The "Young-Old" group may have a unique pulmonary immune microenvironment, resulting in a different ratio of incidence rates between the upper and lower lobes of the lungs compared to other age

The "Young-Old" group may have a unique pulmonary immune microenvironment, resulting in a different ratio of incidence rates between the upper and lower lobes of the lungs compared to other age | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The "Young-Old" group may have a unique pulmonary immune microenvironment, resulting in a different ratio of incidence rates between the upper and lower lobes of the lungs compared to other age Dong-Ning Lu, Hang-Xing Ren, Wan-Chen Zhang, Yan Jiang, Ao Zeng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9155639/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Non-small cell lung cancer (NSCLC) shows substantial heterogeneity across age groups and tumor locations. However, the relationship between age, lobar distribution, and the pulmonary immune microenvironment remains insufficiently understood. This study aimed to identify age-specific patterns of NSCLC occurrence and explore potential immune mechanisms underlying these differences. Methods We retrospectively analyzed 121,436 surgically treated NSCLC cases from Shanghai Pulmonary Hospital between 2015 and 2024. Tumor location and age at onset were assessed across World Health Organization age categories. To investigate potential mechanisms, we analyzed 15 tumor-adjacent single-cell RNA sequencing samples from the E-MTAB-13526 dataset and validated cell-type composition changes using TCGA bulk RNA-seq deconvolution. Cell–cell communication, pseudotime trajectory, transcription factor activity, and functional enrichment analyses were performed to characterize immune alterations. Results The upper-to-lower lobe incidence disparity of NSCLC followed a curved age-related pattern and was most pronounced in the young-old (YO, 60–74 years) group, reaching an approximate ratio of 7:3. Single-cell analysis revealed that the YO group had distinct immune microenvironment features, especially abnormal proportions of myeloid cells and T cells. Among myeloid subsets, alveolar macrophages were markedly increased in the YO group and showed enrichment of metabolic remodeling, oxidative stress, senescence-related signatures, and M2-like polarization. STAT1 and STAT3 activity was elevated, suggesting involvement of the JAK/STAT pathway. In parallel, T-cell composition shifted toward higher CD4 + T-cell proportions and relatively lower CD8 + T-cell proportions in the YO group. Cell–cell communication analysis indicated prominent myeloid–T cell interactions, with MHC-II, prostaglandin, and galectin signaling among the key pathways. Conclusions The YO group exhibits a unique pulmonary immune microenvironment that may contribute to its distinct upper-versus-lower lobe NSCLC distribution. Altered alveolar macrophage abundance and T-cell polarization, potentially mediated through JAK/STAT-related immune remodeling, may represent important mechanisms linking age and tumor location in NSCLC. Non-small cell lung cancer Young-old Alveolar macrophages Tumor immune microenvironment Single-cell RNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lung cancer (LC) is one of the most common malignant tumors worldwide, characterized by high incidence and high mortality. Among men, lung cancer ranks first in both incidence and mortality; among women, it ranks second in both incidence and mortality [ 1 ] . Lung cancer is currently the leading cause of cancer-related death globally, with approximately 2.48 million new cases and 1.84 million deaths each year; due to its large population, Asia bears a relatively heavier lung cancer burden [2] . NSCLC is the most common type of lung cancer, accounting for approximately 80%–85% of all lung cancer cases. Compared with small cell lung cancer, NSCLC generally grows and spreads more slowly, has a broader range of treatment options, and is associated with a relatively better prognosis (especially in early-stage disease). NSCLC has numerous risk factors that have evolved alongside societal development. Current research still consistently identifies smoking and exposure to secondhand smoke as the most important risk factors for NSCLC [3] . Occupational exposures are also an important reason for higher incidence in specific populations, including exposure to polycyclic aromatic hydrocarbons, asbestos, arsenic, and certain forms of crystalline silica, as well as chromium, radon, and cadmium [4–7] . With ongoing societal development, emerging air pollutants—such as particulate matter—have increasingly become major contributing factors [8] . In addition, certain pulmonary diseases, unhealthy psychological factors, and dietary habits may, to some extent, also act as risk factors for NSCLC [9,10] . These environmental factors are important drivers of changes in the NSCLC disease burden. Meanwhile, advances in medical care—such as the widespread adoption of accurate thin-slice CT—and increased public health awareness have also contributed to changes in the NSCLC disease spectrum [11] . Overall, the NSCLC disease spectrum is undergoing the following shifts: adenocarcinoma incidence is increasing relative to squamous cell carcinoma; the incidence of NSCLC among women is increasing relative to men [12] ; and the proportion of early-stage NSCLC is increasing relative to middle-to-late stage disease [13] . Therefore, research focused only on advanced NSCLC or poorly differentiated NSCLC clearly cannot benefit the majority of patients. In light of these changes in the NSCLC disease spectrum, it is necessary to summarize incidence characteristics across populations and to investigate the pathogenesis of early-stage NSCLC, thereby improving understanding of its initiation and progression and providing a basis for early diagnosis and treatment. The occurrence of NSCLC is also associated with age. Age is the most prominent risk-related factor for lung cancer, and the risk rises stepwise with increasing age. Increasing age reflects longer cumulative exposure to risk factors and declining immune competence. NSCLC most commonly occurs after the age of 40, with peak incidence between 55 and 75 years. Although lung cancer incidence is relatively low among people younger than 40, a trend toward younger onset has been observed in recent years; this may be related to prolonged secondhand smoke exposure, air pollution, and unhealthy lifestyles. In younger patients, adenocarcinoma accounts for a higher proportion [14] . However, it should be noted that NSCLC in younger patients may have worse prognosis. Studies suggest that patients in different age groups exhibit distinct molecular features, implying different age-associated tumor risks and highlighting the need for greater caution in treatment selection and post-treatment monitoring to further improve prognosis [15] . The “young-old” (YO) group is an age segment that warrants particular attention in NSCLC. Previous research indicates that YO individuals require focused attention in lung cancer detection and treatment [16] . NSCLC tumor location is related to lung anatomy: the left lung comprises two lobes (upper and lower), while the right lung comprises three lobes (upper, middle, and lower). The total anatomic volume of the right lung (approximately 55%–60%) is larger than that of the left lung (approximately 40%–45%), and the right main bronchus forms a smaller angle with the trachea and is wider, shorter, and straighter. In addition, there are anatomic differences in volume between upper and lower lobes. The upper lobes are more strongly influenced by gaseous factors, whereas the lower lobes are more influenced by heavier substances due to gravity. Studies suggest that differences in ventilation–perfusion ratio, lymphatic flow, and mechanics may contribute to upper-lobe–predominant disease patterns [17] . Over time, these factors may lead to increased upper-lobe density. In addition, Copley SJ et al. used fractal dimension analysis to examine lung complexity across age groups and found that, compared with older adults, younger individuals exhibited higher lung complexity [18] . Although physiological and pathological heterogeneity may exist between different lobes [19,20] and age-related factors may influence lung cancer responses [21] , there has been limited research on mechanisms—particularly the immune microenvironment. These anatomic differences provide additional directions for further exploration of NSCLC pathogenesis. Shanghai Pulmonary Hospital has extensive experience in NSCLC diagnosis and treatment, with an annual surgical volume exceeding 25,000 cases, making it one of the medical institutions with the highest thoracic surgical volume worldwide. We collected thoracic surgery data from 121,436 NSCLC cases treated at Shanghai Pulmonary Hospital over the past decade to explore recent relationships between the NSCLC disease spectrum and population characteristics, and to identify factors associated with the initiation and progression of NSCLC—especially early-stage NSCLC. In population analyses, we found that the difference in upper- versus lower-lobe proportions among YO NSCLC patients differs from that in other age groups. We further used single-cell RNA sequencing (scRNA-seq) to reveal relationships between age and tumor location and to investigate changes in the intrapulmonary immune microenvironment. This work presents possible etiologic factors, focuses on key cell populations, and provides references for future NSCLC diagnosis, treatment, and research. Methods Clinical data and ethics Participants: NSCLC case data from Shanghai Pulmonary Hospital, 2015–2024. Exclusion criteria: (1) incomplete case data; (2) unclear diagnosis. Final included case counts were as follows: 7,701 cases in 2015; 9,873 in 2016; 12,589 in 2017; 13,427 in 2018; 11,984 in 2019; 15,384 in 2022; 23,486 in 2023; and 26,992 in 2024 (data from 2020 and 2021 were not included due to the impact of COVID-19). The main variables collected were tumor location and age at onset (Supplementary1). Data collection was approved by the Ethics Committee of Shanghai Pulmonary Hospital (approval number: K25-612). Data normalization and integration Fifteen tumor-adjacent samples from the E-MTAB-13526 dataset [22] were included. These data were raw scRNA-seq data generated on the 10× Genomics platform. We first used Seurat package [23] to read the gene expression matrix of each sample and build Seurat objects, followed by normalization for each object. The 2,000 most variable genes in each sample were selected as highly variable genes. Batch effects were removed from the expression matrix, and principal component analysis (PCA) was performed for linear dimensionality reduction; the top 10 principal components were used as the basis for downstream analyses. Next, nonlinear dimensionality reduction was performed, a cell-neighbor graph was constructed, and unsupervised clustering was completed. To remove doublets formed during experiments, DoubletFinder [24] was used for doublet detection. Based on clustering-derived annotations, the homotypic doublet proportion was calculated, and a doublet formation rate of 7.5% was assumed. After correction, the number of doublets was determined and removed, and samples were integrated. Cell annotation and cell-type proportions To characterize the functional features of each cluster, DotPlot in Seurat was used to visualize expression of known marker gene sets. Cluster IDs were replaced with corresponding cell-type names based on established marker sets for intrapulmonary cell types, completing cell annotation [25] . Cell-type proportions among all cells were computed using the reshape2 package [26] and displayed by group. BayesPrism Cell-type deconvolution of bulk RNA-seq was performed using BayesPrism90 package [27] to decompose The Cancer Genome Atlas (TCGA) data [28] into cell-type fractions. Marker genes for each cell type were computed using Seurat FindAllMarkers. Genes used in subsequent analyses were required to have an average log-transformed fold-change > 1 and be expressed in >0.25 of cells within the cluster. Mitochondrial and ribosomal protein genes were removed from the marker list. For each cell type, the mean raw counts were computed to generate a single-cell reference expression profile. The single-cell reference and TCGA bulk data were used as inputs to a Bayesian deconvolution framework to estimate distributions of cell fractions in bulk samples. Cell–cell interaction network analysis Using CellChat package [29], target cell subsets were selected based on the annotated Seurat object. The gene expression matrix was extracted to build a CellChat object, and CellChatDB.human was loaded as the ligand–receptor reference database. Overexpressed genes in each cell type were identified, potential ligand–receptor interaction pairs were inferred, and interactions were integrated with the human protein–protein interaction network (PPI.human). Cell–cell communication probabilities were computed at the ligand–receptor level; low-confidence communications were filtered. Cell–cell communication was inferred at the signaling pathway level and aggregated to construct a communication network across cell groups. The netVisualDiffInteraction function was used to visualize differences in interaction number or strength between cell groups. rankNet was used to identify differentially active signaling pathways, and plotGeneExpression was used to visualize communication networks. Pseudotime analysis Pseudotime analysis was performed using monocle2 package [30] . Target cells were extracted from the annotated Seurat object. An AnnotatedDataFrame was built, a Monocle dataset was created, and the detection threshold was set to 0.5 with a negative binomial expression model. The Monocle dataset was normalized. Genes with expression ≥0.1 were retained, and highly variable genes were further selected by dispersion analysis (criteria: mean expression ≥0.1 and empirical dispersion ≥ 1× fitted dispersion). These genes were used as ordering genes. Dimensionality was reduced to 2D, and differentiation trajectories were constructed and ordered to obtain pseudotemporal trajectories. SCENIC analysis SCENIC package [31] was applied to scRNA-seq data using the hg38-500bp_up_and_100bp_down_tss database. The GENIE3 algorithm was used to infer potential regulatory relationships between transcription factors (TFs) and target genes. TF lists were derived from JASPAR 2022 and AnimalTFDB 4.0; GENIE3 parameters were kept at defaults. Using RcisTarget (v1.20.0) and species-specific TF-binding motif databases, co-expression modules were pruned and target genes lacking motif support were filtered, yielding high-confidence regulons. AUCell was used to compute regulon activity scores (AUCell scores) in each cell, and cell-type–specific core regulons were identified based on score distributions. AUC scoring Based on prior studies, gene sets related to biological functions were defined, including immunosuppression, extracellular matrix (ECM) remodeling, myeloid cell recruitment, hypoxia response, oxidative stress, apoptosis, M1 and M2 macrophages, and gene sets related to maturation and senescence. Genes in each set that were present in the Seurat expression matrix were retained, and only gene sets with at least two available genes were kept to reduce bias from overly small sets. The normalized expression matrix was extracted and AUCell was used to rank gene expression for each cell to build a ranked matrix. AUC values (area under the curve) were computed for each cell with aucMaxRank = 0.05 × total genes (i.e., AUC computed using only the top 5% ranked genes per cell), quantifying overall activity of each functional gene set per cell. Differential expression and functional enrichment analyses Differential expression analyses used wilcox.test in Seurat (suitable for scRNA-seq) and edgeR package [32] (suitable for large-scale RNA-seq) via FindMarkers. To ensure validity of the diagnostic model and better characterize differences in the YO group, fold-change (FC) was set at 1.2, with minimum expression proportion 0.25, p 1.2. Candidate genes were functionally enriched using Gene Ontology (GO) and KEGG pathway databases. Fisher’s exact test was used to determine which genes were most associated with particular functions; smaller p-values indicate more significant enrichment. Statistical analysis All statistical analyses were performed using R v4.4.1 and GraphPad Prism v9.01. Student’s t-test was used to compare experimental versus control groups; p < 0.05 was considered statistically significant. Results The Young-Old group shows the greatest disparity between upper and lower lung regions A total of 121,436 surgically treated NSCLC cases from Shanghai Pulmonary Hospital during 2015–2024 were included (Figure 1a). With advances in medical care and the development of Shanghai Pulmonary Hospital, the annual number of NSCLC cases increased from 7,701 in 2015 to 26,992 in 2024. Figure 1b shows yearly case classification by tumor location and age. Notably, according to World Health Organization (WHO) age categories, patients were grouped as Children and adolescents (<18), Young adults (18–44), Middle-aged adults (45–59), Young-old (60–74), Old-old (75–89), and Longevous (≥90). NSCLC cases were mainly concentrated in the Middle-aged and Young-old age groups. Regarding tumor location, the incidence in upper lobes was higher than in lower lobes; right-lung lobes exhibited higher incidence than left-lung lobes. The right upper lobe had the highest incidence, while the middle lobe had the lowest. Further analyses showed that, across all ages, the proportion of NSCLC occurrence in the right versus left lung remained approximately 6:4 (Figure 1c), whereas the upper-versus-lower proportion showed a curved trend (Figure 1d). Figure 1e illustrates this more clearly: the difference between upper and lower incidence proportions showed a clear curved pattern and reached its maximum around ages 60~70—corresponding to the YO group—with an approximate upper-to-lower ratio of 7:3. To identify the specific population driving this pattern, Figure 1f–i shows upper-versus-lower proportions within each age category; only the YO group exhibited a pronounced difference between upper and lower lobes. Abnormal proportions of Myeloid cells and T cells in the YO group We hypothesized that age-associated changes in the pulmonary immune microenvironment contribute to this phenomenon. We used scRNA-seq samples from E-MTAB-13526. This dataset includes 24 samples totaling approximately 900,000 cells and has deep sequencing coverage, offering substantial research value. Among these, 15 tumor-adjacent samples were included and grouped as Young (74), and also as YO versus Others; these groupings were labeled as “age” and “group,” respectively. After dimensionality reduction and clustering of the 15 scRNA-seq samples, batch effects were removed and nine cell types were annotated (Figure 2a). Figure 2b shows the annotated map by age grouping, where Myeloid cells in the YO group clearly differed from the other two groups, and T cells also showed distributional differences. Figure 2c shows marker genes used for annotation. Cell-type proportion patterns are shown in Figure 2d–g: cell types displaying differences in the “group” comparison and non-monotonic (non-stepwise) patterns across Younger/YO/Older were emphasized, particularly Myeloid cells and T cells (highlighted in red). To further validate these findings, 109 tumor-adjacent bulk RNA-seq samples from TCGA LUAD and LUSC were included for BayesPrism analysis. Results were similar to scRNA-seq findings: both Myeloid cells and T cells differed (Figure 2h). Cellchat analysis further showed that Myeloid cells and T cells were prominent in both interaction strength and number in overall cells and in the YO group (Figure 2i–j). Next, cell communication patterns and related ligand–receptor pairs were inferred for overall cells and the YO group (Figure 2k–l). Myeloid–T cell CellChat signaling pathways are shown in Table 1, where MHC-II was the most significant pathway, suggesting recruitment of T cells. Prostaglandin signaling (PGE2–PTGES3–PTGER4) and GALECTIN signaling (LGALS9), among others, were also closely related to myeloid–T cell regulation. source target ligand receptor prob pval Interaction name Interaction name 2 Pathway name annotation evidence Myeloid T cells CXCL16 CXCR6 0.01 0 CXCL16_CXCR6 CXCL16 - CXCR6 CXCL Secreted Signaling KEGG: hsa04060 Myeloid T cells MIF CD74_CXCR4 0.07 0 MIF_CD74_CXCR4 MIF - (CD74+CXCR4) MIF Secreted Signaling PMID: 29637711; PMID: 24760155 Myeloid T cells MIF CD74_CD44 0.06 0.01 MIF_CD74_CD44 MIF - (CD74+CD44) MIF Secreted Signaling PMID: 29637711; PMID: 26175090 Myeloid T cells RETN CAP1 0.03 0 RETN_CAP1 RETN - CAP1 RESISTIN Secreted Signaling PMID: 30809105 Myeloid T cells LGALS9 PTPRC 0.06 0 LGALS9_CD45 LGALS9 - CD45 GALECTIN Secreted Signaling PMID: 30120235 Myeloid T cells LGALS9 CD44 0.05 0 LGALS9_CD44 LGALS9 - CD44 GALECTIN Secreted Signaling PMID: 25065622 Myeloid T cells LGALS9 P4HB 0.01 0 LGALS9_P4HB LGALS9 - P4HB GALECTIN Secreted Signaling PMID:21670307;uniprot Myeloid T cells FN1 ITGA4_ITGB1 0.05 0 FN1_ITGA4_ITGB1 FN1 - (ITGA4+ITGB1) FN1 ECM-Receptor KEGG: hsa04512 Myeloid T cells FN1 CD44 0.12 0 FN1_CD44 FN1 - CD44 FN1 ECM-Receptor KEGG: hsa04512 Myeloid T cells THBS1 CD47 0.01 0 THBS1_CD47 THBS1 - CD47 THBS ECM-Receptor KEGG: hsa04512 Myeloid T cells PGE2-PTGES3 PTGER4 0.02 0 PGE2-PTGES3_PTGER4 PGE2-PTGES3 - PTGER4 Prostaglandin Non-protein Signaling PMID: 34949672;PMID: 21508345 Myeloid T cells Cholesterol-LIPA RORA 0.02 0 Cholesterol-Cholesterol-LIPA_RORA CHOLESTEROL-LIPA - RORA Cholesterol Non-protein Signaling HMRbase;PMID:12467577;PDB:1N83 Myeloid T cells CLEC2B KLRB1 0.01 0 CLEC2B_KLRB1 CLEC2B - KLRB1 CLEC Cell-Cell Contact PMID: 24223577 Myeloid T cells ICAM2 ITGAL_ITGB2 0.00 0 ICAM2_ITGAL_ITGB2 ICAM2 - (ITGAL+ITGB2) ICAM Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DPA1 CD4 0.03 0 HLA-DPA1_CD4 HLA-DPA1 - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DPB1 CD4 0.02 0 HLA-DPB1_CD4 HLA-DPB1 - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DQA1 CD4 0.02 0 HLA-DQA1_CD4 HLA-DQA1 - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DMA CD4 0.01 0 HLA-DMA_CD4 HLA-DMA - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DMB CD4 0.01 0 HLA-DMB_CD4 HLA-DMB - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DQA2 CD4 0.01 0 HLA-DQA2_CD4 HLA-DQA2 - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DOA CD4 0.00 0 HLA-DOA_CD4 HLA-DOA - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DQB1 CD4 0.02 0 HLA-DQB1_CD4 HLA-DQB1 - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DRA CD4 0.03 0 HLA-DRA_CD4 HLA-DRA - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DRB1 CD4 0.03 0 HLA-DRB1_CD4 HLA-DRB1 - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells HLA-DRB5 CD4 0.01 0 HLA-DRB5_CD4 HLA-DRB5 - CD4 MHC-II Cell-Cell Contact KEGG: hsa04514 Myeloid T cells SIGLEC1 SPN 0.00 0 SIGLEC1_SPN SIGLEC1 - SPN SN Cell-Cell Contact KEGG: hsa04514; PMID: 11238599 Myeloid T cells CD55 ADGRE5 0.02 0 CD55_ADGRE5 CD55 - ADGRE5 ADGRE Cell-Cell Contact PMID: 31462748 Table1, The communication network between myeloid cells to T cells Alveolar macrophages show abnormal abundance in the YO group Based on the above analyses, Myeloid cells were extracted for further integration (batch effects removed again) and annotated into nine subclusters (Figure 3a). Figure 3b–c shows Myeloid cells under different groupings, and Figure 3d shows marker genes used for annotation. Further analyses of myeloid subcluster proportions indicated that alveolar macrophages (AMs) and CD14 monocytes were particularly enriched (or depleted) in the YO group: their expression patterns were not consistent with a simple age gradient (highlighted in red; Figure 3e–h). Figure 3i shows pseudotime analysis of myeloid subsets: over pseudotime, myeloid populations transition from the Younger group toward YO and Older groups, with macrophages (e.g., AMs) gradually replacing monocytes (e.g., CD14 and CD16 monocytes). Because AMs are tissue-resident cells with stronger immunosurveillance and antigen presentation capacity than monocytes, the abnormal increase in AM abundance in the YO group may reflect early imbalance in immune homeostasis. Therefore, AMs were extracted and analyzed further by “group”. THBS1 and GATD3A were the most significant differentially expressed genes (Figure 4a). GO enrichment results are shown in Figure 4b–d; notably, pathways related to cellular respiration and energy metabolism were enriched, suggesting that increased AM proportions in the YO group may be associated with metabolic remodeling in AMs. Circle plots corresponding to these pathways are shown in Figure 4e–g. Figure 4h–i shows transcription factor activity in YO and Others AMs: STAT1 and STAT3 were active in the YO group, suggesting that activation of the JAK/STAT pathway may be one factor contributing to AM changes.The specific transcription factor activities can be found in the Supplementary Table1-2. In addition, within YO-group AMs, the proportion of M2 cells was higher than that of M1 cells, as shown by AUC scoring (Figure 4j). AUC scoring of gene sets from major pathways is shown in Figure 4k–p. Compared with the Others group, the YO group showed higher scores for senescence-related, oxidative stress, myeloid cell recruitment, and ECM remodeling gene sets, but lower scores for apoptosis and hypoxia response gene sets, supporting the observation of increased AM proportions in the YO group. Aberrant T-cell polarization in the YO group T cells were also extracted and re-integrated (batch effects removed again). Figure 5a–c shows T-cell subcluster annotation; 14 T-cell subclusters were annotated and displayed by “group” and “age.” Marker genes for T-cell annotation are shown in Figure 5d. Proportions of T-cell subclusters by grouping are shown in Figure 5e–h: most CD4+ T-cell–related subclusters had higher mean proportions in the YO group, whereas CD8+ T-cell–related subclusters had higher mean proportions in the other groups (Others, Younger, Older). T-cell subclusters showing unique changes in the YO group are highlighted in red. Pseudotime analysis of T-cell subclusters suggested that YO and Younger were at earlier stages, transitioning toward Older over pseudotime. In terms of subclusters, early stages were dominated by CD4-related T-cell subclusters, which shifted toward CD8+ T-cell–related subclusters over time (Figure 5i). AUC scoring of major signaling gene sets in T cells is shown in Figure 5j–o. Compared with Others, the YO group showed higher scores for apoptosis, oxidative stress, hypoxia response, and ECM remodeling gene sets, and lower scores for senescence-related and myeloid cell recruitment gene sets. To better illustrate differences between CD4+ and CD8+ T cells across groups, T-cell subclusters were consolidated into three categories: CD4+ T cells, CD8+ T cells, and other T cells. Figure 6a–b more clearly shows differences in CD4+ and CD8+ T cells between YO and Others as well as between Younger and Older. To further explore molecular-level drivers, updated annotation information is shown in Figure 6c. Differential analyses were then performed for CD4 T cells and CD8 T cells comparing YO versus Others; VIM and RPS4Y1 were genes showing large differences in both comparisons (Figure 6d and Figure 6). Figure 6e–j shows GO enrichment for between-group differences in CD4+ T cells, and Figure 6i–k shows GO enrichment for CD8+ T cells. Energy metabolism remodeling pathways were enriched, potentially contributing to shifts between CD4+ and CD8+ T-cell states. Discussion With societal development and advances in medical care, the NSCLC disease spectrum has changed. By analyzing NSCLC cases from Shanghai Pulmonary Hospital over the past decade, this study assessed correlations between age and tumor location at the population level and identified a distinct pattern: in the YO group, the upper-versus-lower lobe incidence gap was substantially larger than in other age groups. Because of anatomic differences, the upper lobes are more susceptible to external exposures, and immune changes may amplify such differences. This study further used scRNA-seq to explore a molecular mechanism underlying this clinical feature. Abnormal AM abundance and shifts in T-cell differentiation may contribute to this phenomenon, and AM changes may act on T cells. Activation of the JAK/STAT pathway may be one factor driving changes in AM abundance. Therefore, AMs may influence T cells, altering the immune landscape in the YO group and ultimately contributing to the pronounced upper-versus-lower incidence disparity. In epidemiologic research on NSCLC, age is a critical factor. With increasing age, pulmonary immune function, lung function, and patterns of lung injury all differ. Studies indicate that incidence and mortality of tracheal, bronchial, and lung cancers rise significantly among patients older than 70 years [33–34] . Accordingly, diagnostic and treatment paradigms must be adjusted based on NSCLC incidence patterns [35] . Research suggests that NSCLC in patients older than 70 years warrants focused attention, strengthening management of different subgroups and promoting effective control of known risk factors [36] . Our study identified a pronounced upper-versus-lower lobe incidence disparity among YO patients, which may be attributable to age-related changes in the pulmonary immune microenvironment. AMs help maintain pulmonary homeostasis and coordinate immune responses to inhaled pathogens and particulates; they are essential immune cells in the lung [37] . Our study showed that AMs in the YO group were markedly higher than in other age groups, potentially driving immune changes that impair tumor immune surveillance and make early malignant transformation less likely to be recognized and cleared. Studies suggest that AMs participate in an immune–metabolic process driving the transition “from precancer to tumor” [38] and can support cancer proliferation by promoting an immunologically permissive tumor microenvironment, forming a vicious cycle [39–40] . Dominance of M2 cells among AMs in NSCLC patients may contribute to immunosuppression [41] . Abnormally increased AM abundance and/or polarization shifts may weaken antigen presentation capacity and promote formation of an immunosuppressive microenvironment [42] . In particular, changes in the proportion of CD8+ and CD4+ T cells are implicated. Studies indicate that cytotoxic CD8+ T cells can be suppressed by AMs [43] , and that the CD4/CD8 ratio is strongly related to treatment response and outcomes and may serve as an independent variable [44–45] , consistent with our findings. Further analyses suggested that activation of the JAK/STAT pathway may be a factor contributing to AM-related changes. Multiple studies have shown that STAT3 activation can promote tumor-associated polarization of AMs, facilitating immunosuppression and tumor immune evasion [46–48] . Together, these results provide potential mechanistic explanations for the upper-versus-lower lobe incidence disparity observed in YO patients. Strengths and limitations This study included statistical analyses of 121,436 NSCLC cases, a very large dataset that provides substantial reliability. Moreover, this study is the first to propose that the YO group (60–74 years) differs from other age groups in pulmonary immune microenvironment features and links these differences to tumor location, providing references for research on lung aging and cancer prevention/control. Single-cell analyses further explored potential molecular mechanisms underlying immune microenvironment changes in the YO group, laying a foundation for future studies. Nevertheless, limitations remain. First, further studies are needed to validate mechanisms underlying immune microenvironment changes in the YO group, including whether JAK/STAT signaling influences AM abundance in the lung and whether AMs recruit or modulate T cells. Second, larger scRNA-seq datasets are needed for independent validation. Third, findings should be further translated into clinical practice to build an age–tumor-location clinical model of NSCLC. Overall, using extensive clinical data, this study innovatively proposes age-specific immune microenvironment changes in the YO group in the context of NSCLC and offers plausible mechanistic hypotheses. Abbreviations LC:lung cancer NSCLC: Non-small cell lung cancer YO: Yong-Old scRNA-seq:single-cell RNA sequencing PCA:principal component analysis TCGA:The Cancer Genome Atlas AMs:alveolar macrophages GO: Gene Ontology Declarations Funding Shanghai Pulmonary Hospital Research Fund (No. FKLY20003) Consent for publication Not Applicable Competing interests The authors declare that they have no competing interests. Availability of data and materials The datasets analysed are available in the BioStudies ,E-MTAB-13526, https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-13526 Acknowledgements We are grateful for the case data provided by Shanghai Pulmonary Hospital and the single-cell sequencing data offered by Marco De Zuani, Ana Cvejic and Haoliang Xue. Ethics approval and consent to participate The ethical exemption has been approved by the Ethics Committee of Shanghai Pulmonary Hospital (K25-612). Clinical trial number Not applicable DL and HR was a major contributor in writing the manuscript. WZ YJ and AZ was responsible for the acquisition of the presented data and was involved in the revision process of the manuscript. YW was responsible for designing the manuscript. XZ analyzed and interpreted the data. DL and XZ participated in the extensive revision process of the manuscript. All authors have read and approved the final manuscript. References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024,74(3):229-263. Thai A A, Solomon B J, Sequist L V, et al. Lung cancer[J]. Lancet, 2021,398(10299):535-554. Huang J, Deng Y, Tin M S, et al. Distribution, Risk Factors, and Temporal Trends for Lung Cancer Incidence and Mortality: A Global Analysis[J]. Chest, 2022,161(4):1101-1111. Wan W, Peters S, Portengen L, et al. Occupational Benzene Exposure and Lung Cancer Risk: A Pooled Analysis of 14 Case-Control Studies[J]. Am J Respir Crit Care Med, 2024,209(2):185-196. van Zandwijk N, Reid G, Frank A L. Asbestos-related cancers: the 'Hidden Killer' remains a global threat[J]. Expert Rev Anticancer Ther, 2020,20(4):271-278. Ge C, Peters S, Olsson A, et al. Respirable Crystalline Silica Exposure, Smoking, and Lung Cancer Subtype Risks. A Pooled Analysis of Case-Control Studies[J]. Am J Respir Crit Care Med, 2020,202(3):412-421. Guseva C I, Gaillen-Guedy A, Wild P, et al. Lung cancer mortality in the French cohort of titanium dioxide workers: some aetiological insights[J]. Occup Environ Med, 2020,77(11):795-797. Chandwani R, Brokamp C, Salfity H, et al. Impact of Environmental Exposures on Lung Cancer in Patients Who Never Smoked[J]. World J Surg, 2023,47(10):2578-2586. Pettit R W, Byun J, Han Y, et al. The shared genetic architecture between epidemiological and behavioral traits with lung cancer[J]. Sci Rep, 2021,11(1):17559. Kachuri L, Johansson M, Rashkin S R, et al. Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility[J]. Nat Commun, 2020,11(1):27. Callister, M. E. J., Sasieni, P., & Robbins, H. A. (2021). Overdiagnosis in lung cancer screening. The Lancet. Respiratory medicine, 9(1), 7–9. Sedeta E, Sung H, Laversanne M, et al. Recent Mortality Patterns and Time Trends for the Major Cancers in 47 Countries Worldwide[J]. Cancer Epidemiol Biomarkers Prev, 2023,32(7):894-905. Leiter, A., Veluswamy, R. R., & Wisnivesky, J. P. (2023). The global burden of lung cancer: current status and future trends. Nature reviews. Clinical oncology, 20(9), 624–639. Trama, A., Tittarelli, A., Barigelletti, G., Botta, L., Gatta, G., Tagliabue, G., Contiero, P., Guzzinati, S., Andreano, A., Manneschi, G., Falcini, F., Castaing, M., Filiberti, R. A., Gasparotti, C., Cirilli, C., Mazzucco, W., Mangone, L., Iacovacci, S., Vitale, M. F., Stracci, F., … Bernasconi, A. (2022). Excess risk of subsequent malignant neoplasms in adolescent and young adult cancer survivors: Results from the first Italian population-based cohort. Cancer, 128(2), 364–372. Hu, M., Tan, J., Liu, Z., Li, L., Zhang, H., Zhao, D., Li, B., Gao, X., Che, N., & Zhang, T. (2022). Comprehensive Comparative Molecular Characterization of Young and Old Lung Cancer Patients. Frontiers in oncology, 11, 806845. Mackintosh, J. A., Marshall, H. M., Slaughter, R., Reddy, T., Yang, I. A., Bowman, R. V., & Fong, K. M. (2019). Interstitial lung abnormalities in the Queensland Lung Cancer Screening Study: prevalence and progression over 2 years of surveillance. Internal medicine journal, 49(7), 843–849. Nemec SF, Bankier AA, Eisenberg RL. Upper lobe–predominant diseases of the lung. Am J Roentgenol. 2013;200:W222–37. Copley SJ, Giannarou S, Hansell DM, Yang G-Z. Effect of aging on lung structure in vivo: assessment with densitometric and fractal analysis of High-resolution computed tomography data. J Thorac Imaging. 2012;27. Boueiz A, Chang Y, Cho MH, Washko GR, San José Estépar R, Bowler RP, et al. Lobar emphysema distribution is associated with 5-year radiological disease progression. Chest. 2018;153:65–76. Khoa ND, Li S, Phuong NL, Kuga K, Yabuuchi H, Kan-O K, et al. Computational fluid-particle dynamics modeling of ultrafine to coarse particles deposition in the human respiratory system, down to the terminal bronchiole. Comput Methods Programs Biomed. 2023;237:107589. Li, Y., Lin, C., Cui, L., Huang, C., Shi, L., Huang, S., Yu, Y., Zhou, X., Zhou, Q., Chen, K., & Shi, L. (2025). Association between age and lung cancer risk: evidence from lung lobar radiomics. BMC medical imaging, 25(1), 204. Marco De Zuani, Ana Cvejic & Haoliang Xue (2024). Single cell RNA-seq atlas of human NSCLC lesions and non-involved tissue. BioStudies, E-MTAB-13526. Hao Y, Stuart T, Kowalski MH, et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology, 2023. McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019). DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Systems, 8(4), 329–337.e4. Madissoon, E., Oliver, A. J., Kleshchevnikov, V., Wilbrey-Clark, A., Polanski, K., Orsi, A. R., et al. (2023). A spatially resolved atlas of the human lung characterizes a gland-associated immune niche. Nature Genetics, 55(1), 66–77. Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21(12), 1–20. Chu, T., Wang, Z., Pe’er, D., & Danko, C. G. (2022). Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nature Cancer, 3(4), 505–517. Weinstein JN, Collisson EA, Mills GB, et al. The Cancer Genome Atlas Pan-Cancer analysis project[J]. Nature Genetics, 2013, 45(10): 1113-1120. Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., et al. (2021). Inference and analysis of cell-cell communication using CellChat. Nature Communications, 12, 1088. Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nature Methods, 14(10), 979–982. Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., et al. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083–1086. Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140. Xing H, Wu C, Yang W, Cai S, Zhang X, Ye X. Tracheal, bronchus, and lung cancer among older adults: thirty-year global burden trends, precision medicine breakthroughs, and lingering barriers. BMC Cancer. 2025;25(1):954. Liu Y, Zheng J, Zhang Y, et al. Aging population of tracheal, bronchus, and lung cancer: global, regional, and national burden-insights from the Global Burden of Disease Study 2021. J Thorac Dis. 2025;17(8):5547-5560. Bandi P, Star J, Ashad-Bishop K, Kratzer T, Smith R, Jemal A. Lung Cancer Screening in the US, 2022. JAMA Intern Med. 2024;184(8):882-891. Xing H, Wu C, Yang W, Cai S, Zhang X, Ye X. Tracheal, bronchus, and lung cancer among older adults: thirty-year global burden trends, precision medicine breakthroughs, and lingering barriers. BMC Cancer. 2025;25(1):954. Aegerter H, Lambrecht BN, Jakubzick CV. Biology of lung macrophages in health and disease. Immunity. 2022;55(9):1564-1580. Huang H, Yang Y, Zhang Q, et al. S100a4+ alveolar macrophages accelerate the progression of precancerous atypical adenomatous hyperplasia by promoting the angiogenic function regulated by fatty acid metabolism. Elife. 2025;13:RP101731. Kuhlmann-Hogan A, Cordes T, Xu Z, et al. EGFR-driven lung adenocarcinomas coopt alveolar macrophage metabolism and function to support EGFR signaling and growth. Cancer Discov. Taniguchi S, Matsui T, Kimura K, et al. In vivo induction of activin A-producing alveolar macrophages supports the progression of lung cell carcinoma. Nat Commun. 2023;14(1):143. Li J, Li J, Wang L, et al. Alveolar macrophages in patients with non-small cell lung cancer. Int J Clin Exp Pathol. 2020;13(7):1867-1872. Prieto LI, Sturmlechner I, Graves SI, et al. Senescent alveolar macrophages promote early-stage lung tumorigenesis. Cancer Cell. 2023;41(7):1261-1275.e6. Sun F, Li L, Xiao Y, et al. Alveolar Macrophages Inherently Express Programmed Death-1 Ligand 1 for Optimal Protective Immunity and Tolerance. J Immunol. 2021;207(1):110-114. Schulze AB, Evers G, Görlich D, et al. Tumor infiltrating T cells influence prognosis in stage I-III non-small cell lung cancer. J Thorac Dis. 2020;12(5):1824-1842. Zhuang W, Wang M, Jiang L, Su Z, Lin S. The peripheral CD4+ T cells predict efficacy in non-small cell lung cancer (NSCLC) patients with the anti-PD-1 treatment. Transl Cancer Res. 2024;13(8):4052-4061. Ao YQ, Gao J, Jin C, et al. ASCC3 promotes the immunosuppression and progression of non-small cell lung cancer by impairing the type I interferon response via CAND1-mediated ubiquitination inhibition of STAT3. J Immunother Cancer. 2023;11(12):e007766. Parakh S, Ernst M, Poh AR. Multicellular Effects of STAT3 in Non-small Cell Lung Cancer: Mechanistic Insights and Therapeutic Opportunities. Cancers (Basel). 2021;13(24):6228. Li L, Sun F, Han L, et al. PDLIM2 repression by ROS in alveolar macrophages promotes lung tumorigenesis. JCI Insight. 2021;6(5):e144394. Additional Declarations No competing interests reported. Supplementary Files Supplementary.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9155639","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611422626,"identity":"4bd6f995-30a0-42c0-9d33-73cc0a3ab564","order_by":0,"name":"Dong-Ning Lu","email":"","orcid":"","institution":"Shanghai Pulmonary Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dong-Ning","middleName":"","lastName":"Lu","suffix":""},{"id":611422627,"identity":"9a903cac-5ddd-4a68-b99c-4d64d080c241","order_by":1,"name":"Hang-Xing Ren","email":"","orcid":"","institution":"Ningbo First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hang-Xing","middleName":"","lastName":"Ren","suffix":""},{"id":611422628,"identity":"9af00921-41c5-402a-8065-fc41325fd5ae","order_by":2,"name":"Wan-Chen Zhang","email":"","orcid":"","institution":"Ningbo First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wan-Chen","middleName":"","lastName":"Zhang","suffix":""},{"id":611422629,"identity":"01c046c2-081f-4848-9b05-b23eb97dbdb3","order_by":3,"name":"Yan Jiang","email":"","orcid":"","institution":"Shanghai Pulmonary Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Jiang","suffix":""},{"id":611422630,"identity":"d5ae85f1-a514-427a-884e-a0826c705adb","order_by":4,"name":"Ao Zeng","email":"","orcid":"","institution":"Shanghai Pulmonary Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ao","middleName":"","lastName":"Zeng","suffix":""},{"id":611422631,"identity":"cc5a9d15-ff2f-4df1-bc7d-57bdb4dfe13b","order_by":5,"name":"Yi-Mu Wu","email":"","orcid":"","institution":"Shanghai Pulmonary Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi-Mu","middleName":"","lastName":"Wu","suffix":""},{"id":611422632,"identity":"048fc62b-cc59-4642-8493-0408fd54e20b","order_by":6,"name":"Xiao Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYNCCChs5Nvb2A0SrZ2xgOJNmzMdzJoEELYwthxPnSTgYEKdefkYC+2PehrT0NgmGBIYfFduIsKLnAGMz7w6b3DbpxgOMPWduE9bCzN4A1HImLbdN5kACM2MbEVrYmBmAWtoOp7NJJBgQp4UHbEvb4QTitUjwHGCcOedMmmEbMJAPEuUXYIgxfHhTYSMv395+8MGPCiK0MDDwf2DigTIPEKMeDBh/EK10FIyCUTAKRiQAAJipOWkMyxrjAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Pulmonary Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-03-18 06:55:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9155639/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9155639/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105456589,"identity":"925183db-561b-41a0-a277-4589b2165db2","added_by":"auto","created_at":"2026-03-26 09:14:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":890293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCollection and analysis of clinical cases. \u003c/strong\u003eFigure1a:The cases recorded by Shanghai Pulmonary Hospital from 2015 to 2024. Figure1b:The cases were grouped according to age and the location of the onset. Figure1c:The incidence rates of the left lung and the right lung with age. Figure1d:The incidence rates of the upper lung and the lower lung with age. Figure1e:The difference in the incidence rates between the upper and lower lungs varies with age.Figure1f-i:The proportion of cases occurring in the upper and lower lobes by each age group, compared by year\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9155639/v1/118487ba906ebb48e1d23ae4.jpg"},{"id":105456588,"identity":"1db3ecc1-3a34-48ec-ac3b-ff42f0abc99e","added_by":"auto","created_at":"2026-03-26 09:14:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2474128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScRNA-seq of 15 adjacent cancer samples. \u003c/strong\u003eFigure2a: Cell annotation UAMP plot for scRNA-seq. Figure2b: Cell annotation UAMP plot for scRNA-seq by age. Figure2c: The makers for each cell for cell annotation. Figure2d-g:Bar charts showing the proportion of each cell type in the overall population, presented in different groups. Figure2h: Using the BayesPrism to map the scRNA-seq results to the bulk RNA-seq of TCGA. Figure2i: The overall results of cell-cell interactions show the number and intensity of interactions. Figure2j: The YO group results of cell-cell interactions show the number and intensity of interactions. Figure2k: Pattern of cell-cell interactions of the overall cells. Figure2l: Pattern of cell-cell interactions of the YO group cells.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9155639/v1/a4ad7c95910ac2f66bd60064.jpg"},{"id":105456587,"identity":"9fee83dd-8522-4e12-bbd6-59f5f4414f63","added_by":"auto","created_at":"2026-03-26 09:14:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1625037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScRNA-seq analysis of Myeloid cell subsets. \u003c/strong\u003eFigure3a: Myeloid cells annotation UAMP plot for scRNA-seq. Figure3b: Myeloid cells annotation UAMP plot for scRNA-seq by age. Figure3c: Myeloid cells annotation UAMP plot for scRNA-seq by group. Figure3d: Violin plot of Myeloid cell subtype annotation maker. Figure3e-h:Bar charts showing the proportion of each Myeloid cell subtype in the overall population, presented in different groups. Figure3i:Pseudotime analysis of myeloid cell subtypes.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9155639/v1/923e5722ff3e127b055d475d.jpg"},{"id":105456591,"identity":"62573a4b-e243-404d-b8c6-a9e81479962e","added_by":"auto","created_at":"2026-03-26 09:14:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3279932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePossible factors for the abnormal increase of AMs in the YO group.\u003c/strong\u003eFigure 4a:Analysis of the different genes between AMs in YO and Others. Figure 4b-d: GO-BP(4b), GO-CC(4c) and GO-MF(4d) enrichment analysis is based on the differential genes. Figure 4e-g: The circle diagram corresponding to the GO-BP(4e), GO-CC(4f) and GO-MF(4g) analysis shows the enriched energy remodeling-related pathways. Figure4h: The active transcription factors in the YO and Others groups. Figure4i: Transcription factor activity heatmap based on sample grouping. Figure 4j: Analysis of the proportion of M1 and M2 cells in AMs of the YO group. Figure4k-p: The biological gene set scores of the AMs in both groups.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9155639/v1/6bd18a60a72e8461b8edad94.jpg"},{"id":105456590,"identity":"0c41f847-a860-432c-bfbf-3df3ebef540e","added_by":"auto","created_at":"2026-03-26 09:14:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2965604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScRNA-seq analysis of T cells subsets. \u003c/strong\u003eFigure5a: T cells annotation UAMP plot for scRNA-seq. Figure5b: T cells annotation UAMP plot for scRNA-seq by age. Figure5c: T cells annotation UAMP plot for scRNA-seq by group. Figure5d:Heatmap of Myeloid cell subtype annotation maker. Figure5e-h:Bar charts showing the proportion of each T cell subtype in the overall population, presented in different groups. Figure5i:Pseudotime analysis of T cell subtypes. Figure5j-o: The biological gene set scores of the T cells in both groups.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9155639/v1/c9f8e25ed7b045d557af23de.jpg"},{"id":105456592,"identity":"96c6c8cd-fc37-4619-a7d5-a94e4a26a50e","added_by":"auto","created_at":"2026-03-26 09:14:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2101056,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePossible factors for the abnormal of T cells in the YO group. \u003c/strong\u003eFigure6a-b:Bar charts showing the proportion of CD4+T and CD8+ T cells, presented in different groups. Figure6c: UAMP plot of CD4+T and CD8+ T cells in different groups. Figure6d: Analysis of the different genes between CD4+T cells in YO and Others. Figure6e-g: GO-BP(6e), GO-CC(6f) and GO-MF(6g) enrichment analysis is based on the differential genes in CD4+T cells. Figure6h: Analysis of the different genes between CD8+T cells in YO and Others. Figure6i-k: GO-BP(6i), GO-CC(6j) and GO-MF(6k) enrichment analysis is based on the differential genes in CD8+T cells.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9155639/v1/778fe8b74fb0ee7f699831f8.jpg"},{"id":107669458,"identity":"fbfa6ceb-ef55-4ff6-9df4-58c32b5935a3","added_by":"auto","created_at":"2026-04-23 20:24:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13703099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9155639/v1/1039fdd4-9d60-4b89-87b8-ccd6512d0d3f.pdf"},{"id":105456593,"identity":"0d7a6e00-f7ef-454e-9257-7d1e450aa724","added_by":"auto","created_at":"2026-03-26 09:14:54","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28644753,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.zip","url":"https://assets-eu.researchsquare.com/files/rs-9155639/v1/1860f11b9dca55d58538514e.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"The \"Young-Old\" group may have a unique pulmonary immune microenvironment, resulting in a different ratio of incidence rates between the upper and lower lobes of the lungs compared to other age","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer (LC) is one of the most common malignant tumors worldwide, characterized by high incidence and high mortality. Among men, lung cancer ranks first in both incidence and mortality; among women, it ranks second in both incidence and mortality \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Lung cancer is currently the leading cause of cancer-related death globally, with approximately 2.48\u0026nbsp;million new cases and 1.84\u0026nbsp;million deaths each year; due to its large population, Asia bears a relatively heavier lung cancer burden \u003csup\u003e[2]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNSCLC is the most common type of lung cancer, accounting for approximately 80%\u0026ndash;85% of all lung cancer cases. Compared with small cell lung cancer, NSCLC generally grows and spreads more slowly, has a broader range of treatment options, and is associated with a relatively better prognosis (especially in early-stage disease). NSCLC has numerous risk factors that have evolved alongside societal development. Current research still consistently identifies smoking and exposure to secondhand smoke as the most important risk factors for NSCLC \u003csup\u003e[3]\u003c/sup\u003e. Occupational exposures are also an important reason for higher incidence in specific populations, including exposure to polycyclic aromatic hydrocarbons, asbestos, arsenic, and certain forms of crystalline silica, as well as chromium, radon, and cadmium \u003csup\u003e[4\u0026ndash;7]\u003c/sup\u003e. With ongoing societal development, emerging air pollutants\u0026mdash;such as particulate matter\u0026mdash;have increasingly become major contributing factors \u003csup\u003e[8]\u003c/sup\u003e. In addition, certain pulmonary diseases, unhealthy psychological factors, and dietary habits may, to some extent, also act as risk factors for NSCLC \u003csup\u003e[9,10]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese environmental factors are important drivers of changes in the NSCLC disease burden. Meanwhile, advances in medical care\u0026mdash;such as the widespread adoption of accurate thin-slice CT\u0026mdash;and increased public health awareness have also contributed to changes in the NSCLC disease spectrum \u003csup\u003e[11]\u003c/sup\u003e. Overall, the NSCLC disease spectrum is undergoing the following shifts: adenocarcinoma incidence is increasing relative to squamous cell carcinoma; the incidence of NSCLC among women is increasing relative to men \u003csup\u003e[12]\u003c/sup\u003e; and the proportion of early-stage NSCLC is increasing relative to middle-to-late stage disease\u003csup\u003e[13]\u003c/sup\u003e. Therefore, research focused only on advanced NSCLC or poorly differentiated NSCLC clearly cannot benefit the majority of patients. In light of these changes in the NSCLC disease spectrum, it is necessary to summarize incidence characteristics across populations and to investigate the pathogenesis of early-stage NSCLC, thereby improving understanding of its initiation and progression and providing a basis for early diagnosis and treatment.\u003c/p\u003e \u003cp\u003eThe occurrence of NSCLC is also associated with age. Age is the most prominent risk-related factor for lung cancer, and the risk rises stepwise with increasing age. Increasing age reflects longer cumulative exposure to risk factors and declining immune competence. NSCLC most commonly occurs after the age of 40, with peak incidence between 55 and 75 years. Although lung cancer incidence is relatively low among people younger than 40, a trend toward younger onset has been observed in recent years; this may be related to prolonged secondhand smoke exposure, air pollution, and unhealthy lifestyles. In younger patients, adenocarcinoma accounts for a higher proportion\u003csup\u003e[14]\u003c/sup\u003e. However, it should be noted that NSCLC in younger patients may have worse prognosis. Studies suggest that patients in different age groups exhibit distinct molecular features, implying different age-associated tumor risks and highlighting the need for greater caution in treatment selection and post-treatment monitoring to further improve prognosis\u003csup\u003e[15]\u003c/sup\u003e. The \u0026ldquo;young-old\u0026rdquo; (YO) group is an age segment that warrants particular attention in NSCLC. Previous research indicates that YO individuals require focused attention in lung cancer detection and treatment \u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNSCLC tumor location is related to lung anatomy: the left lung comprises two lobes (upper and lower), while the right lung comprises three lobes (upper, middle, and lower). The total anatomic volume of the right lung (approximately 55%\u0026ndash;60%) is larger than that of the left lung (approximately 40%\u0026ndash;45%), and the right main bronchus forms a smaller angle with the trachea and is wider, shorter, and straighter. In addition, there are anatomic differences in volume between upper and lower lobes. The upper lobes are more strongly influenced by gaseous factors, whereas the lower lobes are more influenced by heavier substances due to gravity. Studies suggest that differences in ventilation\u0026ndash;perfusion ratio, lymphatic flow, and mechanics may contribute to upper-lobe\u0026ndash;predominant disease patterns \u003csup\u003e[17]\u003c/sup\u003e. Over time, these factors may lead to increased upper-lobe density. In addition, Copley SJ et al. used fractal dimension analysis to examine lung complexity across age groups and found that, compared with older adults, younger individuals exhibited higher lung complexity \u003csup\u003e[18]\u003c/sup\u003e. Although physiological and pathological heterogeneity may exist between different lobes \u003csup\u003e[19,20]\u003c/sup\u003e and age-related factors may influence lung cancer responses \u003csup\u003e[21]\u003c/sup\u003e, there has been limited research on mechanisms\u0026mdash;particularly the immune microenvironment. These anatomic differences provide additional directions for further exploration of NSCLC pathogenesis.\u003c/p\u003e \u003cp\u003eShanghai Pulmonary Hospital has extensive experience in NSCLC diagnosis and treatment, with an annual surgical volume exceeding 25,000 cases, making it one of the medical institutions with the highest thoracic surgical volume worldwide. We collected thoracic surgery data from 121,436 NSCLC cases treated at Shanghai Pulmonary Hospital over the past decade to explore recent relationships between the NSCLC disease spectrum and population characteristics, and to identify factors associated with the initiation and progression of NSCLC\u0026mdash;especially early-stage NSCLC. In population analyses, we found that the difference in upper- versus lower-lobe proportions among YO NSCLC patients differs from that in other age groups. We further used single-cell RNA sequencing (scRNA-seq) to reveal relationships between age and tumor location and to investigate changes in the intrapulmonary immune microenvironment. This work presents possible etiologic factors, focuses on key cell populations, and provides references for future NSCLC diagnosis, treatment, and research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eClinical data and ethics\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Participants: NSCLC case data from Shanghai Pulmonary Hospital, 2015\u0026ndash;2024. Exclusion criteria: (1) incomplete case data; (2) unclear diagnosis. Final included case counts were as follows: 7,701 cases in 2015; 9,873 in 2016; 12,589 in 2017; 13,427 in 2018; 11,984 in 2019; 15,384 in 2022; 23,486 in 2023; and 26,992 in 2024 (data from 2020 and 2021 were not included due to the impact of COVID-19). The main variables collected were tumor location and age at onset (Supplementary1). Data collection was approved by the Ethics Committee of Shanghai Pulmonary Hospital (approval number: K25-612).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData normalization and integration\u003c/strong\u003e\u003cbr\u003eFifteen tumor-adjacent samples from the E-MTAB-13526 dataset \u003csup\u003e[22]\u003c/sup\u003e were included. These data were raw scRNA-seq data generated on the 10\u0026times; Genomics platform. We first used Seurat\u003csup\u003e\u0026nbsp;\u003c/sup\u003epackage\u003csup\u003e[23]\u0026nbsp;\u003c/sup\u003eto read the gene expression matrix of each sample and build Seurat objects, followed by normalization for each object. The 2,000 most variable genes in each sample were selected as highly variable genes. Batch effects were removed from the expression matrix, and principal component analysis (PCA) was performed for linear dimensionality reduction; the top 10 principal components were used as the basis for downstream analyses. Next, nonlinear dimensionality reduction was performed, a cell-neighbor graph was constructed, and unsupervised clustering was completed. To remove doublets formed during experiments, DoubletFinder \u003csup\u003e[24]\u003c/sup\u003e was used for doublet detection. Based on clustering-derived annotations, the homotypic doublet proportion was calculated, and a doublet formation rate of 7.5% was assumed. After correction, the number of doublets was determined and removed, and samples were integrated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell annotation and cell-type proportions\u003c/strong\u003e\u003cbr\u003eTo characterize the functional features of each cluster, DotPlot in Seurat was used to visualize expression of known marker gene sets. Cluster IDs were replaced with corresponding cell-type names based on established marker sets for intrapulmonary cell types, completing cell annotation \u003csup\u003e[25]\u003c/sup\u003e. Cell-type proportions among all cells were computed using the reshape2 package \u003csup\u003e[26]\u003c/sup\u003e and displayed by group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBayesPrism\u003c/strong\u003e\u003cbr\u003eCell-type deconvolution of bulk RNA-seq was performed using BayesPrism90 package\u003csup\u003e\u0026nbsp;[27]\u0026nbsp;\u003c/sup\u003eto decompose The Cancer Genome Atlas (TCGA) data \u003csup\u003e[28]\u003c/sup\u003e into cell-type fractions. Marker genes for each cell type were computed using Seurat FindAllMarkers. Genes used in subsequent analyses were required to have an average log-transformed fold-change \u0026gt; 1 and be expressed in \u0026gt;0.25 of cells within the cluster. Mitochondrial and ribosomal protein genes were removed from the marker list. For each cell type, the mean raw counts were computed to generate a single-cell reference expression profile. The single-cell reference and TCGA bulk data were used as inputs to a Bayesian deconvolution framework to estimate distributions of cell fractions in bulk samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell\u0026ndash;cell interaction network analysis\u003c/strong\u003e\u003cbr\u003eUsing CellChat package \u003csup\u003e[29],\u003c/sup\u003e target cell subsets were selected based on the annotated Seurat object. The gene expression matrix was extracted to build a CellChat object, and CellChatDB.human was loaded as the ligand\u0026ndash;receptor reference database. Overexpressed genes in each cell type were identified, potential ligand\u0026ndash;receptor interaction pairs were inferred, and interactions were integrated with the human protein\u0026ndash;protein interaction network (PPI.human). Cell\u0026ndash;cell communication probabilities were computed at the ligand\u0026ndash;receptor level; low-confidence communications were filtered. Cell\u0026ndash;cell communication was inferred at the signaling pathway level and aggregated to construct a communication network across cell groups. The netVisualDiffInteraction function was used to visualize differences in interaction number or strength between cell groups. rankNet was used to identify differentially active signaling pathways, and plotGeneExpression was used to visualize communication networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePseudotime analysis\u003c/strong\u003e\u003cbr\u003ePseudotime analysis was performed using monocle2 package \u003csup\u003e[30]\u003c/sup\u003e. Target cells were extracted from the annotated Seurat object. An AnnotatedDataFrame was built, a Monocle dataset was created, and the detection threshold was set to 0.5 with a negative binomial expression model. The Monocle dataset was normalized. Genes with expression \u0026ge;0.1 were retained, and highly variable genes were further selected by dispersion analysis (criteria: mean expression \u0026ge;0.1 and empirical dispersion \u0026ge; 1\u0026times; fitted dispersion). These genes were used as ordering genes. Dimensionality was reduced to 2D, and differentiation trajectories were constructed and ordered to obtain pseudotemporal trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCENIC analysis\u003c/strong\u003e\u003cbr\u003eSCENIC package\u003csup\u003e[31]\u0026nbsp;\u003c/sup\u003ewas applied to scRNA-seq data using the hg38-500bp_up_and_100bp_down_tss database. The GENIE3 algorithm was used to infer potential regulatory relationships between transcription factors (TFs) and target genes. TF lists were derived from JASPAR 2022 and AnimalTFDB 4.0; GENIE3 parameters were kept at defaults. Using RcisTarget (v1.20.0) and species-specific TF-binding motif databases, co-expression modules were pruned and target genes lacking motif support were filtered, yielding high-confidence regulons. AUCell was used to compute regulon activity scores (AUCell scores) in each cell, and cell-type\u0026ndash;specific core regulons were identified based on score distributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUC scoring\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Based on prior studies, gene sets related to biological functions were defined, including immunosuppression, extracellular matrix (ECM) remodeling, myeloid cell recruitment, hypoxia response, oxidative stress, apoptosis, M1 and M2 macrophages, and gene sets related to maturation and senescence. Genes in each set that were present in the Seurat expression matrix were retained, and only gene sets with at least two available genes were kept to reduce bias from overly small sets. The normalized expression matrix was extracted and AUCell was used to rank gene expression for each cell to build a ranked matrix. AUC values (area under the curve) were computed for each cell with aucMaxRank = 0.05 \u0026times; total genes (i.e., AUC computed using only the top 5% ranked genes per cell), quantifying overall activity of each functional gene set per cell.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential expression and functional enrichment analyses\u003c/strong\u003e\u003cbr\u003eDifferential expression analyses used wilcox.test in Seurat (suitable for scRNA-seq) and edgeR package \u003csup\u003e[32]\u003c/sup\u003e (suitable for large-scale RNA-seq) via FindMarkers. To ensure validity of the diagnostic model and better characterize differences in the YO group, fold-change (FC) was set at 1.2, with minimum expression proportion 0.25, p \u0026lt; 0.05, and FC \u0026gt; 1.2. Candidate genes were functionally enriched using Gene Ontology (GO) and KEGG pathway databases. Fisher\u0026rsquo;s exact test was used to determine which genes were most associated with particular functions; smaller p-values indicate more significant enrichment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All statistical analyses were performed using R v4.4.1 and GraphPad Prism v9.01. Student\u0026rsquo;s t-test was used to compare experimental versus control groups; p \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe Young-Old group shows the greatest disparity between upper and lower lung regions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A total of 121,436 surgically treated NSCLC cases from Shanghai Pulmonary Hospital during 2015\u0026ndash;2024 were included (Figure 1a). With advances in medical care and the development of Shanghai Pulmonary Hospital, the annual number of NSCLC cases increased from 7,701 in 2015 to 26,992 in 2024. Figure 1b shows yearly case classification by tumor location and age. Notably, according to World Health Organization (WHO) age categories, patients were grouped as Children and adolescents (\u0026lt;18), Young adults (18\u0026ndash;44), Middle-aged adults (45\u0026ndash;59), Young-old (60\u0026ndash;74), Old-old (75\u0026ndash;89), and Longevous (\u0026ge;90). NSCLC cases were mainly concentrated in the Middle-aged and Young-old age groups.\u003c/p\u003e\n\u003cp\u003eRegarding tumor location, the incidence in upper lobes was higher than in lower lobes; right-lung lobes exhibited higher incidence than left-lung lobes. The right upper lobe had the highest incidence, while the middle lobe had the lowest. Further analyses showed that, across all ages, the proportion of NSCLC occurrence in the right versus left lung remained approximately 6:4 (Figure 1c), whereas the upper-versus-lower proportion showed a curved trend (Figure 1d). Figure 1e illustrates this more clearly: the difference between upper and lower incidence proportions showed a clear curved pattern and reached its maximum around ages 60~70\u0026mdash;corresponding to the YO group\u0026mdash;with an approximate upper-to-lower ratio of 7:3. To identify the specific population driving this pattern, Figure 1f\u0026ndash;i shows upper-versus-lower proportions within each age category; only the YO group exhibited a pronounced difference between upper and lower lobes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbnormal proportions of Myeloid cells and T cells in the YO group\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;We hypothesized that age-associated changes in the pulmonary immune microenvironment contribute to this phenomenon. We used scRNA-seq samples from E-MTAB-13526. This dataset includes 24 samples totaling approximately 900,000 cells and has deep sequencing coverage, offering substantial research value. Among these, 15 tumor-adjacent samples were included and grouped as Young (\u0026lt;60), YO (60\u0026ndash;74), and Old (\u0026gt;74), and also as YO versus Others; these groupings were labeled as \u0026ldquo;age\u0026rdquo; and \u0026ldquo;group,\u0026rdquo; respectively.\u003c/p\u003e\n\u003cp\u003eAfter dimensionality reduction and clustering of the 15 scRNA-seq samples, batch effects were removed and nine cell types were annotated (Figure 2a). Figure 2b shows the annotated map by age grouping, where Myeloid cells in the YO group clearly differed from the other two groups, and T cells also showed distributional differences. Figure 2c shows marker genes used for annotation. Cell-type proportion patterns are shown in Figure 2d\u0026ndash;g: cell types displaying differences in the \u0026ldquo;group\u0026rdquo; comparison and non-monotonic (non-stepwise) patterns across Younger/YO/Older were emphasized, particularly Myeloid cells and T cells (highlighted in red).\u003c/p\u003e\n\u003cp\u003eTo further validate these findings, 109 tumor-adjacent bulk RNA-seq samples from TCGA LUAD and LUSC were included for BayesPrism analysis. Results were similar to scRNA-seq findings: both Myeloid cells and T cells differed (Figure 2h). Cellchat analysis further showed that Myeloid cells and T cells were prominent in both interaction strength and number in overall cells and in the YO group (Figure 2i\u0026ndash;j). Next, cell communication patterns and related ligand\u0026ndash;receptor pairs were inferred for overall cells and the YO group (Figure 2k\u0026ndash;l). Myeloid\u0026ndash;T cell CellChat signaling pathways are shown in Table 1, where MHC-II was the most significant pathway, suggesting recruitment of T cells. Prostaglandin signaling (PGE2\u0026ndash;PTGES3\u0026ndash;PTGER4) and GALECTIN signaling (LGALS9), among others, were also closely related to myeloid\u0026ndash;T cell regulation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"760\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003etarget\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eligand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ereceptor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eprob\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction name 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eannotation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eevidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCXCL16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCXCR6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCXCL16_CXCR6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCXCL16 - CXCR6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eCXCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSecreted Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eMIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD74_CXCR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMIF_CD74_CXCR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMIF - (CD74+CXCR4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSecreted Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID: 29637711; PMID: 24760155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eMIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD74_CD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMIF_CD74_CD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMIF - (CD74+CD44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSecreted Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID: 29637711; PMID: 26175090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eRETN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCAP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRETN_CAP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRETN - CAP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eRESISTIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSecreted Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID: 30809105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLGALS9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePTPRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLGALS9_CD45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eLGALS9 - CD45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eGALECTIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSecreted Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID: 30120235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLGALS9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLGALS9_CD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eLGALS9 - CD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eGALECTIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSecreted Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID: 25065622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eLGALS9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eP4HB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLGALS9_P4HB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eLGALS9 - P4HB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eGALECTIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSecreted Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID:21670307;uniprot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eITGA4_ITGB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFN1_ITGA4_ITGB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eFN1 - (ITGA4+ITGB1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eECM-Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFN1_CD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eFN1 - CD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eECM-Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eTHBS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTHBS1_CD47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eTHBS1 - CD47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eTHBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eECM-Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003ePGE2-PTGES3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePTGER4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePGE2-PTGES3_PTGER4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003ePGE2-PTGES3 - PTGER4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eProstaglandin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNon-protein Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID: 34949672;PMID: 21508345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCholesterol-LIPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eRORA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCholesterol-Cholesterol-LIPA_RORA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCHOLESTEROL-LIPA - RORA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eCholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNon-protein Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHMRbase;PMID:12467577;PDB:1N83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCLEC2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eKLRB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCLEC2B_KLRB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCLEC2B - KLRB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eCLEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID: 24223577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eICAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eITGAL_ITGB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eICAM2_ITGAL_ITGB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eICAM2 - (ITGAL+ITGB2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eICAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DPA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DPA1_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DPA1 - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DPB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DPB1_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DPB1 - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DQA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DQA1_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DQA1 - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DMA_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DMA - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DMB_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DMB - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DQA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DQA2_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DQA2 - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DOA_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DOA - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DQB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DQB1_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DQB1 - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DRA_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DRA - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DRB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DRB1_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DRB1 - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eHLA-DRB5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eHLA-DRB5_CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHLA-DRB5 - CD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eMHC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSIGLEC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSIGLEC1_SPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSIGLEC1 - SPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eSN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG: hsa04514; PMID: 11238599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMyeloid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 65px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCD55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 96px;\"\u003e\n \u003cp\u003eADGRE5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCD55_ADGRE5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCD55 - ADGRE5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eADGRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCell-Cell Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePMID: 31462748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable1, The communication network between myeloid cells to T cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlveolar macrophages show abnormal abundance in the YO group\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Based on the above analyses, Myeloid cells were extracted for further integration (batch effects removed again) and annotated into nine subclusters (Figure 3a). Figure 3b\u0026ndash;c shows Myeloid cells under different groupings, and Figure 3d shows marker genes used for annotation. Further analyses of myeloid subcluster proportions indicated that alveolar macrophages (AMs) and CD14 monocytes were particularly enriched (or depleted) in the YO group: their expression patterns were not consistent with a simple age gradient (highlighted in red; Figure 3e\u0026ndash;h). Figure 3i shows pseudotime analysis of myeloid subsets: over pseudotime, myeloid populations transition from the Younger group toward YO and Older groups, with macrophages (e.g., AMs) gradually replacing monocytes (e.g., CD14 and CD16 monocytes).\u003c/p\u003e\n\u003cp\u003eBecause AMs are tissue-resident cells with stronger immunosurveillance and antigen presentation capacity than monocytes, the abnormal increase in AM abundance in the YO group may reflect early imbalance in immune homeostasis. Therefore, AMs were extracted and analyzed further by \u0026ldquo;group\u0026rdquo;. THBS1 and GATD3A were the most significant differentially expressed genes (Figure 4a). GO enrichment results are shown in Figure 4b\u0026ndash;d; notably, pathways related to cellular respiration and energy metabolism were enriched, suggesting that increased AM proportions in the YO group may be associated with metabolic remodeling in AMs. Circle plots corresponding to these pathways are shown in Figure 4e\u0026ndash;g. Figure 4h\u0026ndash;i shows transcription factor activity in YO and Others AMs: STAT1 and STAT3 were active in the YO group, suggesting that activation of the JAK/STAT pathway may be one factor contributing to AM changes.The specific transcription factor activities can be found in the Supplementary Table1-2. In addition, within YO-group AMs, the proportion of M2 cells was higher than that of M1 cells, as shown by AUC scoring (Figure 4j). AUC scoring of gene sets from major pathways is shown in Figure 4k\u0026ndash;p. Compared with the Others group, the YO group showed higher scores for senescence-related, oxidative stress, myeloid cell recruitment, and ECM remodeling gene sets, but lower scores for apoptosis and hypoxia response gene sets, supporting the observation of increased AM proportions in the YO group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAberrant T-cell polarization in the YO group\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;T cells were also extracted and re-integrated (batch effects removed again). Figure 5a\u0026ndash;c shows T-cell subcluster annotation; 14 T-cell subclusters were annotated and displayed by \u0026ldquo;group\u0026rdquo; and \u0026ldquo;age.\u0026rdquo; Marker genes for T-cell annotation are shown in Figure 5d. Proportions of T-cell subclusters by grouping are shown in Figure 5e\u0026ndash;h: most CD4+ T-cell\u0026ndash;related subclusters had higher mean proportions in the YO group, whereas CD8+ T-cell\u0026ndash;related subclusters had higher mean proportions in the other groups (Others, Younger, Older). T-cell subclusters showing unique changes in the YO group are highlighted in red.\u003c/p\u003e\n\u003cp\u003ePseudotime analysis of T-cell subclusters suggested that YO and Younger were at earlier stages, transitioning toward Older over pseudotime. In terms of subclusters, early stages were dominated by CD4-related T-cell subclusters, which shifted toward CD8+ T-cell\u0026ndash;related subclusters over time (Figure 5i). AUC scoring of major signaling gene sets in T cells is shown in Figure 5j\u0026ndash;o. Compared with Others, the YO group showed higher scores for apoptosis, oxidative stress, hypoxia response, and ECM remodeling gene sets, and lower scores for senescence-related and myeloid cell recruitment gene sets.\u003c/p\u003e\n\u003cp\u003eTo better illustrate differences between CD4+ and CD8+ T cells across groups, T-cell subclusters were consolidated into three categories: CD4+ T cells, CD8+ T cells, and other T cells. Figure 6a\u0026ndash;b more clearly shows differences in CD4+ and CD8+ T cells between YO and Others as well as between Younger and Older. To further explore molecular-level drivers, updated annotation information is shown in Figure 6c. Differential analyses were then performed for CD4 T cells and CD8 T cells comparing YO versus Others; VIM and RPS4Y1 were genes showing large differences in both comparisons (Figure 6d and Figure 6). Figure 6e\u0026ndash;j shows GO enrichment for between-group differences in CD4+ T cells, and Figure 6i\u0026ndash;k shows GO enrichment for CD8+ T cells. Energy metabolism remodeling pathways were enriched, potentially contributing to shifts between CD4+ and CD8+ T-cell states.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith societal development and advances in medical care, the NSCLC disease spectrum has changed. By analyzing NSCLC cases from Shanghai Pulmonary Hospital over the past decade, this study assessed correlations between age and tumor location at the population level and identified a distinct pattern: in the YO group, the upper-versus-lower lobe incidence gap was substantially larger than in other age groups. Because of anatomic differences, the upper lobes are more susceptible to external exposures, and immune changes may amplify such differences. This study further used scRNA-seq to explore a molecular mechanism underlying this clinical feature. Abnormal AM abundance and shifts in T-cell differentiation may contribute to this phenomenon, and AM changes may act on T cells. Activation of the JAK/STAT pathway may be one factor driving changes in AM abundance. Therefore, AMs may influence T cells, altering the immune landscape in the YO group and ultimately contributing to the pronounced upper-versus-lower incidence disparity.\u003c/p\u003e\n\u003cp\u003eIn epidemiologic research on NSCLC, age is a critical factor. With increasing age, pulmonary immune function, lung function, and patterns of lung injury all differ. Studies indicate that incidence and mortality of tracheal, bronchial, and lung cancers rise significantly among patients older than 70 years \u003csup\u003e[33\u0026ndash;34]\u003c/sup\u003e. Accordingly, diagnostic and treatment paradigms must be adjusted based on NSCLC incidence patterns \u003csup\u003e[35]\u003c/sup\u003e. Research suggests that NSCLC in patients older than 70 years warrants focused attention, strengthening management of different subgroups and promoting effective control of known risk factors \u003csup\u003e[36]\u003c/sup\u003e. Our study identified a pronounced upper-versus-lower lobe incidence disparity among YO patients, which may be attributable to age-related changes in the pulmonary immune microenvironment.\u003c/p\u003e\n\u003cp\u003eAMs help maintain pulmonary homeostasis and coordinate immune responses to inhaled pathogens and particulates; they are essential immune cells in the lung\u003csup\u003e\u0026nbsp;[37]\u003c/sup\u003e. Our study showed that AMs in the YO group were markedly higher than in other age groups, potentially driving immune changes that impair tumor immune surveillance and make early malignant transformation less likely to be recognized and cleared. Studies suggest that AMs participate in an immune\u0026ndash;metabolic process driving the transition \u0026ldquo;from precancer to tumor\u0026rdquo; \u003csup\u003e[38]\u003c/sup\u003e and can support cancer proliferation by promoting an immunologically permissive tumor microenvironment, forming a vicious cycle \u003csup\u003e[39\u0026ndash;40]\u003c/sup\u003e. Dominance of M2 cells among AMs in NSCLC patients may contribute to immunosuppression \u003csup\u003e[41]\u003c/sup\u003e. Abnormally increased AM abundance and/or polarization shifts may weaken antigen presentation capacity and promote formation of an immunosuppressive microenvironment \u003csup\u003e[42]\u003c/sup\u003e. In particular, changes in the proportion of CD8+ and CD4+ T cells are implicated. Studies indicate that cytotoxic CD8+ T cells can be suppressed by AMs \u003csup\u003e[43]\u003c/sup\u003e, and that the CD4/CD8 ratio is strongly related to treatment response and outcomes and may serve as an independent variable \u003csup\u003e[44\u0026ndash;45]\u003c/sup\u003e, consistent with our findings. Further analyses suggested that activation of the JAK/STAT pathway may be a factor contributing to AM-related changes. Multiple studies have shown that STAT3 activation can promote tumor-associated polarization of AMs, facilitating immunosuppression and tumor immune evasion \u003csup\u003e[46\u0026ndash;48]\u003c/sup\u003e. Together, these results provide potential mechanistic explanations for the upper-versus-lower lobe incidence disparity observed in YO patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study included statistical analyses of 121,436 NSCLC cases, a very large dataset that provides substantial reliability. Moreover, this study is the first to propose that the YO group (60\u0026ndash;74 years) differs from other age groups in pulmonary immune microenvironment features and links these differences to tumor location, providing references for research on lung aging and cancer prevention/control. Single-cell analyses further explored potential molecular mechanisms underlying immune microenvironment changes in the YO group, laying a foundation for future studies.\u003c/p\u003e\n\u003cp\u003eNevertheless, limitations remain. First, further studies are needed to validate mechanisms underlying immune microenvironment changes in the YO group, including whether JAK/STAT signaling influences AM abundance in the lung and whether AMs recruit or modulate T cells. Second, larger scRNA-seq datasets are needed for independent validation. Third, findings should be further translated into clinical practice to build an age\u0026ndash;tumor-location clinical model of NSCLC.\u003c/p\u003e\n\u003cp\u003eOverall, using extensive clinical data, this study innovatively proposes age-specific immune microenvironment changes in the YO group in the context of NSCLC and offers plausible mechanistic hypotheses.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLC:lung cancer\u003c/p\u003e\n\u003cp\u003eNSCLC: Non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003eYO: Yong-Old\u003c/p\u003e\n\u003cp\u003escRNA-seq:single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003ePCA:principal component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCGA:The Cancer Genome Atlas\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAMs:alveolar macrophages\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShanghai Pulmonary Hospital Research Fund (No. FKLY20003)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed are available in the BioStudies ,E-MTAB-13526,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-13526\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the case data provided by Shanghai Pulmonary Hospital and the single-cell sequencing data offered by Marco De Zuani, Ana Cvejic and Haoliang Xue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical exemption has been approved by the Ethics Committee of Shanghai Pulmonary Hospital (K25-612).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDL and HR was a major contributor in writing the manuscript. WZ YJ and AZ was responsible for the acquisition of the presented data and was involved in the revision process of the manuscript. YW was responsible for designing the manuscript. XZ analyzed and interpreted the data. DL and XZ participated in the extensive revision process of the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024,74(3):229-263.\u003c/li\u003e\n\u003cli\u003eThai A A, Solomon B J, Sequist L V, et al. Lung cancer[J]. Lancet, 2021,398(10299):535-554.\u003c/li\u003e\n\u003cli\u003eHuang J, Deng Y, Tin M S, et al. Distribution, Risk Factors, and Temporal Trends for Lung Cancer Incidence and Mortality: A Global Analysis[J]. Chest, 2022,161(4):1101-1111.\u003c/li\u003e\n\u003cli\u003eWan W, Peters S, Portengen L, et al. Occupational Benzene Exposure and Lung Cancer Risk: A Pooled Analysis of 14 Case-Control Studies[J]. Am J Respir Crit Care Med, 2024,209(2):185-196.\u003c/li\u003e\n\u003cli\u003evan Zandwijk N, Reid G, Frank A L. Asbestos-related cancers: the \u0026apos;Hidden Killer\u0026apos; remains a global threat[J]. Expert Rev Anticancer Ther, 2020,20(4):271-278.\u003c/li\u003e\n\u003cli\u003eGe C, Peters S, Olsson A, et al. Respirable Crystalline Silica Exposure, Smoking, and Lung Cancer Subtype Risks. A Pooled Analysis of Case-Control Studies[J]. Am J Respir Crit Care Med, 2020,202(3):412-421.\u003c/li\u003e\n\u003cli\u003eGuseva C I, Gaillen-Guedy A, Wild P, et al. Lung cancer mortality in the French cohort of titanium dioxide workers: some aetiological insights[J]. Occup Environ Med, 2020,77(11):795-797.\u003c/li\u003e\n\u003cli\u003eChandwani R, Brokamp C, Salfity H, et al. Impact of Environmental Exposures on Lung Cancer in Patients Who Never Smoked[J]. World J Surg, 2023,47(10):2578-2586.\u003c/li\u003e\n\u003cli\u003ePettit R W, Byun J, Han Y, et al. The shared genetic architecture between epidemiological and behavioral traits with lung cancer[J]. Sci Rep, 2021,11(1):17559.\u003c/li\u003e\n\u003cli\u003eKachuri L, Johansson M, Rashkin S R, et al. Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility[J]. Nat Commun, 2020,11(1):27.\u003c/li\u003e\n\u003cli\u003eCallister, M. E. J., Sasieni, P., \u0026amp; Robbins, H. A. (2021). Overdiagnosis in lung cancer screening. The Lancet. Respiratory medicine, 9(1), 7\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eSedeta E, Sung H, Laversanne M, et al. Recent Mortality Patterns and Time Trends for the Major Cancers in 47 Countries Worldwide[J]. Cancer Epidemiol Biomarkers Prev, 2023,32(7):894-905.\u003c/li\u003e\n\u003cli\u003eLeiter, A., Veluswamy, R. R., \u0026amp; Wisnivesky, J. P. (2023). The global burden of lung cancer: current status and future trends. Nature reviews. Clinical oncology, 20(9), 624\u0026ndash;639.\u003c/li\u003e\n\u003cli\u003eTrama, A., Tittarelli, A., Barigelletti, G., Botta, L., Gatta, G., Tagliabue, G., Contiero, P., Guzzinati, S., Andreano, A., Manneschi, G., Falcini, F., Castaing, M., Filiberti, R. A., Gasparotti, C., Cirilli, C., Mazzucco, W., Mangone, L., Iacovacci, S., Vitale, M. F., Stracci, F., \u0026hellip; Bernasconi, A. (2022). Excess risk of subsequent malignant neoplasms in adolescent and young adult cancer survivors: Results from the first Italian population-based cohort. Cancer, 128(2), 364\u0026ndash;372.\u003c/li\u003e\n\u003cli\u003eHu, M., Tan, J., Liu, Z., Li, L., Zhang, H., Zhao, D., Li, B., Gao, X., Che, N., \u0026amp; Zhang, T. (2022). Comprehensive Comparative Molecular Characterization of Young and Old Lung Cancer Patients. Frontiers in oncology, 11, 806845.\u003c/li\u003e\n\u003cli\u003eMackintosh, J. A., Marshall, H. M., Slaughter, R., Reddy, T., Yang, I. A., Bowman, R. V., \u0026amp; Fong, K. M. (2019). Interstitial lung abnormalities in the Queensland Lung Cancer Screening Study: prevalence and progression over 2 years of surveillance. Internal medicine journal, 49(7), 843\u0026ndash;849.\u003c/li\u003e\n\u003cli\u003eNemec SF, Bankier AA, Eisenberg RL. Upper lobe\u0026ndash;predominant diseases of the lung. Am J Roentgenol. 2013;200:W222\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eCopley SJ, Giannarou S, Hansell DM, Yang G-Z. Effect of aging on lung structure in vivo: assessment with densitometric and fractal analysis of High-resolution computed tomography data. J Thorac Imaging. 2012;27.\u003c/li\u003e\n\u003cli\u003eBoueiz A, Chang Y, Cho MH, Washko GR, San Jos\u0026eacute; Est\u0026eacute;par R, Bowler RP, et al. Lobar emphysema distribution is associated with 5-year radiological disease progression. Chest. 2018;153:65\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eKhoa ND, Li S, Phuong NL, Kuga K, Yabuuchi H, Kan-O K, et al. Computational fluid-particle dynamics modeling of ultrafine to coarse particles deposition in the human respiratory system, down to the terminal bronchiole. Comput Methods Programs Biomed. 2023;237:107589.\u003c/li\u003e\n\u003cli\u003eLi, Y., Lin, C., Cui, L., Huang, C., Shi, L., Huang, S., Yu, Y., Zhou, X., Zhou, Q., Chen, K., \u0026amp; Shi, L. (2025). Association between age and lung cancer risk: evidence from lung lobar radiomics. BMC medical imaging, 25(1), 204.\u003c/li\u003e\n\u003cli\u003eMarco De Zuani, Ana Cvejic \u0026amp; Haoliang Xue (2024). Single cell RNA-seq atlas of human NSCLC lesions and non-involved tissue. BioStudies, E-MTAB-13526.\u003c/li\u003e\n\u003cli\u003eHao Y, Stuart T, Kowalski MH, et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology, 2023.\u003c/li\u003e\n\u003cli\u003eMcGinnis, C. S., Murrow, L. M., \u0026amp; Gartner, Z. J. (2019). DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Systems, 8(4), 329\u0026ndash;337.e4.\u003c/li\u003e\n\u003cli\u003eMadissoon, E., Oliver, A. J., Kleshchevnikov, V., Wilbrey-Clark, A., Polanski, K., Orsi, A. R., et al. (2023). A spatially resolved atlas of the human lung characterizes a gland-associated immune niche. Nature Genetics, 55(1), 66\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eWickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21(12), 1\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eChu, T., Wang, Z., Pe\u0026rsquo;er, D., \u0026amp; Danko, C. G. (2022). Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nature Cancer, 3(4), 505\u0026ndash;517.\u003c/li\u003e\n\u003cli\u003eWeinstein JN, Collisson EA, Mills GB, et al. The Cancer Genome Atlas Pan-Cancer analysis project[J]. Nature Genetics, 2013, 45(10): 1113-1120.\u003c/li\u003e\n\u003cli\u003eJin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., et al. (2021). Inference and analysis of cell-cell communication using CellChat. Nature Communications, 12, 1088.\u003c/li\u003e\n\u003cli\u003eQiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., \u0026amp; Trapnell, C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nature Methods, 14(10), 979\u0026ndash;982.\u003c/li\u003e\n\u003cli\u003eAibar, S., Gonz\u0026aacute;lez-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., et al. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083\u0026ndash;1086.\u003c/li\u003e\n\u003cli\u003eRobinson, M. D., McCarthy, D. J., \u0026amp; Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139\u0026ndash;140.\u003c/li\u003e\n\u003cli\u003eXing H, Wu C, Yang W, Cai S, Zhang X, Ye X. Tracheal, bronchus, and lung cancer among older adults: thirty-year global burden trends, precision medicine breakthroughs, and lingering barriers. BMC Cancer. 2025;25(1):954.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zheng J, Zhang Y, et al. Aging population of tracheal, bronchus, and lung cancer: global, regional, and national burden-insights from the Global Burden of Disease Study 2021. J Thorac Dis. 2025;17(8):5547-5560.\u003c/li\u003e\n\u003cli\u003eBandi P, Star J, Ashad-Bishop K, Kratzer T, Smith R, Jemal A. Lung Cancer Screening in the US, 2022. JAMA Intern Med. 2024;184(8):882-891.\u003c/li\u003e\n\u003cli\u003eXing H, Wu C, Yang W, Cai S, Zhang X, Ye X. Tracheal, bronchus, and lung cancer among older adults: thirty-year global burden trends, precision medicine breakthroughs, and lingering barriers. BMC Cancer. 2025;25(1):954.\u003c/li\u003e\n\u003cli\u003eAegerter H, Lambrecht BN, Jakubzick CV. Biology of lung macrophages in health and disease. Immunity. 2022;55(9):1564-1580.\u003c/li\u003e\n\u003cli\u003eHuang H, Yang Y, Zhang Q, et al. S100a4+ alveolar macrophages accelerate the progression of precancerous atypical adenomatous hyperplasia by promoting the angiogenic function regulated by fatty acid metabolism. Elife. 2025;13:RP101731.\u003c/li\u003e\n\u003cli\u003eKuhlmann-Hogan A, Cordes T, Xu Z, et al. EGFR-driven lung adenocarcinomas coopt alveolar macrophage metabolism and function to support EGFR signaling and growth. Cancer Discov.\u003c/li\u003e\n\u003cli\u003eTaniguchi S, Matsui T, Kimura K, et al. In vivo induction of activin A-producing alveolar macrophages supports the progression of lung cell carcinoma. Nat Commun. 2023;14(1):143.\u003c/li\u003e\n\u003cli\u003eLi J, Li J, Wang L, et al. Alveolar macrophages in patients with non-small cell lung cancer. Int J Clin Exp Pathol. 2020;13(7):1867-1872.\u003c/li\u003e\n\u003cli\u003ePrieto LI, Sturmlechner I, Graves SI, et al. Senescent alveolar macrophages promote early-stage lung tumorigenesis. Cancer Cell. 2023;41(7):1261-1275.e6.\u003c/li\u003e\n\u003cli\u003eSun F, Li L, Xiao Y, et al. Alveolar Macrophages Inherently Express Programmed Death-1 Ligand 1 for Optimal Protective Immunity and Tolerance. J Immunol. 2021;207(1):110-114.\u003c/li\u003e\n\u003cli\u003eSchulze AB, Evers G, G\u0026ouml;rlich D, et al. Tumor infiltrating T cells influence prognosis in stage I-III non-small cell lung cancer. J Thorac Dis. 2020;12(5):1824-1842.\u003c/li\u003e\n\u003cli\u003eZhuang W, Wang M, Jiang L, Su Z, Lin S. The peripheral CD4+ T cells predict efficacy in non-small cell lung cancer (NSCLC) patients with the anti-PD-1 treatment. Transl Cancer Res. 2024;13(8):4052-4061.\u003c/li\u003e\n\u003cli\u003eAo YQ, Gao J, Jin C, et al. ASCC3 promotes the immunosuppression and progression of non-small cell lung cancer by impairing the type I interferon response via CAND1-mediated ubiquitination inhibition of STAT3. J Immunother Cancer. 2023;11(12):e007766.\u003c/li\u003e\n\u003cli\u003eParakh S, Ernst M, Poh AR. Multicellular Effects of STAT3 in Non-small Cell Lung Cancer: Mechanistic Insights and Therapeutic Opportunities. Cancers (Basel). 2021;13(24):6228.\u003c/li\u003e\n\u003cli\u003eLi L, Sun F, Han L, et al. PDLIM2 repression by ROS in alveolar macrophages promotes lung tumorigenesis. JCI Insight. 2021;6(5):e144394.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung cancer, Young-old, Alveolar macrophages, Tumor immune microenvironment, Single-cell RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-9155639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9155639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-small cell lung cancer (NSCLC) shows substantial heterogeneity across age groups and tumor locations. However, the relationship between age, lobar distribution, and the pulmonary immune microenvironment remains insufficiently understood. This study aimed to identify age-specific patterns of NSCLC occurrence and explore potential immune mechanisms underlying these differences.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 121,436 surgically treated NSCLC cases from Shanghai Pulmonary Hospital between 2015 and 2024. Tumor location and age at onset were assessed across World Health Organization age categories. To investigate potential mechanisms, we analyzed 15 tumor-adjacent single-cell RNA sequencing samples from the E-MTAB-13526 dataset and validated cell-type composition changes using TCGA bulk RNA-seq deconvolution. Cell\u0026ndash;cell communication, pseudotime trajectory, transcription factor activity, and functional enrichment analyses were performed to characterize immune alterations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe upper-to-lower lobe incidence disparity of NSCLC followed a curved age-related pattern and was most pronounced in the young-old (YO, 60\u0026ndash;74 years) group, reaching an approximate ratio of 7:3. Single-cell analysis revealed that the YO group had distinct immune microenvironment features, especially abnormal proportions of myeloid cells and T cells. Among myeloid subsets, alveolar macrophages were markedly increased in the YO group and showed enrichment of metabolic remodeling, oxidative stress, senescence-related signatures, and M2-like polarization. STAT1 and STAT3 activity was elevated, suggesting involvement of the JAK/STAT pathway. In parallel, T-cell composition shifted toward higher CD4\u0026thinsp;+\u0026thinsp;T-cell proportions and relatively lower CD8\u0026thinsp;+\u0026thinsp;T-cell proportions in the YO group. Cell\u0026ndash;cell communication analysis indicated prominent myeloid\u0026ndash;T cell interactions, with MHC-II, prostaglandin, and galectin signaling among the key pathways.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe YO group exhibits a unique pulmonary immune microenvironment that may contribute to its distinct upper-versus-lower lobe NSCLC distribution. Altered alveolar macrophage abundance and T-cell polarization, potentially mediated through JAK/STAT-related immune remodeling, may represent important mechanisms linking age and tumor location in NSCLC.\u003c/p\u003e","manuscriptTitle":"The \"Young-Old\" group may have a unique pulmonary immune microenvironment, resulting in a different ratio of incidence rates between the upper and lower lobes of the lungs compared to other age","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 09:14:48","doi":"10.21203/rs.3.rs-9155639/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41c1e514-34b5-4b12-a42b-5a33d731e5d9","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T20:23:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 09:14:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9155639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9155639","identity":"rs-9155639","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}