Analysis of Expression and Its Clinical Significance of the Secreted Phosphoprotein 1 in Lung Adenocarcinoma

Xuebijing improves inflammation and pyroptosis of acute lung injury by up-regulating miR-181d-5p-mediated SPP1 inactivation

BACKGROUND

Xuebijing (XBJ) is widely applied in the treatment of Acute Lung Injury (ALI). This study focused on the potential mechanism of XBJ in Lipopolysaccharide (LPS)-induced ALI.

METHODS

The rat ALI model was established by injection of LPS (10 mg/kg) and pretreated with XBJ (4 mL/kg) three days before LPS injection. BEAS-2B cell line was stimulated with LPS (1 μg/mL) and ATP (5 mM) to induce pyroptosis, and XBJ (2 g/L) was pretreated 24h before induction. The improvement effects of XBJ on pulmonary edema, morphological changes, and apoptosis in ALI lung tissue were evaluated by lung wet/dry weight ratio, HE-staining, and TUNEL staining. Inflammatory cytokines in lung tissue and cell supernatant were determined by ELISA. pyroptosis was detected by flow cytometry. Meanwhile, the expressions of miR-181d-5p, SPP1, p-p65, NLRP3, ASC, caspase-1, p20, and GSDMD-N in tissues and cells were assessed by RT-qPCR and immunoblotting. The relationship between miR-181d-5p and SPP1 in experimental inflammation was reported by dual luciferase assay.

RESULTS

XBJ could improve inflammation and pyroptosis of ALI by inhibiting contents of inflammatory cytokines, and levels of inflammation- and pyroptosis-related proteins. Mechanistically, XBJ could up-regulate miR-181d-5p and inhibit SPP1 in ALI. miR-181d-5p can target the regulation of SPP1. Depressing miR-181d-5p compensated for the ameliorative effect of XBJ on ALI, and overexpressing SPP1 suppressed the attenuating effect of XBJ on LPS-induced inflammation and pyroptosis.

CONCLUSION

XBJ can regulate the miR-181d-5p/SPP1 axis to improve inflammatory response and pyroptosis in ALI.

Secreted phosphoprotein 1 promotes angiogenesis of glioblastoma through upregulating PSMA expression via transcription factor HIF-1α

Glioblastoma multiforme (GBM) is a highly vascularized malignant brain tumor. Our previous study showed that prostate-specific membrane antigen (PSMA) promotes angiogenesis of GBM. However, the specific mechanism underlying GBM-induced PSMA upregulation remains unclear. In this study, we demonstrate that the GBM-secreted cytokine phosphoprotein 1 (SPP1) can regulate the expression of PSMA in human umbilical vein endothelial cells (HUVECs). Our mechanistic study further reveals that SPP1 regulates the expression of PSMA through the transcription factor HIF1α. Moreover, SPP1 promotes HUVEC migration and tube formation. In addition, HIF1α knockdown reduces the expression of PSMA in HUVECs and blocks the ability of SPP1 to promote HUVEC migration and tube formation. We further confirm that SPP1 is abundantly expressed in GBM, is associated with poor prognosis, and has high clinical diagnostic value with considerable sensitivity and specificity. Collectively, our findings identify that the GBM-secreted cytokine SPP1 upregulates PSMA expression in endothelial cells via the transcription factor HIF1α, providing insight into the angiogenic process and promising candidates for targeted GBM therapy.

Single-Cell RNA Sequencing Unravels Distinct Tumor Microenvironment of Different Components of Lung Adenocarcinoma Featured as Mixed Ground-Glass Opacity

Lung adenocarcinoma featured as mixed ground-glass opacity (mGGO) doubled its volume half of the time in comparison with that featured as pure ground-glass opacity (pGGO). The mechanisms underlying the heterogeneous appearance of mGGO remain elusive. In this study, we macro-dissected the solid (S) components and ground-glass (GG) components of mGGO and performed single-cell sequencing analyses of six paired components from three mGGO patients. A total of 19,391 single-cell profiles were taken into analysis, and the data of each patient were analyzed independently to obtain a common alteration. Cancer cells and macrophages were the dominant cell types in the S and GG components, respectively. Cancer cells in the S components, which showed relatively malignant phenotypes, were likely to originate from both the GG and S components and monitor the surrounding tumor microenvironment (TME) through an intricate cell interaction network. SPP1^hi macrophages were enriched in the S components and showed increased activity of chemoattraction, while macrophages in the GG components displayed an active antimicrobial process with a higher stress-induced state. In addition, the CD47–SIRPA axis was demonstrated to be critical in the maintenance of the GG components. Taken together, our study unraveled the alterations of cell components and transcriptomic features between different components in mGGOs.

Construction and evaluation of a nomogram for predicting survival in patients with lung cancer

BACKGROUND

Lung cancer is a heterogeneous disease with a severe disease burden. Because the prognosis of patients with lung cancer varies, it is critical to identify effective biomarkers for prognosis prediction.

METHODS

A total of 2325 lung cancer patients were integrated into four independent sets (training set, validation set I, II and III) after removing batch effects in our study. We applied the microarray data algorithm to screen the differentially expressed genes in the training set. The most robust markers for prognosis were identified using the LASSO-Cox regression model, which was then used to create a Cox model and nomogram.

RESULTS

Through LASSO and multivariate Cox regression analysis, eight genes were identified as prognosis-associated hub genes, followed by the creation of prognosis-associated risk scores (PRS). The results of the Kaplan-Meier analysis in the three validation sets demonstrate the good predictive performance of PRS, with hazard ratios of 2.38 (95% confidence interval (CI), 1.61-3.53) in the validation set I, 1.35 (95% CI, 1.06-1.71) in the validation set II, and 2.71 (95% CI, 1.77-4.18) in the validation set III. Additionally, the PRS demonstrated superior survival prediction in subgroups by age, gender, p-stage, and histologic type (p < 0.0001). The complex model integrating PRS and clinical risk factors also have a good predictive performance for 3-year overall survival.

CONCLUSIONS

In this study, we developed a PRS signature to help predict the survival of lung cancer. By combining it with clinical risk factors, a nomogram was established to quantify the individual risk assessments.

Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer

Background: SPP1, secreted phosphoprotein 1, is a member of the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. Previous studies have proven SPP1 overexpressed in a variety of cancers and can be identified as a prognostic factor, while no study has explored the function and carcinogenic mechanism of SPP1 in cervical cancer. Methods: We aimed to demonstrate the relationship between SPP1 expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. Next, we validated SPP1 expression of cervical cancer in the Gene Expression Omnibus (GEO) database, including GSE7803, GSE63514, and GSE9750. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of SPP1 as a differentiating factor by the area under curve (AUC) score. Cox regression and logistic regression were performed to evaluate factors associated with prognosis. The SPP1-binding protein network was built by the STRING tool. Enrichment analysis by the R package clusterProfiler was used to explore potential function of SPP1. The single-sample GSEA (ssGSEA) method from the R package GSVA and TIMER database were used to investigate the association between the immune infiltration level and SPP1 expression in cervical cancer. Results: Pan-cancer data analysis showed that SPP1 expression was higher in most cancer types, including cervical cancer, and we got the same result in the GEO database. The ROC curve suggested that SPP1 could be a potential diagnostic biomarker (AUC = 0.877). High SPP1 expression was associated with poorer overall survival (OS) (P = 0.032). Further enrichment and immune infiltration analysis revealed that high SPP1 expression was correlated with regulating the infiltration level of neutrophil cells and some immune cell types, including macrophage and DC. Conclusion: SPP1 expression was higher in cervical cancer tissues than in normal cervical epithelial tissues. It was significantly associated with poor prognosis and immune cell infiltration. Thus, SPP1 may become a promising prognostic biomarker for cervical cancer patients.

MiR-195-3p is a Novel Prognostic Biomarker Associated with Immune Infiltrates of Lung Adenocarcinoma

Background

MicroRNA-195-3p (miR-195-3p) plays an important role in some tumors, but its role in LUAD is unclear. This study explored the expression of miR-195-3p in LUAD and the relationship between the expression of miR-195-3p and the clinical and prognostic characteristics of LUAD patients.

Methods

MiR-195-3p expression and clinical information of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). Kruskal–Wallis test, Wilcoxon signed rank test, logistic regression, and Cox regression were used to assess the relationship between the expression level of miR-195-3p and clinical features in LUAD tissues. Kaplan–Meier survival curves were used to analyze the effect of miR-195-3p expression levels on the prognosis of LUAD patients. Target genes of miR-195-3p were predicted by several software. GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes), and immune infiltration analysis were used to analyze the possible regulatory network of miR-195-3p.

Results

Compared with normal lung tissue, miR-195-3p is down expressed in LUAD tissue (P < 0.001). The low miR-195-3p expression in LUAD was significantly associated with N stage (P = 0.046), pathologic stage (P = 0.011), and gender (P = 0.010). Low miR-195-3p expression predicted a poorer overall survival (HR: 0.60; 95% CI: 0.45–0.81; P = 0.001) and disease-specific survival (HR: 0.55; 95% CI: 0.37–0.80; P = 0.002). The expression of miR-195-3p (HR: 0.488; 95% CI: 0.304–0.784; P = 0.003) was independently correlated with OS in LUAD patients. High expression of miR-195-3p genes, including ABCC2, AGMAT, ARNTL2, ATP6V0A4, CDC25A, CDK1, FAM111B, GJB2, GRIP1, HMGA2, HOXA9, KIF14, SYT2, and TFAP2A, were associated with poor OS in LUAD. GO and KEGG analysis suggested that miR-195-3p was related to the phagosome pathway. MiR-195-3p may promote the function of B cells, dendritic cells, eosinophils, immature dendritic cells, macrophages, Mast cells, NK cells, plasmacytoid dendritic cells, and follicular helper T cells.

Conclusion

Low miR-195-3p expression is significantly associated with poor survival in LUAD, which may be a promising prognostic biomarker for LUAD.

Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma

Objective: Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. The present study aimed to screen the key genes involved in LUAD development and prognosis. Methods: The transcriptome data for 515 LUAD and 347 normal samples were downloaded from The Cancer Genome Atlas and Genotype Tissue Expression databases. The weighted gene co-expression network and differentially expressed genes were used to identify the central regulatory genes for the development of LUAD. Univariate Cox, LASSO, and multivariate Cox regression analyses were utilized to identify prognosis-related genes. Results: The top 10 central regulatory genes of LUAD included IL6, PECAM1, CDH5, VWF, THBS1, CAV1, TEK, HGF, SPP1, and ENG. Genes that have an impact on survival included PECAM1, HGF, SPP1, and ENG. The favorable prognosis genes included KDF1, ZNF691, DNASE2B, and ELAPOR1, while unfavorable prognosis genes included RPL22, ENO1, PCSK9, SNX7, and LCE5A. The areas under the receiver operating characteristic curves of the risk score model in the training and testing datasets were .78 and .758, respectively. Conclusion: Bioinformatics methods were used to identify genes involved in the development and prognosis of LUAD, which will provide a basis for further research on the treatment and prognosis of LUAD.

Macrophage-Related SPP1 as a Potential Biomarker for Early Lymph Node Metastasis in Lung Adenocarcinoma

Lymph node metastasis is a major factor that affects prognosis in patients with lung adenocarcinoma (LUAD). In some cases, lymph node metastasis has already occurred when the primary tumors are still small (i.e., early T stages), however, relevant studies on early lymph node metastasis are limited, and effective biomarkers remain lacking. This study aimed to explore new molecular biomarker for early lymph node metastasis in LUAD using transcriptome sequencing and experimental validation. Here, we performed transcriptome sequencing on tissues from 16 matched patients with Stage-T1 LUAD (eight cases of lymph node metastasis and eight cases of non-metastasis), and verified the transcriptome profiles in TCGA, GSE68465, and GSE43580 cohorts. With the bioinformatics analysis, we identified a higher abundance of M0 macrophages in the metastatic group using the CIBERSORT algorithm and immunohistochemistry (IHC) analysis and the enrichment of the epithelial–mesenchymal transition (EMT) pathway was identified in patients with higher M0 infiltration levels. Subsequently, the EMT hallmark gene SPP1, encoding secreted phosphoprotein 1 (SPP1), was identified to be significantly correlated with macrophage infiltration and M2 polarization, and was determined to be a key risk indicator for early lymph node metastasis. Notably, SPP1 in the blood, as detected by enzyme-linked immunosorbent assay (ELISA) showed a superior predictive capability for early lymph node metastasis [area under the curve (AUC) = 0.74]. Furthermore, a long non-coding RNA (lncRNA, AC037441), negatively correlated with SPP1 and macrophage infiltration, had also been identified and validated to be involved in the regulation of early lymph node metastasis. In conclusion, we revealed the potential role of macrophages in lymph node metastasis and identified the macrophage-related gene SPP1 as a potential biomarker for early lymph node metastasis in LUAD.

Weighted gene co-expression network analysis of hub genes in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a tumor with high incidence. This study aimed to identify the central genes of LUAD. LUAD were analyzed by weighted gene co-expression network (WGCNA), and differentially expressed genes (DEGs) were identified. Samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases and included 515 LUAD samples and 347 normal samples. The WGCNA algorithm generated a total of 10 modules. The top 2 modules (MEturquoise and MEblue) with the highest correlation to LUAD were selected. Ten Hub genes (IL6, CDH1, PECAM1, SPP1, THBS1, HGF, SNCA, CDH5, CAV1, and DLC1) were screened in the intersecting genes of DEGs and WGCNA (MEturquoise and MEblue). Only SPP1 was correlated with LUAD poor survival, indicating that SPP1 may be a key Hub gene for LUAD. The competing endogenous RNA (ceRNA) network was constructed to analyze the regulatory relationship of Hub genes, and SPP1 may be directly regulated by 4 microRNAs (miRNAs) and indirectly regulated by 49 long noncoding RNAs (lncRNAs).

Identification of VWF as a Novel Biomarker in Lung Adenocarcinoma by Comprehensive Analysis

Lung adenocarcinoma (LUAD) is one of the most malignant tumors with high morbidity and mortality worldwide due to the lack of reliable methods for early diagnosis and effective treatment. It’s imperative to study the mechanism of its development and explore new biomarkers for early detection of LUAD. In this study, the Gene Expression Omnibus (GEO) dataset GSE43458 and The Cancer Genome Atlas (TCGA) were used to explore the differential co-expressed genes between LUAD and normal samples. Three hundred sixity-six co-expressed genes were identified by differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) method. Those genes were mainly enriched in ameboidal-type cell migration (biological process), collagen-containing extracellular matrix (cell component), and extracellular matrix structure constituent (molecular function). The protein-protein network (PPI) was constructed and 10 hub genes were identified, including IL6, VWF, CDH5, PECAM1, EDN1, BDNF, CAV1, SPP1, TEK, and SELE. The expression level of hub genes was validated in the GEPIA database, compared with normal tissues, VWF is lowly expressed and SPP1 is upregulated in LUAD tissues. The survival analysis showed increased expression of SPP1 indicated unfavorable prognosis whereas high expression of VWF suggested favorable prognosis in LUAD (p < 0.05). Based on the immune infiltration analysis, the relationship between SPP1 and VWF expression and macrophage, neutrophil, and dendritic cell infiltration was weak in LUAD. Quantitative real-time PCR (qRT-PCR) and western blotting were used to validate the expression of VWF and SPP1 in normal human bronchial epithelial (HBE) cell and three LUAD cell lines, H1299, H1975, and A549. Immunohistochemistry (IHC) was further performed to detect the expression of VWF in 10 cases LUAD samples and matched normal tissues. In summary, the data suggest that VWF is a potential novel biomarker for prognosis of LUAD.

Identification of a Potentially Functional microRNA-mRNA Regulatory Network in Lung Adenocarcinoma Using a Bioinformatics Analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is a common lung cancer with a high mortality, for which microRNAs (miRNAs) play a vital role in its regulation. Multiple messenger RNAs (mRNAs) may be regulated by miRNAs, involved in LUAD tumorigenesis and progression. However, the miRNA-mRNA regulatory network involved in LUAD has not been fully elucidated.

METHODS

Differentially expressed miRNAs and mRNA were derived from the Cancer Genome Atlas (TCGA) dataset in tissue samples and from our microarray data in plasma (GSE151963). Then, common differentially expressed (Co-DE) miRNAs were obtained through intersected analyses between the above two datasets. An overlap was applied to confirm the Co-DEmRNAs identified both in targeted mRNAs and DEmRNAs in TCGA. A miRNA-mRNA regulatory network was constructed using Cytoscape. The top five miRNA were identified as hub miRNA by degrees in the network. The functions and signaling pathways associated with the hub miRNA-targeted genes were revealed through Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The key mRNAs in the protein-protein interaction (PPI) network were identified using the STRING database and CytoHubba. Survival analyses were performed using Gene Expression Profiling Interactive Analysis (GEPIA).

RESULTS

The miRNA-mRNA regulatory network consists of 19 Co-DEmiRNAs and 760 Co-DEmRNAs. The five miRNAs (miR-539-5p, miR-656-3p, miR-2110, let-7b-5p, and miR-92b-3p) in the network were identified as hub miRNAs by degrees (>100). The 677 Co-DEmRNAs were targeted mRNAs from the five hub miRNAs, showing the roles in the functional analyses of the GO analysis and KEGG pathways (inclusion criteria: 836 and 48, respectively). The PPI network and Cytoscape analyses revealed that the top ten key mRNAs were NOTCH1, MMP2, IGF1, KDR, SPP1, FLT1, HGF, TEK, ANGPT1, and PDGFB. SPP1 and HGF emerged as hub genes through survival analysis. A high SPP1 expression indicated a poor survival, whereas HGF positively associated with survival outcomes in LUAD.

CONCLUSION

This study investigated a miRNA-mRNA regulatory network associated with LUAD, exploring the hub miRNAs and potential functions of mRNA in the network. These findings contribute to identify new prognostic markers and therapeutic targets for LUAD patients in clinical settings.

Investigation on Potential Correlation Between Small Nuclear Ribonucleoprotein Polypeptide A and Lung Cancer

SNRPA (small nuclear ribonucleoprotein polypeptide A) gene is essential for the pre-mRNA splicing process. Using the available datasets of TCGA or GEO, we aimed at exploring the potential association between the SNRPA gene and lung cancer by several online tools (such as GEIPA2, MEXPRESS, Oncomine) and bioinformatics analysis software (R or GSEA). SNRPA was highly expressed in the tissues of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma tissue (LUSC), compared with control tissues. The high SNRPA expression was associated with a poor survival prognosis of LUAD cases, while the genetic alteration within SNRPA was linked to the overall survival prognosis of LUSC cases. There was a potential correlation between promoter methylation and the expression of SNRPA for LUAD. Compared with normal tissues, we observed a higher phosphorylation level at the S115 site of SNRPA protein (NP_004587.1) (p = 0.002) in the primary LUAD tissues. The potential ATR kinase of the S115 site was predicted. Besides, SNRPA expression in lung cancer was negatively correlated with the infiltration level of M2 macrophage but positively correlated with that of Follicular B helper T cells, in both LUAD and LUSC. The enrichment analysis of SNRPA-correlated genes showed that cell cycle and ubiquitin mechanism-related issues were mainly observed for LUAD; however, RNA splicing-related cellular issues were mainly for LUSC. In summary, the SNRPA gene was identified as a potential prognosis biomarker of lung cancer, especially lung adenocarcinoma, which sheds new light on the association between the spliceosomal complex component and tumorigenesis.

Key microRNAs and hub genes associated with poor prognosis in lung adenocarcinoma

In the study, we obtained 36 pairs of lung adenocarcinoma (LUAD) tissues and adjacent non-tumorous tissues. Then, we chose a specific hub-target gene of miRNA and used qRT-PCR to evaluate the expression of PECAM1. We found that the expression level of PECAM1 mRNA in LUAD was significantly lower than that in adjacent nontumor tissues (P<0.0001). Univariate and multivariate analyses were conducted on 481 LUAD patients from The Cancer Genome Atlas (TCGA) according to the Cox proportional hazard regression model to evaluate the impact of PECAM1 expression and other clinicopathological factors on survival. The results showed that the low expression of PECAM1 was an important independent predictor of poor overall survival (HR, 0.704; 95% CI, 0.518-0.957; P = 0.025). Based on the Tumor Immune Estimation Resource (TIMER) database, the relationship between PECAM1 expression and B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell infiltration was weak in LUAD (P<0.01). In particular, a more significant positive correlation between PECAM1 expression and HLA-complex members, CD1C, NRP1, and ITGAX expression in dendritic cell was detected in LUAD. The mechanism which PECAM1 involved in the development of LUAD may be closely related to changes in the immune microenvironment.

Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis

Macrophages are key innate immune cells in the tumor microenvironment that regulate primary tumor growth, vascularization, metastatic spread and response to therapies. Macrophages can polarize into two different states (M1 and M2) with distinct phenotypes and functions. To investigate the known tumoricidal effects of M1 macrophages, we obtained RNA expression profiles and clinical data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA). The proportions of immune cells in tumor samples were assessed using CIBERSORT, and weighted gene co-expression network analysis (WGCNA) was used to identify M1 macrophage-related modules. Univariate Cox analysis and LASSO-Cox regression analysis were performed, and four genes (SPP1, DHRS3, SLC11A1, and CFB) with significant differential expression were selected through GEPIA. These four genes can be considered hub genes. The four-gene risk-scoring model may be an independent prognostic factor for THCA patients. The validation cohort and the entire cohort confirmed the results. Univariate and multivariate Cox analysis was performed to identify independent prognostic factors for THCA. Finally, a prognostic nomogram was built based on the entire cohort, and the nomogram combining the risk score and clinical prognostic factors was superior to the nomogram with individual clinical prognostic factors in predicting overall survival. Time-dependent ROC curves and DCA confirmed that the combined nomogram is useful. Gene set enrichment analysis (GSEA) was used to elucidate the potential molecular functions of the high-risk group. Our study identified four genes associated with M1 macrophages and established a prognostic nomogram that predicts overall survival for patients with THCA, which may help determine clinical treatment options for different patients.