Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Spatial and Single-Cell Transcriptomics Unraveled Spatial Evolution of Papillary Thyroid Cancer

Recurrence and metastasis are the major issues for papillary thyroid cancer (PTC). Current morphological and molecular classification systems are not satisfied for PTC diagnosis due to lacking variant-specific morphological criteria and high signal-to-noise in mutation-based diagnosis, respectively. Importantly, intratumor heterogeneity is largely lost in current molecular classification system, which can be resolved by single cell RNA sequencing (scRNA-seq). However, scRNA-seq loses spatial information and morphological features. Herein, scRNA-seq is integrated and spatially-resolved transcriptomics (SRT) to elaborate the mechanisms underlying the spatial heterogeneity, malignancy and metastasis of PTCs by associating transcriptome and local morphology. This results demonstrated that PTC cells evolved with multiple routes, driven by the enhanced aerobic metabolism and the suppressed mRNA translation and protein synthesis and the involvement of cell-cell interaction. Two curated malignant and metastatic footprints can discriminate PTC cells from normal thyrocytes. Ferroptosis resistance contributed to PTC evolution. This results will advance the knowledge of intratumor spatial heterogeneity and evolution of PTCs at spatial and single-cell levels, and propose better diagnostic strategy.

Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA.

Identification of potential targetable genes in papillary, follicular, and anaplastic thyroid carcinoma using bioinformatics analysis

PURPOSE

To perform an extensive exploratory analysis to build a deeper insight into clinically relevant molecular biomarkers in Papillary, Follicular, and Anaplastic thyroid carcinomas (PTC, FTC, ATC).

METHODS

Thirteen Thyroid Cancer (THCA) datasets incorporating PTC, FTC, and ATC were derived from the Gene Expression Omnibus. Genes differentially expressed (DEGs) between THCA and normal were identified and subjected to GO and KEGG analyses. Multiple topological properties were harnessed and protein-protein interaction (PPI) networks were constructed to identify the hub genes followed by survival analysis and validation.

RESULTS

There were 70, 87, and 377 DEGs, and 23, 27, and 53 hub genes for PTC, FTC, and ATC samples, respectively. Survival analysis detected 39 overall and 49 relapse-free survival-relevant hub genes. Six hub genes, BCL2, FN1, ITPR1, LYVE1, NTRK2, TBC1D4, were found common to more than one THCA type. The most significant hub genes found in the study were: BCL2, CD44, DCN, FN1, IRS1, ITPR1, MFAP4, MKI67, NTRK2, PCLO, TGFA. The most enriched and significant GO terms were Melanocyte differentiation for PTC, Extracellular region for FTC, and Extracellular exosome for ATC. Prostate cancer for PTC was the most significantly enriched KEGG pathway. The results were validated using TCGA data.

CONCLUSIONS

The findings unravel potential biomarkers and therapeutic targets of thyroid carcinomas.

QIGTD: identifying critical genes in the evolution of lung adenocarcinoma with tensor decomposition

BACKGROUND

Identifying critical genes is important for understanding the pathogenesis of complex diseases. Traditional studies typically comparing the change of biomecules between normal and disease samples or detecting important vertices from a single static biomolecular network, which often overlook the dynamic changes that occur between different disease stages. However, investigating temporal changes in biomolecular networks and identifying critical genes is critical for understanding the occurrence and development of diseases.

METHODS

A novel method called Quantifying Importance of Genes with Tensor Decomposition (QIGTD) was proposed in this study. It first constructs a time series network by integrating both the intra and inter temporal network information, which preserving connections between networks at adjacent stages according to the local similarities. A tensor is employed to describe the connections of this time series network, and a 3-order tensor decomposition method was proposed to capture both the topological information of each network snapshot and the time series characteristics of the whole network. QIGTD is also a learning-free and efficient method that can be applied to datasets with a small number of samples.

RESULTS

The effectiveness of QIGTD was evaluated using lung adenocarcinoma (LUAD) datasets and three state-of-the-art methods: T-degree, T-closeness, and T-betweenness were employed as benchmark methods. Numerical experimental results demonstrate that QIGTD outperforms these methods in terms of the indices of both precision and mAP. Notably, out of the top 50 genes, 29 have been verified to be highly related to LUAD according to the DisGeNET Database, and 36 are significantly enriched in LUAD related Gene Ontology (GO) terms, including nuclear division, mitotic nuclear division, chromosome segregation, organelle fission, and mitotic sister chromatid segregation.

CONCLUSION

In conclusion, QIGTD effectively captures the temporal changes in gene networks and identifies critical genes. It provides a valuable tool for studying temporal dynamics in biological networks and can aid in understanding the underlying mechanisms of diseases such as LUAD.

Research on the potential mechanism of Deapioplatycodin D against pulmonary fibrosis based on bioinformatics and experimental verification

BACKGROUND

Pulmonary fibrosis (PF) is a group of respiratory diseases that are extremely complex and challenging to treat. Due to its high mortality rate and short survival, it's often referred to as a "tumor-like disease" that poses a serious threat to human health.

OBJECTIVE

We aimed validate the potential of Deapioplatycodin D (DPD) to against PF and clarify the underlying mechanism of action of DPD for the treatment of PF based on bioinformatics and experimental verification. This finding provides a basis for the development of safe and effective therapeutic PF drugs based on DPD.

METHODS

We used LPS-induced early PF rats as a PF model to test the overall efficacy of DPD in vivo. Then, A variety of bioinformatics methods, such as WGCNA, LASSO algorithm and immune cell infiltration (ICI), were applied to analyze the gene microarray related to PF obtained from Gene Expression Omnibus (GEO) to obtained key targets of PF. Finally, an in vitro PF model was constructed based on BEAS-2B cells while incorporating rat lung tissues to validate the regulatory effects of DPD on critical genes.

RESULTS

DPD can effectively alleviate inflammatory and fibrotic markers in rat lungs. WGCNA analysis resulted in a total of six expression modules, with the brown module having the highest correlation with PF. Subsequently, seven genes were acquired by intersecting the genes in the brown module with DEGs. Five key genes were identified as potential biomarkers of PF by LASSO algorithm and validation dataset verification analysis. In the ICI analysis, infiltration of activated B cell, immature B cell and natural killer cells were found to be more crucial in PF. Ultimately, it was observed that DPD could modulate key genes to achieve anti-PF effects.

CONCLUSION

In short, these comprehensive analysis methods were employed to identify critical biomarkers closely related to PF, which helps to elucidate the pathogenesis and potential immunotherapy targets of PF. It also provides essential support for the potential of DPD against PF.

Bioinformatics analysis and experimental validation revealed that Paeoniflorigenone effectively mitigates cerebral ischemic stroke by suppressing oxidative stress and inflammation

Inflammation and oxidative stress are becoming more recognized as risk factors for ischemic stroke. Paeoniflorigenone (PA) has diverse pharmacological effects that include anti-inflammatory and antioxidant properties. However, the specific mechanisms by which PA affects cerebral ischemic stroke have not been studied. Our objective was to investigate the potential targets and mechanisms of PA in preventing cerebral ischemic stroke. We obtained the potential targets of PA from the SwissTargetPrediction, Super-PRED, and SEA Search Server databases. The GSE97537 dataset was utilized to identify gene targets related to ischemic stroke. The overlapping targets were imported into the STRING database to construct a protein-protein interaction network, and enrichment analyses were conducted using R software. Rats were pretreated with PA for three weeks before undergoing MCAO and reperfusion. H&E staining, ELISA, and qRT-PCR analyses were then performed to explore the potential mechanisms of PA. In the study, we identified 439 potential targets for PA and 1206 potential targets for ischemic stroke. Out of these, there were 71 common targets, which were found to be primarily associated with pathways related to oxidative stress and inflammation. The results from animal experiments showed that PA was able to improve nerve function and reduce inflammatory cytokines and oxidative stress in the MCAO-induced ischemic stroke model. Additionally, the expression of core genes in the MCAO + HPA group was significantly lower compared to the MCAO group. Our study revealed that the potential mechanisms by which PA prevents ischemic stroke involve oxidative stress and inflammation. These findings provide important theoretical guidance for the clinical use of PA in preventing and managing ischemic stroke.

Identification of key genes and signalling pathways in clear cell renal cell carcinoma: An integrated bioinformatics approach

BACKGROUND

Clear cell Renal Cell Carcinoma (ccRCC) is one of the most prevalent types of kidney cancer. Unravelling the genes responsible for driving cellular changes and the transformation of cells in ccRCC pathogenesis is a complex process.

OBJECTIVE

In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis.

METHODS

Through GEO2R analysis, 179 common differentially expressed genes (DEGs) were identified among the datasets. The common DEGs were subjected to functional enrichment analysis using ToppFun followed by construction of protein-protein interaction network (PPIN) using Cytoscape. Clusters within the DEGs PPIN were identified using the Molecular Complex Detection (MCODE) Cytoscape plugin. To identify the hub genes, the centrality parameters degree, betweenness, and closeness scores were calculated for each DEGs in the PPIN. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to validate the relative expression levels of hub genes in the normal and ccRCC tissues.

RESULTS

The common DEGs were highly enriched in Hypoxia-inducible factor (HIF) signalling and metabolic reprogramming pathways. VEGFA, CAV1, LOX, CCND1, PLG, EGF, SLC2A1, and ENO2 were identified as hub genes.

CONCLUSION

Among 8 hub genes, only the expression levels of VEGFA, LOX, CCND1, and EGF showed a unique expression pattern exclusively in ccRCC on compared to other type of cancers.

PARP1 modulates METTL3 promoter chromatin accessibility and associated LPAR5 RNA m6A methylation to control cancer cell radiosensitivity

Chromatin remodeling and N6-methyladenosine (m6A) modification are two critical layers in controlling gene expression and DNA damage signaling in most eukaryotic bioprocesses. Here, we report that poly(ADP-ribose) polymerase 1 (PARP1) controls the chromatin accessibility of METTL3 to regulate its transcription and subsequent m6A methylation of poly(A)+ RNA in response to DNA damage induced by radiation. The transcription factors nuclear factor I-C (NFIC) and TATA binding protein (TBP) are dependent on PARP1 to access the METTL3 promoter to activate METTL3 transcription. Upon irradiation or PARP1 inhibitor treatment, PARP1 disassociated from METTL3 promoter chromatin, which resulted in attenuated accessibility of NFIC and TBP and, consequently, suppressed METTL3 expression and RNA m6A methylation. Lysophosphatidic Acid Receptor 5 (LPAR5) mRNA was identified as a target of METTL3, and m6A methylation was located at A1881. The level of m6A methylation of LPAR5 significantly decreased, along with METTL3 depression, in cells after irradiation or PARP1 inhibition. Mutation of the LPAR5 A1881 locus in its 3' UTR results in loss of m6A methylation and, consequently, decreased stability of LPAR5 mRNA. METTL3-targeted small-molecule inhibitors depress murine xenograft tumor growth and exhibit a synergistic effect with radiotherapy in vivo. These findings advance our comprehensive understanding of PARP-related biological roles, which may have implications for developing valuable therapeutic strategies for PARP1 inhibitors in oncology.

Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.

Low expression of SLC34A1 is associated with poor prognosis in clear cell renal cell carcinoma

OBJECTIVE

Clear cell renal cell carcinoma (ccRCC) is a malignant renal tumor that is highly prone to metastasis and recurrence. The exact pathogenesis of this cancer is still not well understood. This study aimed to identify novel hub genes in renal clear cell carcinoma and determine their diagnostic and prognostic value.

METHODS

Intersection genes were obtained from multiple databases, and protein-protein interaction analysis and functional enrichment analysis were performed to identify key pathways related to the intersection genes. Hub genes were identified using the cytoHubba plugin in Cytoscape. GEPIA and UALCAN were utilized to observe differences in mRNA and protein expression of hub genes between KIRC and adjacent normal tissues. The Wilcoxon rank sum test was used to analyze hub gene levels between paired KIRC and matched non-cancer samples. IHC results were obtained from the HPA online database, and according to the median gene expression level, they were divided into a high-expression group and a low-expression group. The correlation of these groups with the prognosis of KIRC patients was analyzed. Logistic regression and the Wilcoxon rank sum test were used to test the relationship between SLC34A1 level and clinicopathological features. The diagnostic value of SLC34A1 was evaluated by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Cox regression analysis was used to analyze the relationship between clinicopathological features, SLC34A1 expression, and KIRC survival rate. LinkedOmics was used to obtain the genes most related to SLC34A1 and their functional enrichment. Genetic mutations and methylation levels of SLC34A1 in KIRC were obtained from the cBioPortal website and the MethSurv website, respectively.

RESULTS

Fifty-eight ccRCC differential genes were identified from six datasets, and they were mainly enriched in 10 functional items and 4 pathways. A total of 5 hub genes were identified. According to the GEPIA database analysis, low expression of SLC34A1, CASR, and ALDOB in tumors led to poor prognosis. Low expression of SLC34A1 mRNA was found to be related to clinicopathological features of patients. SLC34A1 expression in normal tissues could accurately identify tumors (AUC 0.776). SLC34A1 was also found to be an independent predictor of ccRCC in univariate and multivariate Cox analyses. The mutation rate of the SLC34A1 gene was 13%. Eight of the 10 DNA methylated CpG sites were associated with the prognosis of ccRCC. SLC34A1 expression in ccRCC was positively correlated with B cells, eosinophils, neutrophils, T cells, TFH, and Th17 cells, and negatively correlated with Tem, Tgd, and Th2 cells.

CONCLUSION

The expression level of SLC34A1 in KIRC samples was found to be decreased, which predicted a decreased survival rate of KIRC. SLC34A1 may serve as a molecular prognostic marker and therapeutic target for KIRC patients.

Identification and validation of eight estrogen-related genes for predicting prognosis of papillary thyroid cancer

Papillary thyroid cancer (PTC) is one of the most common malignant tumors in female, and estrogen can affect its progression. However, the targets and mechanisms of estrogen action in PTC remain unclear. Therefore, this study focuses on the relationship between estrogen-related genes (ERGs) expression and prognosis in PTC, particularly neuropeptide U (NMU), and its important role in tumor progression. Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differentially expressed genes (DEGs) predominantly enriched in ERGs were identified between PTC and normal tissue. Then, we identified ERGs that contributed most to PTC prognosis, including Transducer of ERBB2 1 (TOB1), trefoil factor 1 (TFF1), phospholipase A and acyltransferase 3 (PLAAT3), NMU, kinesin family member 20A (KIF20A), DNA topoisomerase II alpha (TOP2A), tetraspanin 13 (TSPAN13), and carboxypeptidase E (CPE). In addition, we confirmed that NMU was highly expressed in PTC and explored the effect of NMU on PTC cells proliferation in vitro and in vivo. The results showed that the proliferative capacity of PTC cells was significantly reduced with NMU knockdown. Moreover, the phosphorylation levels of the Kirsten rat sarcoma virus (KRAS) signaling pathway were significantly lower with NMU knockdown. These results suggest that ERGs, especially NMU, may be novel prognostic indicators in PTC.

LPAR5 confers radioresistance to cancer cells associated with EMT activation via the ERK/Snail pathway

BACKGROUND

Epithelial-to-mesenchymal transition (EMT) is a critical event contributing to more aggressive phenotypes in cancer cells. EMT is frequently activated in radiation-targeted cells during the course of radiotherapy, which often endows cancers with acquired radioresistance. However, the upstream molecules driving the signaling pathways of radiation-induced EMT have not been fully delineated.

METHODS

In this study, RNA-seq-based transcriptome analysis was performed to identify the early responsive genes of HeLa cells to γ-ray irradiation. EMT-associated genes were knocked down by siRNA technology or overexpressed in HeLa cells and A549 cells, and the resulting changes in phenotypes of EMT and radiosensitivity were assessed using qPCR and Western blotting analyses, migration assays, colony-forming ability and apoptosis of flow cytometer assays.

RESULTS

Through RNA-seq-based transcriptome analysis, we found that LPAR5 is downregulated in the early response of HeLa cells to γ-ray irradiation. Radiation-induced alterations in LPAR5 expression were further revealed to be a bidirectional dynamic process in HeLa and A549 cells, i.e., the early downregulating phase at 2 ~ 4 h and the late upregulating phase at 24 h post-irradiation. Overexpression of LPAR5 prompts EMT programing and migration of cancer cells. Moreover, increased expression of LPAR5 is significantly associated with IR-induced EMT and confers radioresistance to cancer cells. Knockdown of LPAR5 suppressed IR-induced EMT by attenuating the activation of ERK signaling and downstream Snail, MMP1, and MMP9 expression.

CONCLUSIONS

LPAR5 is an important upstream regulator of IR-induced EMT that modulates the ERK/Snail pathway. This study provides further insights into understanding the mechanism of radiation-induced EMT and identifies promising targets for improving the effectiveness of cancer radiation therapy.

Identification and Validation of Three Hub Genes Involved in Cell Proliferation and Prognosis of Castration-Resistant Prostate Cancer

Background

The acquisition of castration resistance is lethal and inevitable in most prostate cancer patients under hormone therapy. However, effective biomarkers and therapeutic targets for castration-resistant prostate cancer remain to be defined.

Methods

Comprehensive bioinformatics tools were used to screen hub genes in castration-resistant prostate cancer and were verified in androgen-dependent prostate cancer and castration-resistant prostate cancer in TCGA and the SU2C/PCF Dream Team database, respectively. Gene set enrichment analysis and in vitro experiments were performed to determine the potential functions of hub genes involved in castration-resistant prostate cancer progression.

Results

Three hub genes were screened out by bioinformatics analysis: MCM4, CENPI, and KNTC1. These hub genes were upregulated in castration-resistant prostate cancer and showed high diagnostic and prognostic value. Moreover, the expression levels of the hub genes were positively correlated with neuroendocrine prostate cancer scores, which represent the degree of castration-resistant prostate cancer aggression. Meanwhile, in vitro experiments confirmed that hub gene expression was increased in castration-resistant prostate cancer cell lines and that inhibition of hub genes hindered cell cycle transition, resulting in suppression of castration-resistant prostate cancer cell proliferation, which confirmed the gene set enrichment analysis results.

Conclusions

MCM4, CENPI, and KNTC1 could serve as candidate diagnostic and prognostic biomarkers of castration-resistant prostate cancer and may provide potential preventive and therapeutic targets.

Identifying potential prognosis markers in hepatocellular carcinoma via integrated bioinformatics analysis and biological experiments

Background: Hepatocellular carcinoma is one kind of clinical common malignant tumor with a poor prognosis, and its pathogenesis remains to be clarified urgently. This study was performed to elucidate key genes involving HCC by bioinformatics analysis and experimental evaluation.

Methods: We identified common differentially expressed genes (DEGs) based on gene expression profile data of GSE60502 and GSE84402 from the Gene Expression Omnibus (GEO) database. Gene Ontology enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, REACTOME pathway enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used to analyze functions of these genes. The protein-protein interaction (PPI) network was constructed using Cytoscape software based on the STRING database, and Molecular Complex Detection (MCODE) was used to pick out two significant modules. Hub genes, screened by the CytoHubba plug-in, were validated by Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (HPA) database. Then, the correlation between hub genes expression and immune cell infiltration was evaluated by Tumor IMmune Estimation Resource (TIMER) database, and the prognostic values were analyzed by Kaplan-Meier plotter. Finally, biological experiments were performed to illustrate the functions of RRM2.

Results: Through integrated bioinformatics analysis, we found that the upregulated DEGs were related to cell cycle and cell division, while the downregulated DEGs were associated with various metabolic processes and complement cascade. RRM2, MAD2L1, MELK, NCAPG, and ASPM, selected as hub genes, were all correlated with poor overall prognosis in HCC. The novel RRM2 inhibitor osalmid had anti-tumor activity, including inhibiting proliferation and migration, promoting cell apoptosis, blocking cell cycle, and inducing DNA damage of HCC cells.

Conclusion: The critical pathways and hub genes in HCC progression were screened out, and targeting RRM2 contributed to developing new therapeutic strategies for HCC.

Prognostic and tumor immunity implication of inflammatory bowel disease-associated genes in colorectal cancer

BACKGROUND

Epidemiologic studies continue to emphasize that increasing patients with inflammatory bowel disease (IBD) develop to colorectal cancer (CRC). Although the function and mechanisms of IBD-associated genes (IBDGs) in CRC tumorigenesis have been extensively researched, the implications of IBDGs in the prognosis value and tumor immunity of CRC remain unclear.

RESULTS

In this study, the expression, pathological stages and prognostic value of IBDGs in CRC were systematically analyzed, and 7 prognostic genes including CDH1, CCL11, HLA-DRA, NOS2, NAT2, TIMP1 and TP53 were screened through LASSO-Cox regression analysis. Then, a prognostic signature was established based on the 7 prognostic genes, and the model exhibited a good ability in risk stratification of CRC patients. Subsequent results showed that the genetic alterations of the 7 prognostic genes exhibited more significant and extensive influence on immune cells infiltration in colon adenocarcinoma than that in rectal adenocarcinoma. Meanwhile, immune cells infiltration also showed a significant difference between low-risk group and high-risk group. What's more, 7 prognostic genes-based risk stratification was associated with microsatellite instability, and its prognostic characteristics were significantly negatively correlated with mismatch repair genes.

CONCLUSIONS

This study provided a promising insight that the 7 IBDGs could be used as valuable biomarkers for prognostic diagnosis and personalized immunotherapy of CRC patients.

Protein–Protein Interaction (PPI) Network of Zebrafish Oestrogen Receptors: A Bioinformatics Workflow

Protein–protein interaction (PPI) is involved in every biological process that occurs within an organism. The understanding of PPI is essential for deciphering the cellular behaviours in a particular organism. The experimental data from PPI methods have been used in constructing the PPI network. PPI network has been widely applied in biomedical research to understand the pathobiology of human diseases. It has also been used to understand the plant physiology that relates to crop improvement. However, the application of the PPI network in aquaculture is limited as compared to humans and plants. This review aims to demonstrate the workflow and step-by-step instructions for constructing a PPI network using bioinformatics tools and PPI databases that can help to predict potential interaction between proteins. We used zebrafish proteins, the oestrogen receptors (ERs) to build and analyse the PPI network. Thus, serving as a guide for future steps in exploring potential mechanisms on the organismal physiology of interest that ultimately benefit aquaculture research.

A 13-Gene Signature Based on Estrogen Response Pathway for Predicting Survival and Immune Responses of Patients With UCEC

Background: Accumulating evidence suggests that anti-estrogens have been effective against multiple gynecological diseases, especially advanced uterine corpus endometrial carcinoma (UCEC), highlighting the contribution of the estrogen response pathway in UCEC progression. This study aims to identify a reliable prognostic signature for potentially aiding in the comprehensive management of UCEC.

Methods: Firstly, univariate Cox and LASSO regression were performed to identify a satisfying UCEC prognostic model quantifying patients’ risk, constructed from estrogen-response-related genes and verified to be effective by Kaplan-Meier curves, ROC curves, univariate and multivariate Cox regression. Additionally, a nomogram was constructed integrating the prognostic model and other clinicopathological parameters. Next, UCEC patients from the TCGA dataset were divided into low- and high-risk groups according to the median risk score. To elucidate differences in biological characteristics between the two risk groups, pathway enrichment, immune landscape, genomic alterations, and therapeutic responses were evaluated to satisfy this objective. As for treatment, effective responses to anti-PD-1 therapy in the low-risk patients and sensitivity to six chemotherapy drugs in the high-risk patients were demonstrated.

Results: The low-risk group with a relatively favorable prognosis was marked by increased immune cell infiltration, higher expression levels of HLA members and immune checkpoint biomarkers, higher tumor mutation burden, and lower copy number alterations. This UCEC prognostic signature, composed of 13 estrogen-response-related genes, has been identified and verified as effective.

Conclusion: Our study provides molecular signatures for further functional and therapeutic investigations of estrogen-response-related genes in UCEC and represents a potential systemic approach to characterize key factors in UCEC pathogenesis and therapeutic responses.

Identification of a Four-Gene Signature for Determining the Prognosis of Papillary Thyroid Carcinoma by Integrated Bioinformatics Analysis

Purpose

Although well-differentiated papillary thyroid carcinoma (PTC) has an indolent nature and usually an excellent prognosis, some patients experience disease recurrence or death. The aim of this study was to identify prognostic markers to stratify PTC patients.

Patients and Methods

Eight gene-expression profiles (GSE3467, GSE3678, GSE5364, GSE27155, GSE33630, GSE53157, GSE60542, and GSE104005) were obtained from the Gene Expression Omnibus and used to analyze differentially expressed genes (DEGs) between PTC tissues and non-tumor tissues. Univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed to identify prognostic genes and establish a risk-score model based on the integrated DEGs. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were used to validate the prognostic performance of the risk score. A nomogram was constructed based on The Cancer Genome Atlas dataset and Multivariable Cox regression analysis.

Results

A total of 165 upregulated and 207 downregulated DEGs were screened. A four-gene signature including PAPSS2, PCOLCE2, PTX3, and TGFBR3 was identified. The risk-score model showed a strong diagnosis performance for identifying patients with a poor prognosis. KM analysis showed that patients with low risk scores had a significantly more favorable overall survival (OS) than those with high risk scores (p = 0.0002). ROC curves based on the four-gene signature showed better performances in predicting 1-, 3-, and 5-year survival than did the American Joint Committee on Cancer staging system (area under the curve: 0.86 vs 0.84, 0.80 vs 0.63, and 0.79 vs 0.73, respectively). Furthermore, when combined with age and tumor status from the nomogram, the four-gene signature achieved a good performance in guiding postoperative follow-up surveillance of patients with PTC.

Conclusion

The four-gene signature was found to be a novel and reliable biomarker with great potential for clinical application in risk stratification and OS prediction in patients with PTC.

ASF1B: A Possible Prognostic Marker, Therapeutic Target, and Predictor of Immunotherapy in Male Thyroid Carcinoma

Background

Thyroid carcinoma (TC) is the most common malignant endocrine tumor worldwide. Several studies have documented that male patients with TC have a higher rate of metastasis and disease recurrence than female patients. However, the mechanism underlying this observation is not completely clear. The goal of our research was to investigate the potential key candidate genes and pathways related to TC progression in male patients at the molecular level.

Methods

A total of 320 samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Hub genes were screened out using weighted gene coexpression network analysis (WGCNA) and a protein–protein interaction (PPI) network analysis. Survival analysis was used to identify hub genes associated with disease-free survival (DFS) rates. Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression (ESTIMATE) data were used to assess the relationship between hub genes and immune cell infiltration. The molecular mechanism and biological functions of hub genes were explored using RT-qPCR, Western blot, Cell Counting Kit-8 Assay, flow cytometry, Transwell assays, and scratch assays.

Results

Forty-seven hub genes were identified, and the survival analysis demonstrated that anti-silencing function 1B (ASF1B) was the sole independent risk factor for poor DFS in male TC patients. Possible associations between the results from the ESTIMATE analysis showed that the ASF1B expression level was related to the ESTIMATE score, immune score, and T-cell regulatory (Treg) infiltration level. Through in vitro cell function experiments, we verified that knockdown of ASF1B inhibited KTC-1 cell proliferation, promoted cell apoptosis, and blocked cell cycle. The silencing of ASF1B reduced protein kinase B (AKT), phospho-AKT (p-AKT), and forkhead box p3 (FOXP3) in KTC-1 cells. Moreover, FOXP3 overexpression markedly restored the cell migration, invasion, and proliferation abilities repressed by ASF1B knockdown.

Conclusions

Our results indicate that ASF1B can be considered a prognostic marker, therapeutic target, and predictor of immunotherapy response in male thyroid cancer patients. However, further in-depth studies are required to validate this finding.

MiR-1246 regulates the PI3K/AKT signaling pathway by targeting PIK3AP1 and inhibits thyroid cancer cell proliferation and tumor growth

One of the most prevalent forms of endocrine malignancies is thyroid cancer. Herein, we explored the mechanisms whereby miR-1246 is involved in thyroid cancer. Phosphoinositide 3-kinase adapter protein 1 (PIK3AP1) was identified as a potential miR-1246 target, with the online Gene Expression Omnibus (GEO) database. The binding between miR-1246 and PIK3AP1 and the dynamic role of these two molecules in downstream PI3K/AKT signaling were evaluated. Analysis of GEO data demonstrated significant miR-1246 downregulation in thyroid cancer, and we confirmed that overexpression of miR-1246 can inhibit migratory, invasive, and proliferative activity in vitro and tumor growth in vivo. Subsequent studies indicated that miR-1246 overexpression decreased the protein level of PIK3AP1 and the phosphorylation of PI3K and AKT, which were reversed by PIK3AP1 overexpression. At the same time, overexpression of PIK3AP1 also reversed the miR-1246 mimics-induced inhibition proliferative, migratory, and invasive activity, while promoting increases in apoptotic death, confirming that miR-1246 function was negatively correlated with that of PIK3AP1. Subsequently, we found that the miR-1246 mimics-induced inhibition of PI3K/AKT phosphorylation was reversed by the PI3K/AKT activator IGF-1. miR-1246 mimics inhibited proliferative, migratory, and invasive activity while promoting increases in apoptotic death, which were reversed by IGF-1. Furthermore, miR-1246 agomir can inhibit tumor growth in vivo. We confirmed that miR-1246 affects the signaling pathway of PI3K/AKT via targeting PIK3AP1 and inhibits the development of thyroid cancer. Thus, miR-1246 is a new therapeutic target for thyroid cancer.

Long non-coding RNA MIR31HG as a prognostic predictor for malignant cancers: A meta- and bioinformatics analysis

Background

The possible regulatory mechanism of MIR31HG in human cancers remains unclear, and reported results of the prognostic significance of MIR31HG expression are inconsistent.

Methods

The meta‐analysis and related bioinformatics analysis were conducted to evaluate the role of MIR31HG in tumor progression.

Results

The result showed that high MIR31HG expression was not related to prognosis. However, in the stratified analysis, we found that the overexpression of MIR31HG resulted in worse OS, advanced TNM stage, and tumor differentiation in respiratory system cancers. Moreover, our results also found that MIR31HG overexpression was related to shorter OS in cervical cancer patients and head and neck tumors. In contrast, the MIR31HG was lower in digestive system tumors which contributed to shorter overall survival, advanced TNM stage, and distant metastasis. Furthermore, the bioinformatics analysis showed that MIR31HG was highly expressed in normal urinary bladder, small intestine, esophagus, stomach, and duodenum and low in colon, lung, and ovary. The results obtained from FireBrowse indicated that MIR31HG was highly expressed in LUSC, CESC, HNSC, and LUAD and low in STAD and BLCA. Gene Ontology analysis showed that the co‐expressed genes of MIR31HG were most enriched in the biological processes of peptide metabolism and KEGG pathways were most enriched in Ras, Rap1, and PI3K‐Akt signaling pathway.

Conclusion

MIR31HG may serve as a potential biomarker in human cancers.

Low expression of TFF3 in papillary thyroid carcinoma may correlate with poor prognosis but high immune cell infiltration

Background: Papillary thyroid carcinoma (PTC) is one of the most common endocrine malignancies and has a favorable prognosis. However, optimal treatments and prognostic markers have not been clearly identified. Methods: Gene expression data from primary PTC were downloaded from the Gene Expression Omnibus database and subjected to two analyses of differentially expressed genes (DEGs), followed by intersecting individual and integrated DEGs analyses as well as gene set enrichment analysis. Analysis of data from Sequence Read Archive and The Cancer Genome Atlas, immunohistochemistry and qRT-PCR of TFF3 were performed to validate the results. Finally, the relationship between gene expression and disease-free survival as well as immune cell infiltration were investigated. Results: Six critical DEGs and several tumor-enriched signaling pathways were identified. Immunohistochemistry and qRT-PCR validated the low expression of TFF3 in PTC. TFF3 and FCGBP are coexpressed in PTC, and patients with lower gene expression had worse disease-free survival but higher immune cell infiltration. Conclusion: TFF3 was significantly underexpressed and may function with FCGBP synergistically in PTC.

Development and validation of a novel 14-gene signature for predicting lymph node metastasis in papillary thyroid carcinoma

Background

There is still no reasonably accurate method of preoperatively predicting central lymph node metastasis (LNM), and it is essential to develop an effective evaluation model for predicting LNM in papillary thyroid carcinoma (PTC) patients.

Methods

PTC samples were collected from The Cancer Genome Atlas database. Candidate genes were identified as continuously upregulated or downregulated genes in the process of N0 to N1a and N1a to N1b. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct the predictive model for LNM. Multivariate logistic regression analysis was performed to screen the potential factors related to LNM, and a nomogram was established. The risk score of the gene signature model for predicting disease-free survival (DFS) was evaluated by Kaplan-Meier analysis.

Results

A 14-gene signature was developed by LASSO regression for predicting LNM based on 69 differential expression genes (DEGs) that were continuously upregulated or downregulated in the progress of PTC. The receiver operating characteristic (ROC) curves of the 14-gene signature predicting LNM, central LNM and lateral LNM were generated. The area under the ROC (AUC) values were 0.806 [95% confidence interval (CI): 0.7608-0.8815], 0.755 (95% CI: 0.6839-0.8263) and 0.821 (95% CI: 0.7608-0.8815). The nomogram's C-index value, including the 14-gene signature and other potential risk factors, was 0.786 (95% CI: 0.7296-0.8425), and the calibration exhibited fairly good consistency with the perfect prediction. Based on the 14-gene risk score, high-risk PTC patients had a worse DFS.

Conclusions

A novel 14-gene signature was developed for predicting LNM in PTC patients. The risk score also correlated with DFS in PTC patients.

TNRC6C-AS1 Promotes Thyroid Cancer Progression by Upregulating LPAR5 via miR-513c-5p

Background

Considering the combined role of long non-coding RNA (lncRNAs)-microRNA (miRNA)-mRNA in tumorigenesis, the purpose of this study was to investigate how TNRC6C-AS1 regulates the expression of lysophosphatidic acid receptor 5 (LPAR5) by modulating miR-513c-5p, thus influencing the progression of thyroid cancer (THCA).

Methods

qRT-PCR and Western blotting were performed to detect the expression levels of TNRC6C-AS1, miR-513c-5p, and LPAR5 in THCA tissues and cell lines. The viability, proliferation, migration, and invasion were assessed using CCK-8, BrdU, wound healing, and transwell migration assays, respectively. Dual-luciferase reporter assay, RIP assay, and RNA pull-down assay were used to evaluate the relationship between TNRC6C-AS1, miR-513c-5p, and LPAR5.

Results

TNRC6C-AS1 was highly expressed in THCA tissues, and knockout of TNRC6C-AS1 reduced the viability, proliferation, migration, and invasion of THCA cells. TNRC6C-AS1 competitively adsorbed miR-513c-5p. In addition, the biological function of TNRC6C-AS1 was blocked by knocking down the thyroid cell line TNRC6C-AS1 with miR-513c-5p inhibitor transfection. LPAR5 is the target gene for miR-513c-5p, which has the ability to eliminate the influence of miR-513c-5p on THCA cells.

Conclusion

The TNRC6C-AS1/miR-513c-5p/LPAR5 axis is a novel signaling pathway that modulates THCA progression and may be a potential target for cancer therapy.

CENPL, ISG20L2, LSM4, MRPL3 are four novel hub genes and may serve as diagnostic and prognostic markers in breast cancer

As a highly prevalent disease among women worldwide, breast cancer remains in urgent need of further elucidation its molecular mechanisms to improve the patient outcomes. Identifying hub genes involved in the pathogenesis and progression of breast cancer can potentially help to unveil mechanism and also provide novel diagnostic and prognostic markers. In this study, we integrated multiple bioinformatic methods and RNA in situ detection technology to identify and validate hub genes. EZH2 was recognized as a key gene by PPI network analysis. CENPL, ISG20L2, LSM4, MRPL3 were identified as four novel hub genes through the WGCNA analysis and literate search. Among these, many studies on EZH2 in breast cancer have been reported, but no studies are related to the roles of CENPL, ISG20L2, MRPL3 and LSM4 in breast cancer. These four novel hub genes were up-regulated in tumor tissues and associated with cancer progression. The receiver operating characteristic analysis and Kaplan-Meier survival analysis indicated that these four hub genes are promising candidate genes that can serve as diagnostic and prognostic biomarkers for breast cancer. Moreover, these four newly identified hub genes as aberrant molecules in the maintenance of breast cancer development, their exact functional mechanisms deserve further in-depth study.

Development and Validation of a Nomogram and a Comprehensive Prognostic Analysis of an LncRNA-Associated Competitive Endogenous RNA Network Based on Immune-Related Genes for Locally Advanced Rectal Cancer With Neoadjuvant Therapy

Colorectal cancer (CRC) is a common digestive tract tumor worldwide. In recent years, neoadjuvant chemoradiotherapy (CRT) has been the most comprehensive treatment for locally advanced rectal cancer (LARC). In this study, we explored immune infiltration in rectal cancer (RC) and identified immune-related differentially expressed genes (IRDEGs). Then, we identified response markers in datasets in GEO databases by principal component analysis (PCA). We also utilized three GEO datasets to identify the up- and downregulated response-related genes simultaneously and then identified genes shared between the PCA markers and three GEO datasets. Based on the hub IRDEGs, we identified target mRNAs and constructed a ceRNA network. Based on the ceRNA network, we explored prognostic biomarkers to develop a prognostic model for RC through Cox regression. We utilized the specimen to validate the expression of the two biomarkers. We also utilized LASSO regression to screen hub IRDEGs and built a nomogram to predict the response of LARC patients to CRT. All of the results show that the nomogram and prognostic model offer good prognostic value and that the ceRNA network can effectively highlight the regulatory relationship. hsa-mir-107 and WDFY3-AS2 may be prognostic biomarkers for RC.

G Protein-coupled Receptors in Radioiodine-refractory Thyroid Cancer in the Era of Precision Medicine

CONTEXT

Radioiodine-refractory thyroid cancers have poor outcomes and limited therapeutic options (tyrosine kinase inhibitors) due to transient efficacy and toxicity of treatments. Therefore, combinatorial treatments with new therapeutic approaches are needed. Many studies link G protein-coupled receptors (GPCRs) to cancer cell biology.

OBJECTIVE

To perform a specific atlas of GPCR expression in progressive and refractory thyroid cancer to identify potential targets among GPCRs aiming at drug repositioning.

METHODS

We analyzed samples from tumor and normal thyroid tissues from 17 patients with refractory thyroid cancer (12 papillary thyroid cancers [PTCs] and 5 follicular thyroid cancers [FTCs]). We assessed GPCR mRNA expression using NanoString technology with a custom panel of 371 GPCRs. The data were compared with public repositories and pharmacological databases to identify eligible drugs. The analysis of prognostic value of genes was also performed with TCGA datasets.

RESULTS

With our transcriptomic analysis, 4 receptors were found to be downregulated in FTC (VIPR1, ADGRL2/LPHN2, ADGRA3, and ADGRV1). In PTC, 24 receptors were deregulated, 7 of which were also identified by bioinformatics analyses of publicly available datasets on primary thyroid cancers (VIPR1, ADORA1, GPRC5B, P2RY8, GABBR2, CYSLTR2, and LPAR5). Among all the differentially expressed genes, 22 GPCRs are the target of approved drugs and some GPCRs are also associated with prognostic factors.

DISCUSSION

For the first time, we performed GPCR mRNA expression profiling in progressive and refractory thyroid cancers. These findings provide an opportunity to identify potential therapeutic targets for drug repositioning and precision medicine in radioiodine-refractory thyroid cancer.

Integrated bioinformatics analysis and screening of hub genes in papillary thyroid carcinoma

BACKGROUND

With the increasing incidence of papillary thyroid carcinoma (PTC), PTC continues to garner attention worldwide; however its pathogenesis remains to be elucidated. The purpose of this study was to explore key biomarkers and potential new therapeutic targets for, PTC.

METHODS

GEO2R and Venn online software were used for screening of differentially expressed genes. Hub genes were screened via STRING and Cytoscape, followed by Gene Ontology and KEGG enrichment analysis. Finally, survival analysis and expression validation were performed using the UALCAN online software and immunohistochemistry.

RESULTS

We identified 334 consistently differentially expressed genes (DEGs) comprising 136 upregulated and 198 downregulated genes. Gene Ontology enrichment analysis results suggested that the DEGs were mainly enriched in cancer-related pathways and functions. PPI network visualization was performed and 17 upregulated and 13 downregulated DEGs were selected. Finally, the expression verification and overall survival analysis conducted using the Gene Expression Profiling Interactive Analysis Tool (GEPIA) and UALCAN showed that LPAR5, TFPI, and ENTPD1 were associated with the development of PTC and the prognosis of PTC patients, and the expression of LPAR5, TFPI and ENTPD1 was verified using a tissue chip.

CONCLUSIONS

In summary, the hub genes and pathways identified in the present study not only provide information for the development of new biomarkers for PTC but will also be useful for elucidation of the pathogenesis of PTC.

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

BACKGROUND

The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.

METHODS

Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.

RESULTS

A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.

CONCLUSION

In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein-protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

LPAR5 promotes thyroid carcinoma cell proliferation and migration by activating class IA PI3K catalytic subunit p110β

Lysophosphatidic acid receptor 5 (LPAR5) is involved in mediating thyroid cancer progression, but the underlying mechanism needs to be further revealed. In this study, we confirmed that LPAR5 is upregulated in papillary thyroid carcinoma (PTC), especially in BRAF‐like PTC, by analyzing The Cancer Genome Atlas (TCGA) database and performing immunohistochemistry assay in human thyroid cancer tissues. LPAR5‐specific antagonist TC LPA5 4 treatment inhibited CGTH‐W3, TPC‐1, B‐CPAP, and BHT‐101 cell proliferation, CGTH‐W3 and TPC‐1 cell migration significantly. In vivo, TC LPA5 4 treatment could delay CGTH‐W3 xenograft growth in nude mice. We also found that LPAR5‐specific antagonist TC LPA5 4, PI3K inhibitor wortmannin, or mTOR inhibitor rapamycin pretreatment abrogated phosphorylation of Akt and p70S6K1 stimulated by LPA in CGTH‐W3 and TPC‐1 cells. Stimulating CGTH‐W3 cells transfected with pEGFPC1‐Grp1‐PH fusion protein with LPA resulted in the generation of phosphatidylinositol (3,4,5)‐triphosphate, which indicates that PI3K was activated by LPA directly. The p110β‐siRNA instead of p110α‐siRNA transfection abrogated the increase of levels of phosphorylated Akt and S6K1 stimulated by LPA. Furthermore, immunoprecipitation assay confirmed an interaction between LPAR5 and p110β. Overall, we provide new insights that the downregulation of LPAR5 decreased the proliferation and migration phenotype via the PI3K/Akt pathway. Inhibition of LPAR5 or the PI3K/Akt signal may be a novel therapeutic strategy for treating thyroid cancer.

Identification of potential bioactive compounds and mechanisms of GegenQinlian decoction on improving insulin resistance in adipose, liver, and muscle tissue by integrating system pharmacology and bioinformatics analysis

ETHNOPHARMACOLOGICAL RELEVANCE

GegenQinlian Decoction (GQD), a classical formula in traditional Chinese medicine, is widely used in the treatment of diabetes. While studies have demonstrated that GQD is an efficacious treatment for insulin resistance (IR) in type 2 diabetes mellitus (T2DM), the potential bioactive compounds and mechanisms remain unclear.

AIM OF THE STUDY

To further investigate the potential bioactive compounds, targets, and pathways of GQD on improving IR in T2DM for adipose, liver, and muscle tissue using an integrated strategy of system pharmacology and bioinformatics analysis.

MATERIALS AND METHODS

We screened the candidate compounds and targets of GQD and identified IR-associated differentially expressed genes (DEGs) of adipose, liver, and muscle tissue, respectively. Then the intersecting target genes between candidate targets and DEGs were used for "GQD-compounds-targets-tissue" network construction in each type of tissue. The top 10 bioactive compounds acting on each type of tissue were intersected and consequently identified as potential bioactive compounds of GQD. Furthermore, pathway enrichment, protein-protein interaction (PPI) network construction, and hub target identification were performed based on the targets of GQD and the targets of quercetin in each type of tissue, respectively. Finally, to further confirm the role of quercetin, we intersected the hub targets of quercetin and the hub targets of GQD, and the pathways were intersected as well.

RESULTS

132 compounds and 119 potential targets of these compounds were obtained. 1,765, 3,206, and 3594 DEGs were identified between IR and insulin sensitivity (IS) tissue in adipose, liver, and muscle, respectively. There were 21, 23, 45 targets and 103, 73, 123 compounds in the "GQD-compounds-targets-tissue" network of adipose, liver, and muscle tissue, respectively. Then compounds such as quercetin, kaempferol, baicalein, wogonin, isorhamnetin, beta-sitosterol and licochalcone A, were identified as the potential bioactive compounds of GQD, and quercetin had the highest degree among the compounds. Moreover, based on the targets of GQD, hub targets like PPARG, RELA, EGFR, CASP3, VEGFA, AR, ESR1 and CCND1, and signaling pathways such as insulin signaling pathway, endocrine resistance, TNF signaling pathway, PI3K-Akt signaling pathway, AMPK signaling pathway, MAPK signaling pathway, NF-κB signaling pathway, HIF-1 signaling pathway, apoptosis, and VEGF signaling pathway, were filtered out as the underlying mechanisms of GQD on improving diabetic IR. In addition, the hub targets and pathways of quercetin coincided with most of the hub targets and pathways of GQD in each type of tissue, respectively, suggesting that quercetin may be a potential representative compound of GQD.

CONCLUSIONS

Our analysis identifies the potential bioactive compounds, targets, and pathways of GQD on improving IR in T2DM for adipose, liver, and muscle tissue, which shows the characteristics of multi-compounds, multi-targets, multi-pathways, and multi-mechanisms of GQD and lays a solid foundation for further experimental research and clinical application.

Identification of Key Genes Associated with Changes in the Host Response to Severe Burn Shock: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database

Background

Patients with severe burns continue to display a high mortality rate during the initial shock period. The precise molecular mechanism underlying the change in host response during severe burn shock remains unknown. This study aimed to identify key genes leading to the change in host response during burn shock.

Methods

The GSE77791 dataset, which was utilized in a previous study that compared hydrocortisone administration to placebo (NaCl 0.9%) in the inflammatory reaction of severe burn shock, was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs). Functional enrichment analyses of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed. The protein–protein interaction (PPI) network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and then visualized in Cytoscape. In addition, important modules in this network were selected using the Molecular Complex Detection (MCODE) algorithm, and hub genes were identified in cytoHubba, a Cytoscape plugin.

Results

A total of 1059 DEGs (508 downregulated genes and 551 upregulated genes) were identified from the dataset. The DEGs enriched in GO terms and KEGG pathways were related to immune response. The PPI network contained 439 nodes and 2430 protein pairs. Finally, important modules and hub genes were identified using the different Cytoscape plugins. The key genes in burn shock were identified as arginase 1 (ARG1), cytoskeleton-associated protein (CKAP4), complement C3a receptor (C3AR1), neutrophil elastase (ELANE), gamma-glutamyl hydrolase (GGH), orosomucoid (ORM1), and quiescin sulfhydryl (QSOX1).

Conclusion

The DEGs, functional terms and pathways, and hub genes identified in the present study can help shed light on the molecular mechanism underlying the changes in host response during burn shock and provide potential targets for early detection and treatment of burn shock.

Papillary thyroid carcinoma with a high tumor mutation burden has a poor prognosis

BACKGROUND

Tumor mutation burden (TMB) as a prognostic marker for immunotherapy has shown prognostic value in many cancers. However, there is no systematic investigation on TMB in papillary thyroid carcinoma (PTC).

METHODS

Based on the somatic mutation data of 487 PTC patients from The Cancer Genome Atlas (TCGA), TMB was calculated, and we classified the samples into high-TMB (H-TMB) and low-TMB (L-TMB) groups. Bioinformatics methods were used to explore the characteristics and potential mechanism of TMB in PTC.

RESULTS

High TMB predicts shorter progression-free survival (PFS) (P < 0.001). TMB was positively correlated with age, stage, tumor size, metastasis, the male sex and tall cell PTC. Compared to the L-TMB group, the H-TMB group presented with lower immune cell infiltration, a higher proportion of tumor-promoting immune cells (M0 macrophages, activated dendritic cells and monocytes) and a lower proportion of antitumor immune cells (M1 macrophages, CD8⁺ T cells and B cells). Additionally, the characteristics displayed by different TMB groups were not driven by critical driver mutations such as BRAF and RAS.

CONCLUSIONS

PTC patients with high TMB have a worse prognosis. By stratifying PTC patients according to their TMB, advanced PTC patients who are candidates for immunotherapy could be selected.

Comprehensive analysis of location-specific hub genes related to the pathogenesis of colon cancer

The molecular mechanisms underlying colon cancer lesions at different sites are not entirely clear. Herein, we aimed to explore location-specific gene profiles related to the pathogenesis of colon cancer and to identify their function. The robust rank aggregation (RRA) method was used to integrate colon cancer microarray datasets and screen differentially expressed gene (DEG) profiles between left- and right-sided colon cancers. Then, weighted gene co-expression network analysis (WGCNA) was performed to cluster the DEGs into modules and identify hub genes. The selected hub genes were validated using The Cancer Genome Atlas dataset and clinical tissues. We assessed the association of selected hub genes with the methylation status in immune cells. In total, 905 DEGs were identified by RRA; five gene modules and 18 hub genes were related to the clinical traits of colon cancer by WGCNA. Four hub genes were selected and shown to be associated with colon cancers on different sides and distant metastasis in the validation analysis. The four hub genes showed a low methylation status, and their expression was significantly associated with methylation status. Positive correlations were observed between the four hub genes and tumor purity and among the four types of immune cells. Gene set enrichment analysis revealed that the four hub genes were mainly involved in two cancer-related pathways. In conclusion, this study identified a set of location-specific genes related to the pathogenesis of colon cancer. These four hub genes may act as novel candidate targets for the treatment of colon cancer.

Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

Background

Thyroid carcinoma (THCA) is a common endocrine malignant tumor. Papillary carcinoma with low degree of malignancy and good prognosis is the most common. It can occur at any age, but it is more common in young adults. Although the mortality rate is decreased due to early diagnosis, the survival rate varies depending on the type of tumor. Therefore, the purpose of this study is to identify hub biomarkers and novel therapeutic targets for THCA.

Methods

The GSE3467, GSE3678, GSE33630 and GSE53157 were obtained from the GEO database, including 100 thyroid tumors and 64 normal tissues to obtain the intersection of differentially expressed genes, and a protein-protein interaction network was constructed to obtain the HUB gene. The corresponding overall survival information from The Cancer Genome Atlas Project-THCA was then included in this research. The signature mechanism was studied by analyzing the gene ontology and the Kyoto Encyclopedia of Genes and Genome database.

Results

In this research, we identified eight candidate genes (FN1, CCND1, CDH2, CXCL12, MET, IRS1, DCN and FMOD) from the network. Also, expression verification and survival analysis of these candidate genes based on the TCGA database indicate the robustness of the above results. Finally, our hospital samples validated the expression levels of these genes.

Conclusion

The research identified eight mRNA (four up–regulated and four down–regulated) which serve as signatures and could be a potential prognostic marker of THCA.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

Heterogenous nature of gene expression patterns in BRAF-like papillary thyroid carcinomas with BRAFV600E

PURPOSE

Papillary thyroid cancers (PTCs) are the most common type of thyroid cancers, in which BRAF^V600E is the most prevalent driver mutation. It is known that BRAF^V600E-positive PTCs are clinically and molecularly heterogenous in terms of aggressiveness and prognosis. The molecular mechanisms of this heterogeneity were evaluated.

METHODS

The publicly available RNA-seq data for 26 classical (c) and 5 follicular variant (fv) PTCs with BRAF^V600E mutation and the BRAF-like expression signature in the BRAF^V600E-RAS score (BRS) and their respective normal adjacent tissues were downloaded, and analyzed for differentially expressed genes (DEGs). The DEGs were then analyzed with the Gene Ontology annotation and the KEGG pathway dataset.

RESULTS

We found four lines of evidence for heterogeneity of cPTCs. First, the cluster dendrogram and principle component analyses could not completely distinguish the cancer tissues from normal tissues. Second, the DEGs identified in each sample were highly diverse from one another. Third, although the DEGs were enriched in many terms containing the word "extracellular" ("extracellular region", "extracellular space", and so on) when analyzed as groups, the degree of this enrichment was variable when analyzed individually. Fourth, there are only a few intersections in the over-/underexpressed genes annotated with the terms containing the word "extracellular" among the samples examined. Essentially same results were obtained with BRAF-like, fvPTCs with BRAF^V600E. Nevertheless, some frequently over-/underexpressed genes were detected, of which LIPH (lipase H) expression was found to be prognostic and its high expression was favorable for PTCs.

CONCLUSION

Groups of BRAF-like, BRAF^V600E-positive cPTCs and fvPTCs that are homogenous in regard to histopathology, driver mutation and BRS were found to be highly heterogenous in terms of gene expression patterns. Yet, among the genes that were annotated with the terms containing the word "extracellular" and frequently over-/underexpressed, LIPH is a favorable prognostic marker for PTCs.