Abstract Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, with the poor overall prognosis. PTPRC is an essential protein on the surface of cells of the immunological systems However, the role of PTPRC in LUAD has not been clarified. UALCAN, HPA and TCGA database were used to investigate PTPRC expression in LUAD. HPA and Kaplan-Meier plotter database were used to explore the survival curve evaluating the prognostic value of PTPRC for LUAD. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of PTPRC co-expression genes downloaded from cBioPortal database. Tumor immune dysfunction and exclusion (TIDE) was used to predict the immunotherapy response in different PTPRC subgroups. TIMER was used to analyze the correlation of PTPRC and immune cell infiltration or immune checkpoint expression level in LUAD. Serum PTPRC were detected by enzyme linked immunosorbent assay. In present study, the expression of PTPRC in LUAD was lower than that in the normal control group by means of bioinformatics analysis of the TCGA and UALCAN public databases. Moreover, LUAD patients with downregulated PTPRC expression exhibited poor overall survival. Besides, PTPRC was significantly positively correlated with various immune cells in LUAD. TIDE scores were markedly elevated in the PTPRC low-expression subgroup in contrast to the PTPRC high-expression subgroup, And PTPRC was also markedly positively associated with biomarkers of these infiltrated immune cells. PTPRC expression was also positively correlated with PD-1, PD-L1 and CTLA-4 expression. The serum PTPRC levels in LUAD patients were significantly reduced compared to the normal health group. The ROC curve of PTPRC concentration in LUAD was plotted to obtain an AUC = 0.7887, the cutoff value was 31.55 pg/mL, and the sensitivity and specificity were 77.78 and 70.00%, respectively. In summary, our results indicate that down-regulation of PTPRC predicts poor prognosis and associated with immune infiltration in LUAD and PTPRC is an immunotherapeutic predictor and serum biomarker in LUAD correlated with immune cell infiltration. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-15565-w. Keywords: Lung adenocarcinoma, PTPRC, Immune infiltration, Immunotherapy, Biomarker Subject terms: Cancer, Lung cancer, Tumour biomarkers, Tumour immunology Introduction Lung cancer is the most common malignant tumor in terms of morbidity and mortality^[36]1. Non-small-cell lung cancer (NSCLC), including lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD) and large-cell carcinoma (LCC), accounts for 80–85% of lung cancers^[37]2. LUAD is the most common and aggressive subtype of lung cancer, with the largest heterogeneity and invasiveness. Most LUAD patients are diagnosed as advanced stage due to the lack of early typical clinical symptoms and effective diagnostic methods^[38]3and do not meet the conditions of surgical resection, which is an important reason for the poor overall prognosis of LUAD. Therefore, there is a pressing need to explore novel prognostic predictors and therapeutic targets for LUAD. The leucocyte common antigen, protein tyrosine phosphatase receptor type C (PTPRC), also known as CD45, is an essential protein on the surface of cells of the haematological and immunological systems^[39]4. CD45 has an important role in T and B cell antigen receptor signal transduction and controls the immune function by regulating lymphocyte survival, cytokine responses, and TCR signaling. Altered CD45 could result in severe combined immunodeficiency^[40]5. The study found that HIV-1 was associated with CD45, and HIV-1 gp120-induced apoptosis was significantly reduced in CD45-deficient cell lines, whereas apoptosis was significantly increased after reconstitution of CD45^[41]6. Moreover, CD45 deficiency or altered expression is associated with various diseases including leukaemia and lymphoma. Nowakowski et al. found that decreased CD45 expression on chronic lymphocytic leukemia (CLL) cells correlated with better overall survival, and measuring CD45 expression on CLL cells may potentially provide a tool for monitoring patients with CLL^[42]7. However, in patients with multiple myeloma (MM), patients with high levels of CD45 expression on the surface of tumor cells have a good prognosis^[43]8. PTPRC is rarely reported in the study of lung cancer. In a recent study, PTPRC can significantly promote the proliferation, migration and invasion of A549 cells, but the expression of PTPRC in LUAD was downregulated^[44]9. However, the mechanism of PTPRC in LUAD has not been clarified. There is growing evidence that cancer cells can activate different immune pathways that lead to immunosuppressive functions and determine the immune microenvironment of tumors^[45]10. The absence of immune cells and immune escape play important roles in the occurrence and development of LUAD. Therefore, it is an ideal indication of immunotherapy^[46]11. Clinically, immune checkpoint inhibitors (ICIs) targeting PD-1 and PD-L1 exhibit potent and durable antitumor activity in patients with LUAD^[47]12. However, the overall response rate to ICIs is relatively low, and only a subset of patients with LUAD benefits from ICI treatment^[48]13. CD45 modulates signal transduction mediated by B and T cell antigen receptors. The changes in the expression pattern of CD45 isoforms on monocytes and lymphocytes potentially result in alterations of immune response^[49]14. In addition, CD45 activity is essential for an efficient immune response. In NK cells, CD45 expression is highly correlated with NK cell maturation^[50]15. And studies have shown that tumors with high tumor mutation burden (TMB) are thought to be more susceptible to immune checkpoint inhibitors as they express more neoantigens and are therefore more likely to be recognized and targeted by T cells^[51]16. A recent study showed PTPRC expression is positively correlated with TMB and patients of melanoma with high PTPRC expression had a higher probability of responding to immune checkpoint therapy^[52]17. Based on existing research, we hypothesized that PTPRC may serve as a potential predictor of response to immunotherapy in LUAD. Therefore, in this study, we detected the connection between PTPRC and prognosis or immune infiltration in tumor samples of LUAD patients. Furthermore, we estimated the diagnostic power of serum PTPRC levels in LUAD. Our findings showed the role of low PTPRC expression in carcinogenesis and indicate that PTPRC may play an important role in the regulation of immune cell infiltration in LUAD. Results The expression and the prognostic values of PTPRC in LUAD PTPRC expression in LUAD samples and adjacent normal tissues was analyzed using data directly obtained from The Cancer Genome Atlas (TCGA). PTPRC expression was significantly decreased in LUAD tissues (Fig. [53]1A). Furthermore, a marked decrease in PTPRC expression in LUAD was observed in tumor samples compared with normal samples (Fig. [54]1B). The protein expression of PTPRC was further investigated in LUAD by using the UALCAN database, and we found that the PTPRC protein level was obviously decreased in LUAD compared with normal lung tissues (Fig. [55]1C). In order to explore the prognostic values of PTPRC in LUAD with differential PTPRC expression, survival analysis for PTPRC in LUAD was conducted by using the TCGA databases, Kaplan-Meier plotter database and the HPA database. As expected, LUAD patients with downregulated PTPRC expression exhibited poor overall survival (Fig. [56]1D–F). These findings illustrate that PTPRC expression is decreased in LUAD and indicate that PTPRC is significantly associated with the prognosis of LUAD patients. Fig. 1. [57]Fig. 1 [58]Open in a new tab Expression of PTPRC in LUAD. (A) Analysis of PTPRC expression in LUAD and normal tissues in the TCGA database. (B) TCGA database and statistical analyses of PTPRC expression in 58 pairs of LUAD tissues and adjacent normal tissues. (C) The protein of PTPRC expression in LUAD was examined by using the UALCAN database. (D) Survival curves using the TCGA database is shown for OS. (E) Survival curves using the Kaplan-Meier plotter is shown for OS. (F) Survival curves using the HPA database is shown for OS. *p value < 0.05; **p value < 0.01; ***p value < 0.001; ****p value < 0.0001. PTPRC expression and clinical parameters of LUAD patients PTPRC expression among groups of patients according to different clinical parameters were investigated by using the TCGA databases. According to pathologic T stage, a significant decrease in PTPRC expression was observed in LUAD patients in T stage 2, 3 and 4 compared to the T stage 1 (Fig. [59]2A). Based on pathologic stage, PTPRC expression was lower in grade I and grade II patients than grade III and grade IV atients (Fig. [60]2B). In terms of gender, PTPRC expression was significantly lower in male patients (Fig. [61]2C). And there was remarkable downregulated expression of PTPRC in alive LUAD patients compared to dead patients (Fig. [62]2D). Regarding age, the PTPRC level was significantly downregulated in patients of = < 65 years group (Fig. [63]2E). However, smoking (Fig. [64]2F), years of smoking (Fig. [65]2G), nods metastasis status (Fig. [66]2H), and the presence or absence of distal metastases (Fig. [67]2I) did not affect PTPRC expression. Fig. 2. [68]Fig. 2 [69]Open in a new tab Box plots evaluating PTPRC expression among different clinical subgroup using the TCGA database. Analysis is shown for (A) pathologic T stage, (B) pathologic grade, (C) gender, (D) OS events, (E) age, (F) smoking habits, (G) years of smoking (H) pathologic N stage and (I) pathologic M stage. *p < 0.05, **p < 0.01, ***p < 0.001. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis of PTPRC co-expression genes To investigate the function of PTPRC co-expression genes, we performed bioinformatics analysis of 6520 genes. GO analysis showed that the BP enrichment results were mainly related to T cell activation. leukocyte cell-cell adhesion, regulation of T cell activation, positive regulation of leukocyte cell-cell adhesion, negative regulation of immune system process, regulation of leukocyte cell-cell adhesion, regulation of immune effector process, positive regulation of cytokine production, positive regulation of, T cell activation and leukocyte proliferation (Fig. [70]3A). The CC enrichment results were mainly related to external side of plasma membrane, secretory granule membrane, MHC protein complex, organellar ribosome, mitochondrial ribosome, mitochondrial inner membrane, phagocytic vesicle, endocytic vesicle. Fig. 3. [71]Fig. 3 [72]Open in a new tab GO and KEGG enrichment analysis of PTPRC co-expression genes. (A–D) The results of BP, CC, MF and KEGG term enrichment analysis, respectively. membrane raft and membrane microdomain (Fig. [73]3B). MF analysis showed that these genes were mainly involved in immune receptor activity, cytokine binding, cytokine receptor activity, cytokine receptor binding, chemokine binding, MHC protein complex binding, GTPase regulator activity, nucleoside-triphosphatase regulator activity, C-C chemokine binding and C-C chemokine receptor activity (Fig. [74]3C). KEGG analysis showed that PTPRC and related genes were involved in Hematopoietic cell lineage, Cytokine-cytokine receptor interaction, Intestinal immune network for IgA production, Th17 cell differentiation, Th1 and Th2 cell differentiation, B cell receptor signaling pathway, Natural killer cell mediated cytotoxicity, Human T-cell leukemia virus 1 infection, T cell receptor signaling pathway and PD-L1 expression and PD-1 checkpoint pathway in cancer (Fig. [75]3D, Figure S1). These results strongly implied that PTPRC is involved in the regulation of the immune response in LUAD. Correlation analysis between PTPRC expression and infiltrating immune cells Because GO and KEGG analysis found PTPRC is involved in the regulation of the immune response in LUAD, we analyzed the correlation between PTPRC expression and infiltrating immune cells by R package GSVA and TIMER database. The results showed that PTPRC expression levels had a significant positive correlation with the infiltration of T cells, Th1 cells, T helper cells, macrophages, cytotoxic cells, B cells, a DC, DC, Treg, Tcm, iDC, Tem, neutrophils, TFH, pDC, NK CD56dim cells, CD8^+ T cells, mast cells, eosinophils, NK cells and CD4^+ T cells and a negative correlation with the abundance of NK CD56bright cells in LUAD (Fig. [76]4A, B). Then, we used the tumor immune dysfunction and exclusion (TIDE) algorithm to evaluate the potential clinical efficacy of immunotherapy in different PTPRC-expressing subgroups. Our results reveal that the TIDE scores were markedly elevated in the PTPRC low-expression subgroup in contrast to the PTPRC high-expression subgroup, implying that LUAD patients with lower PTPRC expression levels were more likely to be resistant to ICI therapy than those with high PTPRC expression levels (Fig. [77]4C). Additionally, the expression level of PTPRC significantly increased in the partial response (PR) and complete response (CR) groups after treatment compared to the stable disease (SD) and progressive disease (PD) group (Fig. [78]4D). PD1/PD-L1 and CTLA-4 are important immune checkpoints that are responsible for tumor immune escape. To further explore the role of PTPRC in LUAD immune infiltration, the relationship of PTPRC with PD1, PD-L1, and CTLA-4 was assessed. As the (Fig. [79]4E–G) shown, PTPRC expression was significantly correlated with the expression of PD-1, PD-L1 and CTLA-4 in LUAD. These results suggest that PTPRC low expression is associated with cancer immune escape and targeting PTPRC may increase tumor T-cell infiltration, thereby improving the response rate to ICI therapy. Fig. 4. [80]Fig. 4 [81]Open in a new tab Correlation of PTPRC expression with immune infiltration level. (A) The relationship between PTPRC expression level and immune cell enrichment scores in LUAD. (B) Correlation between 24 immune cells and PTPRC expression. PTPRC is significantly associated with tumor purity and is positively correlated with the infiltration of different immune cells using the TIMER database. (C) TIDE scores in low and high expression PTPRC groups. (D) The expression of PTPRC in different groups of primary therapy outcome. (E–G) Spearman correlation of PTPRC with expression of PD-1, PD-L1 and CTLA-4 in LUAD by using GEPIA database. *p < 0.05. Correlation between PTPRC expression and various immune markers in LUAD To deepen our understanding of hepcidin crosstalk with the immune response, we validated the correlations between PTPRC expression and diverse immune signatures in LUAD using the TIMER database. The genes listed in Table [82]1 were used to characterize immune cells, including B cells, CD8^+ T cells, CD4^+ T cells, M1 macrophage, M2 macrophage, neutrophils, and dendritic cells. After adjusting for tumor purity, PTPRC expression was significantly associated with most immune markers in divergent types of immune cells in LUAD (Table [83]1). By using the TIMER database, we also found that the PTPRC expression level was significantly correlated with 35 T cell markers in LUAD (Table [84]2). These findings support that PTPRC was positively linked to immune cell infiltration and played an important role in immune escape in LUAD microenvironment. Table 2. Correlation analysis between PTPRC and biomarkers of different types of T cells in LUAD determined by TIMER database. Immune cell Biomarker R value P value Th1 TBX21 0.639 5.13e-58 STAT4 0.541 9.33e-39 STAT1 0.528 8.71e-37 TNF 0.384 8.38e-19 IFNG 0.476 3.35e-25 Th1-like HAVCR2 0.753 2.44e-91 IFNG 0.476 3.35e-25 CXCR3 0.576 6.41e-45 BHLHE40 0.132 3.36e-03 CD4 0.830 1.77e-126 Th2 STAT6 0.265 2.18e-09 STAT5A 0.698 3.68e-78 Treg FOXP3 0.643 6.44e-59 CCR8 0.753 2.04e-91 TGFB1 0.421 1.40e-22 Resting Treg FOXP3 0.643 6.44e-59 IL2RA 0.719 9.56e-80 Effector Treg T-cell FOXP3 0.643 6.44e-59 CCR8 0.753 2.04e-91 TNFRSF9 0.606 1.10e-50 Effector T-cell CX3CR1 0.425 4.77e-23 FGFBP2 0.220 8.44e-07 FCGR3A 0.680 4.28e-68 Naïve T-cell CCR7 0.626 5.01e-55 SELL 0.679 7.37e-68 Effector memory T-cell DUSP4 -0.071 1.15e-01 GZMK 0.742 1.92e-87 GZMA 0.589 2.05e-47 Resident memory T-cell CD69 0.688 1.84e-70 CXCR6 0.743 8.18e-88 MYADM 0.240 7.22e-08 Memory T-cell SELL 0.679 7.37e-68 IL7R 0.816 7.28e-119 Exhausted T-cell HAVCR2 0.753 2.44e-91 LAG3 0.470 1.86e-28 CXCL13 0.450 6.19e-26 LAYN 0.275 5.67e-10 [85]Open in a new tab Table 1. Correlation analysis between PTPRC and biomarkers of immune cells in LUAD determined by TIMER database. Immune cell Biomarker R value P value B cell CD19 0.450 5.57e-26 CD79A 0.409 2.97e-21 CD8^+ T cell CD8A 0.643 8.39e-59 CD8B 0.517 5.51e-35 CD4^+ T cell CD4 0.830 1.77e-126 M1 macrophage NOS2 0.100 2.57e-02 IRF5 0.438 1.76e-24 PTGS2 -0.078 8.40e-02 M2 macrophage CD163 0.696 1.11e-72 VSIG4 0.613 3.89e-52 MS4A4A 0.682 7.91e-69 Neutrophil CEACAM8 0.284 1.33e-04 ITGAM 0.663 9.68e-64 CCR7 0.626 5.01e-55 Dendritic cell HLA-DPB1 0.637 1.39e-57 HLA-DQB1 0.383 1.11e-18 HLA-DRA 0.651 9.19e-61 HLA-DPA1 0.669 2.88e-65 CD1C 0.379 2.49e-18 NRP1 0.318 4.92e-13 ITGAX 0.650 2.09e-60 [86]Open in a new tab Prognostic analysis of PTPRC expression based on immune cells in LUAD patients Finally, we investigated whether PTPRC affected the overall survival of LUAD patients because of infiltration of different immune cells. We performed prognosis analyses based on the expression levels of PTPRC in LUSC in related immune cell subgroups by using Kaplan-Meier plotter database. The results showed that patients with high expression of PTPRC and enriched infiltration of CD4^+ memory T cells had better prognosis. However, whether eosinophils, regulatory T cells, natural killer cells, macrophages, mesenchymal stem cells and Th2 cells are enriched or decreased, the patients with high expression of PTPRC had better prognosis. Nevertheless, LUAD patients with high PTPRC expression and decreased B cells, CD8^+ T cells eosinophils and Th1 cells had better prognosis (Fig. [87]5A–L). These results indicated that PTPRC may affect the prognosis of LUAD patients in part due to immune infiltration. Fig. 5. [88]Fig. 5 [89]Open in a new tab Kaplan-Meier survival curves according to high and low expression of PTPRC in immune cell subgroups in LUAD. (A) A forest plot shows the prognostic value of PTPRC expression according to different immune cell subgroups in LUAD patients. (B–L) Correlations between PTPRC expression and OS in different immune cell subgroups in LUAD patients were estimated by Kaplan-Meier plotter. The diagnostic value of serum PTPRC in LUAD To estimate the diagnostic power of serum PTPRC levels in LUAD, we detected the levels of PTPRC in the serum of LUAD patients and health controls by ELISA. As expected, the serum PTPRC levels in LUAD patients (26.62 ± 7.38 pg/mL ) were significantly reduced compared to the normal health group (35.62 ± 9.14 pg/mL) (Fig. [90]6A). Compared with stage1-2 group, the concentration of PTPRC in stage 3-4 group were significantly lower (Fig. [91]6B). In addition, there was a significant difference between the grade I–II group and grade III–IV groups (Fig. [92]6C). The ROC curve of PTPRC concentration in LUAD was plotted to obtain an AUC = 0.7887, the cutoff value was 31.55 pg/mL, and the sensitivity and specificity were 77.78% and 70.00%, respectively (Fig. [93]6D). These data suggest that serum PTPRC can be used as potential serum biomarkers for the diagnosis of LUAD. Fig. 6. [94]Fig. 6 [95]Open in a new tab Serum PTPRC as prognostic biomarker in LUAD. (A) The concentrations of PTPRC in health and LUAD. (B) The concentrations of serum PTPRC stage1–2 group and stage 3–4 group. (C) The concentrations of serum PTPRC grade I–II group and grade III–IV group. (D) ROC curve of sensitivity versus specificity of PTPRC in LUAD. ****p value < 0.0001. Discussion LUAD is a lung cancer with increasing morbidity and mortality. Although current immunotherapy has made significant progress, there are still many LUAD patients who do not respond to immunotherapy or who experience adverse reactions^[96]3. As a result, new biomarkers for LUAD patients are required to provide a more precise prediction of patient survival and response to immunotherapy. Growing evidence has shown that the tumor microenvironment (TME) plays a key role in tumorigenesis and progression, particularly in those of ICIs that have a significant impact on LUAD^[97]18. PTPRC is also an important protein on the cell surface of the blood and immune system. However, the function and mechanism of PTPRC in LUAD have rarely been elucidated. In this study, bioinformatic analysis results obtained from the TCGA and UALCAN public databases showed that PTPRC expression was lower in patients with LUAD than in the normal control group. And LUAD patients with downregulated PTPRC expression exhibited poor overall survival. In line with our results, a new study showed that PTPRC was highly expressed in normal lung tissues and low expressed in LUAD and the overall survival (OS) of low PTPRC expression was significantly poorer than that of high PTPRC expression^[98]9. These results substantiated that PTPRC may be an independent prognostic biomarker in LUAD and that it may suppress the development of targeted precision oncology. PTPRC influenced the survival time of patients with LUAD partially through immune cell infiltration, especially CD4^+ memory T cell. Memory CD4^+ T cells have gained increasing attention in immunotherapy^[99]19. In recent years, preclinical and clinical investigations have highlighted the antitumor functions of memory CD4^+ T cells^[100]20. In many types of tumors, the presence of memory CD4^+ T cells is linked to a better prognosis, highlighting their potential significance^[101]21. In our study, LUAD patients with high expression of PTPRC and enriched infiltration of CD4^+ memory T cells had better prognosis. These results indicated that PTPRC could be a novel immune-related therapeutic target in LUAD. However, some studies have shown that the infiltration of memory CD4^+ T cell has an inhibitory effect on cancer progression^[102]22. This dual role of memory CD4^+ T cells may be regulated by various factors, including tumor type, tumor microenvironment, immune regulatory factors, cytokines, and interactions with immune cells^[103]23. Therefore, the functional and therapeutic effects of memory CD4^+ T cells in tumor immunity are complex processes, and further research is needed to gain a deeper understanding of their tumorigenic mechanisms in LUAD. Immune checkpoints have emerged as a vital mechanism that is exploited by tumor cells to evade recognition and attack by T cells. ICI therapy has, to date, proven to be a clinically effective intervention for various cancers^[104]24. Multiple studies have reported the relationship between immune infiltration and prognosis in cancer patients. The number of infiltrating cells in the nests of squamous cell lung carcinoma correlates with the histological subtype of the tumor, and the infiltrating cells in early-stage squamous cell carcinoma correlate with prognosis^[105]25. In breast cancer, cell infiltration is positively associated with shorter RFS (relapse-free survival) and OS^[106]26. Moreover, previous studies have shown that high-density tumor-infiltrating lymphocytes can inhibit tumor progression^[107]27especially in patients who receive immune checkpoint inhibitors^[108]28. To this end, we investigated the correlations between PTPRC and 24 immune cells. Here, we found PTPRC expression levels had a significant positive correlation with most immune cells in LUAD. Moreover, PTPRC is markedly positively associated with the markers of these infiltrated immune cells. These findings indicate that PTPRC is closely associated with the infiltration of multiple immune cells and can be used as a marker of promoting tumor immune response, and it is expected to serve as a predictor of ICI response. In recent years, immunotherapies based on ICI have developed rapidly. They have achieved impressive results in the treatment of several types of cancer. However, only a small percentage of patients have experienced clinical benefits^[109]24. The efficacy of immunotherapy not only depends on the availability of adequate immune cells to infiltrate the TME but also on the expression of immune checkpoints^[110]29. Therefore, there is a need for the development of biomarkers that can predict whether patients will benefit from these therapies. Immune checkpoints, such as PD-1, PD-L1, and CTLA4, play important roles in regulating T-cell responses and have been shown to be effective targets for cancer therapy^[111]30. The expression of constitutive co-suppressor receptors on T cells inhibits. The correlation analysis results showed that PTPRC expression was positively correlated with PD-1, PD-L1, and CTLA-4 expression. Moreover, TIDE integrates expression signatures of T-cell dysfunction and T-cell exclusion to mimic tumor immune evasion, and thus predict clinical responses to ICI^[112]31. In our results, patients in the PTPRC-high pattern had significantly lower TIDE scores than in the PTPRC-low pattern, which suggested that patients with the PTPRC-high pattern benefited more from ICI treatment than those with the PTPRC-low pattern. In the clinical context, biomarkers refer to any measurable molecular indicator for cancer risk, occurrence, or patient outcome. Biomarker testing in cancer involves the profiling of tumor or body fluids to detect changes in DNA, RNA, proteins, or other biomolecules that can provide information related to diagnosis, prognosis, precision medicine/guiding cancer treatment, predicting drug response, or cancer monitoring^[113]32. The majority of patients with LUAD are diagnosed at an advanced stage of the disease, so the implementation of serological markers may provide noteworthy benefits in the early diagnosis of the disease. In this sense, various authors have attempted to define the clinical usefulness of potential serum biomarkers, such as carcinoembryonic antigen (CEA)^[114]33CYFRA21‑1^[115]34, neuron‑specific enolase (NSE)^[116]35 CA 19‑9^[117]36, CA 153^[118]37 and CA125^[119]38. However, the use of serological markers has not been applied in daily clinical practice, despite the fact that combination panels have demonstrated their usefulness not only in the initial diagnosis of these patients, but also in the follow‑up or evaluation and prognosis, acting as potential prognostic and predictive biomarkers^[120]39. A previous report showed proportion of CD45^+EpCAM^+ cells in PBMCs of patients with lung cancer was found to be significantly higher than that of healthy volunteers. And the sensitivity and specificity of the CD45^+EpCAM^+ cell are 81.58% and 88.89%, respectively^[121]40. But our results showed that the serum PTPRC levels in LUAD patients were significantly reduced compared to the normal health group. The ROC curve of PTPRC concentration in LUAD was plotted to obtain an AUC = 0.7713, the cutoff value was 31.28 pg/mL, and the sensitivity and specificity were 72.00% and 73.33%, respectively. The seemingly contradictory results could be caused by the following reasons: Firstly, it might be due to methodological issues; secondly, the types of specimens are different. We measured the PTPRC protein secreted in the serum, while previous studies measured the CD45 molecules on the surface of peripheral blood. Thirdly, the experimental subjects are different. Previous studies included all types of lung cancer, while our study only included patients with lung adenocarcinoma. Further experiments are needed to verify this. Our data suggest that PTPRC can be used as potential serum biomarkers for the diagnosis of LUAD. However, there are some limitations in our study. First, although we investigated the correlation between PTPRC and immune infiltration in LUAD patients, there is a lack of interpretation of the immune analysis according to the different subgroups. Second, the molecular mechanisms and roles of PTPRC in tumor growth, metastasis and immune infiltration need to be explored in further studies. Third, most of the analyses were performed based on mRNA levels of PTPRC in the present study. A deeper analysis, based on protein levels, would make the data more convincing. Fourth, the data in this paper come from the public database, and there is no basic experiment for verification. In future studies, we will focus on the verification of these data. Conclusion In summary, we elucidated that PTPRC was lowly expressed in LUAD and negatively correlated with an unfavorable prognosis in LUAD. Furthermore, our findings indicate that PTPRC may increase tumor T-cell infiltration, thereby improving the response rate to ICI therapy. Overall, our results indicate that PTPRC is an immunotherapeutic predictor and serum biomarker in LUAD correlated with immune cell infiltration. These findings will advance our current understanding of not only the role of PTPRC but also its translational use in LUAD prognosis and immunotherapy. Materials and methods Patients and samples All protocols were performed in accordance with the relevant guidelines and regulations and were approved by Medical Ethics Committee of The First Affiliated Hospital of Hebei North University (approval No. K2021139). Blood samples were collected using standardized procedures. After obtaining patients’ approval, a total of 90 LUAD serum samples were collected before the therapy and 30 healthy individuals were selected as normal controls. All data were obtained from The First Affiliated Hospital of Hebei North University in China between 2022 and 2023. Patients with rheumatoid arthritis and acute inflammatory infections were excluded from this study. Venous blood samples were collected in pyrogen-free tubes, allowed to clot at 4 °C for 1 h, and centrifuged at 2000×g for 10 min. The upper serum layers were carefully obtained, divided into separate vials, and stored at -20 ℃ until the assay was conducted. UALCAN UALCAN ([122]http://ualcan.path.uab.edu/) was used to investigate PTPRC expression and the association between PTPRC and various clinicopathological parameters (cancer stage, nodes metastasis, TP53 mutation status, histological subtypes, age, gender, smoking habits and patient’s race) of LUAD. The human protein atlas (HPA) The Human Protein Atlas ([123]https://www.proteinatlas.org/) was used to explore the survival analysis for PTPRC in LUAD. Gene expression profiling interactive analysis (GEPIA) GEPIA ([124]http://gepia.cancer-pku.cn/index.html) is a web portal for gene expression analysis based on TCGA and GTEx data. In the current study, the correlations of PTPRC expression with immune checkpoints were evaluated by Spearman’s correlation using TCGA-LUAD datasets. Analysis of PTPRC co-expression genes PTPRC co-expression genes were downloaded from cBioPortal database ([125]https://www.cbioportal.org/). 6520 genes were selected (q ≤ 0.01) for subsequent functional analyses. Gene ontology (GO) term and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis DAVID ([126]https://david.ncifcrf.gov/summary.jsp) online analytical tool was used to KEGG Pathway Enrichment Analysis and GO analysis including Biological Process (BP), Cellular Component (CC), Molecular Function (MF) for genes interacted with PTPRC. Enrichment analysis results through the [127]http://www.bioinformatics.com.cn online tools for visualization. KEGG pathway map was downloaded from the KEGG database ([128]https://www.kegg.jp/kegg/)^[129]41. Analyze the correlation between PTPRC and immune infiltration Based on the ssGSEA algorithm provided in R package GSVA [1.46.0] were used to measure the infiltration levels of immune and PTPRC expression with ggplot2 used for visualization. TIMER was used to analyze the correlation of PTPRC and immune cell infiltration in LUAD. TIMER was also applied to investigate the relationship between PTPRC expression and different gene marker sets of immune cells by using the “Correlation” module. The correlations of PTPRC expression with immune infiltration were evaluated by purity-correlated partial Spearman’s correlation and statistical significance. To predict the immunotherapy response in different PTPRC subgroups, tumor immune dysfunction and exclusion (TIDE) scores for 539 TCGA-LUAD samples were calculated using the website [130]http://tide.dfci.harvard.edu/. The TIDE score evaluated two distinct tumor immune evasion mechanisms: the dysfunction of tumor-infiltrating cytotoxic T lymphocytes (CTLs) and the exclusion of CTLs by immunosuppressive factors. Kaplan-Meier plotter database analysis KM Plotter ([131]http://kmplot.com) was used to analyze the prognostic value of PTPRC in LUAD. The patient samples were separated into two groups by median expression (high expression and low expression) to analyze the overall survival (OS) with hazard ratios (HRs) with 95% confidence intervals (95% CIs) and log-rank p-values. Correlations between PTPRC expression and OS in different immune cell subgroups in LUAD patients were also estimated by Kaplan-Meier plotter. The assay of serum PTPRC PTPRC were detected using Human Protein Tyrosinase Receptor C ELISA Kit (Shanghai Yiyan Bio-technologhy Co, Ltd, Shanghai, China) and Enzyme Analyser (Shenzhen Huisong Technology Development Co, Ltd, Shenzhen, China), following the manufacturer’s instructions. Statistics analysis The Student’s t-test was used to compare the differences between the two groups. GraphPad Prism 7 (San Diego, California, U.S.) was used to analyze the data and draw ROC curves. Survival analysis was performed utilizing the Kaplan-Meier method and log-rank testing. Supplementary Information Below is the link to the electronic supplementary material. [132]Supplementary Material 1^ (127.4KB, png) [133]Supplementary Material 2^ (13.6KB, docx) Abbreviations PTPRC Protein tyrosine phosphatase receptor type C LUAD Lung adenocarcinoma NSCLC Non-small-cell lung cancer LUSC Lung squamous cell carcinoma TME Tumor microenvironment PD-L1 Programmed cell death-ligand 1 OS Overall survival Author contributions Conceptualization: Baoli Xiang and Yongwang Hou. Data curation and Formal analysis: Zhichao Yang. Funding acquisition: Yongwang Hou. Investigation: Jiangmin Liu. Methodology: Dandan Xu. Software and Validation: Zhicong Yang. Visualization and Writing—original draft: Yongwang Hou Writing—review & editing: Zhichao Yang and Bin Zhang. Funding This study was supported by the Medical Science Research Project of Hebei (Grant Number 20220596). Data availability The datasets presented in this study can be found in the online repositories, including UALCAN ([134]http://ualcan.path.uab.edu/), The Human Protein Atlas ([135]https://www.proteinatlas.org/), GEPIA ([136]http://gepia.cancer-pku.cn/index.html), cBioPortal database ([137]https://www.cbioportal.org/), DAVID ([138]https://david.ncifcrf.gov/summary.jsp) , Tumor Immune Dysfunction and Exclusion (TIDE) ([139]http://tide.dfci.harvard.edu/), KM Plotter ([140]http://kmplot.com) and KEGG database ([141]https://www.kegg.jp/kegg/). Declarations Competing interests The authors declare no competing interests. Ethics approval and consent to participate The present study protocol was reviewed and approved by Medical Ethics Committee of The First Affiliated Hospital of Hebei North University (Approval No. K2021139). Informed consent was submitted by all subjects when they were enrolled. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Contributor Information Yongwang Hou, Email: 408931519@qq.com. Baoli Xiang, Email: 15530396731@163.com. References