High Expression of MYL9 Indicates Poor Clinical Prognosis of Epithelial Ovarian Cancer

An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators

OBJECTIVE

To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovarian cancer.

METHODS

Patients with ovarian cancer clearly diagnosed in our hospital were collected as a case group (n = 185), and three groups of patients with other malignant otolaryngology tumors (n = 138), patients with benign otolaryngology diseases (n = 339) and those with normal physical examination (n = 92) were used as an overall control group. In this paper, a fully automated segmentation network for magnetic resonance images of ovarian cancer is proposed to improve the reproducibility of tumor segmentation results while effectively reducing the burden on radiologists. A pre-trained Res Net50 is used to the three edge output modules are fused to obtain the final segmentation results. The segmentation results of the proposed network architecture are compared with the segmentation results of the U-net based network architecture and the effect of different loss functions and region of interest sizes on the segmentation performance of the proposed network is analyzed.

RESULTS

The average Dice similarity coefficient, average sensitivity, average specificity (specificity) and average hausdorff distance of the proposed network segmentation results reached 83.62%, 89.11%, 96.37% and 8.50, respectively, which were better than the U-net based segmentation method. For ROIs containing tumor tissue, the smaller the size, the better the segmentation effect. Several loss functions do not differ much. The area under the ROC curve of the machine learning diagnostic model reached 0.948, with a sensitivity of 91.9% and a specificity of 86.9%, and its diagnostic efficacy was significantly better than that of the traditional way of detecting CA125 alone. The model was able to accurately diagnose ovarian cancer of different disease stages and showed certain discriminative ability for ovarian cancer in all three control subgroups.

CONCLUSION

Using machine learning to integrate multiple conventional test indicators can effectively improve the diagnostic efficacy of ovarian cancer, which provides a new idea for the intelligent auxiliary diagnosis of ovarian cancer.

MYL9 expressed in cancer-associated fibroblasts regulate the immune microenvironment of colorectal cancer and promotes tumor progression in an autocrine manner

BACKGROUND

The tumor microenvironment (TME) is an important factor that regulates the progression of colorectal cancer (CRC). Cancer-associated fibroblasts (CAFs) are the main mesenchymal cells in the TME and play a vital role in tumor progression; however, the specific underlying mechanisms require further study.

METHODS

Multiple single-cell and transcriptome data were analyzed and validated. Primary CAFs isolation, CCK8 assay, co-culture assay, western blotting, multiple immunofluorescence, qRT-PCR, ELISA, immunoprecipitation, ChIP, double luciferase, and animal experiments were used to explore the potential mechanism of MYL9 regulation in CRC.

RESULTS

Our findings revealed that MYL9 was predominantly localized and expressed in CAFs rather than in CRC cells, and bioinformatics analysis revealed that high MYL9 expression was strongly associated with poor overall and disease-free survival in various tumors. In addition, high MYL9 expression is closely associated with M2 macrophage infiltration, which can lead to an immunosuppressive microenvironment in CRC, making it insensitive to immunotherapy. Mechanically, MYL9 can regulate the secretion of CAFs on CCL2 and TGF-β1, thus affecting the immune microenvironment and progression of CRC. In addition, MYL9 bounded with IQGAP1 to regulate CCL2 and TGF-β1 secretion through the ERK 1/2 pathway, and CCL2 and TGF-β1 synergistically promoted CRC cells progression through the PI3K-AKT pathway. Furthermore, MYL9 promotes epithelial-mesenchymal transition (EMT) in CRC. During the upstream regulation of MYL9 in CAFs, we found that the EMT transcription factor ZEB1 could bind to the MYL9 promoter in CAFs, enhancing the activity and function of MYL9. Therefore, MYL9 is predominantly expressed in CAFs and can indirectly influence tumor biology and EMT by affecting CAFs protein expression in CRC.

CONCLUSIONS

MYL9 regulates the secretion of cytokines and chemokines in CAFs, which can affect the immune microenvironment of CRC and promote CRC progression. The relationship between MYL9 expression and CRC clinical staging and immunotherapy is closer in CAFs than in tumor cells; therefore, studies using CAFs as a model deserve more attention when exploring tumor molecular targets in clinical research.

MYL9 promotes squamous cervical cancer migration and invasion by enhancing aerobic glycolysis

OBJECTIVE

This study explored the mechanism of squamous cervical cancer (SCC) progression.

METHODS

Reverse transcription-quantitative polymerase chain reaction and western blotting were used to evaluate the expression of myosin light chain 9 (MYL9) in SCC tissues and cell lines. Furthermore, Transwell and Boyden assays were used to assess the function of MYL9 in SCC progression. In addition, the levels of lactate and aerobic glycolysis were used to explore the detailed mechanism of MYL9 in SCC.

RESULTS

The mRNA and protein levels of MYL9 were elevated in SCC tissues, and MYL9 knockdown inhibited the migration and invasion of SCC cell lines. A mechanistic study demonstrated that MYL9 promotes SCC migration and invasion by enhancing aerobic glycolysis and increasing the activity of the Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) pathway.

CONCLUSIONS

MYL9 was upregulated in SCC, and it enhanced JAK2/STAT3 pathway activity and promoted metastasis and glycolysis in SCC.

The autocrine glycosylated-GREM1 interacts with TGFB1 to suppress TGFβ/BMP/SMAD-mediated EMT partially by inhibiting MYL9 transactivation in urinary carcinoma

PURPOSE

Urothelial carcinoma (UC) is a common disease in developed counties. This study aimed to identify autocrine roles and signaling pathways of gremlin 1, DAN family BMP antagonist (GREM1), which inhibits tumor growth and epithelial-mesenchymal transition (EMT) in UC.

METHODS

Systematic in vitro and in vivo studies using genetic engineering, different urinary bladder urothelial carcinoma (UBUC)-derived cell lines, and mouse models were performed, respectively. Further, primary upper tract urothelial carcinoma (UTUC) and UBUC specimens were evaluated by immunohistochemistry.

RESULTS

GREM1 protein levels conferred better disease-specific and metastasis-free survival rates and played an independent prognostic factor in UTUC and UBUC. Hypermethylation is the primary cause of low GREM1 levels. In different UBUC-derived cell lines, the autocrine/secreted and glycosylated GREM1 interacted with transforming growth factor beta 1 (TGFB1) and inhibited TGFβ/BMP/SMAD signaling and myosin light chain 9 (MYL9) transactivation, subsequently cell proliferation and epithelial-mesenchymal transition (EMT). Secreted and glycosylated GREM1 also suppressed tumor growth, metastasis, and MYL9 levels in the mouse model. Instead, cytosolic GREM1 promoted cell proliferation and EMT by activating the tumor necrosis factor (TNF)/AKT/nuclear factor kappa B (NFκB) axis.

CONCLUSIONS

Clinical associations, animal models, and in vitro indications provided solid evidence to show that the epithelial autocrine GREM1 is a novel tumor suppressor in UCs. The glycosylated-GREM1 hampered cell proliferation, migration, invasion, and in vitro angiogenesis through interaction with TGFB1 to inactivate TGFβ/BMP/SMAD-mediated EMT in an autocrine manner.

MYH10 Combines with MYH9 to Recruit USP45 by Deubiquitinating Snail and Promotes Serous Ovarian Cancer Carcinogenesis, Progression, and Cisplatin Resistance

The poor prognosis of serous ovarian cancer (SOC) is due to its high invasive capacity and cisplatin resistance of SOC cells, whereas the molecular mechanisms remain poorly understood. In the present study, the expression and function of non-muscle myosin heavy chain IIB (MYH10) in SOC are identified by immunohistochemistry, in vitro, and in vivo studies, respectively. The mechanism of MYH10 is demonstrated by co-immunoprecipitation, GST pull-down, confocal laser assays, and so on. The results show that the knockdown of MYH10 suppressed SOC cell proliferation, migration, invasion, metastasis, and cisplatin resistance both in vivo and in vitro. Further studies confirm that the MYH10 protein functional domain combines with non-muscle myosin heavy chain IIA (MYH9) to recruit the deubiquitinating enzyme Ubiquitin-specific proteases 45 and deubiquitinates snail to inhibit snail degradation, eventually promoting tumorigenesis, progression, and cisplatin resistance in SOC. In clinical samples, MYH10 expression is significantly elevated in SOC samples compared to the paratumor samples. And the expression of MYH10 is positively correlated with MYH9 expression. MYH10+/MYH9+ co-expression is an independent prognostic factor for predicting SOC patient survival. These findings uncover a key role of the MYH10-MYH9-snail axis in SOC carcinogenesis, progression, and cisplatin resistance, and provide potential novel therapeutic targets for SOC intervention.

An epithelial-mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is closely associated with cancer cell metastasis. Colon adenocarcinoma (COAD) is one of the most common malignancies in the world, and its metastasis leading to poor prognosis remains a challenge for clinicians. The purpose of this study was to explore the prognostic value of EMT-related genes (EMTRGs) by bioinformatics analysis and to develop a new EMTRGs prognostic signature for COAD. Methods: The TCGA-COAD dataset was downloaded from the TCGA portal as the training cohort, and the GSE17538 and GSE29621 datasets were obtained from the GEO database as the validation cohort. The best EMTRGs prognostic signature was constructed by differential expression analysis, Cox, and LASSO regression analysis. Gene set enrichment analysis (GSEA) is used to reveal pathways that are enriched in high-risk and low-risk groups. Differences in tumor immune cell levels were analyzed using microenvironmental cell population counter and single sample gene set enrichment analysis. Subclass mapping analysis and Genomics of Drug Sensitivity in Cancer were applied for prediction of immunotherapy response and chemotherapy response, respectively. Results: A total of 77 differentially expressed EMTRGs were identified in the TCGA-COAD cohort, and they were significantly associated with functions and pathways related to cancer cell metastasis, proliferation, and apoptosis. We constructed EMTRGs prognostic signature with COMP, MYL9, PCOLCE2, SCG2, and TIMP1 as new COAD prognostic biomarkers. The high-risk group had a poorer prognosis with enhanced immune cell infiltration. The GSEA demonstrated that the high-risk group was involved in "ECM Receptor Interaction," "WNT Signaling Pathway" and "Colorectal Cancer." Furthermore, patients with high risk scores may respond to anti-CTLA4 therapy and may be more resistant to targeted therapy agents BI 2536 and ABT-888. Conclusion: Together, we developed a new EMTRGs prognostic signature that can be an independent prognostic factor for COAD. This study has guiding implications for individualized counseling and treatment of COAD patients.

Co-Overexpression of GRK5/ACTC1 Correlates With the Clinical Parameters and Poor Prognosis of Epithelial Ovarian Cancer

Background: The prognosis of epithelial ovarian cancer (EOC) is poor, and the present prognostic predictors of EOC are neither sensitive nor specific.

Objective: The aim of this study was to search the prognostic biomarkers of EOC and to investigate the expression of G protein-coupled receptor kinase 5 (GRK5) and actin alpha cardiac muscle 1 (ACTC1) in EOC tissues (both paraffin-embedded and fresh-frozen tissues) and to explore their association with clinicopathological parameters and prognostic value in patients with EOC.

Methods: A total of 172 paraffin-embedded cancer tissues of EOC patients diagnosed and operated at the memorial hospital of Sun Yat-sen University between December 2009 and March 2017 and 41 paratumor tissues were collected and the expression of GRK5 and ACTC1 was examined using immunohistochemistry. Furthermore, 16 fresh-frozen EOC tissues and their matched paratumor tissues were collected from the Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, between August 2013 and November 2019 and subjected to reverse-transcription quantitative PCR analysis to detect the mRNA expression of GRK5 and ACTC1.

Results: The expression of GRK5 and ACTC1 was both higher in cancer tissues than in paratumor tissues. GRK5 expression was positively correlated with ACTC1 expression. In addition, GRK5, ACTC1, and GRK5/ACTC1 expression was associated with the recurrence-free survival and overall survival of EOC patients. Furthermore, multivariate logistic regression analysis indicated that GRK5+/ACTC1+ co-expression, intestinal metastasis, postoperative chemotherapy, platinum resistance, and hyperthermic intraperitoneal chemotherapy were independent prognostic factors of EOC.

Conclusion: GRK5 and ACTC1 are both upregulated in EOC compared with those in paratumor tissues. The co-expression of GRK5+/ACTC1+ rather than GRK5 or ACTC1 is an independent prognostic biomarker of EOC.