Twist2 promotes kidney cancer cell proliferation and invasion by regulating ITGA6 and CD44 expression in the ECM-receptor interaction pathway

Super-enhancer-driven SLCO4A1-AS1 is a new biomarker and a promising therapeutic target in glioblastoma

Glioblastoma (GBM) is the most common intracranial malignancy, but current treatment options are limited. Super-enhancers (SEs) have been found to drive the expression of key oncogenes in GBM. However, the role of SE-associated long non-coding RNAs (lncRNAs) in GBM remains poorly understood. Here, we screened for an up-regulated lncRNA-SLCO4A1-AS1 expressed in GBM by analyzing data from GSE54791, GSE4536 and TCGA. We systematically analyzed its relationship with clinical characteristics, prognosis, epigenetics, tumor microenvironment (TME), biological functions, and transcription factors. We found that SE-driven SLCO4A1-AS1 was significantly upregulated in GBM and correlated with poor prognosis. Knockdown of SLCO4A1-AS1 decreased glioma cell proliferation, invasive ability, self-renewal ability, and increased apoptosis. Epigenetic analysis revealed that SOX2 and SE could drive SLCO4A1-AS1 expression. In vitro experiments further demonstrated that GBM cells with high SLCO4A1-AS1 expression were more sensitive to VX-11e, and overexpression of SLCO4A1-AS1 could reverse the inhibitory effect of VX-11e on GBM cells. In conclusion, this study revealed that SE-driven SLCO4A1-AS1 may be a potential therapeutic target in GBM.

Identification of Long Noncoding RNAs Expression Profiles Between Gallstone and Gallbladder Cancer Using Next-Generation Sequencing Analysis

Background

Gallstone disease (GS) is an important risk factor for Gallbladder cancer (GBC). However, the mechanisms of the progression of GS to GBC remain unclear. Long non-coding RNA (lncRNA), modulates DNA/RNA/proteins at epigenetic, pre-transcriptional, transcriptional and posttranscriptional levels, and plays a potential therapeutic role in various diseases. This study aims to identify lncRNAs that have a potential impact on GS-promoted GBC progression.

Methods and Results

Six GBC patients without GS, six normal gallbladder tissues, nine gallstones and nine GBC patients with GS were admitted to our hospital. The next-generation RNA-sequencing was performed to analyze differentially expressed (DE) lncRNA and messenger RNA (mRNA) in four groups. Then overlapping and specific molecular signatures were analyzed. We identified 29 co-DEGs and 500 co-DElncRNAs related to gallstone or GBC. The intersection and concatenation of co-DEGs and co-DElncRNA functionally involved in focal adhesion, Transcriptional misregulation in cancers, Protein digestion and absorption, and ECM-receptor interaction signaling pathways may contribute to the development of gallbladder cancer. Further exploration is necessary for early diagnosis and the potential treatment of GBC. FXYD2, MPZL1 and PAH were observed in both co-DEGs and co-DElncRNA and validated by qRT-PCR.

Conclusion

Our data identified a series of DEGs and DElncRNAs, which were involved in the progression of GBC and GS-related metabolism pathways. Compared to GBC, the GS profile was more similar to para-tumor tissues in transcriptome level and lower risk of cancer. Further exploration is necessary from GBC patients with different periods of follow-up gallstone.

Conserved regulatory switches for the transition from natal down to juvenile feather in birds

The transition from natal downs for heat conservation to juvenile feathers for simple flight is a remarkable environmental adaptation process in avian evolution. However, the underlying epigenetic mechanism for this primary feather transition is mostly unknown. Here we conducted time-ordered gene co-expression network construction, epigenetic analysis, and functional perturbations in developing feather follicles to elucidate four downy-juvenile feather transition events. We report that extracellular matrix reorganization leads to peripheral pulp formation, which mediates epithelial-mesenchymal interactions for branching morphogenesis. α-SMA (ACTA2) compartmentalizes dermal papilla stem cells for feather renewal cycling. LEF1 works as a key hub of Wnt signaling to build rachis and converts radial downy to bilateral symmetry. Novel usage of scale keratins strengthens feather sheath with SOX14 as the epigenetic regulator. We show that this primary feather transition is largely conserved in chicken (precocial) and zebra finch (altricial) and discuss the possibility that this evolutionary adaptation process started in feathered dinosaurs.

A Potential biomarker for the early diagnosis of OSCC: saliva and serum PrPC

Background: Oral squamous cell carcinoma (OSCC) is frequently diagnosed at an advanced stage, and the high mortality of patients is mainly due to the delay of diagnosis. Cellular prion protein (PrPC) contributes to the occurrence and development of many malignant tumors. However, little has been known about the clinical and diagnostic value of PrPC in OSCC. This study investigated the levels of PrPC in the saliva and serum of patients with OSCC, OPMD and control group and their diagnostic value. Methods: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Clinical Proteome Tumor Analysis Consortium (CPTAC) databases were analyzed to evaluate the expression of human prion protein gene (PRNP) mRNA and PrPC in OSCC. Enzyme-linked Immunosorbent Assay (ELISA) was utilized to detect the expression of PrPC in saliva and serum samples of OSCC, OPMD and control groups. Furthermore, diagnostic value and clinical significance of PrPC in OSCC was identified. Protein-protein interaction (PPI) network was constructed by STRING. GO and KEGG analysis were performed by ClusterProfiler. Results: The levels of PRNP mRNA and PrPC in OSCC were significantly higher than those in the control group from databases (P<0.05). Besides, salivary and serum PrPC of OSCC patients showed increased levels compared with OPMD and control groups (P<0.05). The expression of salivary and serum PrPC of OSCC was correlated with the degree of differentiation (P<0.05), and the expression of PrPC from CPTAC was related to tumor stage of OSCC (P<0.05). The areas under the diagnostic curves (AUCs) of salivary and serum PrPC were 0.807 and 0.671, respectively. GO and KEGG analysis revealed that PrPC might be related to cell adhesion, cell differentiation, signal transduction and apoptosis, and participate in the pathways of focal adhesion, PI3K-Akt signaling pathway and ECM- receptor interaction in OSCC. Conclusion: PrPC in saliva and serum may be a potential biomarker for early diagnosis of OSCC.

Cadherin dysregulation in gastric cancer: insights into gene expression, pathways, and prognosis

Background

The Cadherin gene family holds immense significance in maintaining the integrity and functionality of stomach tissues, playing crucial roles in cell-cell adhesion, cell migration and differentiation. Dysregulation of cadherin expression and function has been closely associated with various gastric diseases, particularly gastric cancer (GC). Understanding the regulation and clinical implications of cadherin genes in GC is essential to improve our knowledge and to identify new potential prognostic markers and therapeutic targets.

Methods

In this study, we provide an overview on the role of cadherin family genes in GC using bioinformatics analysis. We analyzed the expression, mutational status, and prognostic value of these genes based on available public datasets. Our methodology involved data mining, differential expression analysis, functional enrichment analysis, and survival analysis to explore the association between cadherin gene expression and clinical outcomes in GC patients. Additionally, we investigated the relationship between cadherin expression and immune cell infiltration to gain insights into the tumor microenvironment's role in GC progression.

Results

Our bioinformatics analysis revealed significant differential expression of 16 cadherin genes in GC samples compared to normal tissues. Approximately up to 52% of the analyzed cancer samples exhibited genomic alterations in these cadherins, indicating their potential relevance in GC development. Functional enrichment analysis demonstrated that these differentially expressed cadherins were closely associated with critical cellular processes, including cell adhesion and immune-modulation. Remarkably, lower expression levels of most cadherin genes were linked to improved prognosis in GC patients, suggesting their potential importance as valuable prognostic biomarkers.

Conclusions

The findings deriving from our comprehensive study provide important insights into the dysregulation of cadherin genes in GC and their impact on gene expression, molecular pathways, and prognosis. The associations with clinical outcomes and immune cell infiltration highlight the potential role of cadherin genes as prognostic biomarkers and therapeutic targets in GC.

Role of anoikis-related gene PLK1 in kidney renal papillary cell carcinoma: a bioinformatics analysis and preliminary verification on promoting proliferation and migration

Background: Kidney renal papillary cell carcinoma (KIRP) is a rare malignancy with a very poor prognosis. Anoikis is a specific form of apoptosis involved in carcinogenesis, but the role of anoikis in KIRP has not been explored. Methods: Anoikis-related genes (ARGs) were obtained from the GeneCards database and Harmonizome database and were used to identify different subtypes of KIRP and construct a prognostic model of KIRP. In addition, we also explored the immune microenvironment and enrichment pathways among different subtypes by consensus clustering into different subtypes. Drug sensitivity analysis was used to screen for potential drugs. Finally, we verified the mRNA and protein expression of the independent prognostic gene PLK1 in patient tissues and various cells and further verified the changes in relevant prognostic functions after constructing a PLK1 stable knockdown model using ShRNA. Results: We identified 99 differentially expressed anoikis-related genes (DEGs) associated with KIRP survival, and selected 3 genes from them to construct a prognostic model, which can well predict the prognosis of KIRP patients. Consensus clustering divided KIRP into two subtypes, and there was a significant difference in survival rates between the two subtypes. Immune profiling revealed differing immune statuses between the two subtypes, and functional analysis reveals the differential activity of different functions in different subtypes. Drug sensitivity analysis screened out 15 highly sensitive drugs in the high-risk group and 11 highly sensitive drugs in the low-risk group. Univariate and multivariate Cox regression analysis confirmed that PLK1 was an independent prognostic factor in KIRP, and its mRNA and protein expression levels were consistent with gene differential expression levels, both of which were highly expressed in KIRP. Functional verification of PLK1 in KIRP revealed significant results. Specifically, silencing PLK1 inhibited cell proliferation, clonogenicity, and migration, which indicated that PLK1 plays an important role in the proliferation and migration of KIRP. Conclusion: The prognosis model constructed by ARGs in this study can accurately predict the prognosis of KIRP patients. ARGs, especially PLK1, play an important role in the development of KIRP. This research can help doctors provide individualized treatment plans for KIRP patients and provide researchers with new research ideas.

Chemokine CCL14 affected the clinical outcome and correlated with immune infiltrates in thyroid carcinoma

BACKGROUND

As an important member of the chemokines, CCL14 plays a vital role in cancer progression. However, the role of CCL14 in THCA has not been investigated. This study aimed to reveal the clinical significance of CCL14 in THCA.

MATERIAL AND METHODS

This study evaluated the expression and prognostic value of CCL14 in THCA. Also, the correlation between CCL14 and immune infiltrates was assessed. Enrichment analysis was finally performed to predict CCL14-associated pathways involved in THCA.

RESULTS

The mRNA and protein expressions of CCL14 in THCA tissues were down-regulated compared with normal tissues. CCL14 high expression predicted favorable DFI and PFI but did not influence the DSS and OS. Further, CCL14 showed a good prediction performance on the PFI of patients. Enrichment analysis found that CCL14 was negatively correlated with migration-related pathways such as Notch signaling, ECM-receptor interaction, and cell adhesion molecules. Further, we found that CCL14 was negatively related to immune infiltrates and their gene markers. A negative relationship was also observed between CCL14 and immune checkpoint genes. These results implied the potential effect of CCL14 on the immune response and immune therapy in THCA.

CONCLUSIONS

CCL14 high expression prolonged the DFI and PFI of THCA patients. It was negatively correlated with the migration-related pathways, suggesting that CCL14 might participate in the recurrence of THCA. Further, CCL14 was also shown to be important in immune response and immune therapy in THCA.

Integrin α6 Indicates a Poor Prognosis of Craniopharyngioma through Bioinformatic Analysis and Experimental Validation

Background. Craniopharyngioma (CP) is a benign slow-growing tumor. It tends to affect children, and the number of patients is on rise. Considering the high morbidity and mortality of CP, it is urgent and pivotal to identify new biomarkers to uncover the etiology and pathogenesis of CP. Methods. The “limma” package was utilized to calculate the data from the Gene Expression Omnibus (GEO) database. Based on differentially expressed genes (DEGs), gene ontology and pathway analysis were deduced from the DAVID web tool. Further, we constructed a protein-protein interaction (PPI) network. Weighted correlation network analysis (WGCNA) was utilized to build a coexpression network. Finally, Western blotting and survival analysis were performed to examine the expression level of important metabolism-related genes. Results. Three hundred and eighty-four DEGs were identified between normal tissues and CPs from the GSE94349 and GSE26966 datasets. The Venn diagram for DEGs and hub genes in the ‘turquoise’ module revealed four key genes. Finally, the outcome of the survival analysis suggested that Integrin α6 (ITGA6) significantly affected the overall survival time of the patients with CP. Conclusion. IGTA6, as a metabolism-related molecule, was found to be substantially related to the overall survival of patients with CP.

C1QTNF6 Targeted by MiR-184 Regulates the Proliferation, Migration, and Invasion of Lung Adenocarcinoma Cells

OBJECTIVE

To seek out the mechanism by which C1QTNF6 mediates lung adenocarcinoma (LUAD).

METHODS

Differentially expressed mRNAs and miRNAs in LUAD were analyzed using bioinformatics. In LUAD cells, C1QTNF6 mRNA and miR-184 expression were evaluated with qRT-PCR, and C1QTNF6 protein level was assessed by western blot. Cellular behaviors were assessed by colony formation, CCK-8, Transwell, and wound healing methods. The binding ability of miR-184 to C1QTNF6 was observed by dual-luciferase assay.

RESULTS

High expression of C1QTNF6 in LUAD stimulated cancer cellular behaviors. MiR-184 was lowly expressed in LUAD and downregulated C1QTNF6 expression. MiR-184 restrained LUAD cell processes by targeting C1QTNF6.

CONCLUSION

MiR-184 repressed LUAD cell processes via mediating C1QTNF6. MiR-184 and C1QTNF6 are expected to be indicators for LUAD treatment.

Cysteine cathepsin C: a novel potential biomarker for the diagnosis and prognosis of glioma

Background

Cysteine cathepsin C encoded by the CTSC gene is an important member of the cysteine cathepsin family that plays a key role regulation of many types of tumors. However, whether CTSC is involved in the pathological process of glioma has not yet been reported. We comprehensively analyzed data from multiple databases and for the first time revealed a role and specific mechanism of action of CTSC in glioma, identifying it as a novel and efficient biomarker for the diagnosis and treatment of this brain tumor.

Methods

The expression of CTSC in glioma and its relationship with clinical characteristics and prognosis of patients with glioma were analyzed at different levels by using clinical sample information from several databases. CTSC expression levels in glioma and normal brain tissues, as well as in glioma cells and normal brain cells, was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Gene set enrichment analysis (GSEA) was used to reveal the signaling pathways that CTSC may participate in. The connectivity map was used to reveal small molecules that may inhibit CTSC expression in glioma, and the putative effect of these compounds was verified by RT-qPCR.

Results

Our analyses showed that the expression of CTSC in glioma was higher than that in non-cancerous cells. GSEA showed that CTSC expression may regulate the malignant development of glioma through Toll-like receptor signaling pathways, pathways in cancer, and extracellular matrix receptor interaction signaling pathways. And we proved piperlongumine and scopoletin could inhibit CTSC expression in glioma cells.

Conclusions

CTSC may serve as an efficient molecular target for the diagnosis and therapy of glioma, thereby improving the poor prognosis of patients with glioma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02417-6.

Machine Learning Analysis of Immune Cells for Diagnosis and Prognosis of Cutaneous Melanoma

Tumor infiltration, known to associate with various cancer initiations and progressions, is a promising therapeutic target for aggressive cutaneous melanoma. Then, the relative infiltration of 24 kinds of immune cells in melanoma was assessed by a single sample gene set enrichment analysis (ssGSEA) program from a public database. The multiple machine learning algorithms were applied to evaluate the efficiency of immune cells in diagnosing and predicting the prognosis of melanoma. In comparison with the expression of immune cell in tumor and normal control, we built the immune diagnostic models in training dataset, which can accurately classify melanoma patients from normal (LR AUC = 0.965, RF AUC = 0.99, SVM AUC = 0.963, LASSO AUC = 0.964, and NNET AUC = 0.989). These diagnostic models were also validated in three outside datasets and suggested over 90% AUC to distinguish melanomas from normal patients. Moreover, we also developed a robust immune cell biomarker that could estimate the prognosis of melanoma. This biomarker was also further validated in internal and external datasets. Following that, we created a nomogram with a composition of risk score and clinical parameters, which had high accuracies in predicting survival over three and five years. The nomogram's decision curve revealed a bigger net benefit than the tumor stage. Furthermore, a risk score system was used to categorize melanoma patients into high- and low-risk subgroups. The high-risk group has a significantly lower life expectancy than the low-risk subgroup. Finally, we observed that complement, epithelial-mesenchymal transition, and inflammatory response were significantly activated in the high-risk group. Therefore, the findings provide new insights for understanding the tumor infiltration relevant to clinical applications as a diagnostic or prognostic biomarker for melanoma.

Promising novel biomarkers and candidate small-molecule drugs for lung adenocarcinoma: Evidence from bioinformatics analysis of high-throughput data

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.

Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis

Objective:

Glioblastoma multiforme (GBM) is the most malignant primary brain tumor in adults. This study aimed to identify significant prognostic biomarkers related to GBM.

Methods:

We collected 3 GBM and 3 healthy human brain samples for transcriptome and proteomic sequencing analysis. Differentially expressed genes (DEGs) between GBM and control samples were identified using the edge R package in R. Functional enrichment analyses, prediction of long noncoding RNA target genes, and protein-protein interaction network analyses were performed. Subsequently, transcriptomic and proteomic association analyses, validation using The Cancer Genome Atlas (TCGA) database, and survival and prognostic analyses were conducted. Then the hub genes directly related to GBM were screened. Finally, the expression of key genes was verified by quantitative polymerase chain reaction (qPCR).

Results:

Totally, 1140 transcripts and 503 proteins were significantly up- or down-regulated. A total of 25 genes were upregulated and 62 were downregulated at both the transcriptome and proteome levels. Results from TCGA database showed that 84 of these 87 genes matched with transcriptome sequencing results. A Cox regression analysis suggested that Fibronectin 1(FN1) was a prognostic risk factor. The qPCR results showed that FN1 was significantly upregulated in GBM samples.

Conclusions:

FN1 may play a role in GBM progression through ECM-receptor interaction and PI3K-Akt signaling pathways. FN1 may be considered as a prognostic biomarkers related to GBM.

Identification and functional annotation of differentially expressed long noncoding RNAs in retinoblastoma

Retinoblastoma (RB), the most common intraocular malignancy, typically occurs in pediatric patients under the age of 6 years. The present study aimed to explore the long noncoding RNA (lncRNA) expression profile in RB and identify novel lncRNA biomarkers to facilitate the investigation of molecular mechanisms of RB and improve clinical therapy. Raw microarray data for the comparison of gene expression between three RB and three adjacent normal tissue samples were downloaded from Gene Expression Omnibus (dataset no. GSE111168). After identification of differentially expressed lncRNAs (DELs) and differentially expressed mRNAs (DEMs) in RB, functional enrichment analyses and a DEL-DEM weighted correlation network analysis were performed. A total of 3,915 DELs (1,774 upregulated and 2,141 downregulated) and 3,715 DEMs (1,492 upregulated and 2,223 downregulated) were identified in RB. The DEL-targeted DEMs were highly enriched by genes involved in hexose transport, muscle tissue morphogenesis, the stereocilium membrane, endothelin B receptor binding and γ-filamin/ABP-L, α-actinin and telethonin binding protein of the Z-disc binding. Furthermore, associations of the DELs and DEMs with several pathways were determined, including PI3K/AKT, Hippo and cancer signaling, as well as extracellular matrix-receptor interaction pathways. Coexpression network analysis revealed that the top three DELs, lnc-DAZ1-161, lnc-HDAC7-21 and lnc-OR52A1-55, formed coexpression modules with 181, 156 and 210 DEMs, respectively. In addition, the top three DEMs, namely EIF1AY, GSTM1 and NLRP11, formed coexpression modules with 33, 50 and 41 DELs, respectively. Validation using reverse transcription-quantitative PCR indicated that the expression of representative lncRNAs (lnc-DAZ1-161 and lnc-HDAC7-21) in RB cells in vitro was consistent with that in RB tissues in the database, while the expression of lnc-OR52A1-55 was not consistent with the database. These results suggested that the aberrant lncRNA expression profile in RB is related to the differential regulation of numerous physiological and pathological processes. The lncRNA and mRNA profiles in RB identified may provide novel targets for the investigation of its molecular mechanisms and thus lead to improvements in clinical therapy for RB.

A comprehensive analysis of different gene classes in pancreatic cancer: SIGLEC15 may be a promising immunotherapeutic target

BACKGROUND

Pancreatic cancer (PC) is one of the most lethal cancer types with an extremely poor diagnosis and prognosis. This study aimed to comprehensively analyze the relationships between PC and different gene classes.

METHODS

Numerous genes from different categories were selected from the UALCAN database. Expression and survival analysis of these genes were performed via GEPIA, starBase and Kaplan-Meier Plotter tools. The correlations between PC-related genes and frequently mutated genes in PC as well as myeloid-derived suppressor cells (MDSCs) infiltration levels were explored by TIMER tool. The associations between PC-related genes, immune checkpoints and 182 core cancer-intrinsic CTLs-evasion genes were analyzed by R software. Besides, KEGG analysis were performed for the PC-related genes.

RESULTS

14 genes were identified to be highly expressed in pancreatic cancer and significantly associated with poor prognosis. Besides, high expression of these genes were observed in patients with KRAS or TP53 mutations. Most genes were significantly positively associated with immune checkpoint SIGLEC15, however, showed negative relations to PDCD1, CTLA4, LAG3, TIGIT, PDCD1LG2. In addition, all 14 genes exhibited close relationships with MDSC infiltration levels and various core cancer-intrinsic CTLs-evasion genes, especially DNTTIP1, FADD, ARF6, BCL2L1, CEP55, GALE, PDCD6IP, and RCE1. We also explored the most related pathways with these genes to further reveal the pathogenesis and metastatic mechanisms of PC.

CONCLUSION

Our study analyzed the relationships between 14 PC-related genes and pancreatic cancer from different angles, which may contribute to a better understanding of unsolved mystery in PC.

Identification and Validation of a PPP1R12A-Related Five-Gene Signature Associated With Metabolism to Predict the Prognosis of Patients With Prostate Cancer

Background

Prostate cancer (PCa) is the most common malignant male neoplasm in the American male population. Our prior studies have demonstrated that protein phosphatase 1 regulatory subunit 12A (PPP1R12A) could be an efficient prognostic factor in patients with PCa, promoting further investigation. The present study attempted to construct a gene signature based on PPP1R12A and metabolism-related genes to predict the prognosis of PCa patients.

Methods

The mRNA expression profiles of 499 tumor and 52 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. We selected differentially expressed PPP1R12A-related genes among these mRNAs. Tandem affinity purification-mass spectrometry was used to identify the proteins that directly interact with PPP1R12A. Gene set enrichment analysis (GSEA) was used to extract metabolism-related genes. Univariate Cox regression analysis and a random survival forest algorithm were used to confirm optimal genes to build a prognostic risk model.

Results

We identified a five-gene signature (PPP1R12A, PTGS2, GGCT, AOX1, and NT5E) that was associated with PPP1R12A and metabolism in PCa, which effectively predicted disease-free survival (DFS) and biochemical relapse-free survival (BRFS). Moreover, the signature was validated by two internal datasets from TCGA and one external dataset from the Gene Expression Omnibus (GEO).

Conclusion

The five-gene signature is an effective potential factor to predict the prognosis of PCa, classifying PCa patients into high- and low-risk groups, which might provide potential novel treatment strategies for these patients.

Role of RHOC in evaluating an adverse prognosis in patients with glioma and its potential prognostic value

In recent years, major discoveries have indicated that Ras homology family member C (RHOC) is involved in the occurrence and pathological progression of a number of malignant tumours; nevertheless, the role served by RHOC in glioma remains unclear. The present study aimed to gain further insight into the biological function and expression of RHOC in human glioma based on the Chinese Glioma Genome Atlas (CGGA). The current study analysed ~1,000 glioma samples from the CGGA. First, RHOC expression was analysed according to the clinical features associated with the prognosis of glioma, such as clinical stage, histological type and age. Second, the Kaplan-Meier method was used, revealing that the survival rate of patients with glioma with high RHOC expression was significantly lower than that of patients with low RHOC expression. Receiver operating characteristic curve analysis indicated that RHOC had moderate diagnostic value for patients with glioma. Gene set enrichment analysis indirectly indicated that RHOC mainly participated in the pathological mechanism of glioma through p53, extracellular matrix receptor interaction and focal adhesion. Finally, the aforementioned results were further verified using The Cancer Genome Atlas data and reverse transcription-quantitative PCR technology. To the best of our knowledge, the present study was the first comprehensive in-depth analysis of RHOC, revealing the potential value of RHOC as a novel oncogene in glioma. The current study provided a novel potential biomarker for the diagnosis and prognosis of glioma, and re-examined the pathological mechanism of glioma from a new perspective.

GNG5 is a novel oncogene associated with cell migration, proliferation, and poor prognosis in glioma

Background

Although many biomarkers have been reported for detecting glioma, the prognosis for the disease remains poor, and therefore, new biomarkers need to be identified. GNG5, which is part of the G-protein family, has been associated with different malignant tumors, though the role of GNG5 in glioma has not been studied. Therefore, we aimed to identify the relationship between GNG5 and glioma prognosis and identify a new biomarker for the diagnosis and treatment of gliomas.

Methods

We used data on more than a thousand gliomas from multiple databases and clinical data to determine the expression of GNG5 in glioma. Based on clinical data and CGGA database, we identified the correlation between GNG5 and multiple molecular and clinical features and prognosis using various analytical methods. Co-expression analysis and GSEA were performed to detect GNG5-related genes in glioma and possible signaling pathways involved. ESTIMATE, ssGSEA, and TIMER were used to detect the relationship between GNG5 and the immune microenvironment. Functional experiments were performed to explore the function of GNG5 in glioma cells.

Results

GNG5 is highly expressed in gliomas, and its expression level is positively correlated with pathological grade, histological type, age, and tumor recurrence and negatively correlated with isocitrate dehydrogenase mutation, 1p/19 co-deletion, and chemotherapy. Moreover, GNG5 as an independent risk factor was negatively correlated with the overall survival time. GSEA revealed the potential signaling pathways involved in GNG5 function in gliomas, including cell adhesion molecules signaling pathway. The ssGSEA, ESTIMATE, and TIMER based analysis indicated a correlation between GNG5 expression and various immune cells in glioma. In vivo and in vitro experiments showed that GNG5 could participate in glioma cell proliferation and migration.

Conclusions

Based on the large data platform and the use of different databases to corroborate results obtained using various datasets, as well as in vitro and in vivo experiments, our study reveals for the first time that GNG5, as an oncogene, is overexpressed in gliomas and can inhibit the proliferation and migration of glioma cells and lead to poor prognosis of patients. Thus, GNG5 is a potential novel biomarker for the clinical diagnosis and treatment of gliomas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01935-7.

EPS364, a Novel Deep-Sea Bacterial Exopolysaccharide, Inhibits Liver Cancer Cell Growth and Adhesion

The prognosis of liver cancer was inferior among tumors. New medicine treatments are urgently needed. In this study, a novel exopolysaccharide EPS364 was purified from Vibrio alginolyticus 364, which was isolated from a deep-sea cold seep of the South China Sea. Further research showed that EPS364 consisted of mannose, glucosamine, gluconic acid, galactosamine and arabinose with a molar ratio of 5:9:3.4:0.5:0.8. The relative molecular weight of EPS364 was 14.8 kDa. Our results further revealed that EPS364 was a β-linked and phosphorylated polysaccharide. Notably, EPS364 exhibited a significant antitumor activity, with inducing apoptosis, dissipation of the mitochondrial membrane potential (MMP) and generation of reactive oxygen species (ROS) in Huh7.5 liver cancer cells. Proteomic and quantitative real-time PCR analyses indicated that EPS364 inhibited cancer cell growth and adhesion via targeting the FGF19-FGFR4 signaling pathway. These findings suggest that EPS364 is a promising antitumor agent for pharmacotherapy.

Identification of novel biomarkers and candidate small-molecule drugs in cutaneous melanoma by comprehensive gene microarrays analysis

Background: Melanoma is a pernicious skin cancer with high aggressiveness. This study aimed to identify potential novel biomarkers associated with the prognosis and pathogenesis of cutaneous melanoma and to explore new targeted drugs for melanoma. Methods: Two Gene Expression Omnibus (GEO) microarray datasets, GSE3189 and GSE7553 were combined to analyze the differentially expressed genes (DEGs). To better understand the DEGs in the melanoma pathogenesis, we performed gene enrichment analyses and established a protein-protein interaction network (PPI). The survival analyses for key genes were conducted based on the GEPIA platform. Finally, we mined the CMap database to explore potential small-molecule drugs to target the obtained DEGs. Results: In short, we identified 500 DEGs between cutaneous melanoma samples and normal samples. The PPI network was established with 349 nodes and 1251 edges. Signaling pathway analysis showed that these genes play a vital role in ECM-receptor interactions, the PPAR signaling pathway and pathways in cancer. Eight DEGs with a relatively high degree of connectivity (CDC45, CENPF, DTL, FANCI, GINS2, HJURP, TPX2 and TRIP13) were selected as hub-genes that remarkably correlated to a poor survival rate. Based on 500 DEGs, 20 small-molecule drugs that potentially target genes with abnormal expression in cutaneous melanoma were obtained from the CMap database. Among these compounds, we found that menadione has the greatest therapeutic value for melanoma. Conclusions: In conclusion, we identified the 8 candidate biomarkers and potential key signaling pathways in cutaneous melanoma through comprehensive microarray analyses. The identified candidate drugs have provided several directive significances for the synthesis medicine for melanoma.

Assessment of Significant Pathway Signaling and Prognostic Value of GNG11 in Ovarian Serous Cystadenocarcinoma

BACKGROUND

GNG11 (G protein subunit gamma 11) is a member of guanine nucleotide-binding protein (G protein) gamma family. Few studies elucidated the role of GNG11 in human disease, especially in tumors. The present study initially analyzed the function of GNG11 in ovarian serous cystadenocarcinoma.

METHODS

The differential expression of GNG11 mRNA in ovarian cancer and normal tissues was evaluated through Oncomine, CCLE, Gepia, UCSC Xena and UALCAN databases. The protein expression of GNG11 was assessed via HPA database. Prognosis analysis was performed by Kaplan-Meier Plotter. Restrict survival analysis to subtypes including tumor grade, cancer stage and TP53 mutation status was then carried out. GSEA enrichment analysis was performed to explore the significant pathways associated with GNG11 in ovarian cancer. Finally, the upstream miRNAs of GNG11 were predicted by DIANA, Target Scan, miRDB and miRWalk databases, and the potential key KEGG pathways were subsequently determined by DIANA.

RESULTS

The mRNA expression of GNG11 was down-regulated in ovarian cancer patients (P<0.05). The cancer stage of patients correlated with the expression of GNG11 (P<0.05). Survival analysis indicated that GNG11 high expression statistically shortened the overall survival time of patients (HR=1.26, P=0.0043) compared with low expression group, especially for the patients with earlier stage (HR=2.48, P=0.035) and lower grade (HR=1.72, P=0.0016). Subsequently, the consistent upstream miRNA of GNG11, hsa-miR-22-5p, was predicted from 4 databases. The differential expression profile of hsa-miR-22-5p in blood was observed in ovarian cancer patients. According to the GSEA analysis on GNG11 and KEGG analysis on hsa-miR-22-5p, the consistent pathway of ECM-receptor interaction was observed (all P<0.01). ECM-receptor interaction pathway and differential expression of hsa-miR-22-5p in blood suggested the migration risk of ovarian cancer.

CONCLUSION

High expression of GNG11 indicated the poor prognosis of ovarian cancer patients. GNG11 might play a crucial role in the biological process of ovarian serous cystadenocarcinoma by ECM-receptor interaction pathway, thus affecting the prognosis of patients.

BGN and COL11A1 Regulatory Network Analysis in Colorectal Cancer (CRC) Reveals That BGN Influences CRC Cell Biological Functions and Interacts with miR-6828-5p

Purpose

We explored specific expression profiles of BGN and COL11A1 genes and studied their biological functions in CRC using bioinformatics tools.

Patients and Methods

A total of 68 pairs of cancer and non-cancerous tissues from CRC patients were enrolled in this study. Methods we used in this articles including: qRT-PCR, Western blot analysis, ELISA, GO and KEGG regulatory network analysis, tumor infiltration, luciferase reporter-based protein and etc.

Results

According to The Cancer Genome Atlas (TCGA) data, BGN and COL11A1 expression levels were significantly higher in CRC patient samples than in samples from healthy controls. Moreover, levels were much higher in late-stage CRC than in early-stage disease, warranting evaluation of these genes as CRC prognostic biomarkers. Subsequently, qRT-PCR, Western blot analysis, and ELISA results obtained from analyses of CRC cells, tissues, and patient sera aligned with TCGA results. GO and KEGG regulatory network analysis revealed BGN- and COL11A1-associated genes that were functionally related to extracellular matrix (ECM) receptor pathway activation, with transcription factor genes RELA and NFKB1 positively associated with BGN expression and CEBPZ and SIRT1 with COL11A1 expression. Meanwhile, BGN and COL11A1 expression were separately and significantly correlated to tumor infiltration by six immune cell types. Additionally, kinase genes PLK1 and LYN appeared to be downstream targets of differentially expressed BGN and COL11A1, respectively. In addition, the expression of PLK1 mRNA was down-regulated while BGN was down-regulated. Finally, BGN effects on CRC cell proliferation, cycle, apoptosis, invasion, and migration were studied using molecular biological methods, including luciferase reporter-based protein analysis, qRT-PCR, and Western blot results, which revealed that miR-6828-5p may regulate BGN expression.

Conclusion

We speculate that the use of BGN and COL11A1 as CRC biomarkers would improve CRC staging, while also providing several novel targets for use in the development of more effective CRC treatments.

Discovery and Validation of a Metastasis-Related Prognostic and Diagnostic Biomarker for Melanoma Based on Single Cell and Gene Expression Datasets

Background

Single cell sequencing can provide comprehensive information about gene expression in individual tumor cells, which can allow exploration of heterogeneity of malignant melanoma cells and identification of new anticancer therapeutic targets.

Methods

Single cell sequencing of 31 melanoma patients in GSE115978 was downloaded from the Gene Expression Omniniub (GEO) database. First, the limma package in R software was used to identify the differentially expressed metastasis related genes (MRGs). Next, we developed a prognostic MRGs biomarker in the cancer genome atlas (TCGA) by combining univariate cox analysis and the least absolute shrinkage and selection operator (LASSO) method and was further validated in another two independent datasets. The efficiency of MRGs biomarker in diagnosis of melanoma was also evaluated in multiple datasets. The pattern of somatic tumor mutation, immune infiltration, and underlying pathways were further explored. Furthermore, nomograms were constructed and decision curve analyses were also performed to evaluate the clinical usefulness of the nomograms.

Results

In total, 41 MRGs were screened out from 1958 malignant melanoma cell samples in GSE115978. Next, a 5-MRGs prognostic marker was constructed and validated, which show more effective performance for the diagnosis and prognosis of melanoma patients. The nomogram showed good accuracies in predicting 3 and 5 years survival, and the decision curve of nomogram model manifested a higher net benefit than tumor stage and clark level. In addition, melanoma patients can be divided into high and low risk subgroups, which owned differential mutation, immune infiltration, and clinical features. The low risk subgroup suffered from a higher tumor mutation burden (TMB), and higher levels of T cells infiltrating have a significantly longer survival time than the high risk subgroup. Gene Set Enrichment Analysis (GSEA) revealed that the extracellular matrix (ECM) receptor interaction and epithelial mesenchymal transition (EMT) were the most significant upregulated pathways in the high risk group.

Conclusions

We identified a robust MRGs marker based on single cell sequencing and validated in multiple independent cohort studies. Our finding provides a new clinical application for prognostic and diagnostic prediction and finds some potential targets against metastasis of melanoma.

Comprehensive analysis of cancer hallmarks in cutaneous melanoma and identification of a novel unfolded protein response as a prognostic signature

Molecular pathways regulating the initiation and development of melanoma are potential therapeutic targets for this aggressive skin cancer. Therefore, transcriptome profiles of cutaneous melanoma were obtained from a public database and used to systematically evaluate cancer hallmark pathways enriched in melanoma. Finally, the unfolded protein response pathway was screened out, and the unfolded protein response-related genes were used to develop a robust biomarker that can predict the prognosis of melanoma, especially for younger, metastatic and high Clark level patients. This biomarker was further validated in two other independent datasets. In addition, melanoma patients were divided into high- and low-risk subgroups by applying a risk score system. The high-risk group exhibited higher immune infiltration and higher expression of N6-methyladenosine RNA methylation regulators, and had significantly shorter survival times than the low-risk subgroup. Gene Set Enrichment Analysis revealed that, among the enriched genes, gene sets involved in immune response and the extracellular matrix receptor interaction were significantly activated in the high-risk group. Our findings thus provide a new clinical application for prognostic prediction as well as potential targets for treatment of melanoma.

High-Throughput MicroRNA Profiling of Vitreoretinal Lymphoma: Vitreous and Serum MicroRNA Profiles Distinct from Uveitis

Purpose: Vitreoretinal lymphoma (VRL) is a non-Hodgkin lymphoma of the diffuse large B cell type (DLBCL), which is an aggressive cancer causing central nervous system related mortality. The pathogenesis of VRL is largely unknown. The role of microRNAs (miRNAs) has recently acquired remarkable importance in the pathogenesis of many diseases including cancers. Furthermore, miRNAs have shown promise as diagnostic and prognostic markers of cancers. In this study, we aimed to identify differentially expressed miRNAs and pathways in the vitreous and serum of patients with VRL and to investigate the pathogenesis of the disease. Materials and Methods: Vitreous and serum samples were obtained from 14 patients with VRL and from controls comprising 40 patients with uveitis, 12 with macular hole, 14 with epiretinal membrane, 12 healthy individuals. The expression levels of 2565 miRNAs in serum and vitreous samples were analyzed. Results: Expression of the miRNAs correlated significantly with the extracellular matrix (ECM) ‒receptor interaction pathway in VRL. Analyses showed that miR-326 was a key driver of B-cell proliferation, and miR-6513-3p could discriminate VRL from uveitis. MiR-1236-3p correlated with vitreous interleukin (IL)-10 concentrations. Machine learning analysis identified miR-361-3p expression as a discriminator between VRL and uveitis. Conclusions: Our findings demonstrate that aberrant microRNA expression in VRL may affect the expression of genes in a variety of cancer-related pathways. The altered serum miRNAs may discriminate VRL from uveitis, and serum miR-6513-3p has the potential to serve as an auxiliary tool for the diagnosis of VRL.

Renal carcinoma CD105-/CD44- cells display stem-like properties in vitro and form aggressive tumors in vivo

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer. Prognosis for ccRCC is generally poor since it is largely resistant to chemo- and radiotherapy. Many studies suggested that cancer stem cells/tumor initiating cells (CSCs/TICs) are responsible for development of tumor, disease progression, aggressiveness, metastasis and drug resistance. However, tumorigenic potential of CSCs/TICs isolated from established RCC cell lines - basic ccRCC research model - has never been investigated in vivo. CD105+, CD105-, CD44+ and CD44- as well as CD44-/CD105- CD44+/CD105+ and CD44-/CD105+ cells were isolated from Caki-1 RCC cell line, confirming coexistence of multiple subpopulations of stem-related phenotype in stable cell line. Sorted cells were injected subcutaneously into NOD SCID mice and tumor growth was monitored with MRI and PET/CT. Tumor growth was observed after implantation of CD105+, CD44+, CD44-, CD44-/CD105+ and CD44-/CD105- but not CD105- or CD44+/CD105+. Implantation of CD44-/CD105- cells induced tumors that were characterized by longer T1 and distinct metabolic pattern than other tumors. All the tumors were characterized by low uptake of [18F]FDG. CD105+ and CD44- tumors expresses Nanog and Oct-4, while CD44- tumors additionally expressed endothelial cell marker - CD31.

Identification and analysis of long non-coding RNA related miRNA sponge regulatory network in bladder urothelial carcinoma

Background

The aim of this study was to investigate the regulatory network of lncRNAs as competing endogenous RNAs (ceRNA) in bladder urothelial carcinoma (BUC) based on gene expression data derived from The Cancer Genome Atlas (TCGA).

Materials and methods

RNA sequence profiles and clinical information from 414 BUC tissues and 19 non-tumor adjacent tissues were downloaded from TCGA. Differentially expressed RNAs derived from BUC and non-tumor adjacent samples were identified using the R package “edgeR”. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the “clusterProfiler” package. Gene ontology and protein–protein interaction (PPI) networks were analyzed for the differentially expressed mRNAs using the “STRING” database. The network for the dysregulated lncRNA associated ceRNAs was then constructed for BUC using miRcode, miRTarBase, miRDB, and TargetScan. Cox regression analysis was performed to identify independent prognostic RNAs associated with BUC overall survival (OS). Survival analysis for the independent prognostic RNAs within the ceRNA network was calculated using Kaplan–Meier curves.

Results

Based on our analysis, a total of 666, 1819 and 157 differentially expressed lncRNAs, mRNAs and miRNAs were identified respectively. The ceRNA network was then constructed and contained 59 lncRNAs, 23 DEmiRNAs, and 52 DEmRNAs. In total, 5 lncRNAs (HCG22, ADAMTS9-AS1, ADAMTS9-AS2, AC078778.1, and AC112721.1), 2 miRNAs (hsa-mir-145 and hsa-mir-141) and 6 mRNAs (ZEB1, TMEM100, MAP1B, DUSP2, JUN, and AIFM3) were found to be related to OS. Two lncRNAs (ADAMTS9-AS1 and ADAMTS9-AS2) and 4 mRNA (DUSP2, JUN, MAP1B, and TMEM100) were validated using GEPIA. Thirty key hub genes were identified using the ranking method of degree. KEGG analysis demonstrated that the majority of the DEmRNAs were involved in pathways associated with cancer.

Conclusion

Our findings provide an understanding of the important role of lncRNA–related ceRNAs in BUC. Additional experimental and clinical validations are required to support our findings.

TMT-based quantitative proteomics revealed follicle-stimulating hormone (FSH)-related molecular characterizations for potentially prognostic assessment and personalized treatment of FSH-positive non-functional pituitary adenomas

BACKGROUND

Non-functional pituitary adenoma (NFPA) is highly heterogeneous with different hormone expression subtypes. Of them, follicle-stimulating hormone (FSH)-positive expression is an important subtype of NFPAs. It is well-known that FSH exerted its functions through binding its receptor. However, the expression rate of FSH receptor was significantly higher in aggressive pituitary adenomas. This study aimed to investigate the molecular characteristics of FSH-positive NFPAs for effective stratification of patient, target treatment, prognostic assessment, and personalized treatment of FSH-positive NFPAs.

METHODS

Tandem mass tag (TMT)-based quantitative proteomics was used to investigate differentially expressed proteins (DEPs) between FSH-positive and negative NFPAs. Gene ontology and KEGG pathway enrichment analyses were used to analyze the DEPs. Differentially expressed genes (DEGs) between invasive and non-invasive NFPAs from GEO database were analyzed with pathway enrichment analysis. Protein-protein interaction (PPI) networks were constructed based on DEPs in excetral cellular matrix (ECM)-receptor interaction, focal adhesion, and PI3K-Akt pathways. Cytoscape was used to obtain most significant modules. Western blot was used to validate the expressions of upregulated proteins (ITGA1, ITGA6, and ITGB4), the expression and phosphorylated status of Akt in PI3K-Akt pathway, and the expression of FSH receptors in FSH-positive relative to negative NFPAs.

RESULTS

A total of 594 DEPs (374 upregulated and 220 downregulated) were identified between FSH-positive and negative NFPAs. Nineteen KEGG pathway networks were identified to involve DEPs, and reveal molecular differences between FSH-positive and negative NFPAs, including three important pathways that were significantly associated with tumor invasiveness and aggressiveness: ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways. Further, focal adhesion pathway was also confirmed with invasiveness-related NFPA DEG data that were derived from GEO database. Moreover, the significantly upregulated DEPs (ITGA1, ITGA6, and ITGB4) that were associated with tumor invasiveness and aggressiveness were confirmed by immunoaffinity analysis in FSH-positive vs. negative NFPAs. Also, the phosphorylation level but not its expression level of AKT in PI3K-AKT signaling was significantly increased, and the expression level of FSH receptor was significantly increased in FSH-positive relative to negative NFPAs. Also, overlapping analysis of 594 DEPs and 898 DEGs revealed 45 invasiveness-related DEPs, including 11 upregulated DEPs (ITGA6, FARP1, PALLD, PPBP, LIMA1, SCD, UACA, BAG3, CLU, PLEC, and GATM) that were also upregulated genes in invasive NFPAs, and 8 downregulated DEPs (ALCAM, HP, FSTL4, IL13RA2, NPTX2, DPP6, CRABP2, and SLC27A2) that were also downregulated genes in invasive NFPAs.

CONCLUSIONS

FSH-positive expression was an important NFPA subtype. It was the first time for this study to reveal FSH-related proteomic variations and the corresponding molecular network alterations in FSH-positive relative to negative NFPAs. Also, three signaling pathways (ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways) and involved upregulated proteins (ITGA1, ITGA6, ITGB4, pAKT, and FSHR) were significantly associated with tumor invasiveness and aggressiveness, and a set of invasiveness-related DEPs were identified with overlapping analysis of 594 DEPs in FSH-positive vs. negative NFPAs and 898 DEGs in invasive vs. non-invasive NFPAs. These findings offered the scientific evidence to in-depth understand molecular characteristics of FSH-positive NFPAs, and effectively stratify the post-surgery patients for personalized prognostic assessment and targeted treatment of FSH-positive NFPAs.

Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma

Background

The aim of this study was to identify biomarkers closely related to the pathogenesis and prognosis of oral squamous cell carcinoma (OSCC) by using weighted gene co-expression network analysis (WGCNA) based on integrative transcriptome datasets.

Material/Methods

Gene expression profiles of OSCC were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained and we then performed with Gene ontology (GO) and pathway enrichment analysis as well as protein–protein interactions (PPI) network analysis. WGCNA was used to construct the co-expression network. Multipart results were intersected to acquire the candidate genes, and survival analysis was used to identify the hub genes.

Results

A total of 568 DEGs, including 272 upregulated genes and 296 downregulated genes, were identified. GO and pathway analyses revealed that these DEGs were mainly enriched in extracellular matrix (ECM), ECM organization, structural constituent of muscle, and ECM-receptor interaction. The PPI network of DEGs was established, comprising 428 nodes and 1944 edges. In the co-expression network, pink module was the key module, in which 34 genes with high connectivity were identified. After the intersection of multipart results, 24 common genes were chosen as the candidate genes, among which 7 hub genes (PLAU, SERPINE1, LAMC2, ITGA5, TGFBI, FSCN1, and HLF) were identified using survival analysis.

Conclusions

Seven potential biomarkers were identified as being closely related with the initiation and prognosis of OSCC and might serve as potential targets for early diagnosis and personalized therapy of OSCC.

Identification of potential diagnostic and prognostic biomarkers for prostate cancer

Prostate cancer (PCa) is one of the most common malignant tumors worldwide. The aim of the present study was to determine potential diagnostic and prognostic biomarkers for PCa. The GSE103512 dataset was downloaded, and the differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) analyses of DEGs were performed. The result of GO analysis suggested that the DEGs were mostly enriched in ‘carboxylic acid catabolic process’, ‘cell apoptosis’, ‘cell proliferation’ and ‘cell migration’. KEGG analysis results indicated that the DEGs were mostly concentrated in ‘metabolic pathways’, ‘ECM-receptor interaction’, the ‘PI3K-Akt pathway’ and ‘focal adhesion’. The PPI analysis results showed that Golgi membrane protein 1 (GOLM1), melanoma inhibitory activity member 3 (MIA3), ATP citrate lyase (ACLY) and G protein subunit β2 (GNB2) were the key genes in PCa, and the Module analysis revealed that they were associated with ‘ECM-receptor interaction’, ‘focal adhesion’, the ‘PI3K-Akt pathway’ and the ‘metabolic pathway’. Subsequently, the gene expression was confirmed using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. The results demonstrated that GOLM1 and ACLY expression was significantly upregulated (P<0.05) in PCa compared with that in normal tissues. Receiver operating characteristic and survival analyses were performed. The results showed that area under the curve values of these genes all exceeded 0.85, and high expression of these genes was associated with poor survival in patients with PCa. In conclusion, this study identified GOLM1 and ACLY in PCa, which may be potential diagnostic and prognostic biomarker of PCa.

Bioinformatics analysis identifies hub genes and pathways in nasopharyngeal carcinoma

The aim of the present study was to identify genes associated with and the underlying mechanisms in nasopharyngeal carcinoma (NPC) using microarray data. GSE12452 and GSE34573 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was utilized to obtain differentially expressed genes (DEGs). In addition, the Database for Annotation, Visualization and Integrated Discovery was used to perform pathway enrichment analyses for DEGs using the Gene Ontology (GO) annotation along with the Kyoto Encyclopedia of Genes and Genomes (KEGG). Furthermore, Cytoscape was used to perform module analysis of the protein-protein interaction (PPI) network and pathways of the hub genes were studied. A total of 298 genes were ascertained as DEGs in the two datasets. To functionally categorize these DEGs, we obtained 82 supplemented GO terms along with 7 KEGG pathways. Subsequently, a PPI network consisting of 10 hub genes with high degrees of interaction was constructed. These hub genes included cyclin-dependent kinase (CDK) 1, structural maintenance of chromosomes (SMC) 4, kinetochore-associated (KNTC) 1, kinesin family member (KIF) 23, aurora kinase A (AURKA), ATAD (ATPase family AAA domain containing) 2, NDC80 kinetochore complex component, enhancer of zeste 2 polycomb repressive complex 2 subunit, BUB1 mitotic checkpoint serine/threonine kinase and protein regulator of cytokinesis 1. CDK1, SMC4, KNTC1, KIF23, AURKA and ATAD2 presented with high areas under the curve in receiver operator curves, suggesting that these genes may be diagnostic markers for nasopharyngeal carcinoma. In conclusion, it was proposed that CDK1, SMC4, KNTC1, KIF23, AURKA and ATAD2 may be involved in the tumorigenesis of NPC. Furthermore, they may be utilized as molecular biomarkers in early diagnosis of NPC.

Screening and identification of potential target genes in head and neck cancer using bioinformatics analysis

Head and neck cancer (HNC) is the sixth most common cancer worldwide. Recent studies on the pathogenesis of HNC have identified some biochemical associations of this disease, but the molecular mechanisms are not clear. To explore the genetic alterations in head and neck tumors, to identify new high-specificity and high-sensitivity tumor markers, and to investigate potentially effective therapeutic targets, in silico methods were used to study HNC. The GSE58911 microarray dataset was downloaded from the Gene Expression Omnibus online database to identify potential target genes in the carcinogenesis and progression of HNC. Differentially expressed genes (DEGs) were identified and functional enrichment analysis was performed. In addition, a protein-protein interaction network was also constructed, and gene analysis was undertaken using Search Tool for the Retrieval of Interacting Genes and Cytoscape. A total of 648 differentially expressed genes were identified. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology functional enrichment analysis of DEGs included muscle system process, extracellular matrix organization, actin binding, structural molecule activity, structural constituent of muscle, extracellular region part, ECM-receptor interaction, amoebiasis, focal adhesion, drug metabolism-cytochrome P450, and chemical carcinogenesis. There were 26 hub genes identified and biological process analysis revealed that these genes were mainly enriched in extracellular matrix organization, serine-type endopeptidase activity, extracellular matrix, and complement and coagulation cascades. Survival analysis revealed that interleukin (IL)-8 (C-X-C motif chemokine ligand 8), IL1B, and serpin family A member 1 may be involved in the carcinogenesis of HNC. In summary, the DEGs and hub genes identified in the present study may increase understanding of the molecular mechanisms of development of HNC and provide potential target genes for clinical diagnosis and targeted therapy.

Identification of novel biomarkers and candidate small molecule drugs in non-small-cell lung cancer by integrated microarray analysis

Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC.

Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein–protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database.

Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 down-regulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression.

Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.

Downregulation of HOXA3 in lung adenocarcinoma and its relevant molecular mechanism analysed by RT-qPCR, TCGA and in silico analysis

Recent studies have indicated that homeobox A3 (HOXA3) functions as a carcinogen in colon cancer and the methylation level of HOXA3 is significantly increased in lung adenocarcinoma (LUAD) tissues. However, at least to the best of our knowledge, few studies to date have been performed on HOXA3 in non-small cell lung cancer (NSCLC). Therefore, further studies on HOXA3 expression in NSCLC and the potential regulatory mechanisms are urgently required. In this study, HOXA3 expression in 55 tissues of cases of NSCLC and corresponding non-lung cancer tissues was detected by reverse transcription-quantitative PCR (RT-qPCR). In addition, the clinical significance of HOXA3 expression in NSCLC was evaluated using the Cancer Genome Atlas (TCGA) database. Bioinformatics analysis was then performed to elucidate the potential molecular mechanisms of action of HOXA3. Furthermore, the potential target microRNAs (miRNAs or miRs) of HOXA3 were predicted using miRWalk2.0. Based on Gene Expression Omnibus (GEO) and TGCA databases, standardized mean difference (SMD) and sROC methods were used for meta-analyses of the expression of potential target miRNAs of HOXA3 in NSCLC to evaluate their association with HOXA3. The results revealed that the HOXA3 expression levels in NSCLC, LUAD and lung squamous cell carcinoma (LUSC) were 0.1130±0.1398, 0.1295±0.16890 and 0.0906±0.0846, respectively. These values were all decreased compared with the normal tissues (0.1877±0.1975, 0.2337±0.2405 and 0.1249±0.0873, respectively, P<0.05). The TCGA database also revealed the low expression trend of HOXA3. The downregulation of HOXA3 may play an important role in the progression and the poor prognosis of LUAD. The TCGA database also suggested that HOXA3 in LUAD and LUSC tissues exhibited certain mutational levels. In addition, the methylation levels in the NSCLC, LUAD and LUSC tissues significantly increased [NSCLC: fold change (FC), 1.3226; P<0.001; LUAD: FC, 1.2712; P<0.001; and LUSC: FC, 1.3786; P<0.001]. According to the analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we found that the co-expression HOXA3 genes were mainly associated with the focal adhesion signalling pathway and the ECM-receptor interaction signalling pathway. Furthermore, the predicted miRNA, miR-372-3p, exhibited a high expression in both the NSCLC and LUAD tissues (P<0.05). On the whole, the findings of this study indicate that low HOXA3 expression may play a certain role in LUAD; however, its association with LUSC still requires further investigation. HOXA3 function may be achieved through different pathways or target miRNAs. However, the specific underlying mechanisms need to be confirmed through various functional studies.

Meta-analysis of gene expression and integrin-associated signaling pathways in papillary renal cell carcinoma subtypes

Papillary renal cell carcinoma (PRCC) is the second most common renal cell carcinoma (RCC) that can be further subdivided into type 1 (PRCC1) and type 2 (PRCC2) RCCs based on histological and genetic features. PRCC2 is often more aggressive than PRCC1. While integrin-associated protein complexes mediate tumorigenesis and metastases in many types of cancers it is not known whether integrin-mediated signaling impacts PRCC and differs between PRCC1 and PRCC2. In this study, we combined the analysis of five PRCC gene expression datasets derived from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) by using integrative bioinformatics pipelines. We found 1475 differentially expressed genes among which 37 genes were associated with integrin pathways. In comparison with PRCC1, PRCC2 cases showed upregulated expression of α5-integrin (ITGA5) whereas the expression of α6- (ITGA6) and β8-integrins (ITGB8) was downregulated. Because PRCC2 occurs more frequently in men, the meta-analysis was extended to explore the gender effects. This analysis revealed 8 genes but none of them was related to integrin pathways suggesting that other mechanisms than integrin-mediated signaling underlie the observed gender differences in the pathogenicity of PRCC2.

Comparative transcriptome and histological analyses provide insights into the prenatal skin pigmentation in goat (Capra hircus)

The Youzhou dark goat is a natural mutant with dark skin over the whole body including the visible mucous membranes. In the present study, we characterized 100-day-old fetal skin at the histomorphological and transcriptomic levels in dark-skinned (Youzhou dark goat) and white-skinned (Yudong white goat) goats with deep RNA sequencing, quantitative PCR, and histological methods. Histological analysis indicated that there were marked differences in both melanin distribution and epidermal ultrastructure between the hyperpigmented and normal skin in two breeds of goat. Subsequent analyses suggested that a presumed structure variation (duplication or insertion) in ASIP might be responsible for its lower expression in the hyperpigmented skin (Youzhou dark goat) by determining the distribution of melanocytes across the body at early development stage. Analyses for genes with differential expression between the dark-skinned and white-skinned goats indicated the network composed of ASIP-MC1R, ECM-receptor interaction, and MAPK signaling might play crucial roles in the determination of skin pigmentation in fetal goats. Moreover, we also identified 1,616 novel transcripts in goat skin by RNA sequencing, which may represent two distinct groups of transcript based on their characteristics. Our findings contribute to the understanding of the characteristics of global gene expression in early-stage skin pigmentation and development and describe an animal model for human diseases associated with pigmentation.

Exploration of the mechanism of colorectal cancer metastasis using microarray analysis

The aim of the present study was to investigate the mechanism of metastasis in colorectal cancer (CRC) using microRNA (miRNA) and mRNA expression profiles. The mRNA and miRNA expression profiles of the GSE2509 and GSE56350 datasets were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified using the limma software package. The Database for Annotation, Visualization and Integrated Discovery was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs. The predicted target genes associated with the DEMs were identified using the miRWalk database and the enrichment analysis was conducted using the clusterProfiler package. The miRNA-gene molecular interaction network was visualized using the Cytoscape software platform. A total of 544 DEGs and 42 DEMs were identified. DEGs were annotated in 320 GO terms and 11 KEGG pathways. Overall, 366 miRNA-gene pairs were identified and the miRNA-gene network was visualized. Furthermore, the predicted target genes were mainly classified in 12 pathways. The results of the present study suggest that fibronectin type III domain-containing 3B, cysteine rich transmembrane BMP regulator 1 and forkhead box J2 may be potential therapeutic and prognostic targets of metastatic CRC. In addition, pathways in cancer, the Wnt signaling pathway and extracellular matrix-receptor interaction may play a critical role in CRC metastasis.

Ror2, a Developmentally Regulated Kinase, Is Associated With Tumor Growth, Apoptosis, Migration, and Invasion in Renal Cell Carcinoma

Renal cell carcinoma (RCC) represents one of the most resistant tumors to radiation and chemotherapy. Current therapies for RCC patients are inefficient due to the lack of diagnostic and therapeutic markers. The expression of novel tumor-associated kinases has the potential to dramatically shape tumor cell behavior. Identifying tumor-associated kinases can lend insight into patterns of tumor growth and characteristics. In the present study, we investigated the receptor tyrosine kinase-like orphan receptor 2 (Ror2), a new tumor-associated kinase, in RCC primary tumors and cell lines. Knockdown of Ror2 expression in RCC cells with specific shRNA significantly reduced cell proliferation and induced apoptosis. Using in vitro migration and Matrigel invasion assays, we found that cell migration and invasive ability were also significantly inhibited. In RCC, Ror2 expression correlated with expression of genes involved at the cell cycle and migration, including PCNA, CDK1, TWIST, and MMP-2. Furthermore, in vivo xenograft studies in nude mice revealed that administration of a Ror2 shRNA plasmid significantly inhibited tumor growth. These findings suggest a novel pathway of tumor-promoting activity by Ror2 within renal carcinomas, with significant implications for unraveling the tumorigenesis of RCC.