CLEC3B as a potential diagnostic and prognostic biomarker in lung cancer and association with the immune microenvironment

Secreted protein TNA: a promising biomarker for understanding the adipose-bone axis and its impact on bone metabolism

BACKGROUND

Osteoporosis (OP) is a systemic bone disease characterized by reduced bone mass and deterioration of bone microstructure, leading to increased bone fragility. Platelets can take up and release cytokines, and a high platelet count has been associated with low bone density. Obesity is strongly associated with OP, and adipose tissue can influence platelet function by secreting adipokines. However, the biological relationship between these factors remains unclear.

METHODS

We conducted differential analysis to identify OP platelet-related plasma proteins. And, making comprehensive analysis, including functional enrichment, protein-protein interaction network analysis, and Friends analysis. The key protein, Tetranectin (TNA/CLEC3B), was identified through screening. Then, we analyzed TNA's potential roles in osteogenic and adipogenic differentiation using multiple RNA-seq data sets and validated its effect on osteoclast differentiation and bone resorption function through in vitro experiments.

RESULTS

Six OP-platelet-related proteins were identified via differential analysis. Then, we screened the key protein TNA, which was found to be highly expressed in adipose tissue. RNA-seq data suggested that TNA may promote early osteoblast differentiation. In vitro experiments showed that knockdown of TNA expression significantly increased the expression of osteoclast markers, thereby promoting osteoclast differentiation and bone resorption.

CONCLUSIONS

We identified TNA as a secreted protein that inhibits osteoclast differentiation and bone resorption. While, it potentially promoted early osteoblast differentiation from bioinformatic results. TNA may play a role in bone metabolism through the adipose-bone axis.

Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis

The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, focusing on early immune infiltration. Feature selection was performed on The Cancer Genome Atlas (TCGA) data using least absolute shrinkage and selection Operator (LASSO), random forest (RF), and support vector machine (SVM) algorithms. Six ML algorithms were employed to construct the diagnostic models, which were evaluated through receiver operating characteristic (ROC) curves, precision-recall curves (PRC), and classification error (CE), and validated on the GSE7670 dataset. Additionally, a lasso cox prognostic model was built on the TCGA-LUAD dataset and externally validated using independent Gene Expression Omnibus datasets (GSE30219, GSE31210, GSE50081, and GSE37745). Single-sample gene set enrichment analysis (ssGSEA) was performed to assess immune cell infiltration in stage I LUAD samples, revealing significant differences in immune cell types. These findings demonstrate a positive correlation between immune infiltration in stage I LUAD and Th2 cells, Tcm cells, and T helper cells, while a negative correlation was observed with Macrophages, Eosinophils, and Tem cells. These insights provide novel perspectives for clinical diagnosis and treatment of LUAD.

Insight into the function of tetranectin in human diseases: A review and prospects for tetranectin-targeted disease treatment

Tetranectin (TN), a serum protein, is closely associated with different types of cancers. TN binds plasminogen and promotes the proteolytic activation of plasminogen into plasmin, which suggests that TN is involved in remodeling the extracellular matrix and cancer tissues during cancer development. TN is also associated with other diseases, such as developmental disorders, cardiovascular diseases, neurological diseases, inflammation, and diabetes. Although the functional mechanism of TN in diseases is not fully elucidated, TN binds different proteins, such as structural protein, a growth factor, and a transcription regulator. Moreover, TN changes and regulates protein functions, indicating that TN-binding proteins mediate the association between TN and diseases. This review summarizes the current knowledge of TN-associated diseases and TN functions with TN-binding proteins in different diseases. In addition, potential TN-targeted disease treatment by inhibiting the interaction between TN and its binding proteins is discussed.

Anti-inflammatory therapeutic biomarkers identified of human bone marrow mesenchymal stem cell therapy on aging mice by serum proteomics and peptidomics study

Aging is accompanied by deterioration in physical condition, and creates high risks of diseases. Stem cell therapy exhibited promising potential in delaying aging. However, the unelucidated therapeutic mechanism limits future clinical application. Herein, to systematically understand the response to stem cell transfusion at the molecular level, we performed quantitative serum proteomic and peptidomics analyses in the 24-month-old aging mice model with or without mesenchymal stem cell (MSC) treatment. As a result, a total of 560 proteins and 2131 endogenous peptides were identified, among which, 6 proteins and 9 endogenous peptides derived from 6 precursor proteins were finally identified as therapeutic biomarkers after MSC transfusion on aging mice both by untargeted label-free quantification and targeted parallel reaction monitoring (PRM) quantification. Amazingly, the biological function of these differential proteins was mainly related to inflammation, which is not only the important hallmark of aging, but also the main cause of inducing aging. The reduction of these inflammatory protein content after MSC treatment further suggests the anti-inflammatory effect of MSC therapy reported elsewhere. Therefore, our study provides new evidence for the anti-inflammatory effect of MSC therapy for anti-aging and offers abundant data to support deeper investigations of the therapeutic mechanism of MSC in delaying aging.

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

Background

Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.

Methods

We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.

Results

We identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.

Conclusions

The risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.

Prognostic significance of SH2D5 expression in lung adenocarcinoma and its relation to immune cell infiltration

Objective

Through analyzing the SH2D5 expression profiles, clinical features, and immune infiltration in lung adenocarcinoma (LUAD), the study was intended to discuss the correlations of SH2D5 with prognosis and immune infiltration in LUAD.

Methods

We downloaded transcriptome and clinical data of LUAD patients from TCGA, GEO, and CCLE databases. Sangerbox, R language, GEPIA, UALCAN, and Kaplan-Meier Plotter were adopted to analyze the SH2D5 expression patterns, prognosis, and clinical features. Spearman correlation analysis was performed to determine the association between SH2D5 expression and immune cell infiltration and immune checkpoint genes. The miRNA-SH2D5 relations were predicted by miRDB and starbase. Lastly, quantitative PCR, IHC and Western blot were implemented for validation.

Results

A prominent up-regulation of SH2D5 was noted in the LUAD group relative to the normal group, which was validated by quantitative PCR, IHC and Western blot. SH2D5 expression was inversely related to overall survival (OS) of LUAD patients as well as B cell immune infiltration. Additionally, SH2D5 expression was negatively correlated with dendritic cells resting (p < 0.001), plasma cells (p < 0.001), mast cells resting (p = 0.031) and T cells CD4 memory resting (p = 0.036) in LUAD patients with abundant SH2D5 expression correlated with poor prognosis. Furthermore, enrichment analysis suggested that SH2D5 was associated with lung cancer and immunity. Lastly, we investigated the relationship between the expression of SH2D5 and the use of antitumor drugs.

Conclusion

High SH2D5 expression shares an association with unfavorable prognosis in LUAD, and SH2D5 may also provide new ideas for immunotherapy as a potential therapeutic target.

A novel oncogene trigger transposable element derived-1 promotes oral squamous cell carcinoma progression via evoking immune inhibition

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck squamous cell carcinomas (HNSCC) globally. Its incidence rate is rapidly increasing, and its 5-year survival rate remains at 50%, despite advances in medical science. Trigger transposable element-derived 1 (TIGD1) has been found to be upregulated in various cancer types. However, its biological function in OSCC requires further investigation. We searched the Cancer Genome Atlas database using CIBERSORT and TIMER 2.0 to predict the significance of TIGD1 and evaluate its effect on immune cell infiltration. Gene set enrichment analysis was performed to determine the biological functions of TIGD1. Gain/loss of function techniques were used to explore the biological behavior of TIGD1 in Cal27 and HSC4 cells. Finally, flow cytometry was used to detect dendritic cell markers in an OSCC and dendritic cell co-culture model. Our results show that TIGD1 is upregulated significantly in OSCC and is closely associated with tumor progression and prognosis. TIGD1 functions as an oncogene by increasing cells proliferation, inhibiting apoptosis, promoting cell invasion and migration. TIGD1 is also involved in tumor immune cell infiltration. Its overexpression can inhibit dendritic cell maturation, leading to immune suppression and tumor progression. High TIGD1 expression, which promotes OSCC progression, might be related to decreased dendritic cell maturation and activation. These findings suggest that TIGD1-specific small interfering RNA synthesized in vitro could be a new target for OSCC immunotherapy.

A Molecular and Epidemiological Study of Cholera Outbreak in Sulaymaniyah Province, Iraq, in 2022

Cholera is a disease caused by a Gram-negative bacterium Vibrio cholerae and is among the significant threats to global public health. The disease is mainly spread in the hot months of the year; low sanitation and lack of clean water are the major causes of the disease. In this study, we conducted a molecular and epidemiological study of the recent outbreak in the city of Sulaymaniyah in Iraq. Based on the bacteriological, serological, and molecular identification of the bacterium, it was shown that V. cholerae O1 serotype Ogawa caused the disease. Additionally, the number of positive cholera cases were higher in June compared to July (391 positive cases in June and 23 in July). Moreover, the majority (> 60%) of the cholera cases were recorded among 20-44-year-old people in both months; however, there was no significant difference in the patient genders diagnosed every month. Overall, this is the first report on the recent cholera outbreak in the city of Sulaimaniyah in Iraq.

Human Papillary and Reticular Fibroblasts Show Distinct Functions on Tumor Behavior in 3D-Organotypic Cultures Mimicking Melanoma and HNSCC

Human dermis can be morphologically divided into the upper papillary and lower reticular dermis. Previously, we demonstrated that papillary (PFs) and reticular (RFs) fibroblasts show distinct morphology and gene expression profiles. Moreover, they differently affect tumor invasion and epithelial-to-mesenchymal transition (EMT) in in vitro 3D-organotypic cultures of cutaneous squamous cell carcinoma (cSCC). In this study, we examined if these distinct effects of PFs and RFs can be extrapolated in other epithelial/non-epithelial tumors such as melanoma and head and neck squamous cell carcinoma (HNSCC). To this end, 3D-Full-Thickness Models (FTMs) were established from melanoma (AN and M14) or HNSCC cell lines (UM-SCC19 and UM-SCC47) together with either PFs or RFs in the dermis. The interplay between tumor cells and different fibroblasts was investigated. We observed that all the tested tumor cell lines showed significantly stronger invasion in RF-FTMs compared to PF-FTMs. In addition, RF-FTMs demonstrated more tumor cell proliferation, EMT induction and basement membrane disruption. Interestingly, RFs started to express the cancer-associated fibroblast (CAF) biomarker α-SMA, indicating reciprocal interactions eventuating in the transition of RFs to CAFs. Collectively, in the melanoma and HNSCC FTMs, interaction of RFs with tumor cells promoted EMT and invasion, which was accompanied by differentiation of RFs to CAFs.

A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), we employed mutual information (MI) and recursive feature elimination (RFE) feature selection techniques along with support vector machine (SVM) classification model. We have also utilized Random Forest (RF) as an embedded FS technique. The results were integrated and candidate biomarker genes across all techniques were identified. The proposed framework has identified 12 potential biomarkers that are highly correlated with different LC types, especially LUAD. A predictive model has been trained utilizing the identified biomarker expression profiling and performance of 97.99% was achieved. In addition, upon performing differential gene expression analysis, we could find that all 12 genes were significantly differentially expressed between normal and LUAD tissues, and strongly correlated with LUAD according to previous reports. We here propose that using multiple feature selection methods effectively reduces the number of identified biomarkers and directly affects their biological relevance.

Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma

Background

Numerous studies have found that infiltrating M2 macrophages play an important role in the tumor progression of lung adenocarcinoma (LUAD). However, the roles of M2 macrophage infiltration and M2 macrophage-related genes in immunotherapy and clinical outcomes remain obscure.

Methods

Sample information was extracted from TCGA and GEO databases. The TIME landscape was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to find M2 macrophage-related gene modules. Through univariate Cox regression, lasso regression analysis, and multivariate Cox regression, the genes strongly associated with the prognosis of LUAD were screened out. Risk score (RS) was calculated, and all samples were divided into high-risk group (HRG) and low-risk group (LRG) according to the median RS. External validation of RS was performed using GSE68571 data information. Prognostic nomogram based on risk signatures and other clinical information were constructed and validated with calibration curves. Potential associations of tumor mutational burden (TMB) and risk signatures were analyzed. Finally, the potential association of risk signatures with chemotherapy efficacy was investigated using the pRRophetic algorithm.

Results

Based on 504 samples extracted from TCGA database, 183 core genes were identified using WGCNA. Through a series of screening, two M2 macrophage-related genes (GRIA1 and CLEC3B) strongly correlated with LUAD prognosis were finally selected. RS was calculated, and prognostic risk nomogram including gender, age, T, N, M stage, clinical stage, and RS were constructed. The calibration curve shows that our constructed model has good performance. HRG patients were suitable for new ICI immunotherapy, while LRG was more suitable for CTLA4-immunosuppressive therapy alone. The half-maximal inhibitory concentrations (IC50) of the four chemotherapeutic drugs (metformin, cisplatin, paclitaxel, and gemcitabine) showed significant differences in HRG/LRG.

Conclusions

In conclusion, a comprehensive analysis of the role of M2 macrophages in tumor progression will help predict prognosis and facilitate the advancement of therapeutic techniques.

Construction and validation of a novel prediction system for detection of overall survival in lung cancer patients

BACKGROUND

Many factors have an aberrant effect on the overall survival of lung cancer (LC) patients. In recent years, remarkable progress has been made in immunotherapy, targeted treatment, and promising biomarkers. However, the available treatments and diagnostic methods are not specific for all patients.

AIM

To establish a system for predicting poor survival in patients with LC.

METHODS

The expression matrix and clinical information for this study were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. After the differential analysis of all screened genes, weighted gene coexpression network analysis was performed to analyze hub genes related to patient survival. A logistic regression model was used to construct the scoring system. The expression of the hub genes was verified by performing quantitative reverse transcription-polymerase chain reaction.

RESULTS

A total of 5007 differentially expressed genes were selected for the Weighted Gene Co-expression Network Analysis algorithm. We found that the turquoise module showed the highest correlation with patient prognosis. The gene module with the greatest positive correlation with patient survival was located in the turquoise area. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses performed for the genes contained in the turquoise module indicated the potential roles of the selected genes in the regulation of LC development. In addition, protein–protein interaction analysis was performed to screen hub genes, which identified 100 hub genes located in the core area of the network. We then intersected the 100 hub genes with 75 key genes sorted by module members to identify real hub genes associated with prognosis. Forty-one genes were finally selected. We then used a logistic regression model to determine 11 independent risk genes, namely CCNB2, CDC20, CENPO, FOXM1, HJURP, NEK2, OIP5, PLK1, PRC1, SKA1, UBE2C and SPARC.

CONCLUSION

We constructed a predictive model based on 11 independent risk genes to establish a system predicting the survival status of patients with non-small-cell lung carcinoma.

A Novel Secreted Protein-Related Gene Signature Predicts Overall Survival and Is Associated With Tumor Immunity in Patients With Lung Adenocarcinoma

Secreted proteins are important proteins in the human proteome, accounting for approximately one-tenth of the proteome. However, the prognostic value of secreted protein-related genes has not been comprehensively explored in lung adenocarcinoma (LUAD). In this study, we screened 379 differentially expressed secretory protein genes (DESPRGs) by analyzing the expression profile in patients with LUAD from The Cancer Genome Atlas database. Following univariate Cox regression and least absolute shrinkage and selection operator method regression analysis, 9 prognostic SPRGs were selected to develop secreted protein-related risk score (SPRrisk), including CLEC3B, C1QTNF6, TCN1, F2, FETUB, IGFBP1, ANGPTL4, IFNE, and CCL20. The prediction accuracy of the prognostic models was determined by Kaplan–Meier survival curve analysis and receiver operating characteristic curve analysis. Moreover, a nomogram with improved accuracy for predicting overall survival was established based on independent prognostic factors (SPRrisk and clinical stage). The DESPRGs were validated by quantitative real-time PCR and enzyme-linked immunosorbent assay by using our clinical samples and datasets. Our results demonstrated that SPRrisk can accurately predict the prognosis of patients with LUAD. Patients with a higher risk had lower immune, stromal, and ESTIMATE scores and higher tumor purity. A higher SPRrisk was also negatively associated with the abundance of CD8⁺ T cells and M1 macrophages. In addition, several genes of the human leukocyte antigen family and immune checkpoints were expressed in low levels in the high-SPRrisk group. Our results provided some insights into assessing individual prognosis and choosing personalized treatment modalities.

CLEC3B is a novel causative gene for macular-retinal dystrophy

PURPOSE

Macular degeneration is the leading cause of blindness worldwide. In this study, we aimed to define a new subtype of macular-retinal dystrophy and its genetic predisposition in 5 families.

METHODS

Exome sequencing was performed to determine the putative disease-causing genes in patients with inherited macular disorders confirmed through comprehensive ophthalmic examinations. To validate its functional consequence, adeno-associated virus-mediated mutant gene was delivered into the murine retina, and both structural and functional tests were performed to investigate its pathological effects in vivo.

RESULTS

In total, 5 multigenerational families diagnosed with autosomal dominant maculoretinopathy were found to carry a pathogenic variant in a new gene, CLEC3B, which encodes tetranectin, a plasminogen kringle-4 binding protein. Consistent with the disease phenotypes of patients, mice that received subretinal injections with the CLEC3B variant displayed multiple subretinal hyperreflective deposits, reduced retinal thickness, and decreased electroretinographic responses. Moreover, the optokinetic tracking response indicated that spatial frequency was significantly lower (P < .05), implying impaired visual function in these mice.

CONCLUSION

We have presented a new subtype of macular-retinal dystrophy in 5 families as well as a new pathogenic gene, CLEC3B, providing new insights into maculoretinopathy etiology.

Enhancer RNA SLIT2 Inhibits Bone Metastasis of Breast Cancer Through Regulating P38 MAPK/c-Fos Signaling Pathway

BACKGROUND

Breast cancer (BRCA) is the most common cancer in women, while the bones are one of the most common sites of metastasis. Although new diagnostic methods or radiation or chemotherapies and targeted therapies have made huge advances, the occurrence of bone metastasis is also linked with poorer survival. Enhancer RNAs (eRNAs) have been demonstrated to participate in the progression of tumorigenesis and metastasis. However, the role of eRNAs in BRCA bone metastasis remains largely unclear.

METHOD

Gene expression profiling of 1,211 primary BRCA and 17 bone metastases samples were retrieved from The Cancer Genome Atlas (TCGA) database, and the significant prognostic eRNAs were identified by Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The acceptable accuracy and discrimination of the nomogram were indicated by the receiver operating characteristic (ROC) and the calibration curves. Then target genes of eRNA, immune cell percentage by CIBERSORT analysis, immune genes by single-sample gene set enrichment analysis (ssGSEA), hallmark of cancer signaling pathway by gene set variation analysis (GSVA), and reverse phase protein array (RPPA) protein chip were used to build a co-expression regulation network and identified the key eRNAs in bone metastasis of BRCA. Finally, Cell Counting Kit-8 (CCK8) assay, cell cycle assay, and transwell assay were used to study changes in cell proliferation, migration, and invasiveness. Immunoprecipitation assay and Western blotting were used to test the interaction and the regulation signaling pathways.

RESULTS

The 27 hub eRNAs were selected, and a survival-related linear risk assessment model with a relatively high accuracy (area under curve (AUC): 0.726) was constructed. In addition, seven immune-related eRNAs (SLIT2, CLEC3B, LBPL1, FRY, RASGEF1B, DST, and ITIH5) as prognostic signatures for bone metastasis of BRCA were further confirmed by LASSO and multivariate Cox regression and CIBERSORT analysis. Finally, in vitro assay demonstrated that overexpression of SLIT2 reduced proliferation and metastasis in BRCA cells. Using high-throughput co-expression regulation network, we identified that SLIT2 may regulating P38 MAPK/c-Fos signaling pathway to promote the effects of metastasis.

CONCLUSION

Based on the co-expression network for bone metastasis of BRCA, we screened key eRNAs to explore a prognostic model in predicting the bone metastasis by bioinformatics analysis. Besides, we identified the potential regulatory signaling pathway of SLIT2 in BRCA bone metastasis, which provides a promising therapeutic strategy for metastasis of BRCA.

CircPTCH1 Promotes Migration in Lung Cancer by Regulating MYCN Expression Through miR-34c-5p

BACKGROUND

The incidence rate and mortality rate of lung cancer are the highest in the world. Therefore, further studies are needed to reveal the molecular mechanism of lung cancer progression and development. Previous study demonstrated that the deregulation of circRNAs can regulate cell biological functions in tumorigenesis and development. However, the roles of circPTCH1 in lung cancer have not yet been revealed.

MATERIALS AND METHODS

The expression levels of circPTCH1, miR-34c-5p, and MYCN were measured by RT-PCR in lung cancer tissues and cells; dual-luciferase reporter and RIP assay showed that circRNA served as a sponge for miRNA, and miRNA could target mRNA. In vitro, effects of si-circPTCH1 can regulate lung cancer cells' migration, invasion were detected by CCK-8 assay, wound healing assay, and transwell assay.

RESULTS

Our research demonstrated that the expression of circPTCH1 was upregulated in lung cancer tissues and cell lines and increased in metastatic tissues compared to that of non-metastatic tissues. circPTCH1 sponging miR-34c-5p to target MYCN was revealed by dual-luciferase reporter and a RIP assay. In addition, the expression level of miR-34c-5p was reduced in lung cancer tumor tissues, and MYCN was significantly increased in lung cancer tumor tissues. Pearson correlation analysis showed that miR-34c-5p with circPTCH1 and MYCN had a moderately negative correlation, and there was a moderately positive correlation between circPTCH1 and MYCN. Further, cytological studies found that circPTCH1 reduced lung cancer cells' migration and invasion by targeting MYCN via miR-34c-5p.

CONCLUSION

circPTCH1 plays a tumor enhancement role in lung cancer and that can effectively promote migration, invasion and EMT by targeting the miR-34c-5p/MYCN axis. circPTCH1 may be a novel potential treatment and diagnosis biomarker for lung cancer.

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

BACKGROUND

Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.

RESULTS

In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.

CONCLUSIONS

We established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

Transcriptomic analysis of tumor tissues and organoids reveals the crucial genes regulating the proliferation of lung adenocarcinoma

Background

Accumulative evidence shows that an organoid is a more practical and reliable tool in cancer biology research. This study aimed to identify and validate crucial genes involved in non-small cell lung cancer carcinogenesis and development using the transcriptomic analysis of tumor tissues and organoids.

Methods

Gene set enrichment analysis (GSEA) of tumor tissues, tumor organoids, and normal tissues was performed to reveal the similar and different mechanisms involved in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) carcinogenesis and progression. Differentially expressed gene analysis, prognostic analysis, and gene co-expression network analysis were further used to identify hub genes involved in LUAD and LUSC carcinogenesis and development. Finally, LUAD cell lines and organoids were used to validate these findings.

Results

GSEA analysis was performed to reveal the similar mechanisms involved in LUAD and LUSC carcinogenesis and development, such as P53 signaling pathway, base mismatch repair, DNA replication, cAMP signaling pathway and PPAR pathway. However, comparing with LUSC organoids, LUAD organoids showed downregulation of immune-related pathways, inflammation-related pathways, MAPK signaling pathways, and Rap1 signaling pathways, although these pathways were downregulated in LUAD and LUSC tissues by comparing with normal lung tissues. Further gene co-expression network analysis and prognostic analysis indicated CDK1, CCNB2, and CDC25A as the hub tumor-promoting genes in LUAD but not in LUSC, which were further validated in other datasets. Using LUAD cell lines and organoid models, CDK1 and CCNB2 knockdown were found to suppress LUAD proliferation. However, CDC25A knockdown did not inhibit LUAD cell line proliferation but could effectively suppress LUAD organoid growth, indicating that an organoid could be used as an effective tool to study cancer biology in LUAD.

Conclusions

The results revealed CDK1, CCNB2, and CDC25A as the hub genes involved in LUAD carcinogenesis and development, which could be used as the potential biomarkers and targets for LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03043-6.

Characteristic of molecular subtypes in lung adenocarcinoma based on m6A RNA methylation modification and immune microenvironment

BACKGROUND

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky.

METHODS

The gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model.

RESULT

Consensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094).

CONCLUSION

Our study provides a robust signature for predicting patients' prognosis, which might be helpful for therapeutic strategies discovery of LUAD.

Deregulated glutamate to pro-collagen conversion is associated with adverse outcome in lung cancer and may be targeted by renin-angiotensin-aldosterone system (RAS) inhibition

BACKGROUND

The tumor-microenvironment (TME) represents an attractive therapeutic target in NSCLC and plays an important role for efficacy of cancer therapeutics. We hypothesized that upregulation of collagen synthesis might be associated with adverse outcome in NSCLC. Literature evidence suggests that renin-angiotensin system inhibitors (RASi) decrease collagen deposition. Therefore, we aimed to explore the prognostic role of RASi intake and their influence on the TME in NSCLC.

METHODS

Four publicly available datasets were used to evaluate the impact of key enzymes involved in collagen biosynthesis. To investigate the influence of RASi intake on the TME and prognosis we evaluated a cohort of metastatic NSCLC patients and performed histopathological characterization of the TME. A three-dimensional microtissue in vitro model was developed to define the impact of RASi on collagen synthesis.

RESULTS

Expression of three genes of the collagen synthesis pathway, ALDH18A1, PLOD2 and P4HA1, was upregulated in NSCLC compared to normal lung tissue and linked to shortened overall survival in all investigated cohorts. Together, these genes formed a 'Collagen Signature' which represents an independent unfavourable prognostic factor in two NSCLC cohorts and was linked to alterations of the extracellular matrix deposition and cell cycle pathways. In the cohort of metastatic NSCLC, RASi intake was linked to improved overall response rate and survival. Exploratory in vitro experiments revealed that RASi led to a dose dependent reduction of collagen deposition and degradation of three-dimensional lung cancer cell spheroids.

CONCLUSION

We demonstrate that collagen synthesis is a key upregulated process in the NSCLC TME and its transcriptional readout, the three gene Collagen Signature is independently associated with poor outcome. Pharmacological targeting of this pathways e.g. by RASi bears potential of improving outcome in NSCLC.

Zc3h12d, a Novel of Hypomethylated and Immune-Related for Prognostic Marker of Lung Adenocarcinoma

Background

Zc3h12d is a negative regulator which plays a crucial role in immune modulation. However, the role of zc3h12d in lung adenocarcinoma (LUAD) remains unclear. We aim to explore the prognostic of zc3h12d and investigate the relationship between zc3h12d expression and immune infiltration in LUAD.

Methods

TIMER site was used to analyze the expression of zc3h12d in LUAD. The zc3h12d protein levels in patient tissue samples were detected by immunohistochemistry staining assays. Meanwhile, based on UALCAN database and samples' data from our cohort, we explored the relationship of clinicopathological features and zc3h12d expression to determine the clinical effect of zc3h12d in LUAD. Several databases including GEPIA, Kaplan-Meier plotter and our samples' data were used to explore the prognostic value of zc3h12d in LUAD. Cox regression analysis was established to further evaluate the prognostic value of zc3h12d in LUAD. In addition, zc3h12d promoter methylation was analyzed by UALCAN database. Genetic alteration analysis was observed in the cBioPortal web. GO and KEGG analyses were conducted to elucidate the underlying mechanisms. Finally, the correlation between zc3h12d and tumor-infiltrating immune cells in LUAD was investigated by TIMER database. The B cells level was investigated by flow cytometry analysis of peripheral blood from our LUAD cohort.

Results

Zc3h12d expression was significantly higher in LUAD, compared with adjacent normal tissues. The clinical data from the UALCAN database demonstrated that zc3h12d expression was closely related with cancer stage and nodal metastasis. However, patient sample detection revealed that zc3h12d expression was closely related to pathological N (p = 0.0431) and grade (p = 0.004). Moreover, low zc3h12d expression was associated with poorer overall survival in LUAD. We analyzed the methylation level of zc3h12d in LUAD and found that the methylation levels of zc3h12d promoter in LUAD were significantly reduced. In addition, zc3h12d genetic alterations, including deep deletion, could be found in LUAD. GO and KEGG pathway analysis results indicated that zc3h12d has a certain value in immune infiltration. We investigated the expression of zc3h12d in tumor-immune interactions. It was found that zc3h12d might be associated with the immune infiltration and markers of infiltrating immune cells of LUAD. The results of patient sample detection confirmed that B cells level was significantly lower in the patients with low zc3h12d expression than those in the patients with high zc3h12d expression.

Conclusion

zc3h12d might be considered as a potential biomarker for determining prognosis and immune-related therapeutic target in LUAD.

Expression and gene regulation network of TYMS and BCL2L1 in colorectal cancer based on data mining

Background

The purpose of this study was to study the role of thymidylate synthetase (TYMS) and B-cell lymphoma-2 like 1 (BCL2L1) in the occurrence and development of colorectal cancer and its potential regulatory mechanism.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to examine the expression and prognostic value of TYMS and BCL2L1 in colorectal cancer. C-BioPortal analysis was used to detect the TYMS and BCL2L1 alterations. Through The Human Protein Atlas (THPA), the TYMS and BCL2L1 protein levels were also assessed. The protein protein interaction (PPI) network was built using GeneMANIA analysis, while co-expression genes correlated with TYMS and BCL2L1 were identified using LinkedOmics analysis. Finally, we collected clinical samples to verify the expressions of TYMS and BCL2L1 in colorectal cancer.

Results

TYMS and BCL2L1 were up-regulated, and TYMS and BCL2L1 genomic alterations were not associated with the occurrence of colorectal cancer. TYMS and BCL2L1 were significantly connected with the prognosis of colorectal cancer patients. The genes interacted with TYMS and BCL2L1 were linked to functional networks involving pathway of apoptosis, apoptosis-multiple species, colorectal cancer, platinum drug resistance and p53 signaling pathway. qRT-PCR verification results of TYMS were consistent with the result of TCGA and GEO analysis.

Conclusions

This study display that data mining can efficiently provide information on expression of TYMS and BCL2L1, correlated genes of TYMS and BCL2L1, core pathways and potential functional networks in colorectal cancer, suggesting that TYMS and BCL2L1 may become new prognostic and therapeutic targets for colorectal cancer.

Therapeutic potential of AMPK signaling targeting in lung cancer: Advances, challenges and future prospects

Lung cancer (LC) is a leading cause of death worldwide with high mortality and morbidity. A wide variety of risk factors are considered for LC development such as smoking, air pollution and family history. It appears that genetic and epigenetic factors are also potential players in LC development and progression. AMP-activated protein kinase (AMPK) is a signaling pathway with vital function in inducing energy balance and homeostasis. An increase in AMP:ATP and ADP:ATP ratio leads to activation of AMPK signaling by upstream mediators such as LKB1 and CamKK. Dysregulation of AMPK signaling is a common finding in different cancers, particularly LC. AMPK activation can significantly enhance LC metastasis via EMT induction. Upstream mediators such as PLAG1, IMPAD1, and TUFM can regulate AMPK-mediated metastasis. AMPK activation can promote proliferation and survival of LC cells via glycolysis induction. In suppressing LC progression, anti-tumor compounds including metformin, ginsenosides, casticin and duloxetine dually induce/inhibit AMPK signaling. This is due to double-edged sword role of AMPK signaling in LC cells. Furthermore, AMPK signaling can regulate response of LC cells to chemotherapy and radiotherapy that are discussed in the current review.

Sexual dimorphism in cancer: insights from transcriptional signatures in kidney tissue and renal cell carcinoma

Sexual dimorphism in cancer incidence and outcome is widespread. Understanding the underlying mechanisms is fundamental to improve cancer prevention and clinical management. Sex disparities are particularly striking in kidney cancer: across diverse populations, men consistently show unexplained 2-fold increased incidence and worse prognosis. We have characterized genome-wide expression and regulatory networks of 609 renal tumors and 256 non-tumor renal tissues. Normal kidney displayed sex-specific transcriptional signatures, including higher expression of X-linked tumor suppressor genes in women. Sex-dependent genotype-phenotype associations unraveled women-specific immune regulation. Sex differences were markedly expanded in tumors, with male-biased expression of key genes implicated in metabolism, non-malignant diseases with male predominance and carcinogenesis, including markers of tumor infiltrating leukocytes. Analysis of sex-dependent RCC progression and survival uncovered prognostic markers involved in immune response and oxygen homeostasis. In summary, human kidney tissues display remarkable sexual dimorphism at the molecular level. Sex-specific transcriptional signatures further shape renal cancer, with relevance for clinical management.

The screening of immune-related biomarkers for prognosis of lung adenocarcinoma

Lung adenocarcinoma (LUAD) accounts for a frequently seen non-small cell lung cancer (NSCLC) histological subtype, and it is associated with dismal prognostic outcome. However, the benefits of traditional treatment are still limited, and the efficacies of immunotherapy are quite different. Therefore, it is of great significance to identify novel immune-related therapeutic targets in lung adenocarcinoma. In this study, we identified a set of immune-related biomarkers for prognosis of lung adenocarcinoma, which could provide new ideas for immunotherapy of lung adenocarcinoma. Datasets related to LUAD were filtered from the GEO database. The appropriate packages were used to identify differentially expressed genes (DEGs) and to carry out enrichment analysis, followed by the construction of prognostic biomarkers. The Kaplan-Meier (K-M) curves were plotted to analyze patient survival based on hub genes. Associations between the expression of selected biomarkers and six types of tumor-infiltrating immune cells were evaluated based on the online tool TIMER. After analyzing five GEO datasets(GSE32867, GSE46539, GSE63459, GSE75037 and GSE116959), we discovered altogether 67 DEGs, among which, 15 showed up-regulation while 52 showed down-regulation. Enrichments of integrated DEGs were identified in the ontology categories. CAV1, CFD, FMO2 and CLEC3B were eventually selected as independent prognostic biomarkers, they were correlated with clinical outcomes of LUAD patients. Moreover, a positive correlation was observed between biomarker expression and all different types of immune infiltration, and the expression level of the four biomarkers was all positively related to macrophage.

Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery

Disease progression prediction and therapeutic drug target discovery for Coronavirus disease 2019 (COVID-19) are particularly important, as there is still no effective strategy for severe COVID-19 patient treatment. Herein, we performed multi-platform omics analysis of serial plasma and urine samples collected from patients during the course of COVID-19. Integrative analyses of these omics data revealed several potential therapeutic targets, such as ANXA1 and CLEC3B. Molecular changes in plasma indicated dysregulation of macrophage and suppression of T cell functions in severe patients compared to those in non-severe patients. Further, we chose 25 important molecular signatures as potential biomarkers for the prediction of disease severity. The prediction power was validated using corresponding urine samples and plasma samples from new COVID-19 patient cohort, with AUC reached to 0.904 and 0.988, respectively. In conclusion, our omics data proposed not only potential therapeutic targets, but also biomarkers for understanding the pathogenesis of severe COVID-19.

Characterization of the Tumor Immune Microenvironment in Lung Squamous Cell Carcinoma Using Imaging Mass Cytometry

Background

Lung squamous cell carcinoma (LUSC) is a major subtype of non-small cell lung cancer. The tumor immune microenvironment (TIME) affects the anti-tumor immune response and the patient’s prognosis, although the TIME in LUSC patients is incompletely understood.

Methods

We retrospectively collected surgical specimens from patients with previously untreated primary LUSC. Histopathological examination was used to identify tumor regions and adjacent regions, and imaging mass cytometry was used to characterize the immune cells in those regions. The results were compared between regions and between patients.

Results

We identified heterogeneity in the TIME on comparing different patients with LUSC, although the tumor region and adjacent region both exhibited an immune response to the tumor. The TIME typically included a large number of infiltrating and activated T-cells, especially CD8⁺ T-cells, which closely interacted with the tumor cells in the tumor region. There was limited infiltration of B-cells, NK cells, and NKT cells, while the major immune suppressor cells were CD33⁺ myeloid-derived cells. We also identified a novel population of CD3⁻CD4⁺ cells with high expression of Foxp3 and TNFα, which might modulate the tumor microenvironment and play a proinflammatory role in the TIME.

Conclusions

The TIME of LUSC appears to be immunogenic and heterogenous, with predominant infiltration of activated CD8⁺ T-cells. The interactions between the tumor cells and T-cells facilitate the anti-tumor activity. A novel subpopulation of CD3⁻CD4⁺ cells with high TNFα and Foxp3 expression may modulate the tumor microenvironment and play a proinflammatory role.

CLEC3B as a Potential Prognostic Biomarker in Hepatocellular Carcinoma

C-Type Lectin Domain Family 3 Member B (CLEC3B) encodes proteins associated with tumor invasion and metastasis. However, the interrelation between CLEC3B gene expression, tumor immunity, and prognosis of patients with hepatocellular carcinoma (HCC) is unclear. This study was conducted to investigate the prognostic potential of CLEC3B and its association with tumor tissue infiltration markers. CLEC3B expression was examined using the TIMER and Oncomine databases, with its prognostic potential assessed using the GEPIA and Kaplan–Meier plotter databases. The relationship between CLEC3B and tumor immune cell infiltration biomarkers was analyzed using TIMER. Here, we revealed that CLEC3B expression was decreased in HCC and was correlated with a poor survival rate in patients with HCC. Additionally, the expression of CLEC3B was negatively correlated with differential immune cell infiltration and various immune biomarkers. These results indicate a potential mechanism by which the expression of CLEC3B might adjust tumor immunity by modulating the infiltration of HCC immune cells. Our study demonstrated that CLEC3B could be a potential prognostic biomarker and might be involved in tumor immune cell infiltration in HCC.

Comprehensive Analysis of the Relationships Between Tumor Mutation Burden With Immune Infiltrates in Cervical Cell Carcinoma

We aimed to investigate the prognosis of tumor mutation burden (TMB) in cervical cell carcinoma (CCC) and its potential association with tumor-infiltrating immune cells. The data from TCGA were analyzed, and higher TMB levels conferred high overall survival time, associated with higher T staging (p = 0.006) and older age (p = 2.961e−04). Through “CIBERSORT” package and Wilcoxon rank-sum test, the high TMB group exhibited higher levels of infiltration of T cell CD8 (p = 0.008), T cell CD4 memory activation (p = 0.006), T cell follicular assistance (p = 0.018), and Macrophage M1 (p = 0.037). In addition, 478 TMB-associated differentially expressed genes were identified, and two hub TMB-associated immune genes were identified, including CLEC3B and COL4A2. The TMB prognostic model (TMBPM) based on two hub immune genes showed robust prognostic capability in both training set and testing sets, and the higher the TMBPM score, the worse the prognosis. Finally, survival time was higher for high CLEC3B expression levels (p = 0.038) and lower for high COL4A2 expression levels (p = 0.033). Notably, there is an association between the expression of these two genes and immune infiltration in CCC. CLEC3B expression was most significantly positively correlated with B cells, CD4+ T cells, and Macrophage infiltration. COL4A2 expression was most significantly positively correlated with the presence of Macrophage and Dendritic cell infiltration. In addition, we observed that CLEC3B and COL4A carry mutations in multiple forms that normally suppress immune infiltration, including B cells, CD8+ T cells, and Macrophages.

Systemic Proteome Alterations Linked to Early Stage Pancreatic Cancer in Diabetic Patients

BACKGROUND

Diabetes is a risk factor associated with pancreatic ductal adenocarcinoma (PDAC), and new adult-onset diabetes can be an early sign of pancreatic malignancy. Development of blood-based biomarkers to identify diabetic patients who warrant imaging tests for cancer detection may represent a realistic approach to facilitate earlier diagnosis of PDAC in a risk population.

METHODS

A spectral library-based proteomic platform was applied to interrogate biomarker candidates in plasma samples from clinically well-defined diabetic cohorts with and without PDAC. Random forest algorithm was used for prediction model building and receiver operating characteristic (ROC) curve analysis was applied to evaluate the prediction probability of potential biomarker panels.

RESULTS

Several biomarker panels were cross-validated in the context of detection of PDAC within a diabetic background. In combination with carbohydrate antigen 19-9 (CA19-9), the panel, which consisted of apolipoprotein A-IV (APOA4), monocyte differentiation antigen CD14 (CD14), tetranectin (CLEC3B), gelsolin (GSN), histidine-rich glycoprotein (HRG), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), plasma kallikrein (KLKB1), leucine-rich alpha-2-glycoprotein (LRG1), pigment epithelium-derived factor (SERPINF1), plasma protease C1 inhibitor (SERPING1), and metalloproteinase inhibitor 1 (TIMP1), demonstrated an area under curve (AUC) of 0.85 and a two-fold increase in detection accuracy compared to CA19-9 alone. The study further evaluated the correlations of protein candidates and their influences on the performance of biomarker panels.

CONCLUSIONS

Proteomics-based multiplex biomarker panels improved the detection accuracy for diagnosis of early stage PDAC in diabetic patients.