METTL3 orchestrates glycolysis by stabilizing the c-Myc/WDR5 complex in triple-negative breast cancer

BACKGROUND

The carcinogenic transcription factor c-Myc is the most aggressive oncogene, which drive malignant transformation and dissemination of triple-negative breast cancer (TNBC). Recruitment of many cofactors, especially WDR5, a protein that nucleates H3K4me chromatin modifying complexes, play a pivotal role in regulating c-Myc-dependent gene transcription, a critical process for c-Myc signaling to function in a variety of biological and pathological contexts. For this reason, interrupting the interaction between c-Myc and the transcription cofactor WDR5 may become the most promising new strategy for treating c-Myc driven TNBC.

METHODS

Immunoprecipitation and mass spectrometry (IP-MS) is used to screen proteins that bind c-Myc/WDR5 interactions. The interaction of METTL3 with c-Myc/WDR5 in breast cancer tissues and TNBC cells was detected by Co-IP and immunofluorescence. Subsequently, we further analyzed the influence of METTL3 expression on c-Myc/WDR5 protein expression and its interaction stability by Western blot and Co-IP. The correlation between METTL3 and c-Myc pathway was analyzed by ChIP-seq sequencing and METTL3 knockdown transcriptome data. The effect of METTL3 expression on c-Myc transcriptional activity was detected by ChIP-qPCR and Dual Luciferase Reporter. At the same time, the overexpression vector METTL3-MUT (m6A) was constructed, which mutated the methyltransferase active site (Aa395-398, DPPW/APPA), and further explored whether the interaction between METTL3 and c-Myc/WDR5 was independent of methyltransferase activity. In addition, we also detected the changes of METTL3 expression on TNBC's sensitivity to small molecule inhibitors such as JQ1 and OICR9429 by CCK8, Transwell and clonal formation assays. Finally, we further verified our conclusions in spontaneous tumor formation mouse MMTV-PyMT and nude mouse orthotopic transplantation tumor models.

RESULTS

METTL3 was found to bind mainly to c-Myc/WDR5 protein in the nucleus. It enhances the stability of c-Myc/WDR5 interaction through its methyltransferase independent mechanism, thereby enhancing the transcriptional activity of c-Myc on downstream glucose metabolism genes. Notably, the study also confirmed that METTL3 can directly participate in the transcription of glucose metabolism genes as a transcription factor, and knockdown METTL3 enhances the drug sensitivity of breast cancer cells to small molecule inhibitors JQ1 and OICR9429. The study was further confirmed by spontaneous tumor formation mouse MMTV-PyMT and nude mouse orthotopic transplantation tumor models.

CONCLUSION

METTL3 binds to the c-Myc/WDR5 protein complex and promotes glycolysis, which plays a powerful role in promoting TNBC progression. Our findings further broaden our understanding of the role and mechanism of action of METTL3, and may open up new therapeutic avenues for effective treatment of TNBC with high c-Myc expression.

[Establishing and verifying the threshold value of HLA mixed antigen reagent screening test results]

Objective: To establish the threshold value of human leukocyte antigen (HLA) mixed antigen reagent screening test results, and to verify it by HLA single antigen reagent confirmation test results. Methods: The results of 2 255 serum samples tested for HLA antibodies by HLA mixed antigen reagent in the department of HLA Laboratory, the First Affiliated Hospital of Soochow University from October 2017 to December 2021 were retrospectively analyzed. Among them, 1 139 samples were also tested by single antigen HLA Class-Ⅰ reagent and 1 116 samples were also tested by single antigen HLA Class-Ⅱ reagent. Based on the same antigens coated with both reagents, the Mean Fluorescence Intensity (MFI) and Nomalized Background ratio (NBG ratio) of 12 HLA Class-Ⅰ beads and 5 HLA Class-Ⅱ beads in the HLA mixed antigen reagent and the MFI of 77 anti-HLA class-Ⅰ antibodies and 35 anti-HLA class-Ⅱ antibodies detected by HLA single antigen reagent were recorded. The MFI and NBG ratio of HLA mixed antigen reagent beads in 1 139 or 1 116 samples were segmented according to the positive rate of antibodyies detected by the single antigen reagent corresponding to the antigens coated with each HLA mixed antigen reagent bead, and the results of the HLA mixed antigen screening test were verified by the HLA single antigen reagent confirmation test. Results: The threshold values of MFI and NBG ratio of HLA mixed antigen reagent's 17 beads were established. The MFI of No. 1 to No. 17 beads of HLA mixed antigen reagent ranged from 26.86 to 21 925.58, and the NBG ratio ranged from 0 to 434.65. According to the positive detection rate of HLA single antigen reagent corresponding to the coated antigens, the MFI and NBG ratio of the beads of HLA mixed antigen reagent were divided into positive interval, suspicious positive interval, suspicious negative interval and negative interval. The positive rates of anti-HLA class-Ⅰ antibodies by HLA mixed antigen reagent and single antigen HLA Class-Ⅰ reagent were 87.5% (997/1 139) and 66.3% (755/1 139). The positive rates of anti-HLA class-Ⅱ antibodies were 63.4% (707/1 116) and 44.9% (501/1 116). In the samples with suspicious negative, suspicious positive and positive results of HLA class-Ⅰ、Ⅱ antibodies detected by HLA mixed antigen reagent, the positive detection rates of single antigen HLA Class-Ⅰ reagent were 14.9% (17/114), 41.3% (145/351) and 91.3% (590/646), respectively. The positive detection rates of single antigen HLA Class-Ⅱ reagent were 15.5% (58/375), 26.5% (81/306) and 88.8% (356/401), respectively. Conclusions: In this study, the threshold values of MFI and NBG ratio of HLA mixed antigen reagent screening test are established, and the threshold values are verified by the results of HLA single antigen reagent confirmation test. HLA mixed reagent screening test can be used for screening of HLA antibodies, and if necessary, it should be combined with HLA single antigen confirmatory test for clinical detection of HLA antibodies.

[Establishment and validation of prediction models for human leukocyte antigen haplotypes and human leukocyte antigen genotypes]

Objective: To establish prediction models for human leukocyte antigen (HLA) haplotypes and HLA genotypes, and verify the prediction accuracy. Methods: The prediction models were established based on the characteristic of HLA haplotype inheritance and linkage disequilibrium (LD), as well as the invention patents and software copyrights obtained. The models include algorithm and reference databases such as HLA A-C-B-DRB1-DQB1 high-resolution haplotypes database, B-C and DRB1-DQB1 LD database, G group alleles table, and NMDP Code alleles table. The prediction algorithm involves data processing, comparison with reference data, filtering results, probability calculation and ranking, confidence degree estimation, and output of prediction results. The accuracy of the predictions was verified by comparing them with the correct results, and the relationship between prediction accuracy and the probability distribution and confidence degree of the predicted results was analyzed. Results: The HLA haplotypes and genotypes prediction models were established. The prediction algorithm included the prediction of A-C-B-DRB1-DQB1 haplotypes according to HLA-A, B, DRB1, C, DQB1 genotypes, the prediction of C and DQB1 high-resolution results according to A, B and DRB1 high-resolution results, and the prediction of A, B, DRB1, C and DQB1 high resolution results according to the A, B and DRB1 intermediate or low resolution results. Validation results of "Predicting A-C-B-DRB1-DQB1 haplotypes basing on HLA-A, B, DRB1, C, DQB1 genotypes" model: for 787 data, the accuracy was 94.0% (740/787) with 740 correct predictions, 34 incorrect predictions, and 13 instances with no predicted results. For 847 data, the accuracy was 100% (847/847). The 2 411 and 2 594 haplotype combinations predicted from 787 and 847 data were grouped according to confidence degree, the accuracy was 100% (48/48, 114/114) for a confidence degree of 1, 96.2% (303/315) and 97.8% (409/418) for a confidence degree of 2 respectively. Validation results of "Predicting A, B, DRB1 and C, DQB1 high-resolution genotypes basing on HLA-A, B, DRB1 high, intermediate, or low resolution genotypes" model: when predicting C and DQB1 high resolution genotypes basing on A, B, and DRB1 high resolution genotypes, 89.3% (1 459/1 634) of the predictions were correct. The accuracy for the top 2 predicted probability (GPP) ranking was 79.2% (1 156/1 459), and for the top 10, it was 95.0% (1 386/1 459). Furthermore, when GPP≥90% and GPP 50%-90%, the prediction accuracy was 81.3% (209/257) and 72.8% (447/614) respectively. The accuracy of predicting C and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 high resolution genotypes from the China Marrow Donor Program was 87.0% (20/23). The accuracy of predicting A, B, DRB1, C, and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 intermediate or low-resolution genotypes was 70.0% (7/10) and 52.5% (21/40) respectively. When predicting whether the patient is likely to have a HLA 10/10 matched donor, the accuracy of the top 2 GPP combinations with a proportion of ≥50% was 85.7% (6/7). Conclusions: When using A, B, DRB1, C, DQB1 genotypes to predict A-C-B-DRB1-DQB1 haplotype combinations, the results with a confidence degree of 1 and 2 are reliable. When predicting C and DQB1 genotypes according to A, B and DRB1 genotypes, the top 10 results ranked by GPP are reliable, and the top 2 results with GPP≥50% are more reliable.

[Challenges and solutions in current human leukocyte antigen confirmatory typing of hematopoietic stem cell transplantation]

The impact of human leukocyte antigen (HLA) on hematopoietic stem cell transplantation (HSCT) necessitates high precision in HLA genotyping. Confirmatory typing for patients and their related or unrelated donors before HSCT is critical. This study seeks to standardize HLA confirmatory typing in laboratories by examining the current state of HLA genotyping in the country, building upon the National Standards and Industrial Standards for HLA, and highlighting the significance of confirmatory typing for patients and potential donors prior to HSCT. A retrospective analysis over a decade reveals initial typing errors, indicating potential issues and critical considerations in pre-analytical, analytical, and post-analytical stages. Problems are attributed to three main causes: (1) random human errors, including technical mistakes, sample mix-up, and transcription inaccuracies; (2) limitations of technical methods, such as the varied sequence ranges between confirmatory and initial typing; (3) patient factors, involving high tumor burden, the influence of certain drugs on HLA genotyping results, and the second transplantation. Solutions are proposed for these problems, along with recommendations to standardize HLA confirmatory typing.

Mettl3 synergistically regulates TGF-β/SMAD2/3 to promote proliferation and metastasis of gastric cancer

Transforming Growth factor-β (TGF-β)/Smad signaling is a complex regulatory network that both inhibits and promotes tumorigenesis. However, the mechanisms underlying the function of TGF-β/Smad signaling pathway remain to be fully elucidated. As a methyltransferase, METTL3 is closely related to tumor development, but the role of METTL3 in the proliferation and metastasis of TGF-β/Smad-activated gastric cancer (GC) is unclear. In this study, we identified TGF-β/Smad2/3 axis as an important carcinogenic pathway in GC, which significantly promoted the proliferation and metastasis of GC. Furthermore, we found that Smad3 mRNA could be modified by m6A, which was subsequently recognized and stabilized by IGF2BP2, thereby enhancing Smad3 protein expression and promoting the activation of TGF-β/Smad pathway. Importantly, we also found that METTL3 could combine with p-Smad3 to regulate the transcription of downstream target genes. Therefore, this study revealed a novel mechanism by which METTL3 synergistically regulates TGF-β/Smad2/3 signaling and provide a new potential therapeutic target for the treatment of GC.

IGF2BP2-modified UBE2D1 interacts with Smad2/3 to promote the progression of breast cancer

Recent studies have suggested that ubiquitin-conjugating enzyme E2D1 (UBE2D1) is involved in tumor progression. In this study, we found that UBE2D1 expression was upregulated in breast cancer (BC) and was related to the prognosis of BC patients. Through in vitro and in vivo experiments, we demonstrated the aberrant expression of UBE2D1 promoted the proliferation and migration of BC cells, and the IGF2BP2-mediated N6-methyladenosine (m6A) modification increased the stability of UBE2D1 mRNA. Mechanistically, UBE2D1 expression regulated the activity of TGF-β signaling through modulating the expression and the phosphorylation level of Smad2/3. Furthermore, UBE2D1 directly bound to Smad2/3 and affected the subsequent binding of Smad2 and Smad3, which is a necessary step for TGF-β signaling activation. Thus, our study reveals a pro-oncogenic role of UBE2D1 in the progression of BC and may provide novel strategies for BC treatment.

Hypoxia-Associated Prognostic Markers and Competing Endogenous RNA Co-Expression Networks in Breast Cancer

Objective

Many primary tumors have insufficient supply of molecular oxygen, called hypoxia. Hypoxia is one of the leading characteristics of solid tumors resulting in a higher risk of local failure and distant metastasis. It is quite necessary to investigate the hypoxia associated molecular hallmarks in breast cancer.

Materials and Methods

According to the published studies, we selected 13 hypoxia related gene expression signature to define the hypoxia status of breast cancer using ConsensusClusterPlus package based on the data from The Cancer Genome Atlas (TCGA). Subsequently, we characterized the infiltration of 24 immune cell types under different hypoxic conditions. Furthermore, the differentially expressed hypoxia associated microRNAs, mRNAs and related signaling pathways were analyzed and depicted. On this basis, a series of prognostic markers related to hypoxia were identified and ceRNA co-expression networks were constructed.

Results

Two subgroups (cluster1 and cluster2) were identified and the 13 hypoxia related gene signature were all up-regulated in cluster1. Thus, we defined the cluster1 as “hypoxic subgroup” compared with cluster2. The infiltration of CD8+ T cell and CD4+ T cell were lower in cluster1 while the nTreg cell and iTreg cell were higher, indicating that there was immunosuppressive status in cluster1. We observed widespread hypoxia-associated dysregulation of microRNAs and mRNAs. Next, a risk signature for predicting prognosis of breast cancer patients was established based on 12 dysregulated hypoxia associated prognostic genes. Two microRNAs, hsa-miR-210-3p and hsa-miR-190b, with the most significant absolute logFC value were related to unfavorable and better prognosis, respectively. Several long non-coding RNAs were predicted to be microRNA targets and positively correlated with two selected mRNAs, CPEB2 and BCL11A. Predictions based on the LINC00899/PSMG3-AS1/PAXIP1-AS1- hsa-miR-210-3p-CPEB2 and SNHG16- hsa-miR-190b-BCL11A ceRNA regulation networks indicated that the two genes might act as tumor suppressor and oncogene, respectively.

Conclusion

Hypoxia plays an important role in the initiation and progression of breast cancer. Our research provides potential mechanisms into molecular-level understanding of tumor hypoxia.

Betulinic acid inhibits cell proliferation and migration in gastric cancer by targeting the NF-κB/VASP pathway

Gastric cancer (GC) is one of the most common malignant neoplasms of the digestive system, with China leading in terms of morbidity and mortality rates. Betulinic acid (BA) is a widely-occurring pentacyclic triterpenoid that has been reported to exhibit potent anti-inflammatory, antioxidant, and antitumor activities. BA can combat tumors by inducing apoptosis, regulating cell cycle, and inhibiting autophagy, but its mechanism of action in the context of GC is unclear. A preliminary study found that higher expression of vasodilator-stimulated phosphoprotein (VASP) was correlated with migration in the GC cell line. In this study, BGC-823 cells and MNK45 cells were treated with BA for investigating its effect on the proliferation and migration of cells. Moreover, the expression of VASP and upstream signal molecules were also investigated in this background. The results showed BA could inhibit the proliferation and migration the GC cells. Furthermore, NF-κB acted as a transcription factor to upregulate VASP expression. Moreover, BA could downregulate the expression of VASP at the protein and mRNA level by inhibiting NF-κB activity. In conclusion, these results suggest that BA could inhibit the expression of VASP by negatively regulating NF-κB, thereby inhibiting the proliferation and migration of the GC cells. Our study provides a theoretical basis for exploring the molecular mechanism underlying BA-induced inhibition of proliferation and migration in GC cells.

[Clinical significance of HLA-A, -B, -C, -DRB1, -DQB1 haplotype gene frequencies]

Objective: To analyze family-based haplotype frequencies of HLA-A, -B, -C, -DRB1 and -DQB1 genes and their clinical significance. Methods: The data of HLA genotyping in 3568 families undergoing related haploidentical transplantation between 2012 and 2017 at the First Affiliated Hospital of Soochow University were retrospectively evaluated. The HLA genotyping was performed by PCR amplification with sequence-based typing (PCR-SBT) and sequence-specific oligonucleotide probe (PCR-SSOP) methods. The family genetic analysis and haplotype frequencies were also investigated. Results: All the families were divided into 3 groups, including group1 of 1 422 entire families; group2 of 1 310 patients and either of their parents or one of their children; group3 of 836 patients and their HLA≥5/10 matched sibling donors. In the haplotypes with frequencies greater than 0.1% in group1+ group2, the frequency of A*11∶01-B*40∶01-C*03∶04-DRB1*11∶01-DQB1*03∶01, A*02∶07-B*51∶01-C*14∶02-DRB1*09:01-DQB1*03∶03 were significantly different between group1 and group2 (P=0.029, 0.033) . The frequency of A*11∶01-B*46∶01-C*01∶02∶01G-DRB1*09∶01-DQB1*03∶03 was significantly different between group1 and group3 (P=0.035) . The frequency of A*02∶01-B*40∶01-C*07∶02-DRB1*09∶01-DQB1*03∶03 was significantly different between group1 and group2 (P=0.034) , or group1 and group3 (P=0.034) . The frequency of A*24∶02-B*13∶01-C*03∶04-DRB1*12∶02-DQB1*03:01 was significantly different between group2 and group3 (P=0.046) . Conclusion: In this study, we summarize the prevalence of haplotype frequencies in terms of HLA-A, -B, -C, -DRB1 and-DQB1. Based on the database of family haplotype analysis, patients and donor candidates are sorted with matched HLA genotype while unmatched HLA haplotype. Even in patients without entire family information, HLA haplotype analysis assists in choosing the optimal related or unrelated donors.