Establishing Molecular Subgroups of CD8+ T Cell-Associated Genes in the Ovarian Cancer Tumour Microenvironment and Predicting the Immunotherapy Response

BACKGROUND

The mechanism by which infiltrating CD8+ T lymphocytes in the tumour microenvironment influence the survival of patients with ovarian cancer (OC) remains unclear.

METHODS

To identify biomarkers to optimise OC treatment, 13 immune-cell-line-associated datasets, RNA sequencing data, and clinical data from the GEO, TCGA, and the ICGC were collected. Gene expression in OC was assessed using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining.

RESULTS

We identified 520 genes and three immunological clusters (IC1, IC2, and IC3) associated with CD8+ T cells. Higher IFN scores, immune T cell lytic activity, and immune cell infiltration and upregulated expression of immune-checkpoint-related genes indicated that IC3 is more responsive to immunotherapy, whereas IC1 and IC2 have a poorer prognosis. A 10-gene signature, including SEMA4F, CX3CR1, STX7, PASK, AKIRIN2, HEMGN, GBP5, NSG1, and CXorf65, was constructed, and a multivariate Cox regression analysis revealed a significant association between the 10-gene signature-based risk model and overall survival (p < 0.001). A nomogram was constructed with age and the 10-gene signature. Consistent with the bioinformatics analysis, IHC and qRT-PCR confirmed the accuracy of the signatures in OC tissue samples. The predictive ability of the risk model was demonstrated using the Imvigor210 immunotherapy dataset.

CONCLUSIONS

The development of a novel gene signature associated with CD8+ T cells could facilitate more accurate prognostics and prediction of the immunotherapeutic response of patients with OC.

Clinical analysis of 312 patients with stage IB1-IIA2 cervical squamous cell carcinoma and research on the influencing factors of postoperative recurrence

OBJECTIVE

To investigate the influencing factors of the recurrence of IB1-IIA2 cervical squamous cell carcinoma after surgical treatment, and to explore the relationship between high-risk human papillomavirus (HR-HPV) infection and postoperative cervical squamous cell carcinoma recurrence.

METHODS

Patients (n = 312) diagnosed with stage IB1-IIA2 cervical cancer and treated by radical hysterectomy and lymphadenectomy at this hospital were accrued between January 2014 and December 2016. The clinical data of these patients were analysed, and the association among clinicopathological factors, the association among clinicopathological factors, HPV infection and recurrences was investigated through Cox regression.

RESULTS

The median follow-up time was 59.2 months (with a range of 14-77.9 months). The pre-operative HPV infection rate was 85.3% (266/312), and 74 patients had a high level of HPV-DNA (> 5 × 10⁶ copy number / 10⁴ cells). Twenty-nine patients had a postoperative persistent high level of HPV-DNA (9.3%). On multivariate analysis, deep 1/3 stromal invasion (hazard ratio [HR] 114.79, 95% confidence interval [CI] 2.821-4670.111, p = 0.012*) and postoperative persistence of high HPV-DNA levels within 12 months (HR 269.044, 95% CI 14.437-5013.754, p < 0.001*) and 24 months (HR 31.299, 95% CI 1.191-822.215, p = 0.039*) were associated with a higher local recurrence rate.

CONCLUSION

Continuous high HPV-DNA levels within 24 months of an operation and deep 1/3 interstitial infiltration were independent risk factors for local recurrences of cervical cancer.

Development of the expression and prognostic significance of m 5 C ‐related LncRNAs in breast cancer

BACKGROUND

5-Methylcytosine (m5C) methylation is a major epigenetic RNA modification and is closely related to tumorigenesis in various cancers. This study aimed to explore the prognostic value of m5C-related lncRNAs in breast cancer.

METHODS

Clinical characteristics and RNA-seq expression data from TCGA (The Cancer Genome Atlas) were used in the study. First, we performed differentially expressed gene (DEG) analysis and constructed a PPI network for the 12 m5C regulators. Then, we identified the m5C-related LncRNAs by the "cor. test." An m5C-related lncRNA prognostic risk signature was developed using univariate Cox regression and Lasso-penalized Cox regression analyses. The model's performance was determined using Kaplan-Meier (KM) survival analysis and ROC curves. Finally, a nomogram was constructed for clinical application in evaluating patients with BRCA. We also researched the drug sensitivity of signature lncRNAs and immune cell infiltration. Finally, we validated the expression of the signature lncRNAs through qRT-PCR in a breast cancer cell line and a breast epithelial cell line.

RESULTS

Overall, we constructed an 11-lncRNA risk score signature based on the lncRNAs associated with m5C regulators. According to the median risk score, we divided BRCA patients into high- and low-risk groups. The prognostic risk signature displayed excellent accuracy and demonstrated sufficient independence from other clinical characteristics. The immune cell infiltration analysis showed that the prognostic risk signature was related to the infiltration of immune cell subtypes. Drug sensitivity proved that our prognostic risk signature potentially has therapeutic value.

CONCLUSIONS

The m5C-related lncRNA signature reliably predicted the prognosis of breast cancer patients and may provide new insight into the breast cancer tumor immune microenvironment.

Exosomal circPABPC1 promotes colorectal cancer liver metastases by regulating HMGA2 in the nucleus and BMP4/ADAM19 in the cytoplasm

Liver metastasis is the leading cause of death in colorectal carcinoma (CRC). However, little is known about the mechanisms of transferring effector messages between the primary tumor and the site of metastasis. Exosomes provide a novel transfer message method, and exosomal circular RNAs (circRNAs) play critical regulatory roles in cancer biology. In this study, the results showed that the expression of circPABPC1 was aberrantly upregulated in CRC tissues and exosomes. Exosomal circPABPC1 was considered an oncogene by functional experimental analysis in vitro and in vivo. Mechanistically, circPABPC1 recruited KDM4C to the HMGA2 promoter, reduced its H3K9me3 modification and initiated the transcription process in the nucleus. Moreover, cytoplasmic circPABPC1 promoted CRC progression by protecting ADAM19 and BMP4 from miR-874-/miR-1292-mediated degradation. Our findings indicated that exosomal circPABPC1 is an essential regulator in CRC liver metastasis progression by promoting HMGA2 and BMP4/ADAM19 expression. CircPABPC1 is expected to be a novel biomarker and antimetastatic therapeutic target in CRC.

Immune Subtypes and Immune Landscape Analysis of Endometrial Carcinoma

Some patients with endometrial cancer (EC) suffer from limited survival benefits after immunotherapy, suggesting that there may be a specific pattern associated with immunotherapy. Immune-related genes were extracted from The Cancer Genome Atlas databases. We analyzed the differences among immune subtypes (ISs) in the distribution of the tumor mutational burden, chemotherapy-induced immune response markers, immune checkpoint-related genes, immunotherapy, and chemotherapy. We applied dimensionality reduction and defined the immune landscape of EC. Then, we used the Weighted Gene Co-Expression Network Analysis package to identify the coexpression modules of these immune genes. Finally, hub genes were selected and detected by quantitative PCR and immunohistochemistry. We obtained three ISs. There were differences in the distribution of the tumor mutational burden, chemotherapy-induced immune response markers, and immune checkpoint-related genes among the ISs. Regarding immunotherapy and chemotherapy, the IS2 subtypes were more sensitive to programmed cell death protein 1 inhibitors. In addition, different positions in the immune landscape map exhibited different prognostic characteristics, providing further evidence of the ISs. The IS2 subtypes were significantly positively correlated with yellow module gene list, indicating a good prognosis with high score. SIRPG and SLAMF1 were identified as the final characteristic genes. The quantitative PCR and immunohistochemistry results showed that the expression levels of SIRPG and SLAMF1 were low in human EC tissue. In this study, we identified three reproducible ISs of EC. The immune landscape analysis further revealed the intraclass heterogeneity of the ISs. SIRPG and SLAMF1 were identified to be associated with progression, suggesting that they may be novel immune-related biomarkers of EC.

Single-cell and WGCNA uncover a prognostic model and potential oncogenes in colorectal cancer

Background

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Single-cell transcriptome sequencing (scRNA-seq) can provide accurate gene expression data for individual cells. In this study, a new prognostic model was constructed by scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data of CRC samples to develop a new understanding of CRC.

Methods

CRC scRNA-seq data were downloaded from the GSE161277 database, and CRC bulk RNA-seq data were downloaded from the TCGA and GSE17537 databases. The cells were clustered by the FindNeighbors and FindClusters functions in scRNA-seq data. CIBERSORTx was applied to detect the abundance of cell clusters in the bulk RNA-seq expression matrix. WGCNA was performed with the expression profiles to construct the gene coexpression networks of TCGA-CRC. Next, we used a tenfold cross test to construct the model and a nomogram to assess the independence of the model for clinical application. Finally, we examined the expression of the unreported model genes by qPCR and immunohistochemistry. A clone formation assay and orthotopic colorectal tumour model were applied to detect the regulatory roles of unreported model genes.

Results

A total of 43,851 cells were included after quality control, and 20 cell clusters were classified by the FindCluster () function. We found that the abundances of C1, C2, C4, C5, C15, C16 and C19 were high and the abundances of C7, C10, C11, C13, C14 and C17 were low in CRC tumour tissues. Meanwhile, the results of survival analysis showed that high abundances of C4, C11 and C13 and low abundances of C5 and C14 were associated with better survival. The WGCNA results showed that the red module was most related to the tumour and the C14 cluster, which contains 615 genes. Lasso Cox regression analysis revealed 8 genes (PBXIP1, MPMZ, SCARA3, INA, ILK, MPP2, L1CAM and FLNA), which were chosen to construct a risk model. In the model, the risk score features had the greatest impact on survival prediction, indicating that the 8-gene risk model can better predict prognosis. qPCR and immunohistochemistry analysis showed that the expression levels of MPZ, SCARA3, MPP2 and PBXIP1 were high in CRC tissues. The functional experiment results indicated that MPZ, SCARA3, MPP2 and PBXIP1 could promote the colony formation ability of CRC cells in vitro and tumorigenicity in vivo.

Conclusions

We constructed a risk model to predict the prognosis of CRC patients based on scRNA-seq and bulk RNA-seq data, which could be used for clinical application. We also identified 4 previously unreported model genes (MPZ, SCARA3, MPP2 and PBXIP1) as novel oncogenes in CRC. These results suggest that this model could potentially be used to evaluate the prognostic risk and provide potential therapeutic targets for CRC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12575-022-00175-x.

Plasma cfDNA methylation markers for the detection and prognosis of ovarian cancer

Background

Plasma cell-free DNA (cfDNA) methylation has shown the potential in the detection and prognostic testing in multiple cancers. Herein, we thoroughly investigate the performance of cfDNA methylation in the detection and prognosis of ovarian cancer (OC).

Methods

The OC-specific differentially methylated regions (DMRs) were identified by sequencing ovarian tissue samples from OC (n = 61), benign ovarian disease (BOD, n = 49) and healthy controls (HC, n = 37). Based on 1,272 DMRs, a cfDNA OC detection model (OC-D model) was trained and validated in plasma samples from patients of OC (n = 104), BOD (n = 56) and HC (n = 56) and a prognostic testing model (OC-P model) was developed in plasma samples in patients with high-grade serous OC (HG-SOC) in the training cohort and then tested the rationality of this model with International Cancer Genome Consortium (ICGC) tissue methylation data. Mechanisms were investigated in the TCGA-OC cohort.

Findings

In the validation cohort, the cfDNA OC-D model consisting of 18 DMRs achieved a sensitivity of 94.7% (95% CI: 85.4%‒98.9%) at a specificity of 88.7% (95% CI: 78.7%‒94.9%), which outperformed CA 125 (AUC: 0.967 vs 0.905, P = 0.03). Then the cfDNA OC-P model consisting of 15 DMRs was constructed and associated with a better prognosis of HG-SOC in multivariable Cox regression analysis (HR: 0.29, 95% CI, 0.11‒0.78, P = 0.01) in the training cohort, which was also observed in the ICGC cohort using tissue methylation (HR: 0.56, 95% CI, 0.32‒0.98, P = 0.04). Investigation into mechanisms revealed that the low-risk group had higher homologous recombination deficiency and immune cell infiltration (P < 0.05).

Interpretation

Our study demonstrated the potential utility of cfDNA methylation in the detection and prognostic testing in OC. Future studies with a larger population are warranted.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sector.

ALKBH5-mediated m6A modification of circCCDC134 facilitates cervical cancer metastasis by enhancing HIF1A transcription

Background

Metastasis is the main cause of mortality in cervical cancer (CC). Circular RNAs (circRNAs) have been demonstrated to play a crucial role in carcinoma biology. However, the expression and function of circRNAs in cervical cancer metastasis are still unclear.

Methods

In the present study, we identified a circRNA with an N6-methyladenosine (m6A) modification, circCCDC134, whose expression was increased in CC tissues by circRNA-Seq and qPCR. CircCCDC134 upregulation in CC was fine-tuned by ALKBH5-mediated m6A modification, which enhanced its stability in a YTHDF2-dependent manner. The functional experiments illustrated that circCCDC134 enhanced tumour proliferation and metastasis in vitro and in vivo. For the comprehensive identification of RNA-binding proteins, circRNA pull-down and mass spectrometry (ChIRP-MS), chromatin immunoprecipitation-seq (Chip-seq), RNA binding protein immunoprecipitation (RIP) and luciferase reporter assays were used to perform mechanistic investigations.

Results

The results revealed that circCCDC134 recruited p65 in the nucleus and acted as a miR-503-5p sponge to regulate the expression of MYB in the cytoplasm, ultimately stimulating HIF1A transcription and facilitating CC growth and metastasis. Conclusion: These findings indicate that circCCDC134 is an important therapeutic target and provide new regulatory model insights for exploring the carcinogenic mechanism of circCCDC134 in CC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02462-7.

Single-cell RNA sequencing analysis to explore immune cell heterogeneity and novel biomarkers for the prognosis of lung adenocarcinoma

Background: Single-cell RNA sequencing is necessary to understand tumor heterogeneity, and the cell type heterogeneity of lung adenocarcinoma (LUAD) has not been fully studied.

Method: We first reduced the dimensionality of the GSE149655 single-cell data. Then, we statistically analysed the subpopulations obtained by cell annotation to find the subpopulations highly enriched in tumor tissues. Monocle was used to predict the development trajectory of five subpopulations; beam was used to find the regulatory genes of five branches; qval was used to screen the key genes; and cellchart was used to analyse cell communication. Next, we used the differentially expressed genes of TCGA-LUAD to screen for overlapping genes and established a prognostic risk model through univariate and multivariate analyses. To identify the independence of the model in clinical application, univariate and multivariate Cox regression were used to analyse the relevant HR, 95% CI of HR and p value. Finally, the novel biomarker genes were verified by qPCR and immunohistochemistry.

Results: The single-cell dataset GSE149655 was subjected to quality control, filtration and dimensionality reduction. Finally, 23 subpopulations were screened, and 11-cell subgroups were annotated in 23 subpopulations. Through the statistical analysis of 11 subgroups, five important subgroups were selected, including lung epithelial cells, macrophages, neuroendocrine cells, secret cells and T cells. From the analysis of cell trajectory and cell communication, it is found that the interaction of five subpopulations is very complex and that the communication between them is dense. We believe that these five subpopulations play a very important role in the occurrence and development of LUAD. Downloading the TCGA data, we screened the marker genes of these five subpopulations, which are also the differentially expressed genes in tumorigenesis, with a total of 462 genes, and constructed 10 gene prognostic risk models based on related genes. The 10-gene signature has strong robustness and can achieve stable prediction efficiency in datasets from different platforms. Two new molecular markers related to LUAD, HLA-DRB5 and CCDC50, were verified by qPCR and immunohistochemistry. The results showed that HLA-DRB5 expression was negatively correlated with the risk of LUAD, and CCDC50 expression was positively correlated with the risk of LUAD.

Conclusion: Therefore, we identified a prognostic risk model including CCL20, CP, HLA-DRB5, RHOV, CYP4B1, BASP1, ACSL4, GNG7, CCDC50 and SPATS2 as risk biomarkers and verified their predictive value for the prognosis of LUAD, which could serve as a new therapeutic target.

Identification of Prognostic DNA Methylation Signatures in Lung Adenocarcinoma

Background

Increasing evidence exists of a link between DNA methylation and tumor immunotherapy. However, the impact of DNA methylation on the characteristics of the lung adenocarcinoma microenvironment and its effect on immunotherapy remain unclear.

Method

This study collected TCGA-LUAD related data sets (LUAD) to explore the characteristics and regulation of 20 DNA methylation-related genes. We further identified two DNA methylation subtypes by analysing the expression profiles of these 20 DNA methylation-related genes. Subsequently, the differences in immune cell infiltration (ICI) and the expression of immune-related signaling factors among different DNA methylation subtypes were explored, and the differentially expressed genes (DEGs) among different LUAD DNA methylation subtypes were identified. Using univariate Cox to screen differentially expressed genes meaningful for survival, a DNA methylation score (DMS) was constructed based on the weight of the first and second dimensions after dimensionality reduction by principal component analysis (PCA). Our study found that DMS can better evaluate the prognosis of lung adenocarcinoma.

Results

Based on DMS, LUAD samples were divided into two groups with high and low scores. The differences in clinical characteristics, tumor mutation load, and tumor immune cell infiltration between different DMS groups of LUAD were deeply explored, and the prediction ability of DMS for the benefit of immunotherapy was evaluated.

Conclusions

DMS is a valuable tool for predicting survival, clinicopathological features, and immunotherapeutic efficacy, which may help to promote personalized LUAD immunotherapy in the future.

Gene Signature and Prognostic Value of Ubiquitin-Specific Proteases Members in Hepatocellular Carcinoma and Explored the Immunological Role of USP36

BACKGROUND

Ubiquitination is one of the most common post-translational modifications in cells and dysregulation is closely associated with the development of cancer. However, a comprehensive analysis of the role of ubiquitination in hepatocellular carcinoma (HCC) is still lacking. In this study we analyzed expression and prognostic value of Ubiquitin-Specific Proteases (USPs) in HCC, and the immunological role of USP36 in HCC.

METHODS

Expression data, prognostic data, and DNA methylation data in cases of HCC were obtained from the cancer genome atlas (TCGA). Overexpression of USP36 in HCC was confirmed in the gene expression omnibus (GEO) database and verified by quantitative PCR in 10 pairs of HCC samples. ULCAN was used to analyze the correlation between USP36 and clinicopathological features. TIMER2.0 and DriverDBv3 were used to analyze the USP36 mutational profile. GSEA analysis explored the potential signaling pathways of USP36 affecting HCC. The immune and stromal scores of HCC samples were calculated using the ESTIMATE algorithm. TIMER1.0 was used to explore the correlation between USP36 and immune cell infiltration. Finally, we analyzed the correlation of USP36 expression with immune checkpoint molecules and determined the IC50 values of 6 chemotherapeutic drugs using the pRRophetic software package.

RESULTS

Most USPs are abnormally expressed in HCC, among which USP36 and USP39 are most closely associated with HCC prognosis. We also found that USP36 is associated with TP53 mutational status. GSEA analysis indicated that USP36 may affect HCC progression through the dysregulation of various pathways such as ubiquitin-mediated proteolysis. USP36 expression positively correlated with both macrophage infiltration levels and multiple immune checkpoint molecules. Finally, chemosensitivity analysis indicated that chemosensitivity was lower in cells within the USP36 high expression group.

CONCLUSIONS

Most USPs are abnormally expressed in HCC. Overexpression of USP36 in HCC is closely related to poor prognosis. In particular, the unique immunological role of USP36 may have potential clinical application value.

Establishment and validation of a novel invasion-related gene signature for predicting the prognosis of ovarian cancer

BACKGROUND

Ovarian cancer (OC) is an invasive gynaecologic cancer with a high cancer-related death rate. The purpose of this study was to establish an invasion-related multigene signature to predict the prognostic risk of OC.

METHODS

We extracted 97 invasion-related genes from The Cancer Genome Atlas (TCGA) database. Then, the ConsensusClusterPlus and limma packages were used to calculate differentially expressed genes (DEGs). To calculate the immune scores of the molecular subtypes, we used ESTIMATE to evaluate the stromal score, immune score and ESTIMATE score. MCP-counter and the GSVA package ssgsea were used to evaluate the types of infiltrating immune cells. Survival and nomogram analyses were performed to explore the prognostic value of the signature. Finally, qPCR, immunohistochemistry staining and functional assays were used to evaluate the expression and biological abilities of the signature genes in OC.

RESULTS

Based on the consistent clustering of invasion-related genes, cases in the OC datasets were divided into two subtypes. A significant difference was observed in prognosis between the two subtypes. Most genes were highly expressed in the C1 group. Based on the C1 group genes, we constructed an invasion-related 6-gene prognostic risk model. Furthermore, to verify the signature, we used the TCGA-test and GSE32062 and GSE17260 chip datasets for testing and finally obtained a good risk prediction effect in those datasets. Moreover, the results of the qPCR and immunohistochemistry staining assays revealed that KIF26B, VSIG4 and COL6A6 were upregulated and that FOXJ1, MXRA5 and CXCL9 were downregulated in OC tissues. The functional study showed that the expression of KIF26B, VSIG4, COL6A6, FOXJ1, MXRA5 and CXCL9 can regulate the migration and invasion abilities of OC cells.

CONCLUSION

We developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features. These results suggest that the signature could potentially be used to evaluate the prognostic risk of OC patients.

Bone Mesenchymal Stem Cell-Derived sEV-Encapsulated Thermosensitive Hydrogels Accelerate Osteogenesis and Angiogenesis by Release of Exosomal miR-21

Angiogenesis has been recognized to play an essential role in remodeling new bone (osteogenesis). Small extracellular vesicles (sEVs), the endogenously secreted nanovesicles by cells, exhibit great potential in the regeneration of bone defects and the realization of cell-free therapy. Chitosan, a natural polysaccharide, can form a thermosensitive injectable hydrogel through the addition of β-glycerophosphate. Herein, we developed injectable thermosensitive hydrogel-encapsulated sEVs derived from bone mesenchymal stem cells, which significantly prolonged delivery and release and synergistically enhanced bone regeneration. sEVs were isolated and characterized, and the physicochemical properties, release kinetics, and biocompatibility of the hydrogels were analyzed. In vitro experiments were performed to investigate osteogenic differentiation, cell proliferation and migration, and tube formation. Thereafter, sEVs were added to the chitosan/β-glycerophosphate hydrogel (sEV@CS/β-GP composite) to repair calvarial defects in rats. The results showed that sEV-loaded hydrogels were biocompatible, exhibiting excellent thermosensitive properties and enhancing bone regeneration. Furthermore, mechanistic studies revealed that exosomal miR-21 targeted SPRY2, thereby promoting angiogenesis. Our study provides new insights on the repair of bone defects with multifunctional controlled-sEV-release hydrogels, which shows great potential in the repair of tissues in the future.

Circular RNA ACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription

BACKGROUND & AIMS

Intrahepatic cholangiocarcinoma (ICC) is a primary liver cancer with high aggressiveness and extremely poor prognosis. The role of circular RNAs (circRNAs) in ICC carcinogenesis and progression remains to be determined.

METHODS

CircRNA microarray was performed to screen significantly upregulated circRNAs in paired ICC and non-tumor tissues. Colony formation, transwell, and xenograft models were used to examine the role of circRNAs in ICC proliferation and metastasis. RNA pulldown, mass spectrometry, chromatin immunoprecipitation, RNA-binding protein immunoprecipitation, chromatin isolation by RNA purification, electrophoretic mobility shift assay, and luciferase reporter assays were used to explore the molecular sponge role of the circRNA (via miRNA binding), and the interaction between circRNA and RNA-binding proteins.

RESULTS

Hsa_circ_0050898, which originated from exon 1 to exon 20 of the ACTN4 gene (named circACTN4), was significantly upregulated in ICC. High circACTN4 expression was associated with enhanced tumor proliferation and metastasis in vitro and in vivo, as well as a worse prognosis following ICC resection. In addition, circACTN4 upregulated Yes-associated protein 1 (YAP1) expression by sponging miR-424-5p. More importantly, circACTN4 also recruited Y-box binding protein 1 (YBX1) to stimulate Frizzled-7 (FZD7) transcription. Furthermore, circACTN4 overexpression in ICC cells enhanced the interaction between YAP1 and β-catenin, which are the core components of the Hippo and Wnt signaling pathways, respectively.

CONCLUSIONS

CircACTN4 was upregulated in ICC and promoted ICC proliferation and metastasis by acting as a molecular sponge of miR-424-5p, as well as by interacting with YBX1 to transcriptionally activate FZD7. These results suggest that circACTN4 is a potential prognostic marker and therapeutic target for ICC.

LAY SUMMARY

Intrahepatic cholangiocarcinoma is a primary liver cancer associated with aggressiveness and extremely poor prognosis. It is essential for therapeutic development that we uncover relevant pathogenic pathways. Herein, we showed that a circular RNA (circACTN4) was highly expressed in intrahepatic cholangiocarcinoma and was positively associated with tumor growth and metastasis through key developmental signaling pathways. Thus, circACTN4 could be a prognostic biomarker and therapeutic target for intrahepatic cholangiocarcinoma.

Establishment of a molecular risk model for the prognosis of cervical cancer based on microRNA expression

Background

Globally, the incidence of cervical cancer (CC) is highest among all tumors of the female reproductive system. Numerous studies have shown that the expression level of microRNA (miRNA) is highly correlated with cancer. This study aimed to establish a molecular prognostic model of CC based on miRNAs in order to provide more individualized treatment to CC patients.

Methods

Human tissues were selected from the Cancer Hospital (Chinese Academy of Medical Sciences and Peking Union Medical College) for miRNA gene sequencing. CC transcriptome expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA). We distinguished between common differentially expressed miRNAs of CC miRNA-seq and TCGA-CC. To obtain a molecular prognostic model, R package was used to perform univariate Cox proportional hazard regression and least absolute shrinkage and selection operator (LASSO) Cox regression for common differentially-expressed miRNAs. Next, the model performance was evaluated by survival analysis, receiver operating characteristic (ROC) curve analysis, as well as univariate and multivariate analyses in the TCGA-CC dataset. Quantitative Real-time polymerase chain reaction (qPCR) detection was to verify the expression changes of miRNA. Transwell was used to verify the role of molecules in CC cell migration and invasion.

Results

Thirty-nine miRNAs were distinguished in TCGA-CC and CC miRNA-seq, LASSO regression analysis to obtain the risk model (risk score =−0.310× expression of hsa-miR-142-3p +0.439× expression of hsa-miR-100-3p). The survival analysis, ROC curve analysis, patient risk assessment, and univariate and multivariate analyses showed that the risk score model had good predictive ability in assessing patient survival (P<0.01), risk of death, and independent prognosis (P<0.01). qPCR detection of clinical samples and cells showed that the expression of hsa-miR-142-3p and hsa-miR-100-3p was consistent with the results of the database analysis. The Transwell results indicated that miR-142-3p is an inhibiting factor and miR-100-3p serves as a promoting factor in CC cell migration and invasion.

Conclusions

Twelve miRNA-seq and TCGA-CC tissues were used to build a prognostic model for CC. We have obtained a two-miRNA risk score model. Our results provide a new strategy for the individualized diagnosis and treatment of CC.

Development and Validation of a Novel Gene Signature for Predicting the Prognosis by Identifying m5C Modification Subtypes of Cervical Cancer

Background: 5-Methylcytidine (m5C) is the most common RNA modification and plays an important role in multiple tumors including cervical cancer (CC). We aimed to develop a novel gene signature by identifying m5C modification subtypes of CC to better predict the prognosis of patients.

Methods: We obtained the expression of 13 m5C regulatory factors from The Cancer Genome Atlas (TCGA all set, 257 patients) to determine m5C modification subtypes by the “nonnegative matrix factorization” (NMF). Then the “limma” package was used to identify differentially expressed genes (DEGs) between different subtypes. According to these DEGs, we performed Cox regression and Kaplan-Meier (KM) survival analysis to establish a novel gene signature in TCGA training set (128 patients). We also verified the risk prediction effect of gene signature in TCGA test set (129 patients), TCGA all set (257 patients) and GSE44001 (300 patients). Furthermore, a nomogram including this gene signature and clinicopathological parameters was established to predict the individual survival rate. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, colony formation, migration and invasion assays.

Results: Based on consistent clustering of 13 m5C-modified genes, CC was divided into two subtypes (C1 and C2) and the C1 subtype had a worse prognosis. The 4-gene signature comprising FNDC3A, VEGFA, OPN3 and CPE was constructed. In TCGA training set and three validation sets, we found the prognosis of patients in the low-risk group was much better than that in the high-risk group. A nomogram incorporating the gene signature and T stage was constructed, and the calibration plot suggested that it could accurately predict the survival rate. The expression levels of FNDC3A, VEGFA, OPN3 and CPE were all high in cervical cancer tissues. Downregulation of FNDC3A, VEGFA or CPE expression suppressed the proliferation, migration and invasion of SiHa cells.

Conclusions: Two m5C modification subtypes of CC were identified and then a 4-gene signature was established, which provide new feasible methods for clinical risk assessment and targeted therapies for CC.

Integration of scRNA-Seq and Bulk RNA-Seq to Analyse the Heterogeneity of Ovarian Cancer Immune Cells and Establish a Molecular Risk Model

Background

Considerable evidence suggests that the heterogeneity of ovarian cancer (OC) is a major cause of treatment failure. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to analyse the heterogeneity of the tumour at the single-cell level, leading to a better understanding of cell function at the genetic and cellular levels.

Methods

OC scRNA-seq data were extracted from the Gene Expression Omnibus (GEO) database and the FindCluster () package used for cell cluster analysis. The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a Hallmark gene set score and bulk RNA-seq data were used to analyse the key genes of OC-associated immune cell subsets. CIBERSORT was used to identify immune scores of cells and the “WGCNA” package for the weighted correlation network analysis (WGCNA). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses of subtype groups were performed by GSEA. Then, univariate Cox and lasso regression were performed to further establish a signature. Finally, qPCR and immunohistochemistry staining were used to evaluate the expression of signature genes in OC.

Results

Two scRNA-seq (GSE154600 and GES158937) datasets were integrated to obtain 20 cell clusters. T cells or NK cells (cluster 5, 6, 7, 11), B cells (cluster 16, 19, 20) and myeloid cells (cluster 4, 9, 10) were clustered according to immune cell markers. The ssGSEA revealed that M1- and M2-like myeloid cell-related genes were significantly upregulated in P3 and P4 patients in the GSE154600 data. Immune cell analysis in TCGA-OC showed that a high abundance of M1-like tumour-associated macrophages (TAMS) predicts better survival. WGCNA, univariate Cox and lasso Cox regression established a two-gene signature (RiskScore=-0.059*CXCL13-0.034*IL26). Next, the TCGA-test and TCGA-OC were used to test the risk prediction ability of the signature, showing a good effect in the datasets. Moreover, the qPCR and immunohistochemistry staining revealed that the expression of CXCL13 and IL26 was reduced in OC tissues.

Conclusion

A two-gene signature prognostic stratification system (CXCL13 and IL26) was developed based on the heterogeneity of OC immune cells to accurately evaluate the prognostic risk.

Identification and validation of a novel glycolysis-related gene signature for predicting the prognosis in ovarian cancer

Background

Ovarian cancer (OC) is the most lethal gynaecological tumor. Changes in glycolysis have been proven to play an important role in OC progression. We aimed to identify a novel glycolysis-related gene signature to better predict the prognosis of patients with OC.

Methods

mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype Tissue Expression (GTEx) database. The “limma” R package was used to identify glycolysis-related differentially expressed genes (DEGs). Then, a multivariate Cox proportional regression model and survival analysis were used to develop a glycolysis-related gene signature. Furthermore, the TCGA training set was divided into two internal test sets for validation, while the ICGC dataset was used as an external test set. A nomogram was constructed in the training set, and the relative proportions of 22 types of tumor-infiltrating immune cells were evaluated using the “CIBERSORT” R package. The enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined by single-sample gene set enrichment analysis (ssGSEA) with the “GSVA” R package. Finally, the expression and function of the unreported signature genes ISG20 and SEH1L were explored using immunohistochemistry, western blotting, qRT-PCR, proliferation, migration, invasion and xenograft tumor assays.

Results

A five-gene signature comprising ANGPTL4, PYGB, ISG20, SEH1L and IRS2 was constructed. This signature could predict prognosis independent of clinical factors. A nomogram incorporating the signature and three clinical features was constructed, and the calibration plot suggested that the nomogram could accurately predict the survival rate. According to ssGSEA, the signature was associated with KEGG pathways related to axon guidance, mTOR signalling, tight junctions, etc. The proportions of tumor-infiltrating immune cells differed significantly between the high-risk group and the low-risk group. The expression levels of ISG20 and SEH1L were lower in tumor tissues than in normal tissues. Overexpression of ISG20 or SEH1L suppressed the proliferation, migration and invasion of Caov3 cells in vitro and the growth of xenograft tumors in vivo.

Conclusion

Five glycolysis-related genes were identified and incorporated into a novel risk signature that can effectively assess the prognosis and guide the treatment of OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02045-0.

20(S)-ginsenoside Rh2 as agent for the treatment of LMN-CRC via regulating epithelial-mesenchymal transition

The lymph node metastasis of colorectal cancer (LMN-CRC) seriously threatens the prognosis of patients. Chemotherapy, as the most common treatment, results in severe bone marrow suppression. 20(S)-ginsenoside Rh2 (SGRh2), a major effective constituent of ginseng, has demonstrated therapeutic effects on a variety of diseases, including some tumours. SGRh2 treatment had no effect on other organs. Therefore, ginsenosides are considered a safe and effective antineoplastic drug. However, the effects of SGRh2 on LMN-CRC remain unknown. The present study investigated the potential effect of SGRh2 on LMN-CRC in vitro and in vivo. SW480 and CoLo205 cell lines were treated with SGRh2. SGRh2 dose-dependently decreased CRC cell proliferation by CCK-8, colony formation and Edu assays. The Transwell and scratch assays revealed that SGRh2 inhibits the migratory and invasive abilities of CRC cells in a dose-dependent manner. Furthermore, the results of Western blotting revealed that SGRh2 decreased the expression of matrix metalloproteinase (MMP)-2 and MMP9. In terms of the underlying mechanisms, SGRh2 regulates CRC metastasis by affecting epithelial-mesenchymal transition (EMT), which significantly up-regulated epithelial biomarkers (E-cadherin) and down-regulated mesenchymal biomarkers (N-cadherin and vimentin) and EMT transcriptional factors (Smad-3, Snail-1, and Twist-1). In vivo, SGRh2 significantly inhibited LMN-CRC without affecting other normal organs. Immunohistochemical results showed that SGRh2 treats LMN-CRC by regulating EMT. These results demonstrate that SGRh2 has therapeutic potential for LMN-CRC.

Identification of Energy Metabolism Genes for the Prediction of Survival in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) samples were clustered into three energy metabolism-related molecular subtypes (C1, C2, and C3) with different prognosis using the gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). HCC energy metabolism-related molecular subtype analysis was conducted based on the 594 energy metabolism genes. Differential expression analysis yielded 576 differentially expressed genes (DEGs) among the three subtypes, which were closely related to HCC progression. Six genes were finally selected from the 576 DEGs through LASSO-Cox regression and used in constructing a six-gene signature-associated prognostic risk model, which was validated using the TCGA internal and three GEO external validation cohorts. The risk model showed that high ANLN, ENTPD2, TRIP13, PLAC8, and G6PD expression levels were associated with bad prognosis, and high expression of ADH1C was associated with a good prognosis. The validation results showed that our risk model had a high distinguishing ability of prognosis in HCC patients. The four enriched pathways of the risk model were obtained by gene set enrichment analysis (GSEA) and found to be associated with the tumorigenesis and development of HCC, including the cell cycle, Wnt signaling pathway, drug metabolism cytochrome P450, and primary bile acid biosynthesis. The risk score calculated from the established risk model in 204 samples and other clinical characteristics were used in building a nomogram with a good prognostic prediction ability (C-index = 0.746, 95% CI = 0.714–0.777). The area under the curves (AUCs) of the nomogram model in 1-, 2-, and 3-years were 0.82, 0.77, and 0.79, respectively. Then, qRT-PCR and immunohistochemistry were used to validate the mRNA expression levels of the six genes, and significant differences in mRNA and gene expression were observed among the tumor and adjacent tissues. Overall, our study divided HCC patients into three energy metabolism-related molecular subtypes with different prognosis. Then, a risk model with a good performance in prognostic prediction was built using the TCGA dataset. This model can be used as an independent prognostic evaluation index for HCC patients.

Development and Clinical Validation of a Novel 4-Gene Prognostic Signature Predicting Survival in Colorectal Cancer

In this study, we collected genes related to energy metabolism, used gene expression data from public databases to classify molecular subtypes of colon cancer (COAD) based on the genes related to energy metabolism, and further evaluated the relationships between the molecular subtypes and prognosis and clinical characteristics. Differential expression analysis of the molecular subtypes yielded 1948 differentially expressed genes (DEGs), whose functions were closely related to the occurrence and development of cancer. Based on the DEGs, we constructed a 4-gene prognostic risk model and identified the high expression of FOXD4, ENPEP, HOXC6, and ALOX15B as a risk factor associated with a high risk of developing COAD. The 4-gene signature has strong robustness and a stable predictive performance in datasets from different platforms not only in patients with early COAD but also in all patients with colon cancer. The enriched pathways of the 4-gene signature in the high- and low-risk groups obtained by GSEA were significantly related to the occurrence and development of colon cancer. Moreover, the results of qPCR, immunohistochemistry staining and Western blot assay revealed that FOXD4, ENPEP, HOXC6, and ALOX15B are over expressed in CRC tissues and cells. These results suggesting that the signature could potentially be used as a prognostic marker for clinical diagnosis.

Identification Hub Genes in Colorectal Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Clinical Validation in vivo and vitro

Colorectal cancer (CRC) is the third leading cause of death in the world. However, the key roles of most molecules in CRC remain unclear. This study aimed to identify key modules and hub genes associated with the progression of CRC. The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R. by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified, which were associated with clinical traits. Next, we screened hub genes related to the progression of CRC authenticated by The Cancer Genome Atlas (TCGA) and Oncomine databases. Three hub genes (HCLS1, EVI2B, and CD48) were identified, and survival analysis was further performed. Moreover, the results of qPCR and immunohistochemistry staining revealed that HCLS1, EVI2B, and CD48 are tumor suppressor genes. Further, the functional study verified that over-expression of HCLS1, EVI2B, and CD48 can reduce the proliferation, migration, and invasion ability of CRC cells and significantly suppress CRC tumor growth in vivo. In summary, we identified three hub genes that were associated with the progression of CRC that can be applied in treatment.

Multi-omics analysis at epigenomics and transcriptomics levels reveals prognostic subtypes of lung squamous cell carcinoma

In this study, we identified prognostic biomarkers for lung squamous cell carcinoma (LUSC) by integrating multiple sets of DNA copy number variants (CNV) and methylation variant (MET) data, and performing qPCR and immunohistochemical identification. We examined the expression of CNV and MET in 368 LUSC patients. Gene expression associated with DNA copy number or DNA methylation was identified and four LUSC gene subtypes were defined based on these correlations. The prognosis overall survival (OS) of the iC1 subtype was significantly lower than that in the iC2 and iC4 subtypes. We assessed the immune scores of each subtype and found that the six immune cell scores of the iC3 subtype were significantly higher than the other subtypes (p < 0.01). Three genes associated with prognosis, NFE2L2, ASAH2, and RIMBP2, were identified by comparing the expression of CNV and MET in subtypes. Analysis of mutational differences between subtypes revealed a group of genes with significant mutations between the iC1 and iC4 subtypes. The number of mutations in the NFE2L2 gene in LUSC was significantly higher than that in other genes, and the gene was prognostic. The number of mutations was significantly higher in the best iC4 subtype than the iC1 subtype with the worst prognosis; the other two genes, ASAH2 and RIMBP2, were only found in the worst prognosis of the iC1 subtype. This comprehensive multi-omics analysis of genomics, epigenomics, and transcriptomics data provides new insights into the molecular mechanisms of LUSC and may be helpful in identifying biomolecular markers for early disease diagnosis.

LncRNA HCP5 promotes follicular thyroid carcinoma progression via miRNAs sponge

Long non-coding RNAs (lncRNAs), which are important functional regulators in cancer, have received increased attention in recent years. In this study, next-generation sequencing technology was used to identify aberrantly expressed lncRNAs in follicular thyroid carcinoma (FTC). The long non-coding RNA–HLA complex P5 (HCP5) was found to be overexpressed in FTC. The results of the qPCR analysis were consistent with the sequencing results. In addition, functional experiments showed that overexpression of HCP5 can promote the proliferation, migration, invasiveness and angiogenic ability of FTC cells. Furthermore, according to the sequencing results, HCP5 and alpha-2, 6-sialyltransferase 2 (ST6GAL2) were co-expressed in FTC. We hypothesised that ST6GAL2 may be regulated by HCP5, which would in turn mediate the activity of FTC cells. Through qPCR, immunostaining analyses and functional experiments, we determined that the expression of HCP5 was elevated and was correlated with the levels of ST6GAL2 in FTC tissues and cells. Mechanistic experiments showed that HCP5 functions as a competing endogenous RNA (ceRNA) and acts as a sponge for miR-22-3p, miR-186-5p and miR-216a-5p, which activates ST6GAL2. In summary, our study revealed that HCP5 is a tumour regulator in the development of FTC and that it may contribute to improvement of FTC diagnosis and therapy.