Upregulation of Serine Proteinase Inhibitor Clade B Member 3 (SERPINB3) Expression by Stromal Cell-Derived Factor (SDF-1)/CXCR4/Nuclear Factor kappa B (NF-κB) Promotes Migration and Invasion of Gastric Cancer Cells

Multiple approaches revealed MGc80-3 as a somatic hybrid with HeLa cells rather than a gastric cancer cell line

The short-tandem-repeats (STR) profiles of MGc80-3 and HeLa partially overlap, raising suspicion of contamination in the MGc80-3 cell line. However, there has not been any relevant study demonstrating whether MGc80-3 was fully replaced by HeLa cells, just mixed with HeLa cells (co-existing), or was a somatic hybrid with HeLa cells. In addition to STR profiling, various approaches, including single nucleotide polymorphisms genotyping, polymerase chain reaction, screening for human papillomaviruses type 18 (HPV-18) fragment, chromosome karyotyping, pathological examination of xenografts, tissue-specific-90-gene expression signature and high-throughput RNA sequencing were used to determine the nature of MGc80-3. Our study found that the abnormal STR profile, partially overlapping with that of HeLa cells (64.62% to 71.64%), could not verify MGc80-3 as a HeLa cell line. However, the STR 13.3 repeat allele in the D13S317 locus that seemed to be unique to HeLa cells was detected in MGc80-3. Almost all the MGc80-3 cells exhibited HPV-18 fragments in the genome as well as certain HeLa marker chromosomes, such as M7 and M12. The molecular assay of the 90-gene expression signature still considered MGc80-3 as a stomach cancer using an algorithmic analysis. The expression pattern of multiple genes in MGc80-3 was quite different from that in HeLa cells, which showed that certain characteristics belonged to gastric cancer cell lines. High throughput RNA sequencing showed the distinct patterns of gene expression in MGc80-3. In conclusion, MGc80-3 cell line is a somatic hybrid with HeLa cells rather than a pure gastric cancer cell line.

Identification and validation of a dysregulated TME-related gene signature for predicting prognosis, and immunological properties in bladder cancer

Background

During tumor growth, tumor cells interact with their tumor microenvironment (TME) resulting in the development of heterogeneous tumors that promote tumor occurrence and progression. Recently, there has been extensive attention on TME as a possible therapeutic target for cancers. However, an accurate TME-related prediction model is urgently needed to aid in the assessment of patients' prognoses and therapeutic value, and to assist in clinical decision-making. As such, this study aimed to develop and validate a new prognostic model based on TME-associated genes for BC patients.

Methods

Transcriptome data and clinical information for BC patients were extracted from The Cancer Genome Atlas (TCGA) database. Gene Expression Omnibus (GEO) and IMvigor210 databases, along with the MSigDB, were utilized to identify genes associated with TMEs (TMRGs). A consensus clustering approach was used to identify molecular clusters associated with TMEs. LASSO Cox regression analysis was conducted to establish a prognostic TMRG-related signature, with verifications being successfully conducted internally and externally. Gene ontology (GO), KEGG, and single-sample gene set enrichment analyses (ssGSEA) were performed to investigate the underlying mechanisms. The potential response to ICB therapy was estimated using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and Immunophenoscore (IPS). Additionally, it was found that the expression level of certain genes in the model was significantly correlated with objective responses to anti-PD-1 or anti-PD-L1 treatment in the IMvigor210, GSE111636, GSE176307, or Truce01 (registration number NCT04730219) cohorts. Finally, real-time PCR validation was performed on 10 paired tissue samples, and in vitro cytological experiments were also conducted on BC cell lines.

Results

In BC patients, 133 genes differentially expressed that were associated with prognosis in TME. Consensus clustering analysis revealed three distinct clinicopathological characteristics and survival outcomes. A novel prognostic model based on nine TMRGs (including C3orf62, DPYSL2, GZMA, SERPINB3, RHCG, PTPRR, STMN3, TMPRSS4, COMP) was identified, and a TMEscore for OS prediction was constructed, with its reliable predictive performance in BC patients being validated. MultiCox analysis showed that the risk score was an independent prognostic factor. A nomogram was developed to facilitate the clinical viability of TMEscore. Based on GO and KEGG enrichment analyses, biological processes related to ECM and collagen binding were significantly enriched among high-risk individuals. In addition, the low-risk group, characterized by a higher number of infiltrating CD8+ T cells and a lower burden of tumor mutations, demonstrated a longer survival time. Our study also found that TMEscore correlated with drug susceptibility, immune cell infiltration, and the prediction of immunotherapy efficacy. Lastly, we identified SERPINB3 as significantly promoting BC cells migration and invasion through differential expression validation and in vitro phenotypic experiments.

Conclusion

Our study developed a prognostic model based on nine TMRGs that accurately and stably predicted survival, guiding individual treatment for patients with BC, and providing new therapeutic strategies for the disease.

Retracted: Upregulation of Serine Proteinase Inhibitor Clade B Member 3 (SERPINB3) Expression by Stromal Cell-Derived Factor (SDF-1)/CXCR4/Nuclear Factor kappa B (NF-κB) Promotes Migration and Invasion of Gastric Cancer Cells

The Editors of Medical Science Monitor wish to inform you that the above manuscript has been retracted from publication due to concerns with the credibility and originality of the study, the manuscript content, and the Figure images. Reference: Jing Gong, Yongxi Song, Ling Xu, Xiaofang Che, Kezuo Hou, Tianshu Guo, Yu Cheng, Yunpeng Liu, Xiujuan Qu. Upregulation of Serine Proteinase Inhibitor Clade B Member 3 (SERPINB3) Expression by Stromal Cell-Derived Factor (SDF-1)/CXCR4/Nuclear Factor kappa B (NF-kB) Promotes Migration and Invasion of Gastric Cancer Cells. Med Sci Monit, 2020; 26: e927411. DOI: 10.12659/MSM.927411.

Recent Advances in CXCL12/CXCR4 Antagonists and Nano-Based Drug Delivery Systems for Cancer Therapy

Chemokines can induce chemotactic cell migration by interacting with G protein-coupled receptors to play a significant regulatory role in the development of cancer. CXC chemokine-12 (CXCL12) can specifically bind to CXC chemokine receptor 4 (CXCR4) and is closely associated with the progression of cancer via multiple signaling pathways. Over recent years, many CXCR4 antagonists have been tested in clinical trials; however, Plerixafor (AMD3100) is the only drug that has been approved for marketing thus far. In this review, we first summarize the mechanisms that mediate the physiological effects of the CXCL12/CXCR4 axis. Then, we describe the use of CXCL12/CXCR4 antagonists. Finally, we discuss the use of nano-based drug delivery systems that exert action on the CXCL12/CXCR4 biological axis.

Role of CXCR4 as a Prognostic Biomarker Associated With the Tumor Immune Microenvironment in Gastric Cancer

Background: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, accounting for high rates of morbidity and mortality in the population. The tumor microenvironment (TME), which plays a crucial role in GC progression, may serve as an optimal prognostic predictor of GC. In this study, we identified CXC motif chemokine receptor 4 (CXCR4) as a TME-related gene among thousands of differentially expressed genes (DEGs). We showed that CXCR4 can be used to predict the effect of immunotherapy in patients with GC.

Methods: GC samples obtained from The Cancer Genome Atlas (TCGA) were analyzed for the presence of stroma (stromal score), the infiltration of immune cells (immune score) in tumor tissues, and the tumor purity (estimate score) using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm. DEGs were sorted based on differences in the values of the three scores. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the biological processes and pathways enriched in these DEGs. The correlations of scores with clinicopathological features and overall survival (OS) of patients with GC were assessed by the Kaplan–Meier survival and Cox regression analyses. Through subsequent protein–protein interaction (PPI) network and univariate Cox regression analyses, CXCR4 was identified as a TME-related gene. Gene Set Enrichment Analysis (GSEA) was performed to assess the role of CXCR4 in the TME of GC. The CIBERSORT algorithm was used to further explore the correlation between tumor-infiltrating immune cells (TIICs) and CXCR4. Finally, the TISIDB database was used to predict the efficacy of immunotherapy in patients with GC.

Results: We extracted 1231 TME-related DEGs and by an overlapping screening of PPI network and univariate Cox regression, CXCR4 was identified as a biomarker of TME, which deeply engaged in immune-related biological processes of gastric cancer and have close association with several immunocompetent cells.

Conclusion: CXCR4 may be a useful biomarker of prognosis and an indicator of the TME in GC.