Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

Activation of Hypoxia Inducible Factor-1 Alpha-Mediated DNA Methylation Enzymes (DNMT3a and TET2) Under Hypoxic Conditions Regulates S100A6 Transcription to Promote Lung Cancer Cell Growth and Metastasis

Aims: This research was aimed at investigating the effects of hypoxia inducible factor-1 alpha (HIF-1α)-mediated DNA methylation enzymes (ten-eleven translocase-2 [TET2] and DNA methyltransferase-3a [DNMT3a]) under hypoxic conditions on S100A6 transcription, thereby promoting the growth and metastasis of lung cancer cells. Methods: The expression of HIF-1α or S100A6 in lung cancer cells was interfered with under normoxic and hypoxic conditions, and the cell proliferative, migratory, and invasive properties were assessed. The mechanism of HIF-1α-regulated TET2 and DNMT3 effects on S100A6 transcription under hypoxic conditions was further investigated. Results: Functionally, S100A6 over-expression promoted lung cancer cell proliferation and metastasis. S100A6 over-expression reversed the inhibitory effects of HIF-1α interference on the proliferation and metastasis of lung cancer cells. S100A6 was induced to express in an HIF-1α-dependent manner under hypoxic conditions, and silencing S100A6 or HIF-1α suppressed lung cancer cell proliferation and metastasis under hypoxic conditions. Further, The Cancer Genome Atlas-lung adenocarcinoma database analysis revealed that S100A6 mRNA levels had a negative correlation with methylation levels. Mechanistically, CpG hypomethylation status in the S100A6 promoter hypoxia response element had an association with HIF-1α induction. TET2 was enriched in S100A6 promoter region of lung cancer cells under hypoxic conditions, whereas DNMT3a enrichment was reduced in S100A6 promoter region. HIF-1α-mediated S100A6 activation was linked to DNMT3a-associated epigenetic inactivation and TET2 activation. Innovation: The activation of HIF-1α-mediated DNA methylation enzymes under hypoxic conditions regulated S100A6 transcription, thereby promoting lung cancer cell growth and metastasis. Conclusion: In lung cancer progression, hypoxia-induced factor HIF-1α combined with DNA methylation modifications co-regulates S100A6 transcriptional activation and promotes lung cancer cell growth and metastasis.

Effect of CHRDL1 on angiogenesis and metastasis of colorectal cancer cells via TGF-β/VEGF pathway

Colorectal cancer (CRC) is a common digestive tract tumor with the third incidence and death in the world. There is still an urgent need for effective therapeutic targets and prognostic markers for CRC. Herein, we report a novel potential target and marker, Chordin like-1 (CHRDL1). The function of CHRDL1 has been reported in gastric cancer, breast cancer, and oral squamous cell carcinoma. However, the biological effect of CHRDL1 in CRC remains unrevealed. Transwell and tube formation experiments were used to determine the biological function of CHRDL1. Western blot and rescue experiments were used to determine the specific mechanisms of CHRDL1. Results showed CHRDL1 is significantly downregulated in CRC cell lines and tissues. In vitro, experiments confirmed that CHRDL1 can inhibit cell growth, migration, invasion, angiogenesis and reverse epithelial-mesenchymal transformation. In vivo, experiments proved that it can inhibit tumor growth and metastasis. Mechanistically, we newly find that CHRDL1 exerts biological functions through the transforming growth factor-beta (TGF-β)/vascular endothelial growth factor signaling axis in vitro and in vivo. Therefore, we concluded that CHRDL1 reduces the growth, migration, and angiogenesis of CRC cells by downregulating TGF-β signaling. Our new findings on CHRDL1 may provide a basis for clinical antiangiogenesis therapy and the prognosis of CRC.

Identification of prognostic coagulation-related signatures in clear cell renal cell carcinoma through integrated multi-omics analysis and machine learning

Clear cell renal cell carcinoma is a threat to public health with high morbidity and mortality. Clinical evidence has shown that cancer-associated thrombosis poses significant challenges to treatments, including drug resistance and difficulties in surgical decision-making in ccRCC. However, the coagulation pathway, one of the core mechanisms of cancer-associated thrombosis, recently found closely related to the tumor microenvironment and immune-related pathway, is rarely researched in ccRCC. Therefore, we integrated bulk RNA-seq data, DNA mutation and methylation data, single-cell data, and proteomic data to perform a comprehensive analysis of coagulation-related genes in ccRCC. First, we demonstrated the importance of the coagulation-related gene set by consensus clustering. Based on machine learning, we identified 5 coagulation signature genes and verified their clinical value in TCGA, ICGC, and E-MTAB-1980 databases. It's also demonstrated that the specific expression patterns of coagulation signature genes driven by CNV and methylation were closely correlated with pathways including apoptosis, immune infiltration, angiogenesis, and the construction of extracellular matrix. Moreover, we identified two types of tumor cells in single-cell data by machine learning, and the coagulation signature genes were differentially expressed in two types of tumor cells. Besides, the signature genes were proven to influence immune cells especially the differentiation of T cells. And their protein level was also validated.

A prognostic model based on regulatory T-cell-related genes in gastric cancer: Systematic construction and validation

Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg-related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg-related prognostic genes were screened from Treg-related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg-related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6-gene Treg-related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low-risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1-, 3- and 5-year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low-risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high-risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6-gene Treg-associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients.

Integrating the characteristic genes of macrophage pseudotime analysis in single-cell RNA-seq to construct a prediction model of atherosclerosis

BACKGROUND

Macrophages play an important role in the occurrence and development of atherosclerosis. However, few existing studies have deliberately analyzed the changes in characteristic genes in the process of macrophage phenotype transformation.

METHOD

Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define the cells involved and determine their transcriptomic characteristics. KEGG enrichment analysis, CIBERSORT, ESTIMATE, support vector machine (SVM), random forest (RF), and weighted correlation network analysis (WGCNA) were applied to bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO).

RESULT

Nine cell clusters were identified. M1 macrophages, M2 macrophages, and M2/M1 macrophages were identified as three clusters within the macrophages. According to pseudotime analysis, M2/M1 macrophages and M2 macrophages can be transformed into M1 macrophages. The ROC curve values of the six genes in the test group were statistically significant (AUC (IL1RN): 0.899, 95% CI: 0.764-0.990; AUC (NRP1): 0.817, 95% CI: 0.620-0.971; AUC (TAGLN): 0.846, 95% CI: 0.678-0.971; AUC (SPARCL1): 0.825, 95% CI: 0.620-0.988; AUC (EMP2): 0.808, 95% CI: 0.630-0.947; AUC (ACTA2): 0.784, 95% CI: 0.591-0.938). The atherosclerosis prediction model showed significant statistical significance in both the train group (AUC: 0.909, 95% CI: 0.842-0.967) and the test group (AUC: 0.812, 95% CI: 0.630-0.966).

CONCLUSIONS

IL1RNHigh M1, NRP1High M2, ACTA2High M2/M1, EMP2High M1/M1, SPACL1High M2/M1 and TAGLNHigh M2/M1 macrophages play key roles in the occurrence and development of arterial atherosclerosis. These marker genes of macrophage phenotypic transformation can also be used to establish a model to predict the occurrence of atherosclerosis.

Exploration and Validation of Pancreatic Cancer Hub Genes Based on Weighted Gene Co-Expression Network Analysis and Immune Infiltration Score Analysis

Objective

To find pancreatic cancer (PC)-related hub genes based on weighted gene co-expression network analysis (WGCNA) construction and immune infiltration score analysis and validate them immunohistochemically by clinical cases, to generate new concepts or therapeutic targets for the early diagnosis and treatment of PC.

Material and Methods

In this study, WGCNA and immune infiltration score were utilized to identify the relevant core modules of PC and the hub genes within these core modules.

Results

Using WGCNA analysis, data from PC and normal pancreas integrated with TCGA and GTEX were analyzed and brown modules were chosen from the six modules. Five hub genes, including DPYD, FXYD6, MAP6, FAM110B, and ANK2, were discovered to have differential survival significance via validation tests utilizing survival analysis curves and the GEPIA database. The DPYD gene was the only gene associated with PC survival side effects. Validation of the Human Protein Atlas (HPA) database and immunohistochemical testing of clinical samples showed positive results for DPYD expression in PC.

Conclusion

In this study, we identified DPYD, FXYD6, MAP6, FAM110B, and ANK2, as immune-related candidate markers for PC. Only the DPYD gene had a negative impact on the survival of PC patients. Through validation of the HPA database and immunohistochemical testing of clinical cases, we believe that the DPYD gene brings novel ideas and therapeutic targets in the diagnosis and treatment of PC.

A prognostic cuproptosis-related lncRNA predictive signature for bladder cancer patients

Cuproptosis is a novel form of cell death in tumours. However, the clinical impact and mechanism of cuproptosis in bladder cancer (BC) remain unclear. This study aimed to explore the functions of long noncoding RNAs (lncRNAs) related to cuproptosis in BC and develop a prognostic predictive model. RNA sequencing and clinicopathological data were derived from The Cancer Genome Atlas and randomly divided into training and validation groups. Cuproptosis-related lncRNAs were identified by Cox regression analysis and least absolute shrinkage and selection operator, and the patients were divided into high- and low-risk groups according to the median value of the signature-based risk score. We established a signature of 17 cuproptosis-associated lncRNAs in the training set. In both sets, patients with higher signature-based risk scores had a notably higher probability of death (P ≤ 0.001) and a shorter survival duration. Cox regression analyses confirmed the risk score as an independent predictor of BC prognosis in the entire set. The area under the curve (AUC) values for 1-, 3-, and 5-year survival were 0.767, 0.734, and 0.764, respectively, confirming that the signature could determine the prognosis of BC. A signature-based nomogram was developed, and its prediction accuracy was validated using calibration curves. Several drugs, including Gemcitabine, Oxaliplatin, Mitoxantrone, Camptothecin, Cytarabine and Irinotecan may benefit low-risk BC patients more. Finally, in vitro experiments confirmed that the cuproptosis-related lncRNAs are highly expressed in bladder cancer cells after cuproptosis induced by exogenous copper ions. In conclusion, a cuproptosis-related lncRNA signature independently predicted prognosis in BC, indicating a possible mechanism and clinical treatment approach.

Bioinformatics identify the role of chordin-like 1 in thyroid cancer

The abnormal expression of chordin-like 1 (CHRDL1) is identified in many cancers, while the effect of CHRDL1 in thyroid cancer (THCA) remains unclear. The University of California Santa Cruz, Gene Expression Profiling Interactive Analysis, University of Alabama at Birmingham Cancer, and Gene Expression Omnibus database (GSE33570, GSE33630, and GSE60542) were used for determining the mRNA and methylation expression of CHRDL1 in tumor and normal tissues. Human Protein Atlas was used for exploring the protein expression level of CHRDL1. The genes correlated to CHRDL1 were assessed by cBioPortal database. The prognostic value of CHRDL1 was evaluated through Kaplan-Meier method, cox regression, and nomogram analysis. Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and gene set enrichment analysis were used for predicting potential function of CHRDL1. The relationship between CHRDL1 and immune cell infiltration was determined by Pearson method. The downregulated mRNA and protein expressions of CHRDL1 were identified in THCA through the analysis of data from The Cancer Genome Atlas, Gene Expression Omnibus, and Human Protein Atlas database. The survival analysis showed that the CHRDL1 expression significantly affected disease-free interval (DFI) and progression-free interval, and CHRDL1 was an independent predictor of DFI. Besides, we found that C-C motif chemokine ligand 21 could significantly affect DFI time when it was co-expressed with CHRDL1. Additionally, the function of CHRDL1 was enriched in cell migration, apoptosis, and immune cell receptor. The downregulated expression of CHRDL1 was observed in THCA and caused poor prognosis. CHRDL1 may be involved in signal pathway related to cancer development and immune response, which suggested it could be a potential biomarker.

Identifying liver metastasis-related hub genes in breast cancer and characterizing SPARCL1 as a potential prognostic biomarker

Background

The liver is the third most common metastatic site for advanced breast cancer (BC), and liver metastases predict poor prognoses. However, the characteristic biomarkers of BC liver metastases and the biological role of secreted protein acidic and rich in cysteine-like 1 (SPARCL1) in BC remain unclear. The present study aimed to identify potential biomarkers for liver metastasis of BC and to investigate the effect of SPARCL1 on BC.

Methods

The publicly available GSE124648 dataset was used to identify differentially expressed genes (DEGs) between BC and liver metastases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to annotate these DEGs and understand the biological functions in which they are involved. A protein-protein interaction (PPI) network was constructed to identify metastasis-related hub genes and further validated in a second independent dataset (GSE58708). Clinicopathological correlation of hub gene expression in patients with BC was determined. Gene set enrichment analysis (GSEA) was performed to explore DEG-related signaling pathways. SPARCL1 expression in BC tissues and cell lines was verified by RT-qPCR. Further in vitro experiments were performed to investigate the biological functions of SPARCL1 in BC cells.

Results

We identified 332 liver metastasis-related DEGs from GSE124648 and 30 hub genes, including SPARCL1, from the PPI network. GO and KEGG enrichment analyses of liver-metastasis-related DEGs revealed several enriched terms associated with the extracellular matrix and pathways in cancer. Clinicopathological correlation analysis of SPARCL1 revealed that its expression in BC was associated with age, TNM stage, estrogen receptor status, progesterone receptor status, histological type, molecular type, and living status of patients. GSEA results suggested that low SPARCL1 expression in BC was related to the cell cycle, DNA replication, oxidative phosphorylation, and homologous recombination. Lower expression levels of SPARCL1 were detected in BC tissues compared to adjacent tissues. The in vitro experiments showed that SPARCL1 knockdown significantly increased the proliferation and migration of BC cells, whereas the proliferation and migration were suppressed after elevating the expression of SPARCL1.

Conclusion

We identified SPARCL1 as a tumor suppressor in BC, which shows potential as a target for BC and liver metastasis therapy and diagnosis.

Systematic Analysis and Identification of Molecular Subtypes of TRP-Related Genes and Prognosis Prediction in Lung Adenocarcinoma

Background. Transient receptor potential channel (TRP) is a superfamily of nonselective cation channels, which is a member of calcium ion channels with a vital role in different calcium ion signal transduction pathways. TRP channel expression is often changed in the tumor, although the role of TRP proteins in lung cancer is unknown. Methods. Molecular Signatures Database (MsigDB) provided the TRP gene set. Univariate Cox regression analysis was performed on The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data collection set employing the coxph function of R package survival to find prognosis-related genes. The R package ConsumusClusterPlus was employed for doing the consistency cluster analysis of TCGA-LUAD samples according to the prognosis-related TRP gene. The R-package limma was utilized for investigating the differential expression of TRP subtypes. According to the differentially expressed genes between subtypes, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major genes and develop the risk model. CIBERPORT algorithm, R package maftools, gene set variation analysis (GSVA), and pRRophetic of R-package were employed for measuring the proportion of immune cells among subtypes, genomic mutation difference, pathway enrichment score, and drug sensitivity analysis. Results. A total of 15 TRP-related genes associated with the prognosis of lung adenocarcinoma were found. According to the expression value of 15 genes, lung adenocarcinoma can be sorted into two subcategories. The prognosis of cluster1 is considerably better in comparison with that of cluster2. There were 123 differentially expressed genes between C1 and C2 subtypes, including 6 up- and 117 downregulated genes. There were major variations in the tumor microenvironment between C1 and C2 subtypes. The proportion of CD8 T cells in the C1 subtype was considerably enhanced in comparison with that in the C2 subtype. We further discovered 123 differentially expressed genes among subtypes, and 8 key genes were obtained at the end. The risk score (RS) model developed by the 8-gene signature had good strength in the TCGA validation set, overall set, and Gene Expression Omnibus (GEO) external dataset. There were major variations in immune checkpoint gene expression, patient sensitivity to immunotherapeutic drugs, immune infiltration, and genomic mutations between high and low groups on the basis of RS. Conclusions. The risk model developed on the basis of TRP-related genes can help in predicting the prognosis of patients suffering from lung adenocarcinoma and guide immunotherapy.

Prognostic Roles of Glucose to Lymphocyte Ratio and Modified Glasgow Prognosis Score in Patients With Non-small Cell Lung Cancer

Background

Non-small cell lung cancer (NSCLC) is among the most prevalent malignancies worldwide. Previous studies have shown that the status of inflammation, nutrition and immune are closely related to overall survival (OS) of patients with NSCLC, but little is known about their interactive and combined roles. Hence, we chose glucose to lymphocyte ratio (GLR) and modified Glasgow Prognosis Score (mGPS) as prognostic factors and assessed the prognostic values of them for patients with NSCLC.

Methods

Baseline clinicopathologic and laboratory characteristics of 862 patients with NSCLC were obtained from a multicenter prospective cohort. The Cox proportional hazard regression models were used to determine prognostic values of the clinical factors. A nomogram was also constructed integrating the clinical factors with clinical significance or independent prognostic values. Concordance index (C-index) was utilized to evaluate the prediction accuracy of the TNM stage and the nomogram.

Results

Multivariate analyses demonstrated that GLR [Hazard ratio (HR) = 1.029, 95% confidence interval (CI) = 1.004–1.056, P = 0.023] and mGPS (score of 1: HR = 1.404, 95% CI = 1.143–1.726, P = 0.001; score of 2: HR = 1.515, 95% CI = 1.159–1.980, P = 0.002) were independent prognostic factors for patients with NSCLC. The C-indexes of the TNM stage and the nomogram were 0.642 (95% CI = 0.620–0.663) and 0.694 (95% CI = 0.671–0.717), respectively.

Conclusion

GLR and mGPS were independent prognostic factors for patients with NSCLC. Moreover, our constructed nomogram might be superior in predicting prognosis of patients with NSCLC compared with the TNM stage.

Study on the expression and function of chordin like 1 in oral squamous cell carcinoma

OBJECTIVE

The aim of this study was to investigate the role and related mechanism of chordin like 1 (CHRDL1) in oral squamous cell carcinoma (OSCC).

METHODS

The expressions of CHRDL1 were analyzed in both mRNA and protein levels by bioinformatics analysis, immunohistochemistry, and fluorescence in situ hybridization in OSCC. Survival analysis was used to determine the relationship between CHRDL1 and prognosis. In addition, enrichment analysis was used to suggest signal pathways involved in CHRDL1. Besides, the relationships between CHRDL1 and miRNAs, hypoxia, and immune infiltration were explored.

RESULTS

The mRNA level of CHRDL1 in OSCC was significantly lower than that in normal tissues, while the protein level was significantly higher than that in normal tissues. The high mRNA levels of CHRDL1 suggested a poor prognosis in patients with OSCC. The enrichment results showed that CHRDL1 might be involved in the Calcium signaling pathway, dilated cardiomyopathy, and focal adhesion. 7 immune cells were positively correlated with CHRDL1, while Tgd was negatively correlated with CHRDL1. In addition, we also found that hsa-miR-455-3p directly targeted CHRDL1 and reduced the mRNA levels of CHRDL1.

CONCLUSION

CHRDL1 plays a vital role in promoting cancer in OSCC and is downregulated at the mRNA levels by hsa-miR-455-3p.

Chordin-Like 1 Regulates Epithelial-to-Mesenchymal Transition and Metastasis via the MAPK Signaling Pathway in Oral Squamous Cell Carcinoma

Background

Accumulating evidence suggests that dysregulation of Chordin-like 1 (CHRDL1) is associated with malignant biological behaviors in multiple cancers. However, the exact function and molecular mechanism of CHRDL1 in oral squamous cell carcinoma (OSCC) remain unclear.

Methods

The expression levels of CHRDL1 in OSCC tissues and CAL27 cells were determined by RT-qPCR. Immunohistochemical staining was applied to detect CHRDL1 protein expression in sample tissues from OSCC patients. Gain of function and knockdown by lentivirus were further used to examine the effects of CHRDL1 on cell proliferation, migration, invasion, and adhesion in OSCC. Tail vein injection of CAL27 cells with dysregulated CHRDL1 expression was further used to examine the effect of CHRDL1 on lung colonization. RNA sequencing was performed to explore the molecular mechanisms of CHRDL1 that underlie the progression of OSCC.

Results

CHRDL1 was significantly downregulated in OSCC tissues and CAL27 cells compared to controls. CHRDL1 knockdown enhanced migration, invasion, adhesion, and EMT, but not proliferation, in CAL27 cells. Overexpression of CHRDL1 had the opposite effects. Moreover, CHRDL1 was proven to inhibit tumor metastasis in vivo. Mechanistically, MAPK signaling pathway components, including ERK1/2, p38, and JNK, were found to regulate the malignant biological behaviors of CAL27 cells.

Conclusions

Our results suggest that CHRDL1 has an inhibitory effect on OSCC metastasis via the MAPK signaling pathway, which provides a new possible potential therapeutic target against OSCC.

Meta-analysis of transcriptomics data identifies potential biomarkers and their associated regulatory networks in gallbladder cancer

Aim

This study aimed to identify key genes, non-coding RNAs, and their possible regulatory interactions during gallbladder cancer (GBC).

Background

The early detection of GBC, i.e. before metastasis, is restricted by our limited knowledge of molecular markers and mechanism(s) involved during carcinogenesis. Therefore, identifying important disease-associated transcriptome-level alterations can be of clinical importance.

Methods

In this study, six NCBI-GEO microarray dataseries of GBC and control tissue samples were analyzed to identify differentially expressed genes (DEGs) and non-coding RNAs {microRNAs (DEmiRNAs) and long non-coding RNAs (DElncRNAs)} with a computational meta-analysis approach. A series of bioinformatic methods were applied to enrich functional pathways, create protein-protein interaction networks, identify hub genes, and screen potential targets of DEmiRNAs and DElncRNAs. Expression and interaction data were consolidated to reveal putative DElncRNAs:DEmiRNAs:DEGs interactions.

Results

In total, 351 DEGs (185 downregulated, 166 upregulated), 787 DEmiRNAs (299 downregulated, 488 upregulated), and 7436 DElncRNAs (3127 downregulated, 4309 upregulated) were identified. Eight genes (FGF, CDK1, RPN2, SEC61A1, SOX2, CALR, NGFR, and NCAM) were identified as hub genes. Genes associated with ubiquitin ligase activity, N-linked glycosylation, and blood coagulation were upregulated, while those for cell-cell adhesion, cell differentiation, and surface receptor-linked signaling were downregulated. DEGs-DEmiRNAs-DElncRNAs interaction network identified 46 DElncRNAs to be associated with 28 DEmiRNAs, consecutively regulating 27 DEGs. DEmiRNAs-hsa-miR-26b-5p and hsa-miR-335-5p; and DElnRNAs-LINC00657 and CTB-89H12.4 regulated the highest number of DEGs and DEmiRNAs, respectively.

Conclusion

The current study has identified meaningful transcriptome-level changes and gene-miRNA-lncRNA interactions during GBC and laid a platform for future studies on novel prognostic and diagnostic markers in GBC.