Introduction

Lung cancer is the second most common malignancy worldwide and is a major contributor to cancer-related fatalities1. Among its subtypes, lung adenocarcinoma (LUAD) prevails2. Despite significant strides in understanding and treating LUAD, the 5-year survival rate has been disappointing, at less than 20%, and falls short of the desired outcomes3. In recent years, the treatment approach for LUAD has undergone a significant shift with the advent of immune checkpoint inhibitors4. Emerging biomarkers, such as PD-L1 expression and the tumor mutational burden (TMB), have emerged to predict response to immunotherapy5,6. However, these biomarkers do not comprehensively represent the complex tumor microenvironment, resulting in only a subset of LUAD patients benefiting from immunotherapy7. Therefore, the urgent search for novel biomarkers is key to improving treatment outcomes.

PKHD1L1, short for “polycystic kidney and hepatic disease 1-like protein 1,” was originally identified in mice8, and despite its discovery many years ago, little is known about its specific function. It is a potential contributor to autosomal recessive polycystic kidney disease (ARPKD)9. Additionally, PKHD1L1 plays a crucial role as an outer shell protein in hair cell stereocilia and is essential for normal hearing10. Intriguingly, PKHD1L1 is associated with several significant physiological processes, such as susceptibility to epilepsy11, male longevity12, and childhood obesity13. Further studies revealed that PKHD1L1 encodes fibrocystin-L, which plays a crucial role in cellular immunity9. These findings collectively underscore the integral role of PKHD1L1 in normal physiological functions and suggest its potential in immunotherapy applications. The involvement of the gene in key immune processes suggests that this gene might hold untapped therapeutic potential, particularly in the field of immune-mediated treatments, warranting further exploration into its mechanisms and applications in immunotherapy.

Notably, disrupted expression of PKHD1L1 in various cancers, including breast14, head and neck squamous cell15, cervical16, thyroid17, and kidney18 cancer, may significantly impact tumor progression.

In addition, few studies have addressed the specific function and mechanism of PKHD1L1 in lung cancer, especially LUAD. Moreover, research has indicated that PKHD1L1 plays a pivotal role in the progression of chronic obstructive pulmonary disease (COPD) to LUAD19. These collective findings strongly imply that PKHD1L1 may play a vital role in LUAD. Through data analysis and cell function assays, it was observed that increasing PKHD1L1 expression inhibited malignant tumor cell behavior, providing a protective effect. Building on prior evidence linking PKHD1L1 with T-cell-mediated cellular immunity, our study revealed a similar close association between PKHD1L1 expression and B cells, particularly plasma cells, in LUAD. These findings suggest that PKHD1L1 could be pivotal in orchestrating both cellular and humoral immune responses, potentially affecting and modulating the efficacy of immunotherapeutic interventions. These findings suggest that PKHD1L1 is a promising target for the treatment of LUAD and offers hope for patients.

Materials and methods

Data collection and integration

For a comprehensive evaluation of PKHD1L1 expression in LUAD, we conducted a thorough analysis via the TIMER database. Furthermore, we examined RNA sequencing (RNA-seq) data from LUAD patients in The Cancer Genome Atlas (TCGA) database via the “limma” software package. To expand the scope of our study, we broadened our exploration of PKHD1L1 expression levels by incorporating data from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas (THPA) database. In addition to further assessing the relevant function and mechanism of action of PKHD1L1 in COPD, we downloaded the relevant dataset GSE22148 for COPD from the GEO database and performed an analysis with the matescape database.

Demographic and clinical characteristics

The correlation between PKHD1L1 expression and several clinical characteristics was assessed via chi-square (χ2) statistics. These characteristics included standard TNM stage (both clinical and pathological) on the basis of the American Joint Committee on Cancer (AJCC) classification; pathological stage; sex; smoking status; race; age; overall survival (OS) events; and disease-specific survival (DSS) events.

Survival analysis, significant prognostic marker analysis and the nomogram

The TCGA-LUAD cohort was divided into high- and low-expression groups on the basis of the median PKHD1L1 expression. To explore the association between PKHD1L1 expression and LUAD prognosis, survival curves were generated. Univariate and multivariate Cox regression analyses were also conducted to identify independent factors influencing patient prognosis. We subsequently developed a nomogram based on the results of these regression analyses to predict overall survival probability. The accuracy of the nomogram was assessed via calibration plots. In addition, to further validate the prognostic value of PKHD1L1, we downloaded four different lung cancer-related datasets, GSE30219, GSE31210, GSE3141, and GSE50081, from the GEO database for survival analyses to further assess the prognostic value of PKHD1L1.

Differentially expressed gene analysis

In our study, patients from the TCGA-LUAD cohort were divided into two cohorts on the basis of the median expression of PKHD1L1: those with high expression and those with low expression. We employed the “Limma” R package to identify genes that exhibited differential expression between the two groups, with the criteria set at an adjusted p value less than 0.05 and an absolute log2-fold change greater than 1.5.

Functional enrichment analysis

We conducted a functional enrichment analysis of the differentially expressed genes (DEGs) via the “clusterProfiler” R package, which includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses20,21,22. Additionally, we performed a gene set enrichment analysis (GSEA) via both the “clusterProfiler” and “limma” R tools. Terms with adjusted p values and false discovery rates (FDRs) both less than 0.05 were considered significantly enriched.

Correlation analysis of PKHD1L1 with immunity in LUAD

The CIBERSORT algorithm assisted in investigating the interplay between PKHD1L1 and 22 unique immune cell subsets in TCGA-LUAD patients. The ESTIMATE algorithm was used to determine the extent of immune and stromal cell infiltration in LUAD samples. In our study, we used the limma package in R to calculate the correlation between PKHD1L1 expression and the expression of various immune-related factors, including immunostimulatory and immunosuppressive factors, cytokines, cytokine receptors, and MHC molecules, across different cancers. After determining the correlation coefficients and their significance, we visually analyzed the data via heatmaps created with the ggplot2 package in R. This approach provided a clear and comprehensive view of the associations of PKHD1L1 with immune factors in various cancers, with a specific emphasis on LUAD.

Single-cell data analysis

In this study, we utilized the single-cell RNA sequencing dataset GSE123902, which is linked to lung adenocarcinoma and was generated via the 10X Genomics platform for data acquisition. To ensure the robustness of the data, we rigorously implemented quality control and preprocessing procedures. First, we obtained the annotation information for the probes and subsequently mapped them to their corresponding genes. For genes associated with multiple probes, we opted for the median method to determine their expression levels, resulting in a comprehensive gene expression profile. In our efforts to accurately delineate cellular subpopulations, we carried out additional data filtering. Specifically, we imposed criteria that required each gene to be expressed in at least three cells and that each cell expresses a minimum of 250 genes. Simultaneously, we assessed the mitochondrial gene content and sequencing depth to assess both cell viability and technical quality. Leveraging the “PercentageFeatureSet” function, we computed the proportion of mitochondrial genes and the count of unique molecular identifiers (UMIs) within each cell. Ultimately, we retained cells that encompassed approximately 30% of the mitochondrial genes and exhibited an average of approximately 100 UMIs for subsequent analyses. Following this, we applied log normalization to the data to mitigate technical bias and enhance comparability across cells. The “FindVariableFeatures” function was used to pinpoint variable genes characterized by high dispersion and substantial average expression, as these genes are pivotal in capturing cellular heterogeneity. We subsequently harnessed the “ScaleData” program to normalize the expression values of all the genes within the dataset, ensuring uniform scaling and variance.

We then employed the t-SNE method for data dimensionality reduction, facilitating more insightful visualization and exploration of gene expression patterns. Cell clustering analysis was performed via the “FindNeighbors” and “FindClusters” functions, with the resolution parameter set to 0.1. In this process, “FindNeighbors” computes cell similarity to construct a network, whereas “FindClusters” partitions cells into distinct clusters on the basis of their gene expression profiles. Finally, by cross-referencing the highly expressed genes within each cluster with a database of known gene signatures and cellular markers, we achieved comprehensive annotation of the cell types and identified the specific cell types within each subpopulation. These meticulous steps greatly contributed to our understanding of the single-cell RNA sequencing data, revealing the intricacies of cellular diversity and function.

Correlation analysis of PKHD1L1 expression with immunotherapy efficacy and drug sensitivity in LUAD patients

This research aimed to determine the predictive value of PKHD1L1 expression for the effectiveness of immune checkpoint inhibitors in LUAD patients. We derived an immunoepidemiologic score (IPS) related to PD1 and CTLA4 inhibitors from the Cancer Immunome (TCIA) database. A higher IPS indicates a heightened efficacy of immunotherapy. To assess sensitivity to immunotherapy, we compared the IPS between the high-expression and low-expression PKHD1L1 cohorts via the Kruskal‒Wallis test. The “oncoPredict” R package was utilized to establish the half-maximal inhibitory concentration (IC50) of specific inhibitors. To determine the differences in the IC50 values between the two PKHD1L1 expression groups, we applied the Wilcoxon rank-sum test.

Cell culture and transfection

Three human LUAD cell lines, A549, H1299, and PC9, were obtained from the American Type Culture Collection (ATCC). A549 and H1299 cells were cultured in DMEM/high-glucose medium (Gibco, CT11995500BT), and PC9 cells were cultured in RPMI-1640 medium (Gibco, 11875101), both supplemented with 10% FBS (Biological Industries, 04-001-1 C) and 1% penicillin‒streptomycin (Gibco, 15140122). All the cells were maintained at 37 °C in 5% CO₂ with constant humidity. For PKHD1L1 downregulation, synthetic siRNAs (Guangzhou RiboBio Co., Ltd.) were transfected using Lipofectamine 2000 (Invitrogen, 11668-027). For PKHD1L1 over-expression, the gene was cloned and inserted into the pCDH retroviral vector and cotransfected with pMD2G and pSPAX2 into HEK293T cells via Lipofectamine 2000 (Invitrogen, 11668-027). Harvested retrovirus was used to infect A549, H1299, and PC9 cells, followed by puromycin selection (Selleck, CL13900; 3 µg/mL) for at least 2 weeks.

Cell proliferation assay (CCK-8)

Cell proliferation was assessed via the CCK-8 assay. The cells were seeded at a density of 2000 cells per well in 96-well plates and subjected to continuous measurements for 3 days as needed. CCK-8 solution was added to each well, and the absorbance was monitored at 450 nm via a Tecan Infinite M200 Nonquantal spectrophotometer.

Colony formation experiments

Colony formation experiments were conducted to assess the colony-forming ability of the cells. A total of 2000 cells per well, including A549, H1299, and PC9 cells with PKHD1L1 over-expression, PKHD1L1 knockdown (via RNA interference), and corresponding control cells, were seeded into 6-well plates. The cells were cultured in a standard incubator at 37 °C with 5% CO₂ for one week using the appropriate complete medium for each cell line. After the incubation period, the culture medium was removed, and the cells were fixed with paraformaldehyde. The fixed cells were then stained with crystal violet to visualize and quantify colony formation. High-resolution images of the stained colonies were acquired via a digital camera.

Wound healing experiment

The cells were cultivated in 6-well plates until they reached more than 90% confluence. A wound was subsequently created by scratching the cell layers with a 10 µL sterile pipette tip. The cells were then rinsed twice with sterile PBS buffer and placed in an incubator with controlled temperature and humidity. The wound gaps of the tumor cells were photographed at 0 h and 24 h, and the migration distance of the cells in five different areas of each wound sample was measured.

Migration assay

For the tran-swell cell migration assay, 3 × 104 cells were seeded in the upper tran-swell chambers with more than 300 µL of serum-free culture medium (DMEM; Gibco C11995500BT), while the lower chambers contained more than 700 µL of medium supplemented with 10% FBS (fetal bovine serum, New Zealand, A6904FBS). After incubation for 48 h, the tumor cells that had migrated through the membranes (Corning, 3413; New York, USA) were fixed with methanol and stained with 1% crystal violet (Solarbio, G1064; Beijing, China). Images of the migrated tumor cells were captured from five random fields via a microscope (Leica, DM750, Wetzlar, Germany), and quantitative analysis of cell migration was conducted via ImageJ (v1.53t).

Statistical methods

In this study, baseline patient characteristics and treatments were summarized via descriptive statistics. Group differences were evaluated via chi-square tests, t tests, and one-way analysis of variance (ANOVA). Kaplan‒Meier survival analysis with log-rank tests was performed to compare survival outcomes between patients with high and low PKHD1L1 expression. Trends in cell proliferation were analyzed via repeated-measures ANOVA, whereas paired t tests were applied to compare metrics at different time points within the same group of cells. Quantitative image analysis was conducted via ImageJ software (v1.53t), and statistical and visual data analyses were performed via GraphPad Prism (v9.0) and R (v4.4.1), respectively. All the statistical tests were two-sided, and p values less than 0.05 were considered statistically significant.

Results

PKHD1L1 expression is downregulated in LUAD

Analysis of PKHD1L1 expression across various cancer types was conducted via the TIMER database. The results revealed a significant decrease in PKHD1L1 expression in numerous malignancies, notably in LUAD, compared with that in their normal tissue counterparts (Fig. 1A). This observation led to a detailed assessment of PKHD1L1 expression levels in LUAD through the integration of the TCGA and GEO datasets. This comprehensive analysis consistently demonstrated a marked reduction in PKHD1L1 mRNA expression in LUAD tissues compared with normal lung tissues (Fig. 1B–G). Further validation of PKHD1L1 downregulation in LUAD was achieved by examining immunohistochemical staining data from the THPA database (Fig. 1H). Collectively, these findings underscore a marked reduction in PKHD1L1 expression in LUAD tissues compared with normal tissues. This decrease in expression in LUAD patients was statistically significant, as indicated by p values less than 0.05 (*p < 0.05), 0.01 (**p < 0.01), and 0.001 (***p < 0.001).

Fig. 1
figure 1

PKHD1L1 expression is downregulated in LUAD. (A) PKHD1L1 mRNA expression across various cancers, sourced from the TIMER database. (B) Comparison of PKHD1L1 expression between normal and tumor samples in the TCGA-LUAD dataset. (C) Pairwise expression analysis of PKHD1L1 in normal and tumor samples from the TCGA-LUAD cohort. (D–G) Validation of PKHD1L1 expression levels in normal versus tumor tissues via three independent datasets from the GSE dataset related to LUAD. (H) Protein Atlas (THPA) database confirmation of PKHD1L1 levels in LUAD samples compared with nontumor samples. *p < 0.05, **p < 0.01, and ***p < 0.001 indicate statistical significance.

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Relationships between PKHD1L1 expression and the clinicopathological characteristics of LUAD patients

The TCGA-LUAD cohort was stratified on the basis of median PKHD1L1 mRNA levels, resulting in 270 patients allocated to the high PKHD1L1 expression group and 269 patients to the low PKHD1L1 expression group. A comparison of the clinical characteristics between these groups is presented in Table 1. PKHD1L1 expression was significantly correlated with T stage, N stage, pathological stage, OS events, and DSS events among LUAD patients (all p < 0.05). However, the associations with other clinicopathological factors, such as M stage, did not reach statistical significance (p value > 0.05).

Table 1 Correlations between PKHD1L1 and the clinical characteristics of patients with lung adenocarcinoma.
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PKHD1L1 is a favorable independent prognostic factor for LUAD

Survival analysis revealed a significant correlation between high PKHD1L1 expression and improved survival among TCGA-LUAD patients (p = 0.003) (Fig. 2A). Similarly, the K‒M plotter database supported these results, as a consistent outcome was observed (HR = 0.76, log-rank p = 2e-04) (Fig. 2B). Univariate analysis highlighted the significant prognostic impact of stage, T stage, N stage, and PKHD1L1 expression on LUAD patient outcomes (Fig. 2C). Furthermore, multivariate analysis confirmed the pronounced associations of stage and PKHD1L1 expression with LUAD patient prognosis (Fig. 2D). Collectively, these findings suggest that PKHD1L1 may serve as a promising independent prognostic indicator for LUAD. Additionally, a nomogram was developed that incorporated age, sex, PKHD1L1 expression, T stage and stage to predict LUAD patient survival. This nomogram provided survival predictions for one, three, and five years (Fig. 2E). To further evaluate the prognostic value of PKHD1L1, we selected additional datasets for validation. These datasets included samples from LUAD as well as other types of lung cancer. The results consistently demonstrated a protective effect of PKHD1L1 across various types of lung cancer, with higher PKHD1L1 expression being associated with better patient prognosis. These findings further support and validate the conclusions of our current study (Fig. S1).

Fig. 2
figure 2

Elevated expression of PKHD1L1 is correlated with improved survival in LUAD patients. (A) TCGA-LUAD data-based survival comparison between patient groups with high and low PKHD1L1 expression. (B) The Kaplan‒Meier plotter database was utilized for survival analysis, and the results were correlated with PKHD1L1 expression levels. (C,D) Univariate and multivariate Cox regression analyses of the TCGA-LUAD cohort identified stage and PKHD1L1 expression as independent prognostic indicators. (E) Development of prognostic nomograms integrating PKHD1L1 expression and clinical parameters to predict 1-year, 3-year, and 5-year survival in LUAD patients. Statistical significance is indicated (*p < 0.05, **p < 0.01, ***p < 0.001).

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GO, KEGG and GSEA analyses of PKHD1L1-related signaling pathways in LUAD

In our investigation, a heatmap was constructed to visualize the distribution of DEGs within the high- and low-PKHD1L1 expression groups (Fig. 3A). We subsequently performed GO, KEGG, and GSEA analyses of the markedly upregulated genes. GO analysis revealed the importance of PKHD1L1 in pivotal biological processes such as the humoral immune response, antigen binding, immunoglobulin production and immunoglobulin receptor binding (Fig. 3B, C). KEGG pathway analyses revealed the roles of PKHD1L1 in the pi3k-akt signaling pathway, primary immunodeficiency, and the intestinal immune network for IgA production (Fig. 3D). GSEA further revealed enrichment in pathways such as asthma, olfactory transduction, and systemic lupus erythematosus (Fig. 3E). These collective findings highlight the extensive influence of PKHD1L1 on LUAD prognosis and immunomodulation.

Fig. 3
figure 3

The PKHD1L1-related genes were significantly enriched in multiple immune-related pathways. (A) Heatmap illustrating the top 20 genes most significantly up- and downregulated in the high- and low-PKHD1L1 expression subgroups. (B,C) GO enrichment analysis. (D) KEGG enrichment analysis. (E) GSEA enrichment analysis.

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PKHD1L1 is significantly associated with immunity in LUAD

Using the CIBERSORT algorithm, we conducted a comprehensive analysis of the distribution and abundance of 22 distinct immune cell types within the TCGA-LUAD cohort (Fig. 4A). To elucidate the impact of PKHD1L1 expression, we stratified the LUAD samples into high- and low-PKHD1L1 expression groups and compared the differences in immune cell abundance between these two cohorts (Fig. 4B). This analysis revealed increased levels of plasma cells, T-cell CD8 + T cells, and resting memory CD4 + T cells in the PKHD1L1 high-expression group, whereas activated mast cells, regulatory T cells, and M0 macrophages were less abundant (all p values < 0.05) (Fig. 4C). To further emphasize the statistical significance of these findings, we utilized scatter plots to illustrate the correlations between PKHD1L1 expression and specific immune cell populations, including plasma cells, CD8 + T cells, and resting memory T cells (Fig. 4D–F). (*<0.05, **<0.01, ***<0.001).

Fig. 4
figure 4

PKHD1L1 is significantly associated with plasma cells in LUAD patients. (A) The CIBERSORT algorithm was used to calculate the proportion of immune cells comprising each LUAD sample. (B) Box plots illustrating the differences in the abundances of different immune cells in the high- and low-PD-L1 expression groups. (C) Lollipop plot showing the correlation between PKHD1L1 expression and CIBERSORT scores in 22 immune cell types. (D–F) Scatter plots depicting the correlations between PKHD1L1 and plasma cells, CD8 + T cells, and resting memory CD4 + T cells.

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PKHD1L1 is significantly enriched in plasma cells in LUAD

Dimensional reduction and unsupervised clustering of single-cell RNA-seq data from LUAD samples revealed a complex landscape of cellular subpopulations (Fig. 5A). Seurat-based clustering identified 26 distinct clusters, representing diverse cellular phenotypes within the tumor milieu. Subsequent annotation of these clusters revealed the presence of various immune and stromal cell types, with a distinct compartment of plasma cells identified by their expression of specific marker genes (Fig. 5B). Our analyses demonstrated significant co-localization of PKHD1L1 with a range of plasma cell-specific markers, including CD37, CD69, CD79A, MS4A1 and IGKC (Fig. 5C–H). This colocalization confirmed that PKHD1L1 is predominantly expressed in a cell type-specific manner in association with plasma cells. This expression pattern highlights the potential role of PKHD1L1 in the functional mapping of plasma cells in the LUAD microenvironment. These findings provide a concrete molecular basis for further investigations of how PKHD1L1 affects the pathogenesis and progression of LUAD.

Fig. 5
figure 5

Identification of a PKHD1L1-enriched cluster and B-cell associations via LUAD single-cell analysis. (A, B) Dimensionality reduction clustering analysis of the single-cell dataset GSE123902 revealed that PKHD1L1 had the highest expression in cluster 6. (C–H) Analysis of single-cell GSE123902 data revealed significant associations between PKHD1L1 and B-cell-associated marker genes (e.g., CD37, CD69, CD79A, MS4A1, and IGKC).

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PKHD1L1 is significantly associated with multiple immune-related factors in LUAD

We further investigated the correlations between PKHD1L1 and various regulatory factors, including immunostimulatory factors, immunosuppression factors, cytokines, cytokines receptors, and MHC molecules. Our study revealed significant associations between PKHD1L1 expression and key immune-related factors in LUAD. In particular, PKHD1L1 expression was significantly correlated with immunostimulatory factors such as CD28, CD80, and ENTPD1 (Fig. 6A). Additionally, it exhibited associations with immunosuppression factors, including CD160, BTLA, and CD244 (Fig. 6B). Furthermore, PKHD1L1 was significantly associated with several cytokines, including CCL14, CCL16, and CXCL12, as well as cytokines receptors, such as CCR4, CCR6, and CCR5 (Fig. 7A, B). Finally, notable associations were found between PKHD1L1 and major histocompatibility complex (MHC) molecules, specifically HLA-DMB, HLA-E, and HLA-DQA1 (Fig. 7C). These findings highlight the intricate role of PKHD1L1 in modulating the tumor microenvironment and immune responses in LUAD.

Fig. 6
figure 6

PKHD1L1 is significantly associated with immunostimulatory and immunosuppressive factors in LUAD. The heatmap reveals the association between PKHD1L1 expression and immunostimulatory factor expression across various tumor types, with a specific emphasis on its interaction within LUAD (A). Similarly, the heatmap also displays the relationship of PKHD1L1 with immunosuppressive elements, again with particular attention given to LUAD (B). Red shading signifies positive correlations, whereas blue shading indicates negative correlations, with the color depth denoting the correlation magnitude. Notably, associations (p < 0.05) are distinguished by an asterisk (*). The columns in red indicate significant links between PKHD1L1 expression and immune factor expression in LUAD.

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Fig. 7
figure 7

PKHD1L1 is strongly associated with cytokines, cytokine receptors and the MHC in LUAD. The heatmap in our study illustrates the correlations of PKHD1L1 expression with immune components, such as cytokines, cytokine receptors, and major histocompatibility complex (MHC) molecules, across tumor types, emphasizing its unique role in LUAD. Specifically, (A) the relationships of PKHD1L1 with cytokines, (B) cytokine receptors, and (C) MHC molecules were explored. Positive correlations are shown in red, negative correlations are shown in blue, and the color intensity indicates the correlation strength. Significant correlations are marked with an asterisk (*), with red columns in each heatmap highlighting key associations in LUAD.

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Relationship between PKHD1L1 expression and sensitivity to immunotherapy in LUAD patients

Our study revealed a promising scenario in which increased PKHD1L1 expression is intricately linked with improved treatment outcomes resulting from PD1 and CTLA4 inhibitors. Specifically, the mean IPS significantly increased (p < 0.05) within the PKHD1L1 high-expression subgroup across the CTLA4- PD1+, CTLA4 + PD1- and CTLA4 + PD1 + subgroups. These compelling findings strongly suggest that patients with elevated PKHD1L1 expression may exhibit more favorable responses to PD1 and CTLA4 immunotherapies (Fig. 8A–D).

Fig. 8
figure 8

PKHD1L1 is significantly associated with treatment outcomes in LUAD patients. (A–D) Sensitivity analysis comparing patient outcomes between patients with high and low PKHD1L1 expression levels in the context of immune checkpoint inhibitors. (E–L) Further analyses were performed to determine how these patients respond to chemotherapy and targeted drugs, underscoring the potential of PKHD1L1 as a predictive biomarker for treatment efficacy.

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Relationship between PKHD1L1 expression and drug sensitivity in LUAD patients

We used the “OncoPredict” R package to predict the IC50 values of various chemotherapeutic and targeted therapeutic agents in both the high- and low-PKHD1L1 expression groups. Remarkably, within the spectrum of assessed drugs, including axitinib, AZD1332, AZD8055, BI-2536, foretinib, GSK591, IGF1R_3801 and JQ1, which are commonly employed targeted therapies, a noteworthy discrepancy in their IC50 values emerged between the high-expression group and the low-expression group (all p < 0.05) (Fig. 8E–L).

Over-expression and interference of PKHD1L1 regulate the proliferation and migration of LUAD cell lines

To investigate the functional role of PKHD1L1 in LUAD, we performed both over-expression and interference experiments in the H1299, A549, and PC9 cell lines. CCK8 assays revealed that PKHD1L1 over-expression significantly inhibited H1299 and A549 cell proliferation compared with that in the control group at 24, 48, and 72 h (Fig. 9A, B). Colony formation assays further confirmed this antiproliferative effect, as the number of colonies was markedly reduced upon PKHD1L1 over-expression (Fig. 9C, D). Migration assays also demonstrated that PKHD1L1 over-expression significantly reduced the migration rate of H1299 and A549 cells in both the wound healing (Fig. 9E, F) and trans-well (Fig. 9G, H) assays. To further validate these findings, we expanded the experiments to include the PC9 cell line and performed gene interference using siRNA. PKHD1L1 over-expression in PC9 cells similarly suppressed proliferation, as evidenced by the results of the CCK8 and colony formation assays (Supplementary Figure S2A&C). Conversely, PKHD1L1 interference significantly promoted the proliferation of PC9, H1299 and A549 cells (Supplementary Figs. S2B, D, S3A, B, S4A, B). Migration assays revealed that PKHD1L1 over-expression in PC9 cells reduced wound closure and tran-swell migration rates (Supplementary Fig. S2E, G), whereas interference with PKHD1L1 expression increased the migratory capacity of all three cell lines (Supplementary Figuress. S2F, H, S3C, D, S4C, D). Together, these results consistently demonstrate that PKHD1L1 suppresses the proliferation and migration of LUAD cells. The over-expression of PKHD1L1 inhibits these processes, whereas its interference promotes proliferation and migration across H1299, A549, and PC9 cell lines.

Fig. 9
figure 9

The over-expression of PKHD1L1 significantly inhibited the proliferation and migration of LUAD cell lines. (A–D) CCK8 and colony formation assays were performed on H1299 and A549 cells with and without PKHD1L1 over-expression. These experiments were designed to determine the effect of PKHD1L1 on the proliferation of these LUAD cell lines. (E–H) In addition, we assessed the effect of PKHD1L1 on the migratory capacity of both the control and PKHD1L1-overexpressing cell lines via wound healing and migration assays (*p < 0.05, ** p < 0.01, *** p < 0.00).

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PKHD1L1 expression and functional exploration in COPD

To investigate the role of PKHD1L1 in COPD, we analyzed its expression levels in the GSE22148 dataset and observed significant upregulation of PKHD1L1 in patients with severe COPD compared with moderate COPD patients (Fig. S5A, p < 0.05). Correlation analysis further revealed that PKHD1L1 expression was positively correlated with the expression of MIR21 (Fig. S5B) and FOXO1 (Fig. S5C), two genes associated with the negative regulation of CD8 + T-cell function and plasma cell differentiation. To further explore the biological significance of PKHD1L1, functional enrichment analysis of the top 20 co-expressed genes was performed via the Metascape platform. The results revealed pathways related to cellular senescence (hsa04218) and negative regulation of the MAPK cascade (GO:0043409), both of which are closely linked to T-cell activity and plasma cell function (Fig. S5D). These findings suggest that PKHD1L1 may play a regulatory role in immune responses in the COPD microenvironment.

Discussion

Previous studies have suggested that PKHD1L1 may play a pivotal role in the transition from COPD to LUAD19, sparking our interest in further exploration. In the Supplementary Material of this study, analysis of the GSE22148 dataset revealed that PKHD1L1 expression in the sputum of patients with severe COPD was significantly higher than that in the sputum of patients with moderate COPD. Furthermore, correlation analysis identified MIR21 and FOXO1 as the genes most strongly associated with PKHD1L1, which are closely linked to the negative regulation of CD8 + T-cell function and plasma cell differentiation23,24,25,26, respectively. These findings suggest that PKHD1L1 may play a critical role in the COPD microenvironment by influencing key immune cell functions. Functional enrichment analysis further revealed that the top 20 genes significantly co-expressed with PKHD1L1 were enriched predominantly in pathways related to cellular senescence and the negative regulation of the MAPK cascade, both of which have been widely reported to influence T-cell activity and plasma cell function27,28,29,30. These results provide valuable insights into the potential regulatory role of PKHD1L1 in the chronic inflammatory microenvironment of COPD, where it may modulate immune responses and impact disease progression and transformation. We aimed to investigate the specific mechanisms of PKHD1L1 within the immune microenvironment of LUAD and its potential as a novel immunotherapeutic target. These findings not only shed light on the possible molecular connections between COPD and LUAD but also lay the groundwork for the development of more precise and effective therapeutic strategies for LUAD.

The tumor microenvironment plays a pivotal role in tumorigenesis and progression, with the infiltration of immune cells being a key determinant of patient prognosis31,32,33. Different types and proportions of immune-infiltrating cells can significantly influence tumor progression and treatment outcomes34,35. In this study, we analyzed the composition of 22 immune cell types in LUAD patients via the CIBERSORT algorithm and identified a significant association between PKHD1L1 and various immune-infiltrating cells, particularly plasma cells and CD8 + T cells. Furthermore, the single-cell dataset GSE123902 validated the correlation between PKHD1L1 and plasma cell aggregation, providing robust support for the reliability of CIBERSORT analysis. In LUAD, plasma cells play a crucial role in the tumor microenvironment by secreting antibodies and modulating immune responses to protect the host against pathogens and tumor cells36,37. CD8 + T cells, as cytotoxic T cells, are central to the immune system’s ability to eliminate infected cells, establish immune memory, and regulate immune responses38,39. In recent years, immunotherapeutic strategies targeting plasma cells and CD8 + T cells have garnered increasing attention40,41,42. Exploring the relationship between PKHD1L1 and these immune cells may further open new avenues for therapeutic strategies, offering promising opportunities for LUAD patients. Previous studies have identified fibrocytoplasmic plasmin-L, encoded by PKHD1L1, as a key mediator of T-cell-associated cellular immunity9, suggesting that PKHD1L1 may play a dual role in regulating both humoral and cellular immunity. We hypothesize that PKHD1L1 serves as a critical regulator within the immune microenvironment of LUAD, bridging the interplay between humoral and cellular immunity.

Our study revealed significant correlations between PKHD1L1 and multiple immunostimulatory and immunosuppression genes in LUAD, including PDCD1, TNFRSF9, TNFRSF13B, CD40, CTLA4, IL10, TGFBR1, and HLA-E. These genes are implicated in regulating CD8 + T-cell function43, plasma cell differentiation44, and antibody secretion45, further underscoring the pivotal role of PKHD1L1 in shaping the immune landscape within the LUAD immune microenvironment. Additionally, we examined the associations of PKHD1L1 with cytokines, cytokines receptors, and human leukocyte differentiation antigens in LUAD. The analysis revealed significant correlations with CXCL9, CXCL10, CXCL12, CCL19, CXCR3, CCR5, CXCR4, and CCR7, which are key molecules involved in the migration and function of CD8 + T cells46,47, as well as the differentiation and homing of plasma cells48. These findings suggest that PKHD1L1 plays a central regulatory role in the LUAD immune microenvironment by modulating critical pathways of humoral and cellular immunity. This insight provides valuable insight for developing novel immunotherapeutic strategies and advancing LUAD treatment approaches.

Using the OncoPredict R software package, we analyzed the sensitivity of LUAD patients with varying levels of PKHD1L1 expression to chemotherapeutic agents, including atritin, AZD.1332, JQ1, and flumatinib. Notably, AZD8055, an mTOR inhibitor that targets cancer cell metabolism and proliferation, has been used to treat kidney, breast, and lung cancers and has been extensively studied across various malignancies49,50,51. Similarly, JQ1, a BRD4 inhibitor, has garnered widespread attention for its role in epigenetic regulation and its application in breast cancer, hematological malignancies, and other cancers52,53. These findings suggest that PKHD1L1 has significant potential as a guiding marker for personalized therapy in LUAD patients, offering new opportunities to optimize therapeutic strategies. Further investigations into the role of PKHD1L1 in guiding chemotherapy and immunotherapy could pave the way for more precise and effective treatment options for LUAD while advancing the field of personalized medicine.

Immunotherapy is a cutting-edge approach in LUAD treatment, offering superior targeting, stable efficacy, and fewer side effects by harnessing the patient’s own immune system54,55. Currently, PD-1/PD-L1 and CTLA-4 inhibitors are the primary immunotherapy options for LUAD patients56,57. Through analysis of the TCIA database, we found that LUAD patients with high PKHD1L1 expression presented increased immunogenicity scores, indicating increased sensitivity to immunotherapy. These findings underscore the significant value of PKHD1L1 in guiding immunotherapy strategies. These results provide a robust foundation for personalized LUAD treatment and open new avenues for optimizing therapeutic approaches. In the future, further exploration of the potential of PKHD1L1 in immunotherapy will not only advance precision medicine but also offer new possibilities for the treatment of LUAD, heralding a new chapter in cancer therapy.

In this study, we determined that PKHD1L1 expression was significantly downregulated in LUAD tissues and established it as an independent prognostic marker. Low PKHD1L1 expression was strongly associated with poor prognosis in LUAD patients, highlighting its critical role in LUAD development and progression. To further investigate its function, we conducted experiments in three LUAD cell lines (PC9, H1299, and A549). The results showed that PKHD1L1 over-expression significantly inhibited LUAD cell proliferation and migration, while silencing PKHD1L1 enhanced these abilities. These findings further validated the pivotal role of PKHD1L1 in LUAD progression. Future research will aim to elucidate the molecular mechanisms through which PKHD1L1 influences LUAD, providing new insights and potential strategies for its diagnosis and treatment.

Despite these advances, several limitations remain. For example, the specific mechanisms by which PKHD1L1 contributes to LUAD progression within the chronic inflammatory environment of COPD have not yet been fully elucidated, particularly at the cellular and molecular levels. Future studies will leverage animal models to explore the role of PKHD1L1 in regulating the tumor immune microenvironment during the COPD-to-LUAD transition. Additionally, we aimed to investigate the functions of PKHD1L1 in regulating cellular and humoral immunity in LUAD, with a focus on its potential as an immunotherapy target. Our findings suggest that PKHD1L1 is promising for predicting LUAD patient sensitivity to both immunotherapeutic and chemotherapeutic agents. Future research will further evaluate its therapeutic potential and validate its utility in novel immunotherapy approaches through clinical trials.

Therefore, we aimed to determine whether PKHD1L1 can serve as a reliable biomarker for early diagnosis and treatment response prediction in LUAD patients, as well as a stable and effective therapeutic target. Advancing this research direction is expected to provide LUAD patients with more precise and personalized treatment options, promote clinical translational progress, and drive innovations in LUAD therapy. Ultimately, this work will open new pathways in cancer treatment and help elevate the field of medical oncology to new heights.