Tongue squamous cell carcinoma (TSCC) exhibits an aggressive biological behavior of lymph node and distant metastasis, which contributes to poorer prognosis and results in tongue function loss or death. In addition to known regulators and pathways of cell migration in TSCC, it is important to uncover pivotal switches governing tumor metastasis.

Cancer cell migration-associated transcriptional and epigenetic characteristics were profiled in TSCC, and the specific super-enhancers (SEs) were identified. Molecular function and mechanism studies were used to investigate the pivotal switches in TSCC metastasis.

Ameboidal-type cell migration-related genes accompanied by transcriptional and epigenetic activity were enriched in TSCC. Meanwhile, the higher-ranked SE-related genes showed significant differences between 43 paired tumor and normal samples from the TCGA TSCC cohort. In addition, key motifs were detected in SE regions, and transcription factor-related expression levels were significantly associated with TSCC survival status. Notably, BATF and ATF3 regulated the expression of ameboidal-type cell migration-related MMP14 by switching the interaction with the SE region.

SEs and related key motifs transcriptional regulate tumor metastasis-associated MMP14 and might be potential therapeutic targets for TSCC.

Triple-negative breast cancer (TNBC) represents a highly aggressive subset of breast malignancies characterized by its challenging clinical management and unfavorable prognosis. While TFAP2A, a member of the AP-2 transcription factor family, has been implicated in maintaining the basal phenotype of breast cancer, its precise regulatory role in TNBC remains undefined.

In vitro assessments of TNBC cell growth and migratory potential were conducted using MTS, colony formation, and EdU assays. Quantitative PCR was employed to analyze mRNA expression levels, while Western blot was utilized to evaluate protein expression and phosphorylation status of AKT and ERK. The post-transcriptional regulation of TFAP2A by miR-8072 and the transcriptional activation of SNAI1 by TFAP2A were investigated through luciferase reporter assays. A xenograft mouse model was employed to assess the in vivo growth capacity of TNBC cells.

Selective silencing of TFAP2A significantly impeded the proliferation and migration of TNBC cells, with elevated TFAP2A expression observed in breast cancer tissues. Notably, TNBC patients exhibiting heightened TFAP2A levels experienced abbreviated overall survival. Mechanistically, TFAP2A was identified as a transcriptional activator of SNAI1, a crucial regulator of epithelial-mesenchymal transition (EMT) and cellular proliferation, thereby augmenting the oncogenic properties of TFAP2A in TNBC. Moreover, miR-8072 was unveiled as a negative regulator of TFAP2A, exerting potent inhibitory effects on TNBC cell growth and migration. Importantly, the tumor-suppressive actions mediated by the miR-8072/TFAP2A axis were intricately associated with the attenuation of AKT/ERK signaling cascades and the blockade of EMT processes.

Our findings unravel the role and underlying molecular mechanism of TFAP2A in driving tumorigenesis of TNBC. Targeting the TFAP2A/SNAI1 pathway and utilizing miR-8072 as a suppressor represent promising therapeutic strategies for treating TNBC.

Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour.

To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data.

Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.

For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI.

The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein-protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

Gastric cancer (GC) patients who experience recurrence within the first year following surgery (early recurrence [ER]) exhibit worse prognosis. Herein, we established a microRNA-based liquid biopsy assay to predict ER in GC patients.

A comprehensive biomarker discovery was performed by analysing miRNA expression profiling in 271 primary GC tumours. Thereafter, the expression of these biomarkers was validated in 290 GC cases, which included 218 tissues and 72 pre-treatment sera, from two independent institutions.

A panel of 8 miRNAs was identified during the initial biomarker discovery, and this panel could robustly predict ER in a tissue-based clinical cohort (area under the curve [AUC]: 0.81). Furthermore, a model combining the miRNA panel, microsatellite instability (MSI) status and tumour size exhibited superior predictive performance (AUC: 0.86), and was defined as a Prediction of Early Recurrence in GC (PERGC) signature, which was successfully validated in another independent cohort (AUC: 0.82). Finally, the PERGC signature was translated into a liquid biopsy assay (AUC: 0.81), and a multivariate regression analysis revealed this signature to be an independent predictor for ER (odds ratio: 11.20).

We successfully established a miRNA-based liquid biopsy signature that robustly predicts the risk of ER in GC patients.

The Chromobox homolog 4 (CBX4) has been found to be overexpressed in multiple malignancies. However, the associations between CBX4 and gastric cancer (GC) have remained unclear. This study aimed to determine the biological roles of CBX4 in GC and identify effective therapeutic targets.

The 3-(4,5-dimethylthiazol-2-yl) (MTT) assays were used to screen CBX family members. Differential analysis was utilized to evaluate the CBX4 levels. Kaplan-Meier analysis was used to perform prognostic analysis. Western blotting assay, quantitative polymerase chain reaction (qPCR) assay and immunohistochemistry (IHC) were used to assess CBX4 expressions. Colony formation assay, Cell Counting Kit-8 (CCK-8) assay, and Transwell assay were used to assess progression features of cells. The tail vein injection model was utilized to determine the metastatic efficacy of GC cells. Tumor sphere formation assay was used to assess tumor stemness maintenance ability. Chromatin immunoprecipitation (ChIP)-qPCR assay was used to evaluate the associations between CBX4 and CDC20. A subcutaneous tumor model was used to assess the in vivo growth ability of GC.

The MTT assay revealed that only CBX4 inhibition could lead to notable restriction of GC growth, as compared to others. Differential analysis suggested that CBX4 was upregulated in tumor samples relative to normal tissues. Less favorable overall survival (OS) outcomes were noticed in GC patients with high CBX4 in comparison to those with low CBX4. High CBX4 could notably enhance cell proliferation capacity, migration ability, and in vivo metastatic efficacy. Gene set enrichment analysis (GSEA) indicated the relationships between CBX4 and GC stemness, and CBX4 overexpression could remarkably elevate self-renewal ability of GC cells. In addition, CBX4 could mainly promote CDC20 messenger RNA (mRNA) levels, and targeting CBX4 suppressed the relative CDC20 levels. The ChIP-qPCR assay further demonstrated that CBX4 coordinated with H3K4me3 to bind at the CDC20 promoter region. Additionally, CBX4 depended on CDC20 to drive GC growth. Lastly, downregulated CBX4 could notably inhibit the growth of GC in vivo.

This study highlights the oncogenic roles of CBX4 in GC. CBX4 activates CDC20 to maintain stemness features of GC, thereby creating therapeutic vulnerabilities in the treatment of GC.

The morbidity and mortality of gastric cancer (GC) remain high worldwide. In recent years, circular RNAs (circRNAs) have attracted widespread attention among cancer researchers due to the stable ring structure. The present work aims to find serum circRNA biomarkers that can be used in clinical applications and effective diagnosis.

Hsa_circ_0007507 was extracted through circRNA sequencing. Exonuclease digestion assay, actinomycin D, agarose gel electrophoresis (AGE), and Sanger sequencing verified the potential of hsa_circ_0007507 as a biomarker. Besides, a real-time fluorescent quantitative polymerase chain reaction (RT-qPCR) was established to detect the level of expression of hsa_circ_0007507. Twenty cases of GC and the paired adjacent tissues were collected to verify its overexpression. Then, serum samples from 30 cases of colorectal cancer, 30 cases of thyroid cancer, and 30 cases of breast cancer were collected to verify their organ specificity. Additionally, serum samples from 80 healthy people, 62 gastritis patients, 31 intestinal metaplasia patients, and 100 GC patients were collected, and the diagnostic efficacy was evaluated through analysis of the receiver operating characteristic (ROC) curve. Furthermore, 16 post-operative GC samples, samples of 65 relapsed patients and 36 non-relapsed patients were collected to evaluate the prognosis of GC.

The level of expression of hsa_circ_0007507 in GC tissues was up-regulated (p = 0.0121), which was consistent with the results of circRNA sequencing. Exonuclease digestion assay and actinomycin D confirmed that hsa_circ_0007507 had a stable structure and a longer half-life. In the analysis of organ specificity experiments, serum hsa_circ_0007507 did not have specificity for patients with colorectal cancer (p = 0.5319), thyroid cancer (p = 0.5422), or breast cancer (p = 0.5178). Analysis of diagnostic efficacy indicated that the expression of hsa_circ_0007507 was significantly higher than that of normal people (p <0.0001); the area under the ROC (AUC) was 0.832 (95% CI: 0.771-0.892); the diagnostic power of hsa_circ_0007507 was higher than that of CEA (AUC = 0.765, 95% CI: 0.697-0.833) and CA199 (AUC = 0.587, 95% CI: 0.504-0.67). Through diagnosis using a combination of the three, GC patients could be distinguished from normal people (AUC = 0.849), and higher diagnostic efficiency could be achieved. The expression of serum hsa_circ_0007507 in GC patients significantly decreased after surgery (p = 0.001). Besides, the expression of serum hsa_circ_0007507 in patients with post-operative recurrence was significantly up-regulated again (p = 0.0139).

Serum hsa_circ_0007507 is differentially expressed in GC patients, post-operative GC patients, gastritis patients, intestinal metaplasia patients and relapsed patients, suggesting that serum hsa_circ_0007507 can be used as a new diagnostic and dynamic monitoring biomarker for GC.