frequent appearance in keyword networks. In our statistical analysis of PTM-related lung cancer studies, phosphorylation and methylation emerged as the most commonly studied modifications. In the phosphorylation keyword network, “tyrosine kinase” and “gefitinib” were the most frequently mentioned terms. Gefitinib, a first-generation tyrosine kinase inhibitor, is widely used in NSCLC treatment. Although it shows clinical benefit in advanced NSCLC (21), its efficacy is often limited by acquired resistance (22). Consequently, combination therapy has gained traction. A phase III randomized trial demonstrated that gefitinib combined with pemetrexed and carboplatin significantly improves recurrence-free and overall survival (23). In EGFR-mutant NSCLC, this combination is also more effective than gefitinib monotherapy (24). Thus, the clinical relevance of gefitinib continues to draw considerable research interest. In the “methylation” keyword network, “expression”, “hypermethylation promoter”, and “hypermethylation” are frequently observed. A PubMed search using “promoter hypermethylated” and “lung cancer” retrieved over 900 articles, reflecting its prominent role in this field. Notably, a study by Hu et al. published in Nature revealed that hypermethylation of the stimulator of interferon genes (STING) promoter and enhancer silences its expression in metastatic lung cancer cells, impairing its ability to suppress tumor progression (25). These insights offer new checkpoints for latent metastasis and inform novel diagnostic and therapeutic strategies for disease recurrence. Additionally, PTM-related genes have attracted growing attention. Among them, STAT3 is one of the most extensively studied. Its modifications—phosphorylation, acetylation, and methylation—are known to regulate protein activity (26). In lung cancer, various post-translational modifications of STAT3 profoundly impact its activity, cellular signaling, and tumor progression. Specifically, methylation mediated by PRMT5, serine phosphorylation exhibiting a critical dependency in KRAS-driven lung adenocarcinoma, deacetylation promoting tumorigenesis via HDAC7, and sustained Y705 phosphorylation maintained by DDIAS have all been shown to play key regulatory roles in lung cancer development and progression (27-30). Moreover, ubiquitination of STAT3 has been implicated in NSCLC progression. For instance, activating signal cointegrator 1 complex subunit 3 (ASCC3) has been shown to promote NSCLC development by inhibiting STAT3 ubiquitination-mediated degradation (31).

Finally, we conducted a systematic analysis of PTM genes that have not yet been studied in the context of lung cancer. By comparing PTM-related gene sets from public databases with those extracted from the literature, we identified 7,523 genes that have not been previously investigated in lung cancer. This large number highlights the substantial unexplored potential of PTM genes in this field. Further differential expression and prognostic analyses revealed 134 genes significantly associated with both gene expression and patient prognosis. These genes may play critical roles in lung cancer pathogenesis, early diagnosis, and therapeutic strategies. GO and KEGG pathway enrichment analyses showed that these genes are mainly involved in mitosis and cell cycle regulation. Currently, few studies focus on the intersection of PTMs, mitosis, and cell cycle processes in lung cancer. Existing research suggests that histone modifications are essential for regulating chromatin structure, DNA damage responses, and chromosome segregation (32). To date, only one study has specifically explored the role of PTMs in regulating mitosis in lung cancer. This research identified a novel function of ubiquitin specific peptidase 15 (USP15) and its isoforms in maintaining genomic stability. It demonstrated that phosphorylation can regulate the activity of deubiquitinases in an isoform-specific manner and proposed that the preferential upregulation of USP15 isoform 1 in certain NSCLC cell lines may be linked to isoform imbalance (33). These findings provide valuable insights and may offer new therapeutic targets for lung cancer treatment.

PTM landscapes are tissue-specific: in NSCLC, phosphorylation-driven tyrosine kinase signaling (e.g., EGFR/STAT3 Y705, Ser727) and promoter hypermethylation dominate; breast cancer features histone H4/H4K12 and α-tubulin hypoacetylation plus H1 Thr146-specific phosphorylation; colorectal, pancreatic, and other solid tumors exhibit PRMT3 overexpression and ubiquitin-modifying enzyme dysregulation as key alterations. This organ-dependent PTM preference reflects distinct oncogenic drivers and microenvironments, supporting the development of cancer-type-specific PTM-targeted therapies (34-38).

Although this study has yielded several meaningful findings, it also has limitations inherent to bibliometric methods that warrant further improvement. First, there is a bias in journal sources. Mainstream databases tend to prioritize English-language publications and high-impact factor journals, potentially overlooking high-quality research published in local languages or regional journals. Second, bibliometric analyses typically rely on formally published journal articles, which can exclude informal but academically valuable outputs such as conference proceedings, preprints, and technical reports. In addition, since citation analysis explores the literature with the most citations since publication, it cannot accurately reflect the latest research status. In the future, it is necessary to explore methods that can accurately reflect the relative citation counts. Finally, differences in data coverage, update frequency, and document classification standards across databases may affect the comprehensiveness and accuracy of the analysis. Therefore, when interpreting bibliometric results, it is essential to fully consider these data source limitations and integrate other research methods to achieve a more objective and comprehensive understanding.

Conclusions

This study systematically analyzed the publication trends, distribution of publishing countries/regions, number of authors, keywords and cooperative networks of articles related to lung cancer PTM by bibliometric. In addition, key genes analysis and enrichment of signal pathways were conducted. Finally, the screening and analysis of unstudied genes suggest that there is still a lot of room for exploration for post-translational modification of lung cancer, providing new directions and targets for future research.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO and STREGA reporting checklists. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1262/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1262/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1262/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.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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