Integrated whole genome microarray analysis and immunohistochemical assay identifies COL11A1, GJB2 and CTRL as predictive biomarkers for pancreatic cancer

A novel method for detecting nine hotspot mutations of deafness genes in one tube

Deafness is a common sensory disorder. In China, approximately 70% of hereditary deafness originates from four common deafness-causing genes: GJB2, SLC26A4, GJB3, and MT-RNR1. A single-tube rapid detection method based on 2D-PCR technology was established for nine mutation sites in the aforementioned genes, and Sanger sequencing was used to verify its reliability and accuracy. The frequency of hotspot mutations in deafness genes was analysed in 116 deaf students. 2D-PCR identified 27 genotypes of nine loci according to the melting curve of the FAM, HEX, and Alexa568 fluorescence channels. Of the 116 deaf patients, 12.9% (15/116) carried SLC26A4 mutations, including c.919-2A > G and c.2168A > G (allele frequencies, 7.3% and 2.2%, respectively). The positivity rate (29.3%; 34/116) was highest for GJB2 (allele frequency, 15.9% for c.235delC, 6.0% for c.299_300delAT, and 2.6% for c.176-191del16). Sanger sequencing confirmed the consistency of results between the detection methods based on 2D-PCR and DNA sequencing. Common pathogenic mutations in patients with non-syndromic deafness in Changzhou were concentrated in GJB2 (c.235delC, c.299_300delAT, and c.176-191del16) and SLC26A4 (c.919-2A > G and c.2168 A > G). 2D-PCR is an effective method for accurately and rapidly identifying deafness-related genotypes using a single-tube reaction, and is superior to DNA sequencing, which has a high cost and long cycle.

COL10A1 promotes tumorigenesis by modulating CD276 in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is a lethal malignant tumour. Further study is needed to determine the molecular mechanism and identify novel biomarkers of PAAD.

METHODS

Gene expression data from the GSE62165 microarray were analysed with the online software Morpheus to identify differentially expressed genes (DEGs). The STRING database was used to generate a protein‒protein interaction (PPI) network for these DEGs. Hub genes were identified with Cytoscape. COL10A1 expression in PAAD was analysed via the GEPIA database. COL10A1 expression in pancreatic cancer cell lines was measured by using qRT‒PCR. The LinkedOmics database was utilized to perform survival analysis of pancreatic adenocarcinoma patients grouped based on COL10A1 expression level. CCK-8, wound healing, and Transwell assays were used to study the role of COL10A1 in pancreatic cancer cell viability, migration, and invasion. Differentially expressed genes that were related to COL10A1 in PAAD were analysed via the LinkedOmics portal. After COL10A1 was knocked down, CD276 expression was assessed by western blotting.

RESULTS

COL10A1 was identified as one of the hub genes in PAAD by bioinformatics analysis of the GSE62165 microarray with Morpheus, the STRING database and Cytoscape. GEPIA revealed elevated expression of COL10A1 in PAAD samples vs. normal samples. COL10A1 expression was also increased in pancreatic cancer cells vs. control cells. Survival analysis of PAAD patients via LinkedOmics revealed that high expression of COL10A1 was associated with a poorer prognosis. Knockdown of COL10A1 inhibited the proliferation, migration, and invasion of cells in functional assays. Furthermore, mechanistic studies indicated that CD276 was a target of COL10A1 and that knockdown of COL10A1 decreased CD276 expression. Overexpression of CD276 in cells reversed COL10A1 knockdown-induced repression of proliferation and migration.

CONCLUSIONS

Our research suggests that COL10A1 promotes pancreatic adenocarcinoma tumorigenesis by regulating CD276. This study provides new insight into biomarkers and possible targets for pancreatic cancer treatment.

In silico analysis of differentially expressed-aberrantly methylated genes in breast cancer for prognostic and therapeutic targets

Breast cancer (BC) is the leading cause of death among women across the globe. Abnormal gene expression plays a crucial role in tumour progression, carcinogenesis and metastasis of BC. The alteration of gene expression may be through aberrant gene methylation. In the present study, differentially expressed genes which may be regulated by DNA methylation and their pathways associated with BC have been identified. Expression microarray datasets GSE10780, GSE10797, GSE21422, GSE42568, GSE61304, GSE61724 and one DNA methylation profile dataset GSE20713 were downloaded from Gene Expression Omnibus database (GEO). Differentially expressed-aberrantly methylated genes were identified using online Venn diagram tool. Based on fold change expression of differentially expressed-aberrantly methylated genes were chosen through heat map. Protein-protein interaction (PPI) network of the hub genes was constructed by Search Tool for the Retrieval of Interacting Genes (STRING). Gene expression and DNA methylation level of the hub genes were validated through UALCAN. Overall survival analysis of the hub genes was analysed through Kaplan-Meier plotter database for BC. A total of 72 upregulated-hypomethylated genes and 92 downregulated-hypermethylated genes were obtained from GSE10780, GSE10797, GSE21422, GSE42568, GSE61304, GSE61724, and GSE20713 datasets by GEO2R and Venn diagram tool. PPI network of the upregulated-hypomethylated hub genes (MRGBP, MANF, ARF3, HIST1H3D, GSK3B, HJURP, GPSM2, MATN3, KDELR2, CEP55, GSPT1, COL11A1, and COL1A1) and downregulated-hypermethylated hub genes were constructed (APOD, DMD, RBPMS, NR3C2, HOXA9, AMKY2, KCTD9, and EDN1). All the differentially expressed hub genes expression was validated in UALCAN database. 4 in 13 upregulated-hypomethylated and 5 in 8 downregulated-hypermethylated hub genes to be significantly hypomethylated or hypermethylated in BC were confirmed using UALCAN database (p < 0.05). MANF, HIST1H3D, HJURP, GSK3B, GPSM2, MATN3, KDELR2, CEP55, COL1A1, APOD, RBPMS, NR3C2, HOXA9, ANKMY2, and EDN1 were significantly (p < 0.05) associated with poor overall survival (OS). The identified aberrantly methylated-differentially expressed genes and their related pathways and function in BC can serve as novel diagnostic and prognostic biomarkers and therapeutic targets.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 4 Given name: [Jeewan Ram] Last name [Vishnoi]. Also, kindly confirm the details in the metadata are correct.It is correct.

Substrate specificity of human chymotrypsin-like protease (CTRL) characterized by phage display-selected small-protein inhibitors

Chymotrypsin-like protease (CTRL) is one of the four chymotrypsin isoforms expressed in the human exocrine pancreas. Human genetic and experimental evidence indicate that chymotrypsins B1, B2, and C (CTRB1, CTRB2 and CTRC) are important not only for protein digestion but also for protecting the pancreas against pancreatitis by degrading potentially harmful trypsinogen. CTRL has not been reported to play a similar role, possibly due to its low abundance and/or different substrate specificity. To address this problem, we investigated the specificity of the substrate-binding groove of CTRL by evolving the substrate-like canonical loop of the Schistocerca gregaria proteinase inhibitor 2 (SGPI-2), a small-protein reversible chymotrypsin inhibitor to bind CTRL. We found that phage-associated SGPI-2 variants with strong affinity to CTRL were similar to those evolved previously against CTRB1, CTRB2 or bovine chymotrypsin A (bCTRA), indicating comparable substrate specificity. When tested as recombinant proteins, SGPI-2 variants inhibited CTRL with similar or slightly weaker affinity than bCTRA, confirming that CTRL is a typical chymotrypsin. Interestingly, an SGPI-2 variant selected with a Thr29His mutation in its reactive loop was found to inhibit CTRL strongly, but it was digested rapidly by bCTRA. Finally, CTRL was shown to degrade human anionic trypsinogen, however, at a much slower rate than CTRB2, suggesting that CTRL may not have a significant role in the pancreatic defense mechanisms against inappropriate trypsinogen activation and pancreatitis.

ABERRANT EXPRESSION OF COL14A1, CELRS3, and CTHRC1 IN BREAST CANCER СELLS

BACKGROUND

Collagens, which are the major components of the extracellular matrix involved in the regulation of tumor microenvironment, could be differentially expressed in breast cancer (BC) with different transcriptome profiling.

AIM

To analyze the transcript level expression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, COL14A1, CTHRC1, and CELRS3 genes and the clinical relevance of their differential expression in BC.

MATERIALS AND METHODS

The transcript level expression of the genes was analyzed using the quantitative real-time PCR (qPCR) in tumor tissue of 60 BC patients.

RESULTS

Overexpression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, CTHRC, and CELRS3 anddown-regulated expression of COL14A1 were observed. COL14A1 down-regulation was associated with aggressive, basal, and Her-2/neu BC subtypes (p = 0.031). Overexpression of CELSR3 was found to be associated with the older age of the patients (> 55 years, p = 0.049). Further analysis with the TCGA BC data set has shown a concordance in the differential expression of the above genes. Furthermore, overexpression of CTHRC1 was associated with poor overall survival (OS), particularly with poor prognosis (p = 0.00042) for the luminal BC subtype. On the other hand, CELSR3 overexpression was associated with mucinous tumors and poor prognosis in post-menopausal women. In silicotarget prediction identified several BC-associated miRNAs and members of miR-154, -515, and -10 families to perform a likely regulatory role in the above ECM genes.

CONCLUSION

The present study shows that the expression of COL14A1 and CTHRC1 may serve as potential biological markers for the detection of basal BC and the prognosis of survival for patients with the luminal subtype of BC.

COL11A1-driven positive feedback loop modulates fibroblast transformation and activates pancreatic cancer progression

Pancreatic ductal adenocarcinoma (PDAC) is one of the most common leading causes of cancer death. The cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) aggravate the malignant behavior of PDAC. However, it is still unknown how PDAC induces normal fibroblasts (NFs) to CAFs. In present research, we found that PDAC-derived collagen type XI alpha 1 (COL11A1) promoted the conversion of NFs to CAF-like cells. It included morphological and corresponding molecular marker changes. Activation of the nuclear factor-κB (NF-κB) pathway was involved in this process. Corresponding, CAFs cells could secrete interleukin 6 (IL-6), which promoted the invasion and the epithelial-mesenchymal transition of PDAC cells. Furthermore, IL-6 promoted the expression of transcription factor Activating Transcription Factor 4 by activating the Mitogen-Activated Protein Kinase/extracellular-signal-regulated kinase pathway. The latter directly promotes the expression of COL11A1. This way, a feedback loop of mutual influence was constructed between PDAC and CAFs. Our research proposed a novel concept for PDAC-educated NFs. PDAC-COL11A1-fibroblast-IL-6-PDAC axis might contribute to the cascade between PDAC and TME.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

The prognostic value and biological significance of gap junction beta protein 2 (GJB2 or Cx26) in cervical cancer

Objective

To evaluate the prognostic value and explore the biological significance of gap junction protein beta 2 (GJB2 or Cx26) in cervical cancer (CC).

Methods

We first compared GJB2 expression between CC and normal tissues using public databases and immunohistochemistry (IHC). Based on The Cancer Genome Atlas data (TCGA cohort, n = 304) and tissue microarray samples (OBC cohort, n = 111), we explored the prognostic value of GJB2 for CC patients using bioinformatics analysis and IHC scoring. To explore the biological significance of GJB2, Gene set enrichment analysis (GSEA) and Gene Ontology (GO) were performed. The impact of GJB2 on the immune microenvironment was analyzed by CIBERSORTx and ESTIMATE algorithms. We finally investigated the relationship between GJB2 and drug sensitivity based on the Genomics of Drug Sensitivity in Cancer (GDSC).

Results

The expression of GJB2 was significantly increased in CC over normal tissues. Both the TCGA and OBC cohort found that patients with high GJB2 expression had shorter overall survival (OS) time, and high GJB2 expression was the independent risk factor for prognosis (TCGA: HR, 2.566; 95% CI, 1.066–6.180; p = 0.036; OBC: HR, 2.198; 95% CI, 1.019–4.741; p = 0.045). GJB2 was correlated with patient clinical factors such as tumor size and differentiation grade. The p53 signaling pathway and toll-like receptor pathway may be regulated by GJB2. The abundance of various immune cells was significantly different between the low and high GJB2 expression groups. The ImmuneScore was significantly increased in the high GJB2 expression group. In addition, the expression level of GJB2 was positively correlated with the natural log of the half-maximal inhibitory concentration (LN_IC50) value of cisplatin/paclitaxel (Spearman r = 0.238/0.153, p &lt; 0.001).

Conclusion

GJB2 can serve as a potential prognostic marker of poor survival and a therapeutic target in CC. Moreover, GJB2 may affect the immune microenvironment and is correlated with chemoresistance.

Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients

BACKGROUND

Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only "curative" treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge. Molecular biomarkers are a significant approach for diagnostic and predictive use in PCs. Several prediction models have been developed for patients newly diagnosed with PC that is operable or patients with advanced and metastatic PC; however, these models require further validation. Therefore, precise biomarkers are urgently required to increase the efficiency of predicting a disease-free survival (DFS), OS, and sensitivity to immunotherapy in PC patients and to improve the prognosis of PC.

METHODS

In the present study, we first evaluated the highly and selectively expressed targets in PC, using the GeoMxTM Digital Spatial Profiler (DSP) and then, we analyzed the roles of these targets in PCs using TCGA database.

RESULTS

LAMB3, FN1, KRT17, KRT19, and ANXA1 were defined as the top five upregulated targets in PC compared with paracancer. The TCGA database results confirmed the expression pattern of LAMB3, FN1, KRT17, KRT19, and ANXA1 in PCs. Significantly, LAMB3, FN1, KRT19, and ANXA1 but not KRT17 can be considered as biomarkers for survival analysis, univariate and multivariate Cox proportional hazards model, and risk model analysis. Furthermore, in combination, LAMB3, FN1, KRT19, and ANXA1 predict the DFS and, in combination, LAMB3, KRT19, and ANXA1 predict the OS. Immunotherapy is significant for PCs that are inoperable. The immune checkpoint blockade (ICB) analysis indicated that higher expressions of FN1 or ANXA1 are correlated with lower ICB response. In contrast, there are no significant differences in the ICB response between high and low expression of LAMB3 and KRT19.

CONCLUSIONS

In conclusion, LAMB3, FN1, KRT19, and ANXA1 are good predictors of PC prognosis. Furthermore, FN1 and ANXA1 can be predictors of immunotherapy in PCs.

Gene losses in the common vampire bat illuminate molecular adaptations to blood feeding

Vampire bats are the only mammals that feed exclusively on blood. To uncover genomic changes associated with this dietary adaptation, we generated a haplotype-resolved genome of the common vampire bat and screened 27 bat species for genes that were specifically lost in the vampire bat lineage. We found previously unknown gene losses that relate to reduced insulin secretion (FFAR1 and SLC30A8), limited glycogen stores (PPP1R3E), and a unique gastric physiology (CTSE). Other gene losses likely reflect the biased nutrient composition (ERN2 and CTRL) and distinct pathogen diversity of blood (RNASE7) and predict the complete lack of cone-based vision in these strictly nocturnal bats (PDE6H and PDE6C). Notably, REP15 loss likely helped vampire bats adapt to high dietary iron levels by enhancing iron excretion, and the loss of CYP39A1 could have contributed to their exceptional cognitive abilities. These findings enhance our understanding of vampire bat biology and the genomic underpinnings of adaptations to blood feeding.

COL11A1-Driven Epithelial-Mesenchymal Transition and Stemness of Pancreatic Cancer Cells Induce Cell Migration and Invasion by Modulating the AKT/GSK-3β/Snail Pathway

BACKGROUND

Collagen type XI α1 (COL11A1) is associated with tumorigenesis and development in many human malignancies. Previous reports indicate that COL11A1 may be a significant diagnostic marker for pancreatic ductal adenocarcinoma (PDAC); however, its biological role in PDAC progression remains unclear. In this study, we investigated the influence of COL11A1 on the invasion and migration abilities of pancreatic cancer cells and explored its potential molecular mechanisms.

METHODS

Cell migration and invasion were assessed using Transwell assays in pancreatic cancer cells transfected with siCOL11A1 and pCNV3-COL11A1 plasmids. The protein and mRNA expression levels of N-cadherin, E-cadherin, Vimentin, cluster of differentiation (CD)-24, CD44, serine-threonine kinase (AKT), glycogen synthase kinase (GSK)-3β, phospho (p)-AKTSer473, p-GSK-3βSer9, and Snail were analyzed using Western blotting and real-time polymerase chain reaction (PCR). The effect of COL11A1 on cell stemness was tested using flow cytometry and clone formation assays.

RESULTS

These results demonstrated that COL11A1 significantly promoted the invasion and migration abilities of PDAC cells. Furthermore, COL11A1 facilitated the occurrence of epithelial-mesenchymal transition (EMT) and cell stemness by upregulating the expression levels of p-AKTSer473, p-GSK-3βSer9, and Snail.

CONCLUSIONS

This study suggests that the activation of the AKT/GSK-3β/Snail signaling pathway induced by COL11A1 plays a major role in the progression of PDAC. Therefore, COL11A1 could serve as a potential target for PDAC treatment.

Transposons: Unexpected players in cancer

Transposons are repetitive DNA sequences encompassing about half of the human genome. They play a vital role in genome stability maintenance and contribute to genomic diversity and evolution. Their activity is regulated by various mechanisms considering the deleterious effects of these mobile elements. Various genetic risk factors and environmental stress conditions affect the regulatory pathways causing alteration of transposon expression. Our knowledge of the biological role of transposons is limited especially in various types of cancers. Retrotransposons of different types (LTR-retrotransposons, LINEs and SINEs) regulate a plethora of genes that have a role in cell reprogramming, tumor suppression, cell cycle, apoptosis, cell adhesion and migration, and DNA repair. The regulatory mechanisms of transposons, their deregulation and different mechanisms underlying transposon-mediated carcinogenesis in humans focusing on the three most prevalent types, lung, breast and colorectal cancers, were reviewed. The modes of regulation employed include alternative splicing, deletion, insertion, duplication in genes and promoters resulting in upregulation, downregulation or silencing of genes.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma

BACKGROUND

Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.

METHODS

We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions.

RESULTS

We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival.

CONCLUSIONS

This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.

Collagen XI alpha 1 chain, a potential therapeutic target for cancer

Extracellular matrix (ECM) plays an important role in the progression of cancer. Collagen is the most abundant component in ECM, and it is involved in the biological formation of cancer. Although type XI collagen is a minor fibrillar collagen, collagen XI alpha 1 chain (COL11A1) has been found to be upregulated in a variety of cancers including ovarian cancer, breast cancer, thyroid cancer, pancreatic cancer, non-small-cell lung cancer, and transitional cell carcinoma of the bladder. High levels of COL11A1 usually predict poor prognosis, while COL11A1 is related to angiogenesis, invasion, and drug resistance of cancer. However, little is known about the specific mechanism by which COL11A1 regulates tumor progression. Here, we have organized and summarized the recent developments regarding elucidation of the relationship between COL11A1 and various cancers, as well as the interaction between COL11A1 and intracellular signaling pathways. In addition, we have selected therapeutic agents targeting COL11A1. All these indicate the possibility of using COL11A1 as a target for cancer treatment.

Comprehensive Analysis and Identification of an Immune-Related Gene Signature with Prognostic Value for Prostate Cancer

Background

The tumor microenvironment (TME) has recently been proven to play a crucial role in the development and prognosis of tumors. However, the current knowledge on the potential of the TME in prostate cancer (PCa) remains scarce.

Purpose

This study aims to elucidate the value of TME-related genes for PCa prognosis by integrative bioinformatics analysis.

Materials and Methods

We downloaded the immune and stromal scores of PCa samples via the ESTIMATE and correlated these scores to clinicopathological characteristics and recurrence-free survival (RFS) of patients. Based on these scores, the TME-related differentially expressed genes were identified for functional enrichment analysis. Cox regression analyses were performed to identify prognostic genes and establish a predictive risk model. Moreover, gene set enrichment analysis (GSEA) was performed to evaluate the relationship between risk score and immune pathway.

Results

The stromal and immune scores were associated with clinicopathological characteristics and RFS in PCa patients. In total, 238 intersecting differentially expressed genes were identified. Functional enrichment analysis further revealed that these genes dramatically participated in the immune-related pathways. The immune-related risk model was built with C-type lectin domain containing 7A (CLEC7A) and collagen type XI alpha 1 chain (COL11A1) using Cox regression analyses. Kaplan–Meier survival analysis showed that the expression levels of CLEC7A and COL11A1 were significantly associated with the RFS. Further, the RFS time in high-risk group was significantly shorter than that in low-risk group. The areas under the curve for the risk model in predicting 3- and 5-year RFS rates were 0.694 and 0.731, respectively. GSEA suggested that immunosuppression existed in high-risk PCa patients.

Conclusion

CLEC7A and COL11A1 were selected to build a predictive risk model, which may help clinicians to assess the prognosis of PCa patients and select appropriate targets for immunotherapy.

YEATS2 is a target of HIF1α and promotes pancreatic cancer cell proliferation and migration

Hypoxia is involved in the development of pancreatic cancer (PC). The responses of hypoxia-associated genes and their regulated mechanisms are largely unknown. In this study, through bioinformatic analysis and quantitative real-time polymerase chain reaction, the YEATS domain containing 2 (YEATS2) was determined to be a key hypoxia-associated gene. It was increased in PC cells under hypoxia, upregulated in PC tissues, and predicted poor outcome. YEATS2 inhibition decreased the proliferation and migration of PC cells under both normoxia and hypoxia in vitro as well as proliferation and metastasis in vivo. We found that hypoxia-inducible factor 1α (HIF1α) regulated the expression of YEATS2 via binding to the hypoxia response element (HRE) of YEATS2 and coexpressed with YEATS2 in PC tissues. Overexpression of YEATS2 blocked the inhibitory effects of HIF1α silence on PC cell proliferation and migration under hypoxia. Collectively, our study revealed that YEATS2 is a target gene of HIF1α and promotes PC development under hypoxia.

Integrated bioinformatics analysis identified COL11A1 as an immune infiltrates correlated prognosticator in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is the most common pancreatic cancer, with high mortality rate and limited treatment options. Tumor infiltrating cells and genes in microenvironment are emerging as pivotal players in PAAD progression and prognosis. In this study, we obtained genes expression data set GSE119794 of PAAD, which contains data from 10 tumor and 10 normal samples. A total of 262 differentially expressed genes (DEGs), including 169 up-regulated and 93 down-regulated genes, were obtained based on expression fold change and significance. Combining the pathway analysis of DEGs and GSEA analysis of all genes, four KEGG pathways were enriched. The 4 pathways include pancreatic secretion, protein digestion and absorption, fat digestion and absorption, and PPAR signaling pathways. Functional enrichment of Gene Ontology significantly enriched extracellular matrix, an important component in microenvironment. In the Protein-protein interaction (PPI) network, we screened out 3 hub genes of COL11A1, KRT19 and CXCL5 by CytoHubba. At last, the expression level, prognostic significance and correlation with tumor infiltrates were validated in TCGA database, with GEPIA and TIMER. The validation identified Collagen Type XI Alpha 1 Chain (COL11A1), an extracellular matrix structural constituent, as a hazardous prognosticator with significant correlation with macrophage, neutrophil and dendritic cells. In sum, we identified COL11A1 as an immune infiltrates correlated prognosticator in pancreatic adenocarcinoma.

Paired like homeodomain 1 and SAM and SH3 domain-containing 1 in the progression and prognosis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy with high morbidity and mortality rates. In spite of numerous advancements have been made in therapeutic methods, the prognosis of HNSCC patients remains poor. Therefore, investigation of crucial genes during HNSCC tumorigenesis which could be exploited as biomarkers and therapeutic targets is greatly needed. In this study, original data of four independent datasets was downloaded from the Gene Expression Omnibus database and analyzed through R language to screen out differentially expressed genes. Paired like homeodomain 1 and SAM and SH3 domain-containing 1 were selected to be further explored through multiple online databases. Quantitative real-time polymerase chain reaction analysis and immunohistochemistry assay were adopted to validate the downregulation of paired like homeodomain 1 and SAM and SH3 domain-containing 1 in HNSCC and statistical analysis indicated their close associations with patient prognosis. In vitro experiments demonstrated the inhibitory effect of paired like homeodomain 1 and SAM and SH3 domain-containing 1 on HNSCC progression. Overall, we identified the aberrant downregulation of paired like homeodomain 1 and SAM and SH3 domain-containing 1 in HNSCC and suggested the potential of utilizing them as therapeutic targets or efficient biomarkers for diagnosis and prognosis evaluation. Our findings may provide novel evidences for the development of new strategies for HNSCC treatment.

Reciprocal Interplay Between Fibrillar Collagens and Collagen-Binding Integrins: Implications in Cancer Progression and Metastasis

Cancers are complex ecosystems composed of malignant cells embedded in an intricate microenvironment made of different non-transformed cell types and extracellular matrix (ECM) components. The tumor microenvironment is governed by constantly evolving cell-cell and cell-ECM interactions, which are now recognized as key actors in the genesis, progression and treatment of cancer lesions. The ECM is composed of a multitude of fibrous proteins, matricellular-associated proteins, and proteoglycans. This complex structure plays critical roles in cancer progression: it functions as the scaffold for tissues organization and provides biochemical and biomechanical signals that regulate key cancer hallmarks including cell growth, survival, migration, differentiation, angiogenesis, and immune response. Cells sense the biochemical and mechanical properties of the ECM through specialized transmembrane receptors that include integrins, discoidin domain receptors, and syndecans. Advanced stages of several carcinomas are characterized by a desmoplastic reaction characterized by an extensive deposition of fibrillar collagens in the microenvironment. This compact network of fibrillar collagens promotes cancer progression and metastasis, and is associated with low survival rates for cancer patients. In this review, we highlight how fibrillar collagens and their corresponding integrin receptors are modulated during cancer progression. We describe how the deposition and alignment of collagen fibers influence the tumor microenvironment and how fibrillar collagen-binding integrins expressed by cancer and stromal cells critically contribute in cancer hallmarks.

Dysregulation of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway fuels stage progression of ovarian cancer

Objective: Ovarian cancer is one of the most common and lethal cancer types in women. The molecular mechanism of ovarian cancer progression is still unclear.

Results: Here, we first reported that expression levels of three genes, GJB2, S100A2 and SPOCK2, were significantly higher in advanced stage than that in early stage of ovarian cancer, and upregulation of them indicated poor prognosis of patients with ovarian cancer. Subsequently, 8, 6 and 20 miRNAs were predicted to target GJB2, S100A2 and SPOCK2, respectively. Among these miRNA-mRNA pairs, hsa-miR-363-3p-SPOCK2 axis was the most potential in suppressing progression of ovarian cancer. Mechanistically, we found that hsa-miR-363-3p-SPOCK2 axis was involved in regulation of actin cytoskeleton. Moreover, 6 pseudogenes and 8 lncRNAs were identified to potentially inhibit hsa-miR-363-3p-SPOCK2 axis in ovarian cancer.

Conclusions: Collectively, we elucidate a regulatory role of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway in progression of ovarian cancer, which may provide effective therapeutic approaches and promising prognostic biomarkers for ovarian cancer.

Materials and methods: Differentially expressed genes (DEGs) in ovarian cancer were first screened using GSE12470, after which DEGs expression were validated using GEPIA. Kaplan-Meier analysis was employed to assess the prognostic values. Potential miRNAs were predicted by seven target prediction databases, and upstream lncRNAs and pseudogenes of hsa-miR-363-3p were forecasted through miRNet or starBase. UALCAN and starBase were used to obtain the co-expressed genes of SPOCK. Enrichment analysis for these co-expressed genes was performed by Enrichr.

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

BACKGROUND

The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.

METHODS

Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.

RESULTS

Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = - 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count).

CONCLUSION

Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

Identification of RE1-Silencing Transcription Factor as a Promoter of Metastasis in Pancreatic Cancer

Pancreatic cancer is characterized by its rapid progression and early metastasis. This requires further elucidation of the key promoters for its progression and metastasis. In this study, we identified REST as the hub gene of a gene module which is closely associated with cancer stage by weighted gene correlation network analysis. Validation with the TCGA database, western blot analysis of human pancreatic cancer cell lines (AsPC-1, Capan-2, SW-1990, and PANC-1) and immunohistochemical analysis of paraffin-embedded pancreatic cancer tissue sections showed that REST was enriched in tissue samples of advanced stage and metastatic phenotype cell lines. Survival analysis with the TCGA database and our own follow-up data suggested that patients with higher expression level of REST showed worse overall survival rate. In vitro functional experiments suggested that knockdown of REST suppressed proliferation, migration, invasion and epithelial-mesenchymal transition of AsPC-1 and PANC-1 cells. In vivo experiments (a subcutaneous BALB/c nude mouse model and a superior mesenteric vein injection BALB/c nude mouse model) suggested that knockdown of REST suppressed growth and metastasis of xenograft tumor. Finally, we investigated the underlying molecular mechanism of REST and identified REST as a potential downstream target of MAPK signaling pathway. In conclusion, our results of bioinformatic analysis, in vitro and in vivo functional analysis suggested that REST may serve as a promoter of metastasis in pancreatic cancer.