RNAi-mediated SYT14 knockdown inhibits the growth of human glioma cell line U87MG

Tumor gene expression is associated with venous thromboembolism in patients with ductal pancreatic adenocarcinoma

INTRODUCTION

In patients with pancreatic cancer, the risk of venous thromboembolism (VTE) is high compared to other cancer types, suggesting that tumor-intrinsic features drive hypercoagulability. Tumor gene expression analysis may help unravel the pathogenesis of VTE in these patients and help to identify high-risk patients.

AIM

To evaluate the association between tumor gene expression patterns and VTE in patients with pancreatic cancer.

METHODS

In this retrospective cohort study RNA-sequence data from surgically resected tumor material from patients with pancreatic ductal adenocarcinoma (PDAC) was used to identify genes associated with the presence of venous thromboembolism (i.e., pulmonary embolism or deep-vein thrombosis) within one year follow-up after surgery. Additionally, VTE risk and expression of coagulation related genes in two molecular subtypes of pancreatic cancer was assessed.

RESULTS

Out of 151 patients, 10 (6.6 %) developed deep-vein thrombosis or pulmonary embolism within one year follow-up. Differential expression analysis yielded 89 genes significantly differentially expressed in patients with VTE compared to those without VTE, including ATP6V0A4, SYT14 and ZNF114. The incidence of VTE in classical subtype was higher (n = 9; 7.6 %) than in basal-like subtype (n = 1;4 %), but this difference was not statistically significant (SHR 1.79; 95 % CI 0.22-14.3). Forty-two coagulation-associated genes were identified that were differentially expressed between these molecular subtypes, including F5, PLAU, SERPINE1, and C4BPB.

CONCLUSIONS

Patients with pancreatic cancer and VTE show a different tumor gene expression profile than those without VTE. Multiple coagulation-related genes were differentially expressed in classical versus basal-like molecular subtype, suggesting that there is a difference in pro-thrombotic phenotype.

Construction of a Liver Cancer Prognostic Model Based on Interferon-Gamma-Related Genes for Revealing the Immune Landscape

Inferferon-gamma (LFN-γ) exerts anti-tumor effects, but there is currently no reliable and comprehensive study on prognostic function of IFN-γ-related genes in liver cancer. In this study, IFN-γ-related differentially expressed genes (DEGs) in liver cancer were identified through GO/KEGG databases and open-access literature. Based on these genes, individuals with liver cancer were clustered. A prognostic model was built based on the intersection genes between differential genes in clusters and in liver cancer. Then, model predictive performance was analyzed and validated in GEO dataset. Regression analysis was fulfilled on the model, and a nomogram was utilized to evaluate model ability as an independent prognostic factor and its clinical application value. An immune-related analysis was conducted on both the H- and L-groups, with an additional investigation into link of model genes to drug sensitivity. Significant differential expression of IFN-γ-related genes was observed between the liver cancer and control groups. Subsequently, individuals with liver cancer were classified into two subtypes based on these genes, which displayed a notable difference in survival between the two subtypes. A 10-gene liver cancer prognostic model was constructed, with good prognostic performance and was an independent prognosticator for patient analysis. L-group patients possessed higher immune infiltration levels, immune checkpoint expression levels, and immunophenoscore, as well as lower TIDE scores. Drugs that had high correlations with the feature genes included SPANXB1: PF-04217903, SGX-523, MMP1: PF-04217903, DUSP13: Imatinib, TFF1: KHK-Indazole, and Fulvestrant. We built a 10-gene liver cancer prognostic model. It was found that L-group patients were more suitable for immunotherapy. This study provided valuable information on the prognosis of liver cancer.

Single-cell RNA sequencing revealed subclonal heterogeneity and gene signatures of gemcitabine sensitivity in pancreatic cancer

Introduction: Resistance to gemcitabine is common and critically limits its therapeutic efficacy in pancreatic ductal adenocarcinoma (PDAC). Methods: We constructed 17 patient-derived xenograft (PDX) models from PDAC patient samples and identified the most notable responder to gemcitabine by screening the PDX sets in vivo. To analyze tumor evolution and microenvironmental changes pre- and post-chemotherapy, single-cell RNA sequencing (scRNA-seq) was performed. Results: ScRNA-seq revealed that gemcitabine promoted the expansion of subclones associated with drug resistance and recruited macrophages related to tumor progression and metastasis. We further investigated the particular drug-resistant subclone and established a gemcitabine sensitivity gene panel (GSGP) (SLC46A1, PCSK1N, KRT7, CAV2, and LDHA), dividing PDAC patients into two groups to predict the overall survival (OS) in The Cancer Genome Atlas (TCGA) training dataset. The signature was successfully validated in three independent datasets. We also found that 5-GSGP predicted the sensitivity to gemcitabine in PDAC patients in the TCGA training dataset who were treated with gemcitabine. Discussion and conclusion: Our study provides new insight into the natural selection of tumor cell subclones and remodeling of tumor microenvironment (TME) cells induced by gemcitabine. We revealed a specific drug resistance subclone, and based on the characteristics of this subclone, we constructed a GSGP that can robustly predict gemcitabine sensitivity and prognosis in pancreatic cancer, which provides a theoretical basis for individualized clinical treatment.

Potential roles of synaptotagmin family members in cancers: Recent advances and prospects

With the continuous development of bioinformatics and public database, more and more genes that play a role in cancers have been discovered. Synaptotagmins (SYTs) are abundant, evolutionarily conserved integral membrane proteins composed of a short N-terminus, a variable linker domain, a single transmembrane domain, and two C2 domains, and they constitute a family of 17 isoforms. The synaptotagmin family members are known to regulate calcium-dependent membrane fusion events. Some SYTs play roles in hormone secretion or neurotransmitter release or both, and much evidence supports SYTs as Ca²⁺ sensors of exocytosis. Since 5 years ago, an increasing number of studies have found that SYTs also played important roles in the occurrence and development of lung cancer, gastric cancer, colon cancer, and other cancers. Down-regulation of SYTs inhibited cell proliferation, migration, and invasion of cancer cells, but promoted cell apoptosis. Growth of peritoneal nodules is inhibited and survival is prolonged in mice administrated with siSYTs intraperitoneally. Therefore, most studies have found SYTs serve as an oncogene after overexpression and may become potential prognostic biomarkers for multiple cancers. This article provides an overview of recent studies that focus on SYT family members’ roles in cancers and highlights the advances that have been achieved.

Synaptotagmin 11 scaffolds MKK7-JNK signaling process to promote stem-like molecular subtype gastric cancer oncogenesis

Background

Identifying biomarkers related to the diagnosis and treatment of gastric cancer (GC) has not made significant progress due to the heterogeneity of tumors. Genes involved in histological classification and genetic correlation studies are essential to develop an appropriate treatment for GC.

Methods

In vitro and in vivo lentiviral shRNA library screening was performed. The expression of Synaptotagmin (SYT11) in the tumor tissues of patients with GC was confirmed by performing Immunohistochemistry, and the correlation between the expression level and the patient’s survival rate was analyzed. Phospho-kinase array was performed to detect Jun N-terminal kinase (JNK) phosphorylation. SYT11, JNK, and MKK7 complex formation was confirmed by western blot and immunoprecipitation assays. We studied the effects of SYT11 on GC proliferation and metastasis, real-time cell image analysis, adhesion assay, invasion assay, spheroid formation, mouse xenograft assay, and liver metastasis.

Results

SYT11 is highly expressed in the stem-like molecular subtype of GC in transcriptome analysis of 527 patients with GC. Moreover, SYT11 is a potential prognostic biomarker for histologically classified diffuse-type GC. SYT11 functions as a scaffold protein, binding both MKK7 and JNK1 signaling molecules that play a role in JNK1 phosphorylation. In turn, JNK activation leads to a signaling cascade resulting in cJun activation and expression of downstream genes angiopoietin-like 2 (ANGPTL2), thrombospondin 4 (THBS4), Vimentin, and junctional adhesion molecule 3 (JAM3), which play a role in epithelial-mesenchymal transition (EMT). SNU484 cells infected with SYT11 shRNA (shSYT11) exhibited reduced spheroid formation, mouse tumor formation, and liver metastasis, suggesting a pro-oncogenic role of SYT11. Furthermore, SYT11-antisense oligonucleotide (ASO) displayed antitumor activity in our mouse xenograft model and was conferred an anti-proliferative effect in SNU484 and MKN1 cells.

Conclusion

SYT11 could be a potential therapeutic target as well as a prognostic biomarker in patients with diffuse-type GC, and SYT11-ASO could be used in therapeutic agent development for stem-like molecular subtype diffuse GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02420-3.

Identification of Differentially Expressed and Prognostic lncRNAs for the Construction of ceRNA Networks in Lung Adenocarcinoma

Background

Long noncoding RNAs (lncRNAs) could function as competitive endogenous RNAs (ceRNAs) to competitively adsorb microRNAs (miRNAs), thereby regulating the expression of their target protein-coding mRNAs. In this study, we aim to identify more effective diagnostic and prognostic markers for lung adenocarcinoma (LUAD).

Methods

We obtained differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) for LUAD by using The Cancer Genomes Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) was performed to unveil core gene modules associated with LUAD. The Cox proportional hazards model was performed to determine the prognostic significance of DElncRNAs. The diagnostic and prognostic significance of DElncRNAs was further verified based on the receiver operating characteristic curve (ROC). Cytoscape was used to construct the ceRNA networks comprising the lncRNAs-miRNAs-mRNAs axis based on the correlation obtained from the miRcode, miRDB, and TargetScan.

Results

Compared with normal lung tissues, 2355 DElncRNAs, 820 DEmiRNAs, and 17289 DEmRNAs were identified in LUAD tissues. We generated 8 WGCNA core modules in the lncRNAs coexpression network, 5 modules in the miRNAs, and 12 modules in the mRNAs coexpression network, respectively. One lncRNA module (blue) consisting of 441 lncRNAs, two miRNA modules (blue and turquoise) containing 563 miRNAs, and one mRNA module (turquoise), which consisted of 15162 mRNAs, were mostly significantly related to LUAD status. Furthermore, 67 DEmRNAs were found to be tumor-associated as well as the target genes of the DElncRNAs-DEmiRNAs axis. Survival analyses showed that 6 lncRNAs (LINC01447, WWC2-AS2, OGFRP1, LINC00942, LINC01168, and AC005863.1) were significantly correlated with the prognosis of LUAD patients. Ultimately, the potential ceRNA networks including 6 DElncRNAs, 4 DEmiRNAs, and 22 DEmRNAs were constructed.

Conclusion

Our study indicated that 6 DElncRNAs had the possibilities as diagnostic and prognostic biomarkers for LUAD. The lncRNA-mediated ceRNA networks might provide novel insights into the molecular mechanisms of LUAD progression.

Establishment and Validation of a Comprehensive Prognostic Model for Patients With HNSCC Metastasis

Objective

To identify biomarkers related to head and neck squamous cell carcinoma (HNSCC) metastasis and establish a prognostic model for patients with HNSCC.

Methods

HNSCC mRNA expression data of metastasis and non-metastatic samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. After screening the differentially expressed genes (DEGs) in the two datasets, a prognostic model, including clinical factors and biomarkers, was established, and verified in 36 samples of HNSCC by quantitative real-time transcription (qRT)-PCR. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene sets enrichment analysis (GSEA) were consulted to explore the functions of the DEGs.

Results

In total, 108 DEGs were identified. GSEA, GO, and KEGG analyses showed that these DEGs were mainly involved in the proliferation and metastasis of HNSCC. Six genes that were significantly related to metastasis, immune cell infiltration and prognosis were further identified to construct a prognostic gene signature. The reliability of the gene signature was verified in 36 samples of HNSCC. A prognostic model, including tumor stage, risk level, and a nomogram for prediction were further established. Receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), C-index, and calibration plots showed that the model and nomogram perform well.

Conclusion

We constructed a six-gene signature and a nomogram with high performance in predicting the prognosis of patients with HNSCC metastasis.

Identification of novel susceptibility loci for non-syndromic cleft lip with or without cleft palate

Although several genome‐wide association studies (GWAS) of non‐syndromic cleft lip with or without cleft palate (NSCL/P) have been reported, more novel association signals are remained to be exploited. Here, we performed an in‐depth analysis of our previously published Chinese GWAS cohort study with replication in an extra dbGaP case‐parent trios and another in‐house Nanjing cohort, and finally identified five novel significant association signals (rs11119445: 3’ of SERTAD4, P = 6.44 × 10⁻¹⁴; rs227227 and rs12561877: intron of SYT14, P = 5.02 × 10⁻¹³ and 2.80 × 10⁻¹¹, respectively; rs643118: intron of TRAF3IP3, P = 4.45 × 10⁻⁶; rs2095293: intron of NR6A1, P = 2.98 × 10⁻⁵). The mean (standard deviation) of the weighted genetic risk score (wGRS) from these SNPs was 1.83 (0.65) for NSCL/P cases and 1.58 (0.68) for controls, respectively (P = 2.67 × 10⁻¹⁶). Rs643118 was identified as a shared susceptible factor of NSCL/P among Asians and Europeans, while rs227227 may contribute to the risk of NSCL/P as well as NSCPO. In addition, sertad4 knockdown zebrafish models resulted in down‐regulation of sox2 and caused oedema around the heart and mandibular deficiency, compared with control embryos. Taken together, this study has improved our understanding of the genetic susceptibility to NSCL/P and provided further clues to its aetiology in the Chinese population.

Intra-locked G-quadruplex structures formed by irregular DNA G-rich motifs

G-rich DNA sequences with tracts of three or more continuous guanines (G≥3) are known to have high propensity to adopt stable G-quadruplex (G4) structures. Bioinformatic analyses suggest high prevalence of G-rich sequences with short G-tracts (G≤2) in the human genome. However, due to limited structural studies, the folding principles of such sequences remain largely unexplored and hence poorly understood. Here, we present the solution NMR structure of a sequence named AT26 consisting of irregularly spaced G2 tracts and two isolated single guanines. The structure is a four-layered G4 featuring two bi-layered blocks, locked between themselves in an unprecedented fashion making it a stable scaffold. In addition to edgewise and propeller-type loops, AT26 also harbors two V-shaped loops: a 2-nt V-shaped loop spanning two G-tetrad layers and a 0-nt V-shaped loop spanning three G-tetrad layers, which are named as VS- and VR-loop respectively, based on their distinct structural features. The intra-lock motif can be a basis for extending the G-tetrad core and a very stable intra-locked G4 can be formed by a sequence with G-tracts of various lengths including several G2 tracts. Findings from this study will aid in understanding the folding of G4 topologies from sequences containing irregularly spaced multiple short G-tracts.

SYT16 is a prognostic biomarker and correlated with immune infiltrates in glioma: A study based on TCGA data

BACKGROUND

Glioma is the most lethal primary brain tumor. Lower-grade glioma (LGG) is the crucial pathological type of Glioma. Immune-infiltration of the tumor microenvironment positively associated with overall survival in LGG. SYT16 is a gene has not been reported in cancer. We assess the role of SYT16 in LGG, via the publicly available TCGA database.

METHODS

Gene Expression Profiling Interactive Analysis (GEPIA) was used to analyze the expression of SYT16 in LGG. We evaluated the influence of SYT16 on survival of LGG patients by survival module. Then, datasets of LGG were downloaded from TCGA. The correlations between the clinical information and SYT16 expression were analyzed using logistic regression. Univariable survival and Multivariate Cox analysis was used to compare several clinical characteristics with survival. we also explore the correlation between SYT16 and cancer immune infiltrates using CIBERSORT and correlation module of GEPIA. Gene set enrichment analysis (GSEA) was performed using the TCGA dataset. In addition, we use TIMER to explore the collection of SYT16 Expression and Immune Infiltration Level in LGG and to explore cumulative survival in LGG.

RESULTS

The univariate analysis using logistic regression, indicated that increased SYT16 expression significantly correlated with the tumor grade. Moreover, multivariate analysis revealed that the up-regulated SYT16 expression is an independent prognostic factor for good prognosis. Specifically, SYT16 expression level has significant negative correlations with infiltrating levels of B cell, CD4+ T cells, Macrophages, Neutrophils and DCs in LGG. In addition, GSEA identified ingle organism behavior, gated channel activity, cognition, transporter complex and ligand gated channel activity  in Gene Ontology (GO) were differentially enriched in the high SYT16 expression phenotype pathway. Neuroactive ligand receptor interaction, calcium signaling pathway, long term potentiation, type II diabetes mellitus and long term depression were identified as differentially enriched  pathway in Kyoto Encyclopedia of Genes and Genomes (KEGG).

CONCLUSION

SYT16 is a Prognostic Biomarker and Correlated with Immune Infiltrates in LGG.