{"paper_id":"44a6a49d-9816-4e51-8478-d4a54908a05e","body_text":"Lysophosphatidic Acid Receptor 6: A Prognostic Biomarker for Lung Adenocarcinoma via Correlating Immune Infiltration | 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 Primary research Lysophosphatidic Acid Receptor 6: A Prognostic Biomarker for Lung Adenocarcinoma via Correlating Immune Infiltration Jian He, Mei Meng, Hui Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-653591/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background LPAR6 is the most recently determined GPCR of LPA, and very few of study have demonstrated the performance of LPAR6 in cancers. Moreover, the relationship of LPAR6 to prognosis potential and tumor infiltration immune cells in different cancers still unclarified. Methods The mRNA expression of LPAR6 and its clinical characteristics were evaluated on various databases. The association between LPAR6 and immune infiltrates of various types of cancer were investigated via TIMER. IHC for LPAR6 in LUAD and LUSC tissue microarray with patients’ information was detected. Results We constructed a systematic prognostic landscape in various types of cancer base on the mRNA expression level. We enclosed that higher LPAR6 expression level was associated with better OS in some types of malignancy. Moreover, LPAR6 significantly affects the prognostic potential of various cancers in TCGA, especially in lung cancer. Tissue microarray’s results demonstrated that higher protein level of LPAR6 was correlated with better overall survival of LUAD rather than LUSC cohorts. Further research found that the underlying mechanism of this phenome might be the expression level of LPAR6 was positively associated with infiltrating statuses of devious immunocytes in LUAD rather than in LUSC, that is, LPAR6 expression potentially contributes to the activation and recruiting of CD8 + T, naive T, effector T cell and natural killer cell and inactivates Tregs, decrease T cell exhaustion and regulate T-helper cells in LUAD. Conclusions Our discovery implies that LPAR6 is associated with prognostic potential and immune-infiltrating levels in LUAD. These discoveries imply that LPAR6 could be a promising biomarker for indicating prognosis potential and immune infiltration level in LUAD cohorts. Cancer Biology Oncology LPAR6 tumor infiltration lymphocytes prognosis lung adenocarcinoma biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Lung cancer is one of the most common malignancies around the world, and metastasis is a crucial biological procedure leading to a poor prognosis [ 1 ]. It is the top one diagnosed malignancy in China and the second most common malignancy in the U.S., also the leading cause of cancer-related deaths both in China and the U.S. [ 2 ]. Scientists have made great efforts to treat various types of lung cancer, but there is still a large amount of time and effort to do. According to histopathological classification, lung cancer could be catalogued into two broad subtypes, non-small-cell lung cancer (NSCLC) and small cell lung cancer (SCLC), and the NSCLC is more prevalent [ 3 ]. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the first and the second most common subtype NSCLC respectively [ 4 ]. Surgery is the primary treatment option during the early stages of the NSCLC, while in late stages, surgery is combined with chemotherapies, and/or radiotherapy [ 4 ]. However, despite these treatment procedures, the prognosis of the patients remains not good, also the post-treatment recurrence is the main cause of the disease, the total 5-year survival rate for all stages is only 16.6% [ 4 ]. NSCLC has been regarded as a kind of non-immunogenic disease in the past twenty years. However, more and more knowledge of tumor immune interactions has opposed this model in lung cancer and other types of malignancy. Immune-related interaction mechanisms act as a crucial role in oncogenesis and development, and immune therapy is considered a promising approach for cancer treatment [ 4 , 5 ], based on this, scientists are attempting to employ the body’s own immune system to fight and prevent malignancies [ 6 ]. Recently, immunotherapies, including adoptive cell transfers, monoclonal antibodies and vaccines, have become more and more applied to the clinic treatment of many types of cancers, for example, melanoma, and more recently for lung cancer [ 7 ]. During the last decade, the finding of antibodies that target the immune checkpoints has revolutionized the treatment of NSCLC, such as PD-1 and PD-L1 [ 8 ], and these two therapy approaches (PD-1 and PD-L1), has demonstrated promising anti-tumor performance in NSCLC and melanoma [ 9 – 11 ]. In addition, more and more research has demonstrated that TILs (tumor-infiltrating lymphocytes) play a key role in modulating the response to chemotherapy and heighten the clinical prognosis potential of various types of cancer [ 12 , 13 ], such as tumor-associated macrophages (TAMs) [ 14 – 16 ] and tumor-infiltrating neutrophils (TINs), they also associate with the prognosis [ 17 – 20 ]. So, it is an essential and urgent requirement for the explanations of the immunophenotypes of tumor immune interactions and the identification of new immune therapy targets for lung cancers. LPA is a kind of lipid that involved in the proliferation of tumor cells via its G-protein coupled (GPC) receptors [ 21 , 22 ] and one of their receptors-LPAR6 is a newly identified receptor of LPA [ 23 , 24 ], and it has been demonstrated to be related to many types of tumor, including prostate [ 25 ], liver [ 26 , 27 ], colorectal [ 28 , 29 ] and pancreatic cancer [ 30 ]. But the function of LPAR6 remains highly controversial since in colorectal cancer, the scientists found that LPAR6 might act as a tumor suppressor whereas act as a facilitator in the other types of tumors [ 25 – 27 , 30 , 31 ]. All these indicate that LPAR6 plays a key role in cancer, but the relationship between LPAR6 and tumor biology and the underlying mechanism involved is still not well understood. Bioinformatics is an emerging procedure that supports us to make full usage of numerous high throughput data to analyze the level of specific genes in various types of cancer [ 31 ]. So in this work, we investigated the mRNA expression level of LPAR6 and the correlation with prognosis patterns of cancer patients in databases. In addition, we analyzed the correlation of LPAR6 with tumor-infiltrating immune cells (TIICs) in various tumor microenvironments via TIMER. Moreover, IHC staining for LPAR6 in two separate lung cancer cohorts with patients’ information was detected to analyze the correlation of the expression of LPAR6 and the clinicopathological parameters of lung cancer. All these discoveries shed light on the crucial role of LPAR6 in lung cancers as well as provide a potential correlation and the mechanism involved between LPAR6 and tumor-immune interactions. 2. Materials And Methods 2.1 Ethics approval This project was permitted by Independent Ethics Committee of Shanghai Jiao Tong University School of Medicine. 2.2 Gene expression level of the LPAR6 gene analysis The mRNA expression level of the LPAR6 in different types of cancers was investigated via Oncomine database, TIMER and GEPIA2 database [ 32 ]. The threshold in Oncomine database was as follows: P -value of 0.0001, fold change of 1.5, and gene ranking top 5%. 2.3 Prognosis Analysis The association between LPAR6 expression level and survival rate in different types of cancers was investigated by the database PrognoScan and GEPIA2, which searching for relationships between gene expression level and the prognoses of patients, such as OS and DFS, across a large collection of publicly available cancer microarray datasets [ 33 , 34 ]. The threshold was adjusted to a Cox P -value < 0.05. 2.4 Correlation Analysis The correlation between LPAR6 expression and survival rate as well as different cancer staging in various cancers was determined by Kaplan-Meier plotter [ 35 ]. The HR with 95% confidence intervals and log-rank P -value were also analyzed. 2.5 Methylation analysis UALCAN [ 36 ] could be used to investigate methylation and relative mRNA expression levels, as well as the survival of a specific target gene across several clinicopathological features, such as stages and age. The t-test was performed to compare the statistical significance between the two independent groups. 2.6 GeneMANIA analysis GeneMANIA is identified single genes related to a set of input genes [ 37 ] to construct the LPAR6 biological network based on a set of functional association data, including coexpression, genetic and protein interaction pathways, colocalization and protein domain homology. 2.7 LinkedOmics analysis. Thirty-two types of cancer and over ten thousand patients from TCGA were included in the LinkedOmics database [ 38 ]. LinkFinder was used to determine the differentially expressed genes (DEGs) in TCGA. LUAD and LUSC cohorts whose expression levels correlated with those of LPAR6 . The results were investigated by using Pearson’s correlation coefficient. LinkInterpreter was employed to identify the pathways and networks [ 39 ]. 2.8 Immune infiltrates level and gene correlation analysis We investigated LPAR6 expression in various types of malignancy and the association of LPAR6 expression level with the abundance of immune infiltrating, including CD4 + T cells, CD8 + T cells, B cells, macrophages, neutrophils, and DCs, via gene modules in TIMER, which is a comprehensive resource for systematic analysis of immune infiltrates across diverse types of cancer [ 40 – 43 ]. In addition, associations between LPAR6 expression level and marker genes of TIICs were explored via correlation modules. The marker genes of TIICs included markers of T cells (CD8+, general), B cells, TAMs (tumor association macrophages), monocytes, macrophages (M1 and M2), natural killer (NK) cells, neutrophils, dendritic cells (DCs), T-helper (Th1, Th2 and Th17) cells, follicular helper T (Tfh) cells, Tregs, and exhausted T cells. The gene marker sets are referenced in our previous studies [ 44 , 45 ]. The expression level of the genes was demonstrated by using log2 RSEM. GEPIA2 database was employed to confirm the significantly correlated genes in-depth, which [ 34 ] is a web server with gene expression analysis based on GTEx and TCGA databases. Furthermore, GEPIA2 was employed to generate curves of OS and DFS. 2.9 Immunohistochemical staining for LPAR6 in lung cancer patient cohort tissue microarrays Here, two tissue microarrays were constructed using formalin-fixed, paraffin-embedded (FFPE) tissue samples from LUAD (LUC1601) and LUSC (LUC1602) patients, each TMA chip containing 74 and 78 paired tumors and adjacent normal tissues were purchased from the Superbiotek Co., Ltd., (Shanghai, China) respectively. Clinicopathological data including subtype, histological grading, and tumor/nodal stage and information about patient follow-up could be retrieved from the database of the Shanghai Jiao Tong University School of Medicine. The tissue sections underwent immunohistochemical staining using a primary antibody to LPAR6 (Thermo Fisher/ Invitrogen, USA) (Cat No. PA5-33901) at a dilution of 1: 100. Sections of the TMAs were used to investigate the protein levels of LPAR6 following the general standard IHC staining protocols. 2.10 Statistical Analysis The statistical analysis as the our previous work. The results produced via Oncomine are exhibited as mentioned in part 2.1. The consequence of Kaplan-Meier plots, GEPIA, and PrognoScan are exhibited with HR and p or Cox p -values from a log-rank test. And the correlation coefficient of gene expression was evaluated by Spearman’s correlation and p- values < 0.05 were considered statistically significant. Protein level was determined by the staining intensity and the distribution of the positive cells, which were performed by two independent pathologists blinded to the clinical information of the patients as described [ 45 ]. 3. Results 3.1 The expression levels of LPAR6 in different human cancers To study the varied mRNA expression level of LPAR6 expression in tumor and normal tissues, the LPAR6 mRNA expression levels were analyzed using the dominant online database (Oncomine and GEPIA2). This study enclosed that the LPAR6 expression was higher in brain and CNS cancer, gastric, kidney, liver cancer,, lymphoma and pancreatic cancer compared to the normal tissues and lower expression level of LPAR6 was observed in breast, bladder, colorectal, cervical, lung, esophageal, prostate cancer and some other types of cancer compared to the adjacent normal tissues (cancer vs. normal) (Fig. 1 A). The detail of the expression level of LPAR6 expression in different cancer types is summarized in Supplementary Table 1 . To evaluate LPAR6 expression level in cancers, we determined the levels of LPAR6 expression employing the RNA-Seq datasets of multiple cancer types in the Cancer Genome Atlas (TCGA). The varied expression levels between tumor and adjacent normal tissues for LPAR6 across each type of TCGA tumors is demonstrated in Fig. 1 B. The expression level of LPAR6 was significantly lower in the tumor tissue of BLCA, BRCA, COAD, HNSC, KICH, LUAD, PRAD, READ and UCEC compared with adjacent normal tissues and was significantly higher in ESCA, KIRC, KIRP, THCA compared with adjacent normal tissues (Fig. 1 B). GEPIA2 generates dot plots to profile gene/isoform expression across various types of cancer and paired normal tissue samples, and each dot representing a distinct sample. The differential mRNA expression level of LPAR6 between tumor and matched TCGA normal and GTEx data across all TCGA tumors by GEPIA2 is demonstrated in Fig. 1 C. LPAR6 expression was significantly higher in GBM, KIRC, LAML, LGG, PAAD, THYM and lower in ACC, ESCA, KICH, LUAD, PRAD, TGCT, UCEC and UCS compared with normal GTEx tissues. From these above, we found that the expression pattern are different in two types of lung cancers, LUAD and LUSC. 3.2 Prognostic potential of LPAR6 across various types of cancer We determined whether the mRNA expression level of LPAR6 was associated with the prognosis specific across cancer patient cohorts. The effects of LPAR6 expression on the various survival rates were assessed by using the PrognoScan database. The detailed relationship between the expression level of LPAR6 and prognosis potential of various cancers are listed in Supplementary Table 2. Notably, the expression level of LPAR6 impacts OS in breast and lung cancer significantly (Fig. 2 A-M). Two cohorts (GSE3141 and GSE4573) of lung cancer demonstrated that high expression level of LPAR6 was associated with better prognosis (OS HR = 0.53, 95% CI = 0.36 to 0.80, Cox P = 0.00206181; OS HR = 0.53, 95% CI = 0.31 to 0.91, Cox P = 0.0219869). (Fig. 2 A, B, D). So it is conceivable that high LPAR6 expression is an independent risk factor and leads to a better prognosis in lung cancer patients, and a hazard ratio below 0 indicates LPAR6 expression is a protective factor. Also, high LPAR6 expression significantly impacts DSS in bladder cancer and RFS and DFS in breast cancer (Fig. 2 C, E, F). Moreover, three cohorts (GSE19615, GSE9195 and GSE11121) of breast cancer demonstrated that higher expression level of LPAR6 was correlated with a better prognosis potential of DMFS (Fig. 2 G-I). And higher LPAR6 expression level was associated with better prognosis potential in some other types of cancer (Fig. 2 J-M). To further analyze the prognostic characteristics of LPAR6 gene in different types of cancer, we employed Kaplan-Meier plotter database to access the LPAR6 prognostic value. Similarly, a better prognosis potential in breast and lung cancer was shown to correlate with higher LPAR6 expression (Fig. 2 N-P, T-V). In addition to microarray analysis data of LPAR6, the RNA-Seq was also used to analyze the prognosis of LPAR6 in various types of cancers via the same database. A better prognosis in breast cancer is shown to be associated with a higher LPAR6 expression level (Fig. 2 Q-S). The different correlation patterns between adenocarcinoma and squamous cell carcinoma of lung cancer attracted our attention (Fig. 2 V, W). These data confirmed the prognostic value of LPAR6 in some specific types of cancers, that is, the increased or decreased LPAR6 expression has different prognostic values depending on the type of cancers. In addition to using Kaplan-Meier and PrognoScan plotter databases, TCGA database were also employed to determine the prognostic characteristics of LPAR6 in different types of cancer via GEPIA2. We assessed the relationships between the level of LPAR6 and prognostic potential in 33 types of cancer. LPAR6 expression significantly impacts prognosis in 3 types of cancers, including ACC, LGG ( Supplementary Fig. 1 ). High LPAR6 expression levels were associated with a better prognosis of OS in SKCM but have less influence on DFS. These results demonstrated the prognostic value of LPAR6 in some types of cancers and that differential LPAR6 expression has different prognostic values depending on the type of cancers. 3.3 The expression level of LPAR6 impacts the lung cancer prognosis in different stages and treatments In this part, we studied the association with the expression level of LPAR6 and different clinical characteristics in order to better disclosure the relevance and mechanisms of the expression level of LPAR6 in cancers, especially in different clinical stages, of lung cancer patients. We found that high expression of LPAR6 was associated with better OS only in Stage 1 and Stage 2 of LUAD (OS HR = 0.27, P = 4.6E-10; OS HR = 0.51, P = 0.0073) not in LUSC ( Table 1 ). This interesting phenomenon combines with the different survival rate patterns of LUAD and LUSC in Figs. 2 V and 2 W may indicate the correlation of LPAR6 expression and the prognosis of different cancers depends on the different mechanisms in the tumorigenesis and development. Table 1 Correlation of the mRNA expression level of LPAR6 in different stage and clinical prognostic potential in lung Cancer with different clinicopathological factors. Clinicopathological Characteristics Overall survival (n = 364 ) LUAD (n = 720) LUSC (n = 524) N Hazard ratio P-value N Hazard ratio P-value Sex Female 318 0.39 (0.26–0.58) 1.4E-10 129 1.69 (0.94–3.01) 0.075 Male 344 0.66 (0.48–0.93) 0.015 342 0.79 (0.59–1.04) 0.087 Smoking history Never 143 0.4 (0.17–0.96) 0.034 9 --- --- Smoker 246 0.49 (0.3–0.79) 0.0029 820 0.89 (0.72–1.09) 0.26 Stage 1 370 0.27 (0.17–0.42) 4.6E-10 172 0.75 (0.49–1.14) 0.17 2 136 0.51 (0.31–0.84) 0.0073 100 1.42 (0.76–2.65) 0.27 3 24 2.1 (0.71–6.21) 0.17 43 0.48 (0.24–0.96) 0.035 4 4 --- --- 0 --- --- Bold values indicate P < 0.05. 3.4 Low Promoter Methylation Levels of LPAR6 Impacts the Clinicopathological Parameters of Liver Cancer and Lung Cancer in Patients The lower promoter methylation levels of LPAR6 were detected in the earlier stage, implying that lower promoter methylation levels of LPAR6 were correlated with the earlier stages of the progress of lung cancer ( Fig. 3 ) . We also found that late-stage (stage 4) with the lowest promoter methylation levels of LPAR6 in LUSC whereas it is the highest methylation level in entire cancer progress in LUAD cohorts (stage 1–4) (Fig. 3 A, 3 E), and the lowest promoter methylation levels of LPAR6 appears in an earlier stage (stage 2) of LUAD while in the late stage of LUSC. What interested us is that the same pattern was detected in nodal metastasis analysis, which implies that in the later stage, the promoter methylation levels of LPAR6 are correlated with nodal metastasis in some way (Fig. 3 D, 3 H). The promoter methylation levels of LPAR6 share a similar pattern in LUAD and LUSC among different races and different ages respectively, that is, both in the African-America group and younger group of these two cohorts with the lowest promoter methylation levels of LPAR6 (Fig. 3 B, 3 F). 3.5 Interaction network of LPAR6 An interaction network of LPAR6 was constructed to determine potential interactions between LPAR6 and other cancer‑associated proteins. The data demonstrated that LPAR6 has co‑expression with 19 proteins, shared protein domains with ADRB2 and physical interactions with DMD (dystrophin) (Fig. 4 A). LinkedOmics were then used to analysis the genes that co-expressed with LPAR6 in lung cancers. The volcano plot elucidated that the expression of genes were negative correlated with that of LPAR6 [green spot; FDR < 0.05], while genes expression is positively correlated with LPAR6 (red spot; FDR < 0.05; Fig. 4 B). The top 50 positively and negatively correlated genes are showed in Fig. 4 B. These results imply that LPAR6 serves an important role in cancer development. Biological process and molecular function analyses were conducted using gene set enrichment analysis, which showed that LPAR6‑associated DEGs were involved in several kinds of immune biology process such as ‘interleukin production’, ‘respiratory burst’, ‘leukocyte proliferation’, ‘T cell activation’, ‘adaptive immune response’ were involved in LUAD and LUSC respectively. (Fig. 5 ). All these data indicate that LPAR6 serves a key role in immune system activation, cellular responses to stimulation, metabolism and many other processes. 3.6 The expression level of LPAR6 is correlated with immune infiltration level in lung cancers TILs have been proved as an independent predictor of survival in cancers [ 46 , 47 ]. So, in this study, we determined whether the mRNA expression level of LPAR6 correlates with the immune infiltration levels in various types of cancer. We analyzed the correlations of LPAR6 expression with immune infiltration levels in nearly forty types of cancer. The results show that the expression level of LPAR6 has significant negative correlations with tumor purity in 26 types of cancer which indicating LPAR6 somehow related to recruiting lymphocytes to tumor and significant correlations with B cell infiltration levels in 13 types of cancers. In addition, the expression level of LPAR6 has significant correlations with infiltrating levels of CD8 + T cells in 24 types of cancer, CD4 + T cells in 26 types of cancer, macrophages in 20 types of cancer, neutrophils in 33 types of cancer, and dendritic cells in 21 types of cancer. (Supplementary Table 3 and Supplementary Fig. 2). Given the correlation of the expression level of LPAR6 with immune infiltration level in diverse types of cancer, we next investigated the distinct types of cancers in which LPAR6 was correlated with prognosis and immune infiltration. Tumor purity is a crucial factor that influences the analysis of immune infiltration in clinical tumor samples by genomic approaches [ 34 , 41 ]. So we selected the cancer types in which LPAR6 expression levels have a significant negative correlation with tumor purity in TIMER and a significant correlation with prognosis. Interestingly, we found that the expression level of LPAR6 expression correlates with better OS and high immune infiltration levels in breast cancer, liver cancer and LUAD but not in LUSC. The LPAR6 expression level of LUAD and LUSC are all significantly negatively related to tumor purity (Fig. 6 ). LPAR6 expression level has significant positive correlations with the infiltrating levels of B cell, CD8 + T cell, CD4 + T cells, Macrophages, Neutrophils and DCs in LUAD (Fig. 6 ). What interested us is that the correlation with immune cells demonstrated a different pattern in LUAD and LUSC of lung cancers. These findings strongly suggest that LPAR6 plays a specific role in immune infiltration in different types of lung cancer, and leads to a better prognosis in LUAD instead of in LUSC. 3.7 Correlation Analysis Between LPAR6 Expression and Immune Marker Sets To study the association between LPAR6 and different types of TIICs, we focused on the correlations between the expression level of LPAR6 and immune marker sets of various immune cells of LUAD and LUSC. The correlations between LPAR6 expression level and immune marker gene sets of different immune cells, including CD8 + T cells, T cells (general), B cells, monocytes, TAMs, M1 and M2 macrophages, neutrophils, NK cells and DCs were determined in LUAD and LUSC ( Table 3 and Fig. 7 ). We also investigated the different types of T cells (Th1, Th2, Tfh, Th17, Tregs and exhausted T cells). After adjustment by purity, the correlation results revealed the LPAR6 expression level was significantly correlated with most immune marker sets of various immune cells and different subtypes of T cells, especially effect T cells in LUAD. However, none of these gene markers was significantly correlated with the LPAR6 expression level in LUSC and other cancer with poor prognosis ( Table 3 and Fig. 7 ). These results demonstrated that the mRNA expression levels of the marker genes in T cells (general, CD8+, Naive T, Effector T), natural killer cell, M1 macrophages and DCs have strong correlations with LPAR6 expression in LUAD ( Table 3 ). More specifically, we demonstrated NOS2, IRF5, PTGS2 of M1 phenotype are significantly correlate with LPAR6 expression in LUAD ( P < 0.0001; Fig. 4 A–H). It is reported that M1 could prevent tumor development. In-depth studies need to be done on whether LPAR6 is a crucial factor that mediating the de-polarization of macrophages and remodel tumor microenvironment. In addition, for Treg cells, LPAR6 does not demonstrate a correlation with the Tregs markers such as STAT5B in LIHC ( Table 3 ). Furthermore, we determined the association between the expression level of LPAR6 and the above marker sets of monocytes and various types of T cells in normal and tumor tissue in LUAD and LUSC. ( Supplementary-Table 4 , Fig. 8 ). 3.8 Different correlation patterns between tumor and normal tissue in LUAD patients The more interesting thing is that the expression levels of most marker sets of these immunocytes have strong correlations with LPAR6 expression in tumor tissue of LUAD patients. In the LUSC, there was no significant correlation between LPAR6 and markers of immune cells ( Fig. 9 , Supplementary-Table 4 ). This finding suggests that there are different correlation patterns between tumor and normal tissue in LUAD patients. This exciting finding indicates that LPAR6 may regulate macrophage de-polarization in the tumor microenvironment of the LUAD and LPAR6 might be a novel target for LUAD therapy. High LPAR6 expression relates to a high infiltration level of DCs in the tumor tissue of LUAD patients, DC markers such as HLA-DQB1, CD1C and NRP1 show significant correlations with LPAR6 expression both in the tumor tissue in LUAD ( Supplementary-Table 4 ). These results further reveal that there is a strong relationship between LPAR6 and DCs infiltration. 3.9 Higher expression of LPAR6 was correlated with clinicopathological parameters in LUAD cohort and was correlated with increased overall survival (OS) of LUAD and LUSC patients We analyzed the protein level of LPAR6 in two independent lung cancer patient cohorts with 74 and 77 paired lung cancer and normal tissues respectively. LPAR6 is mainly expressed in the cytoplasm of the cells (Fig. 10 A), and the protein level was lower in the lung cancer tissues compared with the normal tissues (Fig. 10 B, D). Next step, we investigated the relationship between the LPAR6 protein level and the clinical characteristics of the LUAD and LUSC patient cohorts. Lung cancer patients with higher LPAR6 levels demonstrated better OS than those patients with relatively lower levels in LUAD patient cohorts, but not in LUSC patient cohorts (Fig. 10 C, E). Moreover, we found that lower LPAR6 was negatively correlated with the clinical stage of lung cancer and the lymph node metastasis of patients (Fig. 10 F, G). In summary, we demonstrated that the LPAR6 was downregulated in the tumor tissue of LUAD patients and its expression was positive associated with the overall survival for LUAD patients base on the databases and TMA cohorts. The results further confirm that LPAR6 is specifically correlated with immune infiltrating cells in LUAD which suggests that LPAR6 plays a vital role in immune cells recruiting in the tumor tissue in LUAD patients. LPAR6 and its modulation on tumor microenvironment may serve as a novel therapeutic target for LUAD. 4. Discussion LPA receptors are GPCR that bind to the LPA and trigger multiple downstreaming cellular responses, including cell proliferation, cytoskeletal rearrangements, apoptosis and motility [ 48 – 50 ]. Previously, five LPA receptors ( LPAR 1-5) are well characterized and extensively studied [ 51 ]. LPAR6 is a recently determined GPCR, alias as ARWH1, HYPT8, LAH3, P2RY5, at first was considered as purinergic receptor P2Y5 that involved in inherited hair loss [ 23 , 52 ]. Although LPAR6 has not been extensively studied, it was reported that the LPAR6 suppresses tumor cell migration in colorectal cancer [ 28 ], and the expression of LPAR6 was decreased in P53-mutated cases [ 29 ]. It was also reported that the LPA axis plays an important role in HCC by recruiting and trans-differentiating of peritumoral fibroblasts into TAMs [ 53 , 54 ]. This offers scientists a promising hint that LPAR6 is involved in the TME. Immunotherapy is a new genre of treatment for patients and has a tightly association with TME [ 29 ]. In this study, we announced that different expression levels of LPAR6 are associated with the prognostic potential in various cancer types. Higher level of LPAR6 is associated with a better prognosis in three types of cancers, including liver cancer, lung cancer and breast cancer. Moreover, our data demonstrated that the immune infiltration levels and diverse immune marker sets of the different subtypes of lung cancers (LUAD and LUSC) are associated with the expression level of LPAR6. To this end, our study provides insights into elucidating the potential role of LPAR6 in tumor immunology and its usage as a biomarker and novel therapy target for LUAD. In this work, we determined the LPAR6 expression levels and constructed a systematic prognostic landscape in various types of cancers by using independent datasets in Oncomine and 33 type cancers of TCGA data in GEPIA2. The variation expression level of LPAR6 between cancer and normal tissues was observed in many cancer types. Based on the Oncomine database, we found that LPAR6, compared to normal tissues, was highly expressed in brain and CNS, kidney, gastric cancer, leukemia, lymphoma, liver and pancreatic cancer while some data sets showed that LPAR6 has a lower mRNA expression level in bladder, breast, cervical, colorectal, esophageal, lung and prostate cancer (Fig. 1 A). However, the redetermination of the TCGA data demonstrated that LPAR6 expression was higher expressed in ESCA, KIRC, KIRP and THCA, but significantly lower expressed in BLCA, COAD, BRCA, HNSC, KICH, PRAD, LUAD, UCEC, READ and slightly lower in LIHC compared with adjacent normal tissues (Fig. 1 B). The vary in the expression levels of LPAR6 in different types of cancer among various databases might be a reflection in data collection approaches and underlying mechanisms involved in different biological properties. Nevertheless, in these databases, we found similar prognostic associations between LPAR6 expression in bladder, breast, cervical, colorectal, esophageal, lung and prostate cancers. Investigation of the TCGA database enclosed that the higher LPAR6 expression level is correlated with better prognostic potential in ACC, LGG, SKCM ( Supplementary-Figure2 ). Furthermore, the determination of patient cohorts from PrognoScan and Kaplan-Meier Plotter demonstrated a high level of LPAR6 expression is correlated with better prognosis in breast, lung, bladder, colorectal, eye and ovarian cancer (Fig. 2 ). In two datasets of PrognoScan, high LPAR6 expression levels could be considered as an independent risk factor for better prognosis in LUAD. Moreover, a high level of LPAR6 expression was shown to be correlated with a better prognosis of LUAD in the early stage with the lowest HR [0.27 (0.17–0.42)] for a better OS when LPAR6 was highly expressed in LUAD, rather than in LUSC. These together strongly suggest that LPAR6 could be a prognostic biomarker in LUAD. Another crucial aspect of this work is that the mRNA expression level of LPAR6 is correlated with diverse immune infiltration levels in cancer, especially in LUAD. Here, we demonstrate that there’s a strong positive correlation between the infiltration level of T cells (CD8 + and CD4+), neutrophils, macrophages and DCs and LPAR6 expression in LUAD (Figs. 3 A, 3 C). Moreover, the correlation patterns of the infiltration level are different in two kinds of lung cancers (LUAD and LUSC). The correlation between LPAR6 expression and the marker genes of immune cells implicates the role of LPAR6 in regulating tumor immunology in these types of cancers. A possible explanation for this striking effect might be that LPAR6 orchestrates the function of multiple immune marker gene sets. This supports the argument that the LPAR6 expression levels are important contributors to human malignancies and indicating the prognosis of specific types of cancer. Firstly, gene markers of M1 macrophages such as PTGS2 and IRF5 show significant correlations with LPAR6 expression in LUAD respectively (Tables 2 ). Since macrophages are functionally plastic cells. Type 1 macrophages (M1) producing type 1 cytokines prevent tumors from developing, whereas type 2 macrophages (M2) inducing type 2 cytokines facilitate tumor growth. Especially in the tumor tissue of LUAD, both NOS2 and IRF5 show significant correlations with LPAR6 expression and PTGS2 shows a significant correlation with LPAR6 expression in the tumor tissue ( Supplementary - Table 4 ). These results reveal the potential regulating role of LPAR6 in de-polarization macrophages against tumor that activated macrophages can be re-polarized into opposite functional phenotypes by microenvironmental modifications and then inhibit tumor growth. Table 2 Correlation analysis between LPAR6 and relate markers of immune cells Description Gene markers LUAD LUSC None Purity None Purity Cor P Cor P Cor P Cor P CD8 + T cell CD8A 0.363 *** 0.238 *** 0.121 * 0.065 0.157 CD8B 0.366 *** 0.281 *** 0.231 *** 0.196 *** T cell (general) CD3D 0.451 *** 0.318 *** 0.197 *** 0.136 * CD3E 0.434 *** 0.283 *** 0.163 ** 0.092 0.045 CD2 0.49 *** 0.358 *** 0.171 ** 0.101 0.0277 Naive T-Cell CCR7 0.39 *** 0.222 *** 0.175 *** 0.109 0.0175 LEF1 0.351 *** 0.241 *** -0.002 0.962 0.014 0.766 TCF7 0.237 *** 0.102 0.0232 0.095 0.0336 0.049 0.289 SELL 0.421 *** 0.26 *** 0.187 *** 0.115 0.12 Effector T-Cell CX3CR1 0.41 *** 0.353 *** 0.113 0.0113 0.055 0.23 FGFBP2 0.247 *** 0.185 *** -0.04 0.378 -0.02 0.656 FCGR3A 0.448 *** 0.354 *** 0.042 0.35 -0.044 0.34 Effector memory T-Cell PDCD1 0.316 *** 0.175 *** 0.112 0.0121 0.045 0.323 DUSP4 -0.074 0.0915 -0.075 0.0966 -0.017 0.707 -0.057 0.212 GZMK 0.444 *** 0.309 *** 0.172 ** 0.106 0.0204 GZMA 0.408 *** 0.295 *** 0.197 *** 0.143 * IFNG 0.304 *** 0.201 *** 0.101 0.0235 0.061 0.183 Resident memory T-Cell CD69 0.518 *** 0.423 *** 0.247 *** 0.192 *** ITGAE 0.295 *** 0.228 *** 0.109 0.0149 0.09 0.0493 CXCR6 0.434 *** 0.305 *** 0.161 ** 0.095 0.0389 MYADM 0.162 ** 0.064 0.156 -0.132 * -0.197 *** B cell CD19 0.341 *** 0.192 *** 0.157 ** 0.083 0.0694 CD79A 0.312 *** 0.171 ** 0.155 ** 0.076 0.096 Monocyte CD86 0.55 *** 0.455 *** 0.164 ** 0.079 0.085 CD115 (CSF1R) 0.495 *** 0.388 *** 0.071 0.111 -0. 031 0.498 TAM CCL2 0.424 *** 0.331 *** 0.148 ** 0.086 0.0611 CD68 0.387 *** 0.291 *** 0.012 0.785 -0.087 0.0576 IL10 0.523 *** 0.433 *** 0.211 *** 0.151 ** M1 Macrophage INOS (NOS2) 0.14 * 0.075 0.0955 0.104 0.0203 0.106 0.0203 IRF5 0346 *** 0.254 *** -0. 101 0.0236 -0.129 ** COX2 (PTGS2) 0.009 0.833 0.017 0.705 0.27 *** 0.244 *** M2 Macrophage CD163 0.376 *** 0.281 *** 0.032 0.472 -0.058 0.209 VSIG4 0.438 *** 0.358 *** 0.061 0.171 -0.021 0.649 MS4A4A 0.501 *** 0.412 *** 0.109 0.0145 0.028 0.536 Neutrophils CD66b (CEACAM8) 0.114 * 0.09 0.0464 0.023 0.613 0.006 0.894 CD11b (ITGAM) 0.405 *** 0.29 *** 0.091 0.0412 -0.006 0.898 CCR7 0.39 *** 0.222 *** 0.175 *** 0.109 0.0175 Natural killer cell KIR2DL1 0.117 * 0.064 0.155 0.081 0.0704 0.052 0.258 KIR2DL3 0.209 *** 0.13 * 0.013 0.776 0.013 0.776 KIR2DL4 0.179 *** 0.11 0.0145 0.08 0.0744 0.039 0.4 KIR3DL1 0.149 ** 0.075 0.098 0.005 0.902 -0.048 0.294 KIR3DL2 0.168 ** 0.078 0.083 0.015 0.737 -0.038 0.41 KIR3DL3 0.039 0.38 0.006 0.899 -0.116 * -0.142 * KIR2DS4 0.143 * 0.065 0.149 0.052 0.245 0.026 0.572 Dendritic cell HLA-DPB1 0.463 *** 0.353 *** 0.098 0.0279 0.014 0.759 HLA-DQB1 0.315 *** 0.195 *** 0.098 0.0279 0.014 0.759 HLA-DRA 0.476 *** 0.376 *** 0.135 * 0.062 0.178 HLA-DPA1 0.447 *** 0.343 *** 0.106 0.0172 0.029 0.521 BDCA-1 (CD1C) 0.382 *** 0.294 *** 0.196 *** 0.131 * BDCA-4 (NRP1) 0.18 *** 0.137 * 0.052 0.247 -0.014 0.767 CD11c (ITGAX) 0.474 *** 0.364 *** 0.168 ** 0.082 0.074 Th1 TBX21 (T-bet) 0.355 *** 0.216 *** 0.119 * 0.051 0.266 STAT4 0.394 *** 0.267 *** 0.195 *** 0.122 * STAT1 0.193 *** 0.075 0.094 0.032 0.477 -0.018 0.689 IFNG (IFN-g) 0.304 *** 0.201 *** 0.101 0.0235 0.061 0.183 TNF-a (TNF) 0.414 *** 0.291 *** 0.277 *** 0.229 *** Th2 GATA3 0.365 *** 0.231 *** 0.187 *** 0.143 * STAT6 0.007 0.873 0.019 0.681 0.257 *** 0.259 *** STAT5A 0.459 *** 0.33 *** 0.15 ** 0.082 0.0722 IL13 0.209 *** 0.128 0.0213 0.076 0.0818 0.037 0.423 Tfh BCL6 0.022 0.612 0.018 0.684 0.08 0.0729 0.105 0.0223 IL21 0.118 ** 0.038 0.402 0.034 0.452 -0.013 0.782 Th17 STAT3 -0. 138 ** -0. 147 ** 0.129 * 0.109 0.0168 IL17A 0.177 *** 0.11 0.014 0.051 0.254 0.025 0.58 Treg FOXP3 0.364 *** 0.207 *** 0.145 * 0.063 0.172 CCR8 0.382 *** 0.242 *** 0.162 ** 0.083 0.0706 STAT5B 0.212 *** 0.18 *** -0.071 0.114 -0.076 0.096 TGFB1 (TGFb) 0.304 *** 0.201 *** 0.123 ** 0.123 *** T cell exhaustion PDCD1 (PD-1) 0.316 *** 0.175 *** 0.112 0.0121 0.045 0.323 CTLA4 0.444 *** 0.304 *** 0.217 *** 0.152 ** LAG3 0.275 *** 0.152 *** 0.095 0.0329 0.034 0.457 HAVCR2 (TIM-3) 0.539 *** 0.442 *** 0.094 0.0357 0.008 0.867 GZMB 0.268 *** 0.15 *** 0.137 ** 0.077 0.0926 TAM, tumor-associated macrophage; Th, T helper cell; Tfh, Follicular helper T cell; Treg, regulatory T cell; Cor, R value of Spearman’s correlation; None, correlation without adjustment. Purity, correlation adjusted by purity. * P < 0.01; ** P < 0.001; *** P < 0.0001. Secondly, our results indicated that LPAR6 has the potential to activate CD8 + T cell, naive T-cell, effector T-cell and natural killer cell and inactivate Tregs, and decrease T cell exhaustion. CD8A, a crucial surface protein on T cells, is highly correlated with LPAR6 expression in LUAD which are types of cancers with better prognosis. And CD8A did not demonstrate a significant correlation pattern in LUSC (Table 3). This pattern also occurs with the general T cell markers such as CD3D, CD3E, CD2 and most markers of naive T-cell, effector T-cell, effector memory T-cell and natural killer cells. Such as LEF1 which has been proved to be a predictor of better treatment response in AML, because high LEF1 expression level was associated with favorable relapse-free survival in patients and predicted a significantly better overall survival for AML patients [ 55 ]. Thirdly, different correlation patterns can be found between LPAR6 expression and the regulation of several markers of T helper cells (Th1, Th2, Tfh, and Th17) in these different cancers. IFN-g is a Th1 cytokine with both pro-and anti-cancer properties [ 56 ] which are highly correlated with LPAR6 expression in LUAD, whereas did not demonstrate significant correlations in LUSC (Table 3). IL-13 is an important immunoregulatory cytokine mainly produced by activated type II helper T cells and is widely involved in tumorigenesis and development, fibrosis and inflammation [ 57 , 58 ]. We found that IL-13 is highly correlated with LPAR6 expression in LUAD, but did not demonstrate significant correlations in LUSC (adjusted by purity), and a similar situation is in the IL-21. So these would be explanations that why LPAR6 indication poor prognosis in LUSC and a better prognosis in LUAD. All these correlations above could be indications of a potential mechanism where LPAR6 regulates T cell functions in LUAD. Together these findings suggest that the LPAR6 plays an important role in the recruitment and regulation of effective T cells infiltrating in LUAD leading to a better prognosis. 5. Conclusions In this study, we offered a potential explanation for the mechanism that why LPAR6 expression correlates with immune infiltration level and better prognostic potential in some specific types of cancer, especially in LUSC. Hence, the interactions between LPAR6 and the immunocytes in the tumor microenvironment could be a potential mechanism for the correlation of LPAR6 expression with immune infiltration level and better prognosis in LUAD patients. Abbreviations TMA Tissue microarrays TCGA The Cancer Genome Atlas DEG Differentially expressed gene TIICs Tumor-infiltrating immune cells TILs Tumor-infiltrating lymphocytes TINs Tumor-infiltrating neutrophils TAMs Tumor association macrophages FDR False Discovery Rate TME Tumor microenvironment Declarations Ethics approval and consent to participate This project was permitted by Independent Ethics Committee of Shanghai Jiao Tong University School of Medicine. Consent for publication Not applicable Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Conflict of interest The authors declare there is none conflicts of interest. This research conforms to all the laws and ethical guidelines that apply in the country where I carried it out. Authors' contributions J. H. contributed to idea, conception, and study design. J. H. collected and analyzed the datasets, performed TMA analysis. J. H., M.M. and R.G. wrote the manuscript and generating the figures. H. W. revised and proofread the article. Funding This project is funded by the grants of National Natural Science Foundation of China (NSFC 81702730) and Start-up Plan for New Young Teacher of SHSMU (KJ30214190026) of JH and National Natural Science Foundation of China (NSFC 81630086), National Key Research & Development Program of China (2018YFC2000700) of HW. Acknowledgments We would like to thank to the funder. Database Website Reference Oncomine https://www.oncomine.org/resource/login.html 32 GEPIA2 http://gepia.cancer-pku.cn/ 34 PrognoScan http://dna00.bio.kyutech.ac.jp/PrognoScan/ 33 Kaplan-Meier plotter http://kmplot. com/analysis/ 35 UALCAN http://ualcan.path.uab.edu 36 GeneMANIA http://genemania.org/ 37 LinkedOmics database http://www.linkedomics.org/login.php 38 TIMER https://cistrome.shinyapps.io/timer/ 40 References Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin 2018. American Cancer Society. Key statistics for lung cancer. www.cancer.org/cancer/non-small-cell-lung-cancer/about/key-statistics.html. Yang L, Wang L, Zhang Y. Immunotherapy for lung cancer: advances and prospects. Am J Clin Exp Immunol. 2016; 5: 1-20. Dela Cruz CS, Tanoue LT, Matthay RA. Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med. 2011; 32 (4): 605-44. Gelsomino F, Lamberti G, Parisi C, Casolari L, Melotti B, Sperandi F, Ardizzoni A. 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Nature medicine. 2006; 1 (1): 99-106. Shimamura T, Fujisawa T, Husain SR, Joshi B, Puri RK. Interleukin 13 mediates signal transduction through interleukin 13 receptor alpha2 in pancreatic ductal adenocarcinoma: role of IL-13 Pseudomonas exotoxin in pancreatic cancer therapy. Clinical cancer research. 2010; 2 (2): 577-86. Supplementary Files SIFig1.pdf Supplementary Figure 1 | Correlation of LPAR6 expression with prognostic values in diverse types of cancer. Overall survival and disease free curves comparing the high and low expression of LPAR6 in adrenocortical carcinoma (ACC ) (A-B), bladder urothelial carcinoma (BLCA ) (C-D), breast invasive carcinoma (BRCA ) (E-F), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (G-H), cholangio carcinoma (CHOL) (I-J), colon adenocarcinoma(COAD ) (K-L), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) (M-N), esophageal carcinoma (ESCA) (O-P), glioblastoma multiforme (GBM) (Q-R), head and Neck squamous cell carcinoma (HNSC ) (S-T), kidney chromophobe (KICH) (U-V), kidney renal clear cell carcinoma (KIRC) (W-X), kidney renal papillary cell carcinoma (KIRP) (Y-Z), acute myeloid leukemia (LAML) (AA-AB), brain lower grade glioma(LGG) (AC-AD), liver hepatocellular carcinoma(LIHC) (AE-AF), lung adenocarcinoma (LUAD) (AG-AH), lung squamous cell carcinoma (LUSC) (AI-AJ), mesothelioma (MESO) (AK-AL), ovarian serous cystadenocarcinoma (OV) (AM-AN), pancreatic adenocarcinoma (PAAD) (AO-AP), pheochromocytoma and paraganglioma (PCPG) (AQ-AR), prostate adenocarcinoma (PRAD) (AS-AT), rectum adenocarcinoma (READ) (AU-AV), sarcoma (SARC) (AW-AX), skin cutaneous melanoma (SKCM) (AY-AZ), stomach adenocarcinoma (STAD) (BA-BB), testicular germ cell tumors (TGCT) (BC-BD), thyroid carcinoma (THCA) (BE-BF), thymoma (THYM) (BG-BH), uterine corpus endometrial carcinoma (UCEC) (BI-BJ), uterine carcinosarcoma (UCS) (BK-BL), uveal melanoma (UVM) (BM-BN). SIFig2.pdf Supplementary Figure 2 | The correlation of the expression of LPAR6 with immune infiltration level in cancers. SITable1.docx SITable2.xlsx SITable3.docx SITable4.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 16 Aug, 2021 Review # 2 received at journal 15 Aug, 2021 Review # 1 received at journal 29 Jul, 2021 Reviewer # 2 agreed at journal 24 Jul, 2021 Reviews received at journal 24 Jul, 2021 Reviewers invited by journal 24 Jul, 2021 Reviewer # 1 agreed at journal 23 Jul, 2021 Editor invited by journal 24 Jun, 2021 Editor assigned by journal 23 Jun, 2021 Submission checks completed at journal 23 Jun, 2021 First submitted to journal 23 Jun, 2021 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. 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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-653591\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Primary research\",\"associatedPublications\":[],\"authors\":[{\"id\":36222235,\"identity\":\"2044f62b-14de-47dc-89e7-5e3b3a576154\",\"order_by\":0,\"name\":\"Jian He\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxmYehgMJP2wYGBtAXDbitDA++NiTxsDYRqwWBgYeZsMZbIehqonRwtzOe0yah+e8PfP8HgOGD2WHGfhnNxByGF+aNI/F7cTGNh4DxhnnDjNI3DlASAuPGdCW2wmMQC3MvG2HGQwkEojRwnbOHqzlL5FajIHeP8AIchgzI5FaDIGBnAz0S1rBwZ5z6TwSNwhoMew/YwCMSjt7w+bDGx/8KLOW459BSEsDEuMAkObBrx4I5DEYo2AUjIJRMArQAQBGZD5k/2p6bQAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0000-0002-1426-7799\",\"institution\":\"Shanghai Jiao Tong University School of Medicine\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jian\",\"middleName\":\"\",\"lastName\":\"He\",\"suffix\":\"\"},{\"id\":36222236,\"identity\":\"750914b4-06e4-40d7-a331-f654bd5b5c4c\",\"order_by\":1,\"name\":\"Mei Meng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Jiao Tong University School of Medicine\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mei\",\"middleName\":\"\",\"lastName\":\"Meng\",\"suffix\":\"\"},{\"id\":36222237,\"identity\":\"4652d392-1fbe-4c8d-bff2-7af2191ce40f\",\"order_by\":2,\"name\":\"Hui Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Jiao Tong University School of Medicine\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hui\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2021-06-23 18:04:36\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-653591/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-653591/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":11023907,\"identity\":\"9dee17c2-7f9c-47ea-a1c6-3ae259e0ebf0\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:55:10\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1998697,\"visible\":true,\"origin\":\"\",\"legend\":\"LPAR6 mRNA expression levels in different types of human cancers in different databases. (A) Increased or decreased LPAR6 in data sets of different cancers compared with normal tissues. Cell color is determined by the best gene rank percentile for the analyses within the cell. (B) Human LPAR6 expression levels in different tumor types from TCGA database. One category of cancer is in one box, and paired tissue (tumor and adjacent) are in grey boxes. . p\\u003c0.1, * p\\u003c0.05, **p\\u003c0.01, ***p\\u003c0.001. (C) LPAR6 expression profile across all tumor samples and paired normal tissues (Dot plot) via GEPIA. Each dots represent the expression of samples. \",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/397aef25f9e2dbd4a0a4b73c.png\"},{\"id\":11024109,\"identity\":\"65293ded-000d-48dc-8726-6ed858590f8f\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 22:01:10\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":732196,\"visible\":true,\"origin\":\"\",\"legend\":\"Kaplan-Meier survival curves comparing the high and low expression of LPAR6 in different types of cancer in the PrognoScan (A–M) and Kaplan-Meier plotter databases (N–W). (A-C) Survival curves of OS in two lung cancer cohorts [GSE3141 (n =111, P = 0.00206181) and GSE4573 (n =129 , P = 0.0219869)] and DSS in bladder cancer cohort [GSE13507 (n =165, P = 0.0067285) ]. (D-F) Survival curves of OS, RFS and DFS in the breast cancer cohort [GSE1456-GPL96 (n =159, P = 0.00575883; P= 0.0000252; P= 0.000210173)]. (G-I) Survival curves of DMFS in the breast cancer cohort [GSE19615 (n=159, P = 0.00575883), GSE9195 (n=159, P = 0.0466683), GSE11121 (n=200 , P =0.0389008)]. (J-L) Survival curves of DSS in the breast, DFS in the colorectal and DMFS in the eye cancer cohort [GSE3494 (n=236, P = 0.00294205), GSE17537 (n=55, P = 0.0257972), GSE22138 (n=63, P = 0.00092478)]. (M) Survival curves of PFS in the ovarian cancer cohort [GSE17260 (n=110, P = 0.0392865)]. (N-P) Survival curves of OS (n =1402), RFS (n=3951) and DMSF (n=1746) in the breast cancer cohorts. (Q-S) Survival curves of OS (n=364), FPS (n=370) and RFS (n=316) in the liver cancer cohort. (T, U) Survival curves of OS (n=1926) and PPS (n=344) of the lung cancer. (V,W) Survival curves of OS of the lung adenocarcinoma (n=720) and squamous cell carcinoma (n=524).\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/69c4acc2bbd450033035c778.png\"},{\"id\":11024407,\"identity\":\"8a448652-2d57-4b6b-afae-7c2d7735b6d9\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 22:04:10\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":616358,\"visible\":true,\"origin\":\"\",\"legend\":\"Promoter methylation levels of LPAR6 impacts the clinicopathological parameters in LUAD and LUSC cohorts. \\nFIGURE 4 | Biological interaction network of LPAR6. LPAR6 interaction network in TCGA, different colors represent diverse bioinformatics methods (A) and differentially expressed genes in correlation with LPAR6 and heat maps of positively and negatively correlated genes with LPAR6 in LUAD and LUSC were analyzed by Pearson test (B). Red indicates positive and blue indicates negative. \\n\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/9a08fe67f5e74b0352625b2d.png\"},{\"id\":11024056,\"identity\":\"79a60c13-5a4d-43ec-ba3b-47c63ab4bfac\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:58:10\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3473534,\"visible\":true,\"origin\":\"\",\"legend\":\"Biological interaction network of LPAR6. LPAR6 interaction network in TCGA, different colors represent diverse bioinformatics methods (A) and differentially expressed genes in correlation with LPAR6 and heat maps of positively and negatively correlated genes with LPAR6 in LUAD and LUSC were analyzed by Pearson test (B). Red indicates positive and blue indicates negative. \",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/b7b82331ee64871874223203.png\"},{\"id\":11024061,\"identity\":\"289fc888-af80-46f4-964c-d6f87ba0b43f\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:58:10\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1181704,\"visible\":true,\"origin\":\"\",\"legend\":\"Enriched gene ontology annotations of biological process and molecular function analysis of LPAR6 correlated genes in LUAD (A), LUSC (B). Dark blue and orange indicate FDR≤0.05, light blue and orange indicate FDR \\u003e0.05. FDR, false discovery rate.\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/e2333c431830866b9bc380c3.png\"},{\"id\":11023915,\"identity\":\"46953273-fcb1-4a1f-8949-6246238a19c6\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:55:10\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2461018,\"visible\":true,\"origin\":\"\",\"legend\":\"Correlation of LPAR6 expression with immune infiltration level in (A) LUAD, (B) LUSC. (A) LPAR6 expression is significantly negatively related to tumor purity and has significant strong positive correlations with the level of B cells, CD8+ T cells, macrophages, neutrophils, and DCs in LUAD (n = 515). (B) LPAR6 expression is significantly negatively related to tumor purity and has weak positive correlations with infiltrating levels of neutrophils in LUSC but no significant correlation with infiltrating levels of B cells, CD8+ T cells, macrophages and DCs (n = 501). \",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/eccd77a2bfa594d942ddf55d.png\"},{\"id\":11023918,\"identity\":\"cb935008-0199-48ca-956b-a5dc52de3f50\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:55:11\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3683934,\"visible\":true,\"origin\":\"\",\"legend\":\"Correlation Analysis Between LPAR6 Expression and Immune Marker Sets in LUAD and LUSC. Markers include CD8A and CD8B of CD8+ T cell; CD3D, CD3E and CD2 of general T cell; FOXP3, CCR8, STAT5B and TGFB1 of Treg; PDCD1, CTLA4, LAG3, HAVCR2 and GZMB of exhausted T cells; CD163, VSIG4 and MS4A4A of M2 macrophages; CD86 and CSF1R of monocytes; HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C, NRP1 and ITGAX of Dendritic cell. (A–Z) Scatterplots of correlations between LPAR6 expression and gene markers of CD8+ T cell (A, B), general T cell (C-E), Treg (F-I), T cell exhaustion (J-N), M2 macrophage (O-Q), monocyte (R-S) and dendritic cell (T-Z) in LUAD. (AA–AZ) Scatterplots of correlations between LPAR6 expression and gene markers of CD8+ T cell (AA, AB), general T cell (AC-AE), Treg (AF-AI), T cell exhaustion (AJ-AN), M2 macrophage (AO-AQ), monocyte (AR-AS) and dendritic cell (AT-AZ) in LUSC.\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/f6d7edf63fcede2bb42a6e46.png\"},{\"id\":11024059,\"identity\":\"8269bdb1-c046-4830-944b-130eaf27f1fd\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:58:10\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4788118,\"visible\":true,\"origin\":\"\",\"legend\":\"Correlation Analysis Between LPAR6 Expression and various T cell Marker Sets in LUAD and LUSC. (A–AE) Scatterplots of correlations between LPAR6 expression and gene markers of Naive T-Cell (CCR7, LEF1, TCF7, SELL) (A-D), Effector T-Cell (CX3CR1, FGFBP2, FCGR3A) (E-G), Effector memory T-Cell (PDCD1, DUSP4, GZMK, GZMA, IFNG) (H-L), Central memory T-Cell (CCR7, SELL, IL7R) (A, D, M), Resident memory T-Cell (CD69, ITGAE, CXCR6, MYADM) (N-Q), T cell exhaustion (HAVCR2, TIGIT, LAG3, PDCD1, CXCL13, LAYN) (R-T, H, U-V), Resting Treg (FOXP3, IL2RA) (W, X), Effector Treg (FOXP3, CTLA4, CCR8, TNFRSF9) (W, Y-AA), Th1-like (HAVCR2, IFNG, CXCR3, BHLHE40, CD4) (AB, L, AC-AE) in LUAD; (AF–BJ) Scatterplots of correlations between LPAR6 expression and gene markers of Naive T-Cell (CCR7, LEF1, TCF7, SELL) (AF-AI), Effector T-Cell (CX3CR1, FGFBP2, FCGR3A) (AJ-AL), Effector memory T-Cell (PDCD1, DUSP4, GZMK, GZMA, IFNG) (AM-AQ), Central memory T-Cell (CCR7, SELL, IL7R) (AF, AI, AR), Resident memory T-Cell (CD69, ITGAE, CXCR6, MYADM) (AS-AV), T cell exhaustion (HAVCR2, TIGIT, LAG3, PDCD1, CXCL13, LAYN) (AW-AY, AM, AZ-BA), Resting Treg (FOXP3, IL2RA) (BB-BC), Effector Treg (FOXP3, CTLA4, CCR8, TNFRSF9) (BB, BD, BE-BF), Th1-like (HAVCR2, IFNG, CXCR3, BHLHE40, CD4) (BG, AQ, BI-BJ) in LUSC.\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/94ee616e09f142c5d0a3d420.png\"},{\"id\":11024055,\"identity\":\"d7125392-9dc5-44f7-9841-f284516e7d10\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:58:10\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1373577,\"visible\":true,\"origin\":\"\",\"legend\":\"Correlation analysis between LPAR6 expression and various immune cells in normal and tumor tissue of LUAD and LUSC. (A–R) Scatterplots of correlations between LPAR6 expression and naive T-cell (A, B), effector T-cell (C, D), effector memory T-cell (E, F), central memory T-cell (G, H), resident memory T-cell (I, J), T cell exhaustion (K, L), resting Treg (M, N), effector Treg (O, P), Th1-like (Q, R) in the normal and tissue of LUAD; (S–AJ) Scatterplots of correlations between LPAR6 expression and gene markers of naive T-cell (S, T), effector T-cell (U, V), effector memory T-cell (W, X), central memory T-cell (Y, Z), resident memory T-cell (AA, AB), T cell exhaustion (AC, AD), resting Treg (AE, AF), effector Treg (AG, AH), Th1-like (AI-AJ) in LUSC.\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/180c86bc7a258cbfa57a55bc.png\"},{\"id\":11023919,\"identity\":\"03e1d44e-c8f2-4a56-9861-78a99dd67665\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:55:11\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3279184,\"visible\":true,\"origin\":\"\",\"legend\":\"Higher expression of LPAR6 was correlated with clinicopathological parameters in LUAD cohort and was associated with increased overall survival (OS) of LUAD and LUSC patients. (A) Immunohistochemistry staining of the LPAR6 in the tumor and adjacent normal tissues. Red arrows indicated the cytoplasm-stained LPAR6. Bar, 50 μm; (B) The immunoreactive score (IRS) of the cytoplasm LPAR6 staining in 74 paired lung cancer tissues in LUAD cohort; (C) The Kaplan–Meier plot of the OS for lung cancer patients with relatively higher or lower LPAR6 expression levels in LUAD cohort (N = 74; Log-rank test, P =0.02); (D) The IRS of the cytoplasm LPAR6 staining in 78 paired lung cancer tissues in LUSC cohort; (E) The Kaplan–Meier plot of the overall survival for lung cancer patients with relatively higher or lower LPAR6 expression levels in LUSC cohort (N = 78; log-rank test, P =0.04 ). (F) The proportion of LPAR6 expression level (higher or lower) in different clinical stages, lymph node metastasis, organ metastasis and tumor size of patients in LUAD and LUSC cohorts.\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/3db129fc307eb0e401589e61.png\"},{\"id\":13702148,\"identity\":\"240bf5e2-18bf-4afd-8e2b-9272f84bb668\",\"added_by\":\"auto\",\"created_at\":\"2021-09-17 13:33:57\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6903077,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/df5f72f9-5c93-43cb-a613-088c096b9d58.pdf\"},{\"id\":11024111,\"identity\":\"1c45eb97-0b1c-4945-b862-321935003807\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 22:01:10\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":973350,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementary Figure 1 | Correlation of LPAR6 expression with prognostic values in diverse types of cancer. Overall survival and disease free curves comparing the high and low expression of LPAR6 in adrenocortical carcinoma (ACC ) (A-B), bladder urothelial carcinoma (BLCA ) (C-D), breast invasive carcinoma (BRCA ) (E-F), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (G-H), cholangio carcinoma (CHOL) (I-J), colon adenocarcinoma(COAD ) (K-L), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) (M-N), esophageal carcinoma (ESCA) (O-P), glioblastoma multiforme (GBM) (Q-R), head and Neck squamous cell carcinoma (HNSC ) (S-T), kidney chromophobe (KICH) (U-V), kidney renal clear cell carcinoma (KIRC) (W-X), kidney renal papillary cell carcinoma (KIRP) (Y-Z), acute myeloid leukemia (LAML) (AA-AB), brain lower grade glioma(LGG) (AC-AD), liver hepatocellular carcinoma(LIHC) (AE-AF), lung adenocarcinoma (LUAD) (AG-AH), lung squamous cell carcinoma (LUSC) (AI-AJ), mesothelioma (MESO) (AK-AL), ovarian serous cystadenocarcinoma (OV) (AM-AN), pancreatic adenocarcinoma (PAAD) (AO-AP), pheochromocytoma and paraganglioma (PCPG) (AQ-AR), prostate adenocarcinoma (PRAD) (AS-AT), rectum adenocarcinoma (READ) (AU-AV), sarcoma (SARC) (AW-AX), skin cutaneous melanoma (SKCM) (AY-AZ), stomach adenocarcinoma (STAD) (BA-BB), testicular germ cell tumors (TGCT) (BC-BD), thyroid carcinoma (THCA) (BE-BF), thymoma (THYM) (BG-BH), uterine corpus endometrial carcinoma (UCEC) (BI-BJ), uterine carcinosarcoma (UCS) (BK-BL), uveal melanoma (UVM) (BM-BN). \",\"description\":\"\",\"filename\":\"SIFig1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/323722e14f3bea2e8d67d1a1.pdf\"},{\"id\":11023908,\"identity\":\"02bb3ad6-710c-4ba9-ab58-2055f1aa63d5\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:55:10\",\"extension\":\"pdf\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":949639,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementary Figure 2 | The correlation of the expression of LPAR6 with immune infiltration level in cancers.\",\"description\":\"\",\"filename\":\"SIFig2.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/25af9255e4ff10f4bfc1c5cc.pdf\"},{\"id\":11023906,\"identity\":\"2923c869-773c-412f-b1f1-87a6954c3329\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:55:10\",\"extension\":\"docx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":27188,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SITable1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/3729c4a9a4f47cb48f15d45f.docx\"},{\"id\":11023921,\"identity\":\"a9cf5571-9206-436d-ab1a-c3697f18acf3\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:55:11\",\"extension\":\"xlsx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":64161,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SITable2.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/8590832765a2a97e78573a0c.xlsx\"},{\"id\":11024062,\"identity\":\"911a3342-2ffd-413d-8f01-71fa77789f08\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:58:11\",\"extension\":\"docx\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16762,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SITable3.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/dfdd15be6920f5e6c1f814d2.docx\"},{\"id\":11023917,\"identity\":\"c932801a-2c0b-49a4-ae24-8bdbf8317d0c\",\"added_by\":\"auto\",\"created_at\":\"2021-07-01 21:55:10\",\"extension\":\"docx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":42470,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SITable4.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-653591/v1/64c66e3671d42c91b56fd593.docx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"\\u003cp\\u003eLysophosphatidic Acid Receptor 6: A Prognostic Biomarker for Lung Adenocarcinoma via Correlating Immune Infiltration\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\" \\u003cp\\u003eLung cancer is one of the most common malignancies around the world, and metastasis is a crucial biological procedure leading to a poor prognosis [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. It is the top one diagnosed malignancy in China and the second most common malignancy in the U.S., also the leading cause of cancer-related deaths both in China and the U.S. [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Scientists have made great efforts to treat various types of lung cancer, but there is still a large amount of time and effort to do. According to histopathological classification, lung cancer could be catalogued into two broad subtypes, non-small-cell lung cancer (NSCLC) and small cell lung cancer (SCLC), and the NSCLC is more prevalent [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the first and the second most common subtype NSCLC respectively [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Surgery is the primary treatment option during the early stages of the NSCLC, while in late stages, surgery is combined with chemotherapies, and/or radiotherapy [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. However, despite these treatment procedures, the prognosis of the patients remains not good, also the post-treatment recurrence is the main cause of the disease, the total 5-year survival rate for all stages is only 16.6% [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eNSCLC has been regarded as a kind of non-immunogenic disease in the past twenty years. However, more and more knowledge of tumor immune interactions has opposed this model in lung cancer and other types of malignancy. Immune-related interaction mechanisms act as a crucial role in oncogenesis and development, and immune therapy is considered a promising approach for cancer treatment [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e], based on this, scientists are attempting to employ the body\\u0026rsquo;s own immune system to fight and prevent malignancies [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Recently, immunotherapies, including adoptive cell transfers, monoclonal antibodies and vaccines, have become more and more applied to the clinic treatment of many types of cancers, for example, melanoma, and more recently for lung cancer [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. During the last decade, the finding of antibodies that target the immune checkpoints has revolutionized the treatment of NSCLC, such as PD-1 and PD-L1 [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], and these two therapy approaches (PD-1 and PD-L1), has demonstrated promising anti-tumor performance in NSCLC and melanoma [\\u003cspan additionalcitationids=\\\"CR10\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. In addition, more and more research has demonstrated that TILs (tumor-infiltrating lymphocytes) play a key role in modulating the response to chemotherapy and heighten the clinical prognosis potential of various types of cancer [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e], such as tumor-associated macrophages (TAMs) [\\u003cspan additionalcitationids=\\\"CR15\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e] and tumor-infiltrating neutrophils (TINs), they also associate with the prognosis [\\u003cspan additionalcitationids=\\\"CR18 CR19\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. So, it is an essential and urgent requirement for the explanations of the immunophenotypes of tumor immune interactions and the identification of new immune therapy targets for lung cancers.\\u003c/p\\u003e \\u003cp\\u003eLPA is a kind of lipid that involved in the proliferation of tumor cells via its G-protein coupled (GPC) receptors [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e] and one of their receptors-LPAR6 is a newly identified receptor of LPA [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], and it has been demonstrated to be related to many types of tumor, including prostate [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e], liver [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e], colorectal [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e] and pancreatic cancer [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. But the function of LPAR6 remains highly controversial since in colorectal cancer, the scientists found that LPAR6 might act as a tumor suppressor whereas act as a facilitator in the other types of tumors [\\u003cspan additionalcitationids=\\\"CR26\\\" citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. All these indicate that LPAR6 plays a key role in cancer, but the relationship between LPAR6 and tumor biology and the underlying mechanism involved is still not well understood.\\u003c/p\\u003e \\u003cp\\u003eBioinformatics is an emerging procedure that supports us to make full usage of numerous high throughput data to analyze the level of specific genes in various types of cancer [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. So in this work, we investigated the mRNA expression level of LPAR6 and the correlation with prognosis patterns of cancer patients in databases. In addition, we analyzed the correlation of LPAR6 with tumor-infiltrating immune cells (TIICs) in various tumor microenvironments via TIMER. Moreover, IHC staining for LPAR6 in two separate lung cancer cohorts with patients\\u0026rsquo; information was detected to analyze the correlation of the expression of LPAR6 and the clinicopathological parameters of lung cancer.\\u003c/p\\u003e \\u003cp\\u003eAll these discoveries shed light on the crucial role of LPAR6 in lung cancers as well as provide a potential correlation and the mechanism involved between LPAR6 and tumor-immune interactions.\\u003c/p\\u003e \"},{\"header\":\"2. Materials And Methods\",\"content\":\" \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Ethics approval\\u003c/h2\\u003e \\u003cp\\u003e This project was permitted by Independent Ethics Committee of Shanghai Jiao Tong University School of Medicine.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Gene expression level of the LPAR6 gene analysis\\u003c/h2\\u003e \\u003cp\\u003eThe mRNA expression level of the LPAR6 in different types of cancers was investigated via Oncomine database, TIMER and GEPIA2 database [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. The threshold in Oncomine database was as follows: \\u003cem\\u003eP\\u003c/em\\u003e-value of 0.0001, fold change of 1.5, and gene ranking top 5%.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Prognosis Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe association between LPAR6 expression level and survival rate in different types of cancers was investigated by the database PrognoScan and GEPIA2, which searching for relationships between gene expression level and the prognoses of patients, such as OS and DFS, across a large collection of publicly available cancer microarray datasets [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. The threshold was adjusted to a Cox \\u003cem\\u003eP\\u003c/em\\u003e-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Correlation Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe correlation between LPAR6 expression and survival rate as well as different cancer staging in various cancers was determined by Kaplan-Meier plotter [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. The HR with 95% confidence intervals and log-rank \\u003cem\\u003eP\\u003c/em\\u003e-value were also analyzed.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Methylation analysis\\u003c/h2\\u003e \\u003cp\\u003eUALCAN [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e] could be used to investigate methylation and relative mRNA expression levels, as well as the survival of a specific target gene across several clinicopathological features, such as stages and age. The t-test was performed to compare the statistical significance between the two independent groups.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 GeneMANIA analysis\\u003c/h2\\u003e \\u003cp\\u003eGeneMANIA is identified single genes related to a set of input genes [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e] to construct the LPAR6 biological network based on a set of functional association data, including coexpression, genetic and protein interaction pathways, colocalization and protein domain homology.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7 LinkedOmics analysis.\\u003c/h2\\u003e \\u003cp\\u003eThirty-two types of cancer and over ten thousand patients from TCGA were included in the LinkedOmics database [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. LinkFinder was used to determine the differentially expressed genes (DEGs) in TCGA. LUAD and LUSC cohorts whose expression levels correlated with those of \\u003cem\\u003eLPAR6\\u003c/em\\u003e. The results were investigated by using Pearson\\u0026rsquo;s correlation coefficient. LinkInterpreter was employed to identify the pathways and networks [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.8 Immune infiltrates level and gene correlation analysis\\u003c/h2\\u003e \\u003cp\\u003eWe investigated LPAR6 expression in various types of malignancy and the association of LPAR6 expression level with the abundance of immune infiltrating, including CD4\\u0026thinsp;+\\u0026thinsp;T cells, CD8\\u0026thinsp;+\\u0026thinsp;T cells, B cells, macrophages, neutrophils, and DCs, via gene modules in TIMER, which is a comprehensive resource for systematic analysis of immune infiltrates across diverse types of cancer [\\u003cspan additionalcitationids=\\\"CR41 CR42\\\" citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. In addition, associations between LPAR6 expression level and marker genes of TIICs were explored via correlation modules. The marker genes of TIICs included markers of T cells (CD8+, general), B cells, TAMs (tumor association macrophages), monocytes, macrophages (M1 and M2), natural killer (NK) cells, neutrophils, dendritic cells (DCs), T-helper (Th1, Th2 and Th17) cells, follicular helper T (Tfh) cells, Tregs, and exhausted T cells. The gene marker sets are referenced in our previous studies [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. The expression level of the genes was demonstrated by using log2 RSEM.\\u003c/p\\u003e \\u003cp\\u003eGEPIA2 database was employed to confirm the significantly correlated genes in-depth, which [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e] is a web server with gene expression analysis based on GTEx and TCGA databases. Furthermore, GEPIA2 was employed to generate curves of OS and DFS.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.9 Immunohistochemical staining for LPAR6 in lung cancer patient cohort tissue microarrays\\u003c/h2\\u003e \\u003cp\\u003eHere, two tissue microarrays were constructed using formalin-fixed, paraffin-embedded (FFPE) tissue samples from LUAD (LUC1601) and LUSC (LUC1602) patients, each TMA chip containing 74 and 78 paired tumors and adjacent normal tissues were purchased from the Superbiotek Co., Ltd., (Shanghai, China) respectively. Clinicopathological data including subtype, histological grading, and tumor/nodal stage and information about patient follow-up could be retrieved from the database of the Shanghai Jiao Tong University School of Medicine.\\u003c/p\\u003e \\u003cp\\u003eThe tissue sections underwent immunohistochemical staining using a primary antibody to LPAR6 (Thermo Fisher/ Invitrogen, USA) (Cat No. PA5-33901) at a dilution of 1: 100. Sections of the TMAs were used to investigate the protein levels of LPAR6 following the general standard IHC staining protocols.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.10 Statistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe statistical analysis as the our previous work. The results produced via Oncomine are exhibited as mentioned in part 2.1. The consequence of Kaplan-Meier plots, GEPIA, and PrognoScan are exhibited with HR and \\u003cem\\u003ep\\u003c/em\\u003e or Cox \\u003cem\\u003ep\\u003c/em\\u003e-values from a log-rank test. And the correlation coefficient of gene expression was evaluated by Spearman\\u0026rsquo;s correlation and \\u003cem\\u003ep-\\u003c/em\\u003evalues\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were considered statistically significant.\\u003c/p\\u003e \\u003cp\\u003eProtein level was determined by the staining intensity and the distribution of the positive cells, which were performed by two independent pathologists blinded to the clinical information of the patients as described [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv class=\\\"Section2\\\" id=\\\"Sec14\\\"\\u003e\\n \\u003ch2\\u003e3.1 The expression levels of \\u003cem\\u003eLPAR6\\u003c/em\\u003e in different human cancers\\u003c/h2\\u003e\\n \\u003cp\\u003eTo study the varied mRNA expression level of \\u003cem\\u003eLPAR6\\u003c/em\\u003e expression in tumor and normal tissues, the LPAR6 mRNA expression levels were analyzed using the dominant online database (Oncomine and GEPIA2). This study enclosed that the LPAR6 expression was higher in brain and CNS cancer, gastric, kidney, liver cancer,, lymphoma and pancreatic cancer compared to the normal tissues and lower expression level of LPAR6 was observed in breast, bladder, colorectal, cervical, lung, esophageal, prostate cancer and some other types of cancer compared to the adjacent normal tissues (cancer \\u003cem\\u003evs.\\u003c/em\\u003e normal) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). The detail of the expression level of \\u003cem\\u003eLPAR6\\u003c/em\\u003e expression in different cancer types is summarized in \\u003cstrong\\u003eSupplementary Table\\u0026nbsp;1\\u003c/strong\\u003e. To evaluate LPAR6 expression level in cancers, we determined the levels of LPAR6 expression employing the RNA-Seq datasets of multiple cancer types in the Cancer Genome Atlas (TCGA). The varied expression levels between tumor and adjacent normal tissues for LPAR6 across each type of TCGA tumors is demonstrated in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB. The expression level of LPAR6 was significantly lower in the tumor tissue of BLCA, BRCA, COAD, HNSC, KICH, LUAD, PRAD, READ and UCEC compared with adjacent normal tissues and was significantly higher in ESCA, KIRC, KIRP, THCA compared with adjacent normal tissues (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). GEPIA2 generates dot plots to profile gene/isoform expression across various types of cancer and paired normal tissue samples, and each dot representing a distinct sample. The differential mRNA expression level of LPAR6 between tumor and matched TCGA normal and GTEx data across all TCGA tumors by GEPIA2 is demonstrated in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC. LPAR6 expression was significantly higher in GBM, KIRC, LAML, LGG, PAAD, THYM and lower in ACC, ESCA, KICH, LUAD, PRAD, TGCT, UCEC and UCS compared with normal GTEx tissues. From these above, we found that the expression pattern are different in two types of lung cancers, LUAD and LUSC.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"Section2\\\" id=\\\"Sec15\\\"\\u003e\\n \\u003ch2\\u003e3.2 Prognostic potential of \\u003cem\\u003eLPAR6\\u003c/em\\u003e across various types of cancer\\u003c/h2\\u003e\\n \\u003cp\\u003eWe determined whether the mRNA expression level of LPAR6 was associated with the prognosis specific across cancer patient cohorts. The effects of LPAR6 expression on the various survival rates were assessed by using the PrognoScan database. The detailed relationship between the expression level of LPAR6 and prognosis potential of various cancers are listed in \\u003cstrong\\u003eSupplementary Table\\u0026nbsp;2.\\u003c/strong\\u003e Notably, the expression level of LPAR6 impacts OS in breast and lung cancer significantly (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA-M). Two cohorts (GSE3141 and GSE4573) of lung cancer demonstrated that high expression level of LPAR6 was associated with better prognosis (OS HR\\u0026thinsp;=\\u0026thinsp;0.53, 95% CI\\u0026thinsp;=\\u0026thinsp;0.36 to 0.80, Cox \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.00206181; OS HR\\u0026thinsp;=\\u0026thinsp;0.53, 95% CI\\u0026thinsp;=\\u0026thinsp;0.31 to 0.91, Cox \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0219869). (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA, B, D). So it is conceivable that high LPAR6 expression is an independent risk factor and leads to a better prognosis in lung cancer patients, and a hazard ratio below 0 indicates LPAR6 expression is a protective factor. Also, high LPAR6 expression significantly impacts DSS in bladder cancer and RFS and DFS in breast cancer (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC, E, F). Moreover, three cohorts (GSE19615, GSE9195 and GSE11121) of breast cancer demonstrated that higher expression level of LPAR6 was correlated with a better prognosis potential of DMFS (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG-I). And higher LPAR6 expression level was associated with better prognosis potential in some other types of cancer (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eJ-M).\\u003c/p\\u003e\\n \\u003cp\\u003eTo further analyze the prognostic characteristics of LPAR6 gene in different types of cancer, we employed Kaplan-Meier plotter database to access the LPAR6 prognostic value. Similarly, a better prognosis potential in breast and lung cancer was shown to correlate with higher LPAR6 expression (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eN-P, T-V).\\u003c/p\\u003e\\n \\u003cp\\u003eIn addition to microarray analysis data of LPAR6, the RNA-Seq was also used to analyze the prognosis of LPAR6 in various types of cancers via the same database. A better prognosis in breast cancer is shown to be associated with a higher LPAR6 expression level (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eQ-S). The different correlation patterns between adenocarcinoma and squamous cell carcinoma of lung cancer attracted our attention (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eV, W). These data confirmed the prognostic value of LPAR6 in some specific types of cancers, that is, the increased or decreased LPAR6 expression has different prognostic values depending on the type of cancers.\\u003c/p\\u003e\\n \\u003cp\\u003eIn addition to using Kaplan-Meier and PrognoScan plotter databases, TCGA database were also employed to determine the prognostic characteristics of LPAR6 in different types of cancer via GEPIA2. We assessed the relationships between the level of LPAR6 and prognostic potential in 33 types of cancer. LPAR6 expression significantly impacts prognosis in 3 types of cancers, including ACC, LGG (\\u003cstrong\\u003eSupplementary Fig.\\u0026nbsp;1\\u003c/strong\\u003e). High LPAR6 expression levels were associated with a better prognosis of OS in SKCM but have less influence on DFS. These results demonstrated the prognostic value of LPAR6 in some types of cancers and that differential LPAR6 expression has different prognostic values depending on the type of cancers.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"Section2\\\" id=\\\"Sec16\\\"\\u003e\\n \\u003ch2\\u003e3.3 The expression level of LPAR6 impacts the lung cancer prognosis in different stages and treatments\\u003c/h2\\u003e\\n \\u003cp\\u003eIn this part, we studied the association with the expression level of LPAR6 and different clinical characteristics in order to better disclosure the relevance and mechanisms of the expression level of LPAR6 in cancers, especially in different clinical stages, of lung cancer patients.\\u003c/p\\u003e\\n \\u003cp\\u003eWe found that high expression of LPAR6 was associated with better OS only in Stage 1 and Stage 2 of LUAD (OS HR\\u0026thinsp;=\\u0026thinsp;0.27, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;4.6E-10; OS HR\\u0026thinsp;=\\u0026thinsp;0.51, \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.0073) not in LUSC \\u003cstrong\\u003e(\\u003c/strong\\u003eTable\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u003cstrong\\u003e).\\u003c/strong\\u003e This interesting phenomenon combines with the different survival rate patterns of LUAD and LUSC in Figs.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eV and \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eW may indicate the correlation of LPAR6 expression and the prognosis of different cancers depends on the different mechanisms in the tumorigenesis and development.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003ctable border=\\\"1\\\" id=\\\"Tab1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eCorrelation of the mRNA expression level of \\u003cem\\u003eLPAR6\\u003c/em\\u003e in different stage and clinical prognostic potential in lung Cancer with different clinicopathological factors.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eClinicopathological Characteristics\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003eOverall survival (n\\u0026thinsp;=\\u0026thinsp;364 )\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLUAD (n\\u0026thinsp;=\\u0026thinsp;720)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLUSC (n\\u0026thinsp;=\\u0026thinsp;524)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHazard ratio\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHazard ratio\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSex\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e318\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.39 (0.26\\u0026ndash;0.58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1.4E-10\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e129\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.69 (0.94\\u0026ndash;3.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e344\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.66 (0.48\\u0026ndash;0.93)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.015\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e342\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.79 (0.59\\u0026ndash;1.04)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.087\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSmoking history\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e143\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.4 (0.17\\u0026ndash;0.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.034\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e---\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e---\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e246\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.49 (0.3\\u0026ndash;0.79)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.0029\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e820\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.89 (0.72\\u0026ndash;1.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eStage\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.27 (0.17\\u0026ndash;0.42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e4.6E-10\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e172\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.75 (0.49\\u0026ndash;1.14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e136\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.51 (0.31\\u0026ndash;0.84)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.0073\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.42 (0.76\\u0026ndash;2.65)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.1 (0.71\\u0026ndash;6.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.48 (0.24\\u0026ndash;0.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.035\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e---\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e---\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e---\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e---\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\"\\u003e\\u003cem\\u003eBold values indicate P\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003e0.05.\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003ch2\\u003e3.4 Low Promoter Methylation Levels of LPAR6 Impacts the Clinicopathological Parameters of Liver Cancer and Lung Cancer in Patients\\u003c/h2\\u003e\\n \\u003cp\\u003eThe lower promoter methylation levels of LPAR6 were detected in the earlier stage, implying that lower promoter methylation levels of LPAR6 were correlated with the earlier stages of the progress of lung cancer \\u003cstrong\\u003e(\\u003c/strong\\u003eFig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e\\u003cstrong\\u003e)\\u003c/strong\\u003e. We also found that late-stage (stage 4) with the lowest promoter methylation levels of LPAR6 in LUSC whereas it is the highest methylation level in entire cancer progress in LUAD cohorts (stage 1\\u0026ndash;4) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA, \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE), and the lowest promoter methylation levels of LPAR6 appears in an earlier stage (stage 2) of LUAD while in the late stage of LUSC. What interested us is that the same pattern was detected in nodal metastasis analysis, which implies that in the later stage, the promoter methylation levels of LPAR6 are correlated with nodal metastasis in some way (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD, \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eH). The promoter methylation levels of LPAR6 share a similar pattern in LUAD and LUSC among different races and different ages respectively, that is, both in the African-America group and younger group of these two cohorts with the lowest promoter methylation levels of LPAR6 (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB, \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"Section2\\\" id=\\\"Sec17\\\"\\u003e\\n \\u003ch2\\u003e3.5 Interaction network of LPAR6\\u003c/h2\\u003e\\n \\u003cp\\u003eAn interaction network of LPAR6 was constructed to determine potential interactions between LPAR6 and other cancer‑associated proteins. The data demonstrated that LPAR6 has co‑expression with 19 proteins, shared protein domains with ADRB2 and physical interactions with DMD (dystrophin) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). LinkedOmics were then used to analysis the genes that co-expressed with LPAR6 in lung cancers. The volcano plot elucidated that the expression of genes were negative correlated with that of LPAR6 [green spot; FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05], while genes expression is positively correlated with LPAR6 (red spot; FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). The top 50 positively and negatively correlated genes are showed in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB. These results imply that LPAR6 serves an important role in cancer development. Biological process and molecular function analyses were conducted using gene set enrichment analysis, which showed that LPAR6‑associated DEGs were involved in several kinds of immune biology process such as \\u0026lsquo;interleukin production\\u0026rsquo;, \\u0026lsquo;respiratory burst\\u0026rsquo;, \\u0026lsquo;leukocyte proliferation\\u0026rsquo;, \\u0026lsquo;T cell activation\\u0026rsquo;, \\u0026lsquo;adaptive immune response\\u0026rsquo; were involved in LUAD and LUSC respectively. (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). All these data indicate that LPAR6 serves a key role in immune system activation, cellular responses to stimulation, metabolism and many other processes.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"Section2\\\" id=\\\"Sec18\\\"\\u003e\\n \\u003ch2\\u003e3.6 The expression level of LPAR6 is correlated with immune infiltration level in lung cancers\\u003c/h2\\u003e\\n \\u003cp\\u003eTILs have been proved as an independent predictor of survival in cancers [\\u003cspan class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. So, in this study, we determined whether the mRNA expression level of LPAR6 correlates with the immune infiltration levels in various types of cancer. We analyzed the correlations of LPAR6 expression with immune infiltration levels in nearly forty types of cancer. The results show that the expression level of LPAR6 has significant negative correlations with tumor purity in 26 types of cancer which indicating LPAR6 somehow related to recruiting lymphocytes to tumor and significant correlations with B cell infiltration levels in 13 types of cancers. In addition, the expression level of LPAR6 has significant correlations with infiltrating levels of CD8\\u0026thinsp;+\\u0026thinsp;T cells in 24 types of cancer, CD4\\u0026thinsp;+\\u0026thinsp;T cells in 26 types of cancer, macrophages in 20 types of cancer, neutrophils in 33 types of cancer, and dendritic cells in 21 types of cancer. \\u003cstrong\\u003e(Supplementary Table\\u0026nbsp;3 and Supplementary Fig.\\u0026nbsp;2).\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eGiven the correlation of the expression level of LPAR6 with immune infiltration level in diverse types of cancer, we next investigated the distinct types of cancers in which LPAR6 was correlated with prognosis and immune infiltration. Tumor purity is a crucial factor that influences the analysis of immune infiltration in clinical tumor samples by genomic approaches [\\u003cspan class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. So we selected the cancer types in which LPAR6 expression levels have a significant negative correlation with tumor purity in TIMER and a significant correlation with prognosis. Interestingly, we found that the expression level of LPAR6 expression correlates with better OS and high immune infiltration levels in breast cancer, liver cancer and LUAD but not in LUSC.\\u003c/p\\u003e\\n \\u003cp\\u003eThe LPAR6 expression level of LUAD and LUSC are all significantly negatively related to tumor purity (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). LPAR6 expression level has significant positive correlations with the infiltrating levels of B cell, CD8\\u0026thinsp;+\\u0026thinsp;T cell, CD4\\u0026thinsp;+\\u0026thinsp;T cells, Macrophages, Neutrophils and DCs in LUAD (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). What interested us is that the correlation with immune cells demonstrated a different pattern in LUAD and LUSC of lung cancers. These findings strongly suggest that LPAR6 plays a specific role in immune infiltration in different types of lung cancer, and leads to a better prognosis in LUAD instead of in LUSC.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"Section2\\\" id=\\\"Sec19\\\"\\u003e\\n \\u003ch2\\u003e3.7 Correlation Analysis Between LPAR6 Expression and Immune Marker Sets\\u003c/h2\\u003e\\n \\u003cp\\u003eTo study the association between LPAR6 and different types of TIICs, we focused on the correlations between the expression level of LPAR6 and immune marker sets of various immune cells of LUAD and LUSC. The correlations between LPAR6 expression level and immune marker gene sets of different immune cells, including CD8\\u0026thinsp;+\\u0026thinsp;T cells, T cells (general), B cells, monocytes, TAMs, M1 and M2 macrophages, neutrophils, NK cells and DCs were determined in LUAD and LUSC (\\u003cstrong\\u003eTable\\u0026nbsp;3 and\\u003c/strong\\u003e Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). We also investigated the different types of T cells (Th1, Th2, Tfh, Th17, Tregs and exhausted T cells). After adjustment by purity, the correlation results revealed the LPAR6 expression level was significantly correlated with most immune marker sets of various immune cells and different subtypes of T cells, especially effect T cells in LUAD. However, none of these gene markers was significantly correlated with the LPAR6 expression level in LUSC and other cancer with poor prognosis (\\u003cstrong\\u003eTable\\u0026nbsp;3 and\\u003c/strong\\u003e Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eThese results demonstrated that the mRNA expression levels of the marker genes in T cells (general, CD8+, Naive T, Effector T), natural killer cell, M1 macrophages and DCs have strong correlations with LPAR6 expression in LUAD (\\u003cstrong\\u003eTable\\u0026nbsp;3\\u003c/strong\\u003e). More specifically, we demonstrated NOS2, IRF5, PTGS2 of M1 phenotype are significantly correlate with LPAR6 expression in LUAD (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001; Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA\\u0026ndash;H). It is reported that M1 could prevent tumor development. In-depth studies need to be done on whether LPAR6 is a crucial factor that mediating the de-polarization of macrophages and remodel tumor microenvironment. In addition, for Treg cells, LPAR6 does not demonstrate a correlation with the Tregs markers such as STAT5B in LIHC (\\u003cstrong\\u003eTable\\u0026nbsp;3\\u003c/strong\\u003e). Furthermore, we determined the association between the expression level of LPAR6 and the above marker sets of monocytes and various types of T cells in normal and tumor tissue in LUAD and LUSC. (\\u003cstrong\\u003eSupplementary-Table\\u0026nbsp;4\\u003c/strong\\u003e, Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"Section2\\\" id=\\\"Sec20\\\"\\u003e\\n \\u003ch2\\u003e3.8 Different correlation patterns between tumor and normal tissue in LUAD patients\\u003c/h2\\u003e\\n \\u003cp\\u003eThe more interesting thing is that the expression levels of most marker sets of these immunocytes have strong correlations with LPAR6 expression in tumor tissue of LUAD patients. In the LUSC, there was no significant correlation between LPAR6 and markers of immune cells \\u003cstrong\\u003e(\\u003c/strong\\u003eFig. \\u003cspan class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cstrong\\u003eSupplementary-Table\\u0026nbsp;4\\u003c/strong\\u003e). This finding suggests that there are different correlation patterns between tumor and normal tissue in LUAD patients. This exciting finding indicates that LPAR6 may regulate macrophage de-polarization in the tumor microenvironment of the LUAD and LPAR6 might be a novel target for LUAD therapy. High LPAR6 expression relates to a high infiltration level of DCs in the tumor tissue of LUAD patients, DC markers such as HLA-DQB1, CD1C and NRP1 show significant correlations with LPAR6 expression both in the tumor tissue in LUAD (\\u003cstrong\\u003eSupplementary-Table\\u0026nbsp;4\\u003c/strong\\u003e). These results further reveal that there is a strong relationship between LPAR6 and DCs infiltration.\\u003c/p\\u003e\\n \\u003ch2\\u003e3.9 Higher expression of LPAR6 was correlated with clinicopathological parameters in LUAD cohort and was correlated with increased overall survival (OS) of LUAD and LUSC patients\\u003c/h2\\u003e\\n \\u003cp\\u003eWe analyzed the protein level of LPAR6 in two independent lung cancer patient cohorts with 74 and 77 paired lung cancer and normal tissues respectively. LPAR6 is mainly expressed in the cytoplasm of the cells (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eA), and the protein level was lower in the lung cancer tissues compared with the normal tissues (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eB, D). Next step, we investigated the relationship between the LPAR6 protein level and the clinical characteristics of the LUAD and LUSC patient cohorts. Lung cancer patients with higher LPAR6 levels demonstrated better OS than those patients with relatively lower levels in LUAD patient cohorts, but not in LUSC patient cohorts (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eC, E). Moreover, we found that lower LPAR6 was negatively correlated with the clinical stage of lung cancer and the lymph node metastasis of patients (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eF, G).\\u003c/p\\u003e\\n \\u003cp\\u003eIn summary, we demonstrated that the LPAR6 was downregulated in the tumor tissue of LUAD patients and its expression was positive associated with the overall survival for LUAD patients base on the databases and TMA cohorts. The results further confirm that LPAR6 is specifically correlated with immune infiltrating cells in LUAD which suggests that LPAR6 plays a vital role in immune cells recruiting in the tumor tissue in LUAD patients. LPAR6 and its modulation on tumor microenvironment may serve as a novel therapeutic target for LUAD.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\" \\u003cp\\u003eLPA receptors are GPCR that bind to the LPA and trigger multiple downstreaming cellular responses, including cell proliferation, cytoskeletal rearrangements, apoptosis and motility [\\u003cspan additionalcitationids=\\\"CR49\\\" citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. Previously, five LPA receptors (\\u003cem\\u003eLPAR\\u003c/em\\u003e1-5) are well characterized and extensively studied [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. LPAR6 is a recently determined GPCR, alias as ARWH1, HYPT8, LAH3, P2RY5, at first was considered as purinergic receptor P2Y5 that involved in inherited hair loss [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. Although LPAR6 has not been extensively studied, it was reported that the LPAR6 suppresses tumor cell migration in colorectal cancer [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], and the expression of LPAR6 was decreased in P53-mutated cases [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. It was also reported that the LPA axis plays an important role in HCC by recruiting and trans-differentiating of peritumoral fibroblasts into TAMs [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]. This offers scientists a promising hint that LPAR6 is involved in the TME. Immunotherapy is a new genre of treatment for patients and has a tightly association with TME [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn this study, we announced that different expression levels of LPAR6 are associated with the prognostic potential in various cancer types. Higher level of LPAR6 is associated with a better prognosis in three types of cancers, including liver cancer, lung cancer and breast cancer. Moreover, our data demonstrated that the immune infiltration levels and diverse immune marker sets of the different subtypes of lung cancers (LUAD and LUSC) are associated with the expression level of LPAR6. To this end, our study provides insights into elucidating the potential role of LPAR6 in tumor immunology and its usage as a biomarker and novel therapy target for LUAD.\\u003c/p\\u003e \\u003cp\\u003eIn this work, we determined the LPAR6 expression levels and constructed a systematic prognostic landscape in various types of cancers by using independent datasets in Oncomine and 33 type cancers of TCGA data in GEPIA2. The variation expression level of LPAR6 between cancer and normal tissues was observed in many cancer types. Based on the Oncomine database, we found that LPAR6, compared to normal tissues, was highly expressed in brain and CNS, kidney, gastric cancer, leukemia, lymphoma, liver and pancreatic cancer while some data sets showed that LPAR6 has a lower mRNA expression level in bladder, breast, cervical, colorectal, esophageal, lung and prostate cancer (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). However, the redetermination of the TCGA data demonstrated that LPAR6 expression was higher expressed in ESCA, KIRC, KIRP and THCA, but significantly lower expressed in BLCA, COAD, BRCA, HNSC, KICH, PRAD, LUAD, UCEC, READ and slightly lower in LIHC compared with adjacent normal tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). The vary in the expression levels of \\u003cem\\u003eLPAR6\\u003c/em\\u003e in different types of cancer among various databases might be a reflection in data collection approaches and underlying mechanisms involved in different biological properties. Nevertheless, in these databases, we found similar prognostic associations between LPAR6 expression in bladder, breast, cervical, colorectal, esophageal, lung and prostate cancers. Investigation of the TCGA database enclosed that the higher LPAR6 expression level is correlated with better prognostic potential in ACC, LGG, SKCM (\\u003cb\\u003eSupplementary-Figure2\\u003c/b\\u003e). Furthermore, the determination of patient cohorts from PrognoScan and Kaplan-Meier Plotter demonstrated a high level of \\u003cem\\u003eLPAR6\\u003c/em\\u003e expression is correlated with better prognosis in breast, lung, bladder, colorectal, eye and ovarian cancer (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). In two datasets of PrognoScan, high LPAR6 expression levels could be considered as an independent risk factor for better prognosis in LUAD. Moreover, a high level of LPAR6 expression was shown to be correlated with a better prognosis of LUAD in the early stage with the lowest HR [0.27 (0.17\\u0026ndash;0.42)] for a better OS when LPAR6 was highly expressed in LUAD, rather than in LUSC. These together strongly suggest that LPAR6 could be a prognostic biomarker in LUAD.\\u003c/p\\u003e \\u003cp\\u003eAnother crucial aspect of this work is that the mRNA expression level of LPAR6 is correlated with diverse immune infiltration levels in cancer, especially in LUAD. Here, we demonstrate that there\\u0026rsquo;s a strong positive correlation between the infiltration level of T cells (CD8\\u0026thinsp;+\\u0026thinsp;and CD4+), neutrophils, macrophages and DCs and LPAR6 expression in LUAD (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). Moreover, the correlation patterns of the infiltration level are different in two kinds of lung cancers (LUAD and LUSC). The correlation between LPAR6 expression and the marker genes of immune cells implicates the role of LPAR6 in regulating tumor immunology in these types of cancers. A possible explanation for this striking effect might be that LPAR6 orchestrates the function of multiple immune marker gene sets. This supports the argument that the LPAR6 expression levels are important contributors to human malignancies and indicating the prognosis of specific types of cancer.\\u003c/p\\u003e \\u003cp\\u003eFirstly, gene markers of M1 macrophages such as PTGS2 and IRF5 show significant correlations with LPAR6 expression in LUAD respectively (Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Since macrophages are functionally plastic cells. Type 1 macrophages (M1) producing type 1 cytokines prevent tumors from developing, whereas type 2 macrophages (M2) inducing type 2 cytokines facilitate tumor growth. Especially in the tumor tissue of LUAD, both NOS2 and IRF5 show significant correlations with LPAR6 expression and PTGS2 shows a significant correlation with LPAR6 expression in the tumor tissue (\\u003cb\\u003eSupplementary\\u003c/b\\u003e-\\u003cb\\u003eTable\\u0026nbsp;4\\u003c/b\\u003e). These results reveal the potential regulating role of LPAR6 in de-polarization macrophages against tumor that activated macrophages can be re-polarized into opposite functional phenotypes by microenvironmental modifications and then inhibit tumor growth.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCorrelation analysis between \\u003cem\\u003eLPAR6\\u003c/em\\u003e and relate markers of immune cells\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"10\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eDescription\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eGene markers\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eLUAD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c10\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eLUSC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003ePurity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e 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\\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCD8\\u0026thinsp;+\\u0026thinsp;T cell\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD8A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.363\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.238\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e*\\u003c/p\\u003e 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\\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.197\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.136\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD3E\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.434\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.283\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.163\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.092\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.358\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.171\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.101\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.0277\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNaive T-Cell\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCCR7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.222\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.175\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.109\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.0175\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLEF1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.351\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.241\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.962\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.014\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.766\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTCF7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.237\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.0232\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.095\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0336\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.049\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.289\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSELL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.421\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.187\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.115\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEffector T-Cell\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCX3CR1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.353\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.113\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0113\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.055\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFGFBP2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.247\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.185\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.378\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.656\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFCGR3A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.448\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.354\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.042\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.044\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEffector memory T-Cell\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePDCD1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.316\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.175\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.112\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.323\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDUSP4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.074\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0915\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.075\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.0966\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.017\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.707\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.057\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.212\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGZMK\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.444\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.309\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.172\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.106\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.0204\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGZMA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.408\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.295\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.197\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.143\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIFNG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.304\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.201\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.101\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0235\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.061\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.183\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eResident memory T-Cell\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.518\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.423\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.247\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.192\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eITGAE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.295\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.228\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.109\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0149\\u003c/p\\u003e 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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.157\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.083\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.0694\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD79A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.312\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.171\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.155\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.076\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.096\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMonocyte\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.455\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.164\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.079\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.085\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD115 (CSF1R)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.495\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.388\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.071\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.111\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0. 031\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.498\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTAM\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCCL2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.424\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.331\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.148\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.086\\u003c/p\\u003e \\u003c/td\\u003e 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\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.087\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.0576\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIL10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.523\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.433\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.211\\u003c/p\\u003e \\u003c/td\\u003e 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\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.0955\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.104\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0203\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.106\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.0203\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIRF5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0346\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.254\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0. 101\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0236\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.129\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCOX2 (PTGS2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.833\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.017\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.705\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.244\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eM2 Macrophage\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD163\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.376\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.281\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.032\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.472\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.058\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.209\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVSIG4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.438\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.358\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.061\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.171\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.021\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.649\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMS4A4A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.501\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.412\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.109\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0145\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.028\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.536\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNeutrophils\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD66b (CEACAM8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.114\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.0464\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.613\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.894\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCD11b (ITGAM)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.405\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.091\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0412\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.898\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCCR7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.222\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.175\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.109\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.0175\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNatural killer cell\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKIR2DL1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.117\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.064\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.155\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.081\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0704\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.052\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.258\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKIR2DL3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.209\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.13\\u003c/p\\u003e 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\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.0145\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0744\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.039\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKIR3DL1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.149\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.075\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.098\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.902\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.048\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.294\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKIR3DL2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.168\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.078\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.083\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.737\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" 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\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.112\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.323\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCTLA4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.444\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.304\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.217\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.152\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLAG3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.275\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.152\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.095\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0329\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.034\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.457\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHAVCR2\\u003c/p\\u003e \\u003cp\\u003e(TIM-3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.539\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.442\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.094\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.0357\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.867\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGZMB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.268\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.077\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.0926\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"10\\\"\\u003e\\u003cem\\u003eTAM, tumor-associated macrophage; Th, T helper cell; Tfh, Follicular helper T cell; Treg, regulatory T cell; Cor, R value of Spearman\\u0026rsquo;s correlation; None, correlation without adjustment. Purity, correlation adjusted by purity.\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"10\\\"\\u003e*\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003e0.01;\\u003c/em\\u003e **\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003e0.001;\\u003c/em\\u003e ***\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003e0.0001.\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSecondly, our results indicated that LPAR6 has the potential to activate CD8\\u0026thinsp;+\\u0026thinsp;T cell, naive T-cell, effector T-cell and natural killer cell and inactivate Tregs, and decrease T cell exhaustion. CD8A, a crucial surface protein on T cells, is highly correlated with LPAR6 expression in LUAD which are types of cancers with better prognosis. And CD8A did not demonstrate a significant correlation pattern in LUSC (Table\\u0026nbsp;3). This pattern also occurs with the general T cell markers such as CD3D, CD3E, CD2 and most markers of naive T-cell, effector T-cell, effector memory T-cell and natural killer cells. Such as LEF1 which has been proved to be a predictor of better treatment response in AML, because high LEF1 expression level was associated with favorable relapse-free survival in patients and predicted a significantly better overall survival for AML patients [\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThirdly, different correlation patterns can be found between LPAR6 expression and the regulation of several markers of T helper cells (Th1, Th2, Tfh, and Th17) in these different cancers. IFN-g is a Th1 cytokine with both pro-and anti-cancer properties [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e] which are highly correlated with LPAR6 expression in LUAD, whereas did not demonstrate significant correlations in LUSC (Table\\u0026nbsp;3). IL-13 is an important immunoregulatory cytokine mainly produced by activated type II helper T cells and is widely involved in tumorigenesis and development, fibrosis and inflammation [\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]. We found that IL-13 is highly correlated with \\u003cem\\u003eLPAR6\\u003c/em\\u003e expression in LUAD, but did not demonstrate significant correlations in LUSC (adjusted by purity), and a similar situation is in the IL-21. So these would be explanations that why LPAR6 indication poor prognosis in LUSC and a better prognosis in LUAD.\\u003c/p\\u003e \\u003cp\\u003eAll these correlations above could be indications of a potential mechanism where LPAR6 regulates T cell functions in LUAD. Together these findings suggest that the LPAR6 plays an important role in the recruitment and regulation of effective T cells infiltrating in LUAD leading to a better prognosis.\\u003c/p\\u003e \"},{\"header\":\"5. Conclusions\",\"content\":\" \\u003cp\\u003eIn this study, we offered a potential explanation for the mechanism that why LPAR6 expression correlates with immune infiltration level and better prognostic potential in some specific types of cancer, especially in LUSC. Hence, the interactions between LPAR6 and the immunocytes in the tumor microenvironment could be a potential mechanism for the correlation of LPAR6 expression with immune infiltration level and better prognosis in LUAD patients.\\u003c/p\\u003e \"},{\"header\":\"Abbreviations\",\"content\":\"\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eTMA\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eTissue microarrays\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eTCGA\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eThe Cancer Genome Atlas\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eDEG\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eDifferentially expressed gene\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eTIICs\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eTumor-infiltrating immune cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eTILs\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eTumor-infiltrating lymphocytes\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eTINs\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eTumor-infiltrating neutrophils\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eTAMs\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eTumor association macrophages\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eFDR\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eFalse Discovery Rate\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"27.305605786618443%\\\"\\u003e\\n\\u003cp\\u003eTME\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"72.69439421338156%\\\"\\u003e\\n\\u003cp\\u003eTumor microenvironment\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis project was permitted by Independent Ethics Committee of Shanghai Jiao Tong University School of Medicine.\\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\\u003eAvailability of data and materials\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare there is none conflicts of interest. This research conforms to all the laws and ethical guidelines that apply in the country where I carried it out.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJ. H.\\u0026nbsp;contributed to idea, conception, and study design.\\u0026nbsp;J. H. collected and analyzed the datasets, performed TMA analysis. J. H., M.M. and R.G. wrote the manuscript\\u0026nbsp;and generating the figures. H. W. revised and proofread the article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis project is funded by the grants of National Natural Science Foundation of China (NSFC 81702730) and Start-up Plan for New Young Teacher of SHSMU (KJ30214190026) of JH and National Natural Science Foundation of China (NSFC 81630086), National Key Research \\u0026amp; Development Program of China (2018YFC2000700) of HW.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank to the funder.\\u003c/p\\u003e\\n\\u003cbr\\u003e\\n\\u003ctable border=\\\"1\\\" width=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"30.37542662116041%\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDatabase\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"55.460750853242324%\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWebsite\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"14.16382252559727%\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eReference\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"30.37542662116041%\\\"\\u003e\\n\\u003cp\\u003eOncomine\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"55.460750853242324%\\\"\\u003e\\n\\u003cp\\u003ehttps://www.oncomine.org/resource/login.html\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"14.16382252559727%\\\"\\u003e\\n\\u003cp\\u003e32\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"30.37542662116041%\\\"\\u003e\\n\\u003cp\\u003eGEPIA2\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"55.460750853242324%\\\"\\u003e\\n\\u003cp\\u003ehttp://gepia.cancer-pku.cn/\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"14.16382252559727%\\\"\\u003e\\n\\u003cp\\u003e34\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"30.37542662116041%\\\"\\u003e\\n\\u003cp\\u003ePrognoScan\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"55.460750853242324%\\\"\\u003e\\n\\u003cp\\u003ehttp://dna00.bio.kyutech.ac.jp/PrognoScan/\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"14.16382252559727%\\\"\\u003e\\n\\u003cp\\u003e33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"30.37542662116041%\\\"\\u003e\\n\\u003cp\\u003eKaplan-Meier plotter\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" 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width=\\\"55.460750853242324%\\\"\\u003e\\n\\u003cp\\u003ehttps://cistrome.shinyapps.io/timer/\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd valign=\\\"top\\\" width=\\\"14.16382252559727%\\\"\\u003e\\n\\u003cp\\u003e40\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eSiegel RL, Miller KD, Jemal A. 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Nature. 2017; 548 (7667): 356-60.\\u003c/li\\u003e\\n \\u003cli\\u003eKetscher A, Jilg CA, Willmann D, et al. LSD1 controls metastasis of androgen-independent prostate cancer cells through PXN and LPAR6. Oncogenesis. 2014; 3: e120.\\u003c/li\\u003e\\n \\u003cli\\u003eMazzocca A,\\u0026nbsp;Dituri F,\\u0026nbsp;De Santis F,\\u0026nbsp;Filannino A,\\u0026nbsp;Lopane C,\\u0026nbsp;Betz RC, et al. Lysophosphatidic\\u0026nbsp;acid\\u0026nbsp;receptor\\u0026nbsp;LPAR6\\u0026nbsp;supports\\u0026nbsp;the\\u0026nbsp;tumorigenicity\\u0026nbsp;of\\u0026nbsp;hepatocellular\\u0026nbsp;carcinoma. Cancer Res.\\u0026nbsp;2015; 75 (3): 532-43.\\u003c/li\\u003e\\n \\u003cli\\u003eSokolov E, Eheim AL, Ahrens WA, et al. Lysophosphatidic acid receptor expression and function in human hepatocellular carcinoma. J Surg Res. 2013; 180 (1): 104-13.\\u003c/li\\u003e\\n \\u003cli\\u003eTakahashi K, Fukushima K, Onishi Y, Inui K,\\u0026nbsp;Node Y,\\u0026nbsp;Fukushima N, et al. 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Gene expression markers of Tumor Infiltrating Leukocytes. J Immunother Cancer. 2017; 5:18.\\u003c/li\\u003e\\n \\u003cli\\u003eChen P,\\u0026nbsp;Duan X,\\u0026nbsp;Li X,\\u0026nbsp;Li J,\\u0026nbsp;Ba Q,\\u0026nbsp;Wang H. HIPK2 suppresses tumor growth and progression of hepatocellular carcinoma through promoting the degradation of HIF-1\\u0026alpha;. Oncogene.\\u0026nbsp;2020 Apr;39(14):2863-2876.\\u003c/li\\u003e\\n \\u003cli\\u003eAzimi F, Scolyer RA, Rumcheva P, MoncrieffM, Murali R, McCarthy SW, et al. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol. 2012; 30: 2678-83.\\u003c/li\\u003e\\n \\u003cli\\u003eOhtani H. Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer. Cancer Immun. 2007; 7:4.\\u003c/li\\u003e\\n \\u003cli\\u003eMills GB and Moolenaar WH. The emerging role of lysophosphatidic acid in cancer. Nat Rev Cancer. 2003; 3: 582-91.\\u003c/li\\u003e\\n \\u003cli\\u003evan Corven EJ, Groenink A, Jalink K, Eichholtz T, Moolenaar WH. Lysophosphatidate- induced cell proliferation: identification and dissection of signaling pathways mediated by G proteins. Cell. 1989; 59: 45-54.\\u003c/li\\u003e\\n \\u003cli\\u003eMoolenaar WH, van Meeteren LA, Giepmans BN. The ins and outs of lysophosphatidic acid signaling. Bioessays. 2004; 26: 870-81.\\u003c/li\\u003e\\n \\u003cli\\u003eChoi JW, Herr DR, Noguchi K, Yung YC, Lee CW, Mutoh T, et al. LPA receptors: subtypes and biological actions. Annu Rev Pharmacol Toxicol. 2010; 50: 157-86.\\u003c/li\\u003e\\n \\u003cli\\u003ePasternack SM, von Kugelgen I, Al Aboud K, Lee YA, Ruschendorf F, Voss K, et al. G protein-coupled receptor P2Y5 and its ligand LPA are involved in maintenance of human hair growth. Nat Genet. 2008; 40: 329-34.\\u003c/li\\u003e\\n \\u003cli\\u003eMazzocca A, Dituri F, De Santis F, Filannino A, Lopane C, Betz RC, et al. Lysophosphatidic acid receptor LPAR6 supports the tumorigenicity of hepatocellular carcinoma.\\u0026nbsp;\\u003ca href=\\\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Lysophosphatidic+Acid+Receptor+LPAR6+Supports+the+Tumorigenicity+of+Hepatocellular+Carcinoma\\\" title=\\\"Cancer research.\\\"\\u003eCancer Res.\\u003c/a\\u003e 2015; 75 (3): 532-43.\\u003c/li\\u003e\\n \\u003cli\\u003eMazzocca A, Dituri F, Lupo L, Quaranta M, Antonaci S,Giannelli G. Tumorsecreted lysophostatidic acid accelerates hepatocellular carcinoma progression by promoting differentiation of peritumoral fibroblasts in myofibroblasts. Hepatology. 2011; 54: 920-30.\\u003c/li\\u003e\\n \\u003cli\\u003eFu Y, Zhu H, Wu W, et al. Clinical significance of lymphoid enhancer- binding factor 1 expression in acute myeloid leukemia. Leuk Lymphoma. 2014; 55(2): 371-377.\\u003c/li\\u003e\\n \\u003cli\\u003eGanapathi SK,\\u0026nbsp;Beggs AD,\\u0026nbsp;Hodgson SV,\\u0026nbsp;Kumar D. Expression\\u0026nbsp;and\\u0026nbsp;DNA methylation\\u0026nbsp;of TNF, IFNG\\u0026nbsp;and\\u0026nbsp;FOXP3\\u0026nbsp;in\\u0026nbsp;colorectal\\u0026nbsp;cancer\\u0026nbsp;and their\\u0026nbsp;prognostic significance. Br J Cancer. 2014; 111 (8): 1581-9.\\u003c/li\\u003e\\n \\u003cli\\u003eFichtner-Feigl S, Strober W, Kawakami K, Puri RK, Kitani A. IL-13 signaling through the IL-13 alpha2 receptor is involved in induction of TGF-beta1 production and fibrosis. Nature medicine. 2006; 1 (1): 99-106.\\u003c/li\\u003e\\n \\u003cli\\u003eShimamura T, Fujisawa T, Husain SR, Joshi B, Puri RK. Interleukin 13 mediates signal transduction through interleukin 13 receptor alpha2 in pancreatic ductal adenocarcinoma: role of IL-13 Pseudomonas exotoxin in pancreatic cancer therapy. Clinical cancer research. 2010; 2 (2): 577-86.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"cancer-cell-international\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ccin\",\"sideBox\":\"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)\",\"snPcode\":\"12935\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12935/3\",\"title\":\"Cancer Cell International\",\"twitterHandle\":\"@OncoBioMed\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"LPAR6, tumor infiltration lymphocytes, prognosis, lung adenocarcinoma, biomarker\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-653591/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-653591/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eLPAR6 is the most recently determined GPCR of LPA, and very few of study have demonstrated the performance of LPAR6 in cancers. Moreover, the relationship of LPAR6 to prognosis potential and tumor infiltration immune cells in different cancers still unclarified.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThe mRNA expression of LPAR6 and its clinical characteristics were evaluated on various databases. The association between LPAR6 and immune infiltrates of various types of cancer were investigated via TIMER. IHC for LPAR6 in LUAD and LUSC tissue microarray with patients\\u0026rsquo; information was detected.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eWe constructed a systematic prognostic landscape in various types of cancer base on the mRNA expression level. We enclosed that higher LPAR6 expression level was associated with better OS in some types of malignancy. Moreover, LPAR6 significantly affects the prognostic potential of various cancers in TCGA, especially in lung cancer. Tissue microarray\\u0026rsquo;s results demonstrated that higher protein level of LPAR6 was correlated with better overall survival of LUAD rather than LUSC cohorts. Further research found that the underlying mechanism of this phenome might be the expression level of LPAR6 was positively associated with infiltrating statuses of devious immunocytes in LUAD rather than in LUSC, that is, LPAR6 expression potentially contributes to the activation and recruiting of CD8\\u0026thinsp;+\\u0026thinsp;T, naive T, effector T cell and natural killer cell and inactivates Tregs, decrease T cell exhaustion and regulate T-helper cells in LUAD.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eOur discovery implies that LPAR6 is associated with prognostic potential and immune-infiltrating levels in LUAD. These discoveries imply that LPAR6 could be a promising biomarker for indicating prognosis potential and immune infiltration level in LUAD cohorts.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Lysophosphatidic Acid Receptor 6: A Prognostic Biomarker for Lung Adenocarcinoma via Correlating Immune Infiltration\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2021-07-01 21:55:08\",\"doi\":\"10.21203/rs.3.rs-653591/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Major revision\",\"date\":\"2021-08-16T23:43:51+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2021-08-16T00:00:00+00:00\",\"index\":2,\"fulltext\":\"Recommendation: Reviewer's comments unavailable due to the journal's policy.\\n\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2021-07-30T00:00:00+00:00\",\"index\":1,\"fulltext\":\"Recommendation: Reviewer's comments unavailable due to the journal's policy.\\n\"},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2021-07-25T00:00:00+00:00\",\"index\":2,\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2021-07-24T19:31:07+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2021-07-24T12:55:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2021-07-24T00:00:00+00:00\",\"index\":1,\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"Cancer Cell International\",\"date\":\"2021-06-25T00:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2021-06-24T00:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2021-06-23T23:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Cancer Cell International\",\"date\":\"2021-06-23T11:17:36+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"cancer-cell-international\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ccin\",\"sideBox\":\"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)\",\"snPcode\":\"12935\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12935/3\",\"title\":\"Cancer Cell International\",\"twitterHandle\":\"@OncoBioMed\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"5543679f-1c84-47bb-9bd2-26d79c60e1d2\",\"owner\":[],\"postedDate\":\"July 1st, 2021\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":5410157,\"name\":\"Cancer Biology\"},{\"id\":5410158,\"name\":\"Oncology\"}],\"tags\":[],\"updatedAt\":\"2021-10-28T05:10:01+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2021-07-01 21:55:08\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-653591\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-653591\",\"identity\":\"rs-653591\",\"version\":[\"v1\"]},\"buildId\":\"-HB7Z8yhvgn0wM9Nzuekk\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}