Cell cycle-related FAM64A could be activated by TGF-β signaling to promote glioma progression

doi:10.21203/rs.3.rs-1500149/v1

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Cell cycle-related FAM64A could be activated by TGF-β signaling to promote glioma progression

https://doi.org/10.21203/rs.3.rs-1500149/v1

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Background

FAM64A has been found to promote tumor cell proliferation in various tumors. However, the regulatory mechanism and clinical significance of FAM64A in gliomas still remain unclear.

Methods

FAM64A expression in gliomas with different grades was determined by TCGA database mining and immunoblotting assay. FAM64A silenced U87 cells were constructed with shRNA, and cell proliferation, migration, and cell cycle and were detected by CCK8 assay, scratch assay, and flow cytometry respectively. Intercellular crosstalk analysis and transcription factor prediction were performed to query the potential regulatory mechanisms. TGF-b and its downstream transcription factors were detected in glioma samples with different grades to validate the regulatory roles of TGF-b signaling.

Results

FAM64A was highly expressed in glioblastoma and associated with a poor prognosis. FAM64A silenced U87 cell lines displayed disrupted proliferation and migration ability. More cells would be stuck in the S phase. Expression profiles at the single-cell resolution further indicated that FAM64A could play a role in a cell-cycle-dependent way to promote the proliferation of glioma cells. FAM64A expression could be induced by TGF-β signal. And colocalization of SMAD4 and FAM64A could be observed in high-grade glioma tissues.

Conclusions

FAM64A could serve as a malignant biomarker in gliomas, and it could be induced via TGF-β pathway activation., which could explain the crosstalk between glioma cells and also the microenvironment.

FAM64A

Cell cycle

TGF-β

glioma

progression

single-cell RNA-seq

Glioma is the most prevalent and malignant brain tumor, and the prognosis remains poor due to its rapid progression [1, 2]. The genetic and epigenetic markers are given increasing priority since the update of the 2016 WHO CNS5 tumor classification [3]. IDH mutation, H3K27M mutation, cell cycle related CDKN2a, and other molecular events all contributed to the profiling of gliomas, which might indicate totally different therapeutic responses and clinical outcomes [4, 5]. Given self-sufficiency in growth signals was one of the hallmarks of cancer [6–8], cell-cycle-related genes like CDK4 and CDK6 have been attracted attention. and clinical trials are launched about their inhibitors [9, 10].

Family with sequence similarity 64 member A (FAM64A, aka RCS1, PIMREG) is a cell cycle-related protein [11], which could control the metaphase-to-anaphase transition during cell division [12]. Besides its physiological functions, FAM64A in abnormal expression is associated with tumor proliferation in multiple cancer cells including breast cancer and prostate cancer [13–17]. It was reported that the up-regulation of FAM64A would promote breast cancer cell proliferation by enhancing epithelial-to-mesenchymal transition (EMT) and cell stemness [18]. In the meantime, knockdown of FAM64A would suppress proliferation and migration of breast cancer [13]. Additionally, FAM64A could regulate Th17 differentiation and the immune environment by interacting with STAT3 [19]. Given the great abundance of FAM64A and its diverse function, FAM64A might be a promising prognostic biomarker and potential tumor therapeutic target. However, the previous study was based on bulk RNA-seq analysis, and many published single-cell RNA (scRNA) datasets could provide new perspectives for evaluating the functions and role of FAM64A in the microenvironment at single-cell resolution [20].

In the current study, we evaluate the impact of FAM64A on glioma proliferation and query the regulatory mechanism. Our single-cell RNA analysis strategy will further improve our understanding of the crosstalk of FAM64A in glioma microenvironment.

Data collection and processing

All clinical information and bulk RNA-seq and array expression data were downloaded from Gliovis (http://gliovis.bioinfo.cnio.es/). All the data were preprocessed by normalizing. Single-cell expression profiles were obtained from a previous study published in the GEO database (GEO Accession No. GSE131928). Data analysis was conducted using R 4.0 (R Core Team, 2020).

Clinical Samples

Glioma tissues were resected in 2020 from patients in the Department of Neurosurgery, Huashan Hospital of Fudan University. Normal brain tissues were gathered from traumatic brain injury patients. The experiments were approved by the Human Ethics Committee of Huashan Hospital, and informed consent was signed by all patients.

Cell culture and cell line construction

U87-MG cells cell lines were maintained in DMEM (PWL035), supplemented with 10% FBS (Gibco) and 1% PenStrepGlutamine in a humidified incubator with 5% CO2 at 37%.

Scramble-shRNA lentivirus, and FAM64A-shRNA lentivirus were purchased from Hanbio (Shanghai, China). U87-MG cells were transfected with lentivirus according to the manufacturers’ instructions. 2 μg/mL puromycin were used to select stable clonal cell lines.

Immunoblotting and Immunofluorescence

RIPA lysis buffer was used for both tissues and cell protein extraction. After centrifugation, the supernatant was gathered, then the 5× loading buffer was added and boiled for 5 min. Western blot was conducted as previously described [21]. The data analysis and statistics were performed through ImageJ as reported previously [22]. Anti-FAM64A anti-body (#ab118102) produced in rabbit was purchased from Abcam. Anti-b-Tubulin antibody (#M30109) was purchased from Abmart.

In vitro cell growth and migration assays

In vitro cell growth assays were performed by cell-counting kit-8 (CCK8) assays. U87-MG cells were seeded in 96-well plates and were incubated with 10% CCK8 solution at 37 °C for 2 h, and then the absorbance at 450 nm was measured. DMEM supplemented with 10% FBS was added along with denoted ligands to detect whether FAM64A could be induced by TGF-b signaling.

In vitro migration assays were performed by wound healing assay. ShFAM64A and shScramble U87-MG cells were plated in 24-well culture plates. Cell monolayers were allowed to rest for 12 h and a wound was made by scraping the middle of the cell monolayer with a P10 pipette tip. Cells were washed thrice with PBS twice and photographed. Migration was monitored and photographed 12 h later in DMEM supplemented with 10% FBS. Quantitation was performed on images at least three independent experiments for each experimental condition.

Immunofluorescence

Cells and glioma tissues were fixed with prechilled 4% paraformaldehyde for 30min and blocked with 1% bovine serum albumin (BSA) for 1 hour. Immunofluorescence staining was performed with antibody overnight at 4˚C. Then the sections were incubated with the secondary antibodies for 1 hour. The nucleus was stained with DAPI (1:500, Proteintech). Olympus IX73 microscopic system was used for imaging. Confocal images were acquired with a Leica SP8 confocal microscope (Leica).

Flow cytometry analysis of cell-cycle progression

Cell cycle analysis was conducted with propidium iodide (PI) (Biosharp, China). 5 × 10⁶ cells were harvested after digestion and fixed with ice-cold 70% ethanol at 4°C for 1 hour. After being washed with PBS, 100 µL RNase A and 400 µL PI were mixed well and added to the suspensions in a dark place for 30 min. Finally, a flow cytometer and data fitting software were employed for the cell cycle analysis.

Single-cell transcriptome data analysis

R package Seurat was applied to aggregate all samples [23], and perform quality control on raw gene expression matrix obtained from Cell Ranger pipeline. The filtering criteria for each cell were: (1) the number of genes expressed ranges from 200 to 4000, and (2) the percentage of the mitochondrial genes is less than 25%. Cells that did not satisfy the above criteria were discarded. Gene expressions were normalized by log normalization with factor 10,000, and the top 2000 highly variable genes were selected for PCA. Harmony was utilized for batch-effect correction based on the top 50 principal components of PCA [24]. The corresponding cell types of the clusters were annotated using SingleR with the reference datasets of human immune cells. The annotation was further verified manually following known gene markers.

Transcription factor prediction

CistromeDB toolkit (http://dbtoolkit.cistrome.org/) was used to predict the function of this TF [25]. FAM64A (NM_001195228) was used as input. Transcription factors and chromatin regulators with a 10 kb half-decay distance to the transcription start site were predicted. The cutoff value of a relatively high regulatory potential (RP) score was set as 0.3.

Nomogram and chemotherapeutic responses evaluation

A nomogram was built on the predictive model as a graphical presentation. Chemotherapeutic responses of glioma were assessed by R package “pRRophetic” based on gene expression profiles [26]. Cgp2016 dataset was used as a reference dataset.

Statistical analysis

Comparisons between groups were done using either Student’s t-test or one-way ANOVA test on GraphPad Prism 8. Difference between groups is considered significant at p-values<0.05. P value for multiple comparisons was adjusted by the Tukey–Kramer post hoc test. Kaplan–Meier analysis was used to analyze the data and used the log-rank p-value to compare the two groups.

FAM64A could serve as a malignant marker in gliomas

Pan-cancer analysis showed FAM64A were higher expressed in tumor tissues (Supple Fig. 1A). The count of FAM64A transcripts increased with the malignancy of glioma, as it was higher in glioblastoma (GBM, n = 199) than grade 2 glioma (n = 275, P < 0.001) and grade 3 glioma (n = 293, P < 0.001, Fig. 1A) according to the integrated profiles from TCGA gliomas (LGG and GBM). Histological subgroup analysis also indicated a higher FAM64A expression in GBM, and no favorable expression on astrocyte or oligodendrocyte lineage was observed (Fig. 1B). In the meantime, IDH wild-type gliomas had a higher FAM64A expression compared with IDH mutant gliomas (P < 0.001, Suppl. Fig. 1B). FAM64A was more commonly expressed in mesenchymal and classical subtypes compared with neural and proneural subtypes (Suppl. Fig. 1C). survival analysis showed that FAM64A was a significant risk factor of TCGA-LGG cohort and could predict a poor prognosis (P = 2.42e-6, HR = 2.5, Fig. 1C). This differential FAM64A expression between normal brain tissues and gliomas was further validated by immunoblotting (Fig. 1D), as grade 4 gliomas expressed most highly FAM64A (Fig. 1E, P < 0.001).

A nomogram plot including FAM64A, WHO grade, age, IDH mutation, was successfully constructed to predict glioma prognosis (Fig. 1F). Meanwhile, FAM64A expression could also predict the sensitivity to chemotherapy based on Cancer Genome Project (GGP), and patients with low FAM64A might be more responsive to BI2536, a polo-like kinase (PLK) inhibitor (P = 2.2e-16, Pearson correlation r = -0.48, Fig. 1G).

FAM64A could promote the proliferation of glioma cells

A glioblastoma single cell RNA-seq (scRNA-seq) dataset (GSE131928) was analyzed to specify FAM64A expression profiles in different cell types of glioma tissues. After removing the batch effect, the principal component analysis showed a remarkable separation of different cell groups (Suppl. Fig. 2A). The infiltration of T cells, monocytes/macrophage/microglia in glioma were verified by classic markers (Suppl. Fig. 2B). It was clear that FAM64A was mainly expressed in glioma and glial cells and affected their proliferation (Fig. 2A).

FAM64A knockdown U87 cell lines (sh-FAM64A) and its control (sh-Scramble) were successfully established (Suppl. Fig. 2C). Sh-FAM64A group exhibited significant higher cell viability after 24h (P = 0.014), 48h (P = 0.001) and 72h (P < 0.001) with CCK8 assay. The immunofluorescent assay also indicated that sh-FAM64A expressed less Ki-67 (P = 0.017) compared with sh-Scramble (Fig. 2C). Scratch wound healing assay showed FAM64A knockdown could decrease migration significantly as well (P = 0.003, Fig. 2D).

With differential expression analysis according to TCGA-GBM cohort, pathways associated with cell division, like cell cycle and oocyte meiosis, were significantly enriched by KEGG analysis (Fig. 2E). GSEA analysis also further verified that cell-cycle associated genes were upregulated in FAM64A high expressed gliomas (Fig. 2F).

FAM64A expression could change dynamically in a cell cycle-dependent manner

To demonstrate the details about how FAM64A affected cell-cycle, cell-cycle analysis was conducted on scRNA-seq profile. Neoplasm cells were extracted according to their copy number variation (CNV) and cell-cycle analysis showed cells in different cell cycle are characterized with distinct expression profile (Suppl. Fig. 3A, 3B). FAM64A was highly expressed in glioma cells in G2M phase. Pseudotime and RNA velocity analysis revealed the temporal trajectory of cells with different FAM64A expressions and showed cells with high FAM64A expression were in late pseudotime. The primitive glioma cells (in blue rectangle) have two evolutionary trajectories. Part of them would enter the stage of expressing FAM64A highly. This finding indicates that primitive cells might be activated by some signaling which determines their fate (Fig. 3B). Flow cytometrical analysis validated the effect of FAM64A on cell-cycle. Intriguingly, sh-FAM64A cells had a higher S phase proportion compared with control, indicating FAM64A knockdown would induce S phase arrest (P = 0.002, Fig. 3C).

FAM64A could be induced by TGF-b pathway activation

Intercellular crosstalk analysis of GSE131928 showed a strong TGF-b signaling flow between tumor-associated macrophages and microglia (TAMs) and neoplasm cells. TAMs showed the highest TGF-b signaling communication probability towards high FAM64A cells (Fig. 4A), suggesting that TGF-b might be responsible for the induction of FAM64A expression. CistromeDB toolkit further demonstrated that TGF-b downstream transcription factors (SMAD2/3, SMAD3, and SMAD4) had a regulatory potential higher than 0.3 on FAM64A, indicating that FAM64A could be regulated by TGF-b pathway (Fig. 4B). In vitro cultivation with exogenous TGF-b (5ng/mL) confirmed that FAM64A could be upregulated with enhanced TGF-b signaling (Fig. 4C).

To investigate the association between TGF-b and FAM64A, glioma samples of different WHO grades were collected and TGF-b, FAM64A and TGF- b downstream transcription factor level were detected. ELISA and immunoblotting assays showed the concentration of TGF-b had a positive correlation with FAM64A expression, while grade 4 gliomas have the highest TGF-b level (one-way ANOVA, P = 0.004). In the meantime, Smad3 and Smad4 were also higher expressed in high-grade glioma (Fig. 4D). These proteins showed a high correlation between each other (Fig. 4D). Immunofluorescence also showed increased number of FAM64A and SMAD4 colocalization in high grade gliomas (Fig. 4E). The proportions of cells expressing FAM64A and SMAD4 in grade 4 gliomas were higher than grade 2 or 3 gliomas (one-way ANOVA, FAM64A P = 0.002; SMAD4 P = 0.002, Fig. 4F) and have distinct correlation (Pearson r = 0.76, P = 0.017). Colocalization of FAM64A and SMAD4 could be observed in grade 4 gliomas (Fig. 4E).

Cell cycle related proteins have attracted attention in the molecular diagnosis and treatment in gliomas [27–29]. Herein we found FAM64A knockdown could lead to S phase arrest and suppress glioma proliferation, which is in accordance with previous study on other solid tumors [11, 13, 30]. Despite its significant role, no studies investigated the regulatory mechanism of FAM64A so far.

TGF-b pathway could play important roles in gliomas. Under physiological conditions, TGF-b is in low expression in the brain. Meanwhile, TGF-b is broadly expressed in gliomas, especially glioblastoma [31]. TGF-b is believed as an oncogenic factor of glioma [31, 32]. The elevated TGF-b level confers poor prognosis [32]. All these characteristics are very close to FAM64A. Therefore, we further explored the association of FAM64A and TGF-b signaling and validated FAM64A could be induced through TGF-b pathway. Previous studies revealed the TGF-b was secreted by tumor cells and immune cells during the progression of glioma [33, 34]. Taken together, we speculated TAMs shift to immunosuppression status with tumor progression and promote tumor growth through intercellular communication like TGF-b signaling. When the upstream TGF-b signaling pathway was disordered, FAM64A expression is activated to accelerate cell cycle and proliferation (Fig. 5).

The findings provide novel insights about the anti-microenvironment therapy in glioma. It is widely acknowledged that glioma tissues comprise not only malignant cells but also immune cells, epithelial cells, vascular cells, and signaling molecules [35]. Traditional treatment focused on killing malignant cells, but tumor microenvironment (TME) is likely to lead tumor immune evasion and drug resistance [36]. Therefore, glioma is susceptible to relapses and the prognosis is far from satisfactory. Microenvironment-targeting therapy is very promising and attracted increased attention [37–39]. TGF-b signaling was acknowledged to promote proliferation of malignant cells and regulate inflammation [40]. We also could target the TGF-b pathway to modulate the FAM64A expression and inhibit the glioma proliferation [41]. Currently, there are some kinds of TGF-b target medicine (ISTH1047 and ISTH0047) for glioma treatment and the preclinical findings are very promising [42, 43]. We also found FAM64A could predict glioma sensitivity to PLK inhibitors, like BI2536. PLK inhibitions have been found to inhibit proliferation by inducing cell cycle arrest [44]. Though it was not applied in GBM treatment currently, FAM64A may help guide PLK inhibition administration in GBM.

Although some strengths exist, there are also some limitations in the current study. Tumor progression is the result of a complex and multifaceted interaction in TME. Due to the blood-brain barrier (BBB), TME in brain tumors is distinguished from other solid tumors [45]. In current study, only neoplasm cell lines were investigated for FAM64A functions. But there is a major difference between glioma cell-lines and intracranial tumors. To bridge this gap, we analyzed published scRNA datasets of GBM samples and performed cell crosstalk analysis, which indicated TGF-b pathway played important roles in the interactions between neoplasm cells and immune cells (mainly tumor-associated macrophages, microglia in CNS) [46].

FAM64A could still serve as an efficient biomarker to predict the glioma prognosis.

TGF-b Transforming growth factor b

GBM Glioblastoma

LGG Low grade glioma

TCGA The Cancer Genome Atlas

scRNA single cell RNA

IDH Isocitrate dehydrogenase

PLK polo-like kinase

TME tumor microenvironment

Ethics approval and consent to participate

Glioma tissues used in the current study were resected in 2020 from patients in the Department of Neurosurgery, Huashan Hospital of Fudan University. Normal brain tissues were gathered from traumatic brain injury patients. The experiments were approved by the Human Ethics Committee of Huashan Hospital (KY2015-256). Informed consent was signed by all patients.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and analyzed during the current study are available from Gliovis (http://gliovis.bioinfo.cnio.es/) and GEO database (https://www.ncbi.nlm.nih.gov/geo/). Analysis scripts are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing financial interests.

Funding

This work was supported by the National Natural Science Foundation of China (82072785, 82072784, 8210113482). The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Authors' contributions

W. H., W. W., and Y. M. conceived the general framework of this study and revised the manuscript. J. Z., M. F. and L. Z. performed the cell experiments. M. F., Y. F., Q. W., X. F., and J. Z. analyzed data. M. F. and L. Z. interpreted the results. F. F. collected clinical samples. M. F. and J. Z. wrote the manuscript. All authors have read and approved the final manuscript.

Authors’ information

Department of neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, China
National Center for Neurological Disorders, China
Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, China
Neurosurgical Institute of Fudan University, China
Shanghai Clinical Medical Center of Neurosurgery, China
Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China

Wen PY, Packer RJ: The 2021 WHO Classification of Tumors of the Central Nervous System: clinical implications. Neuro Oncol 2021, 23:1215–1217.
Ostrom QT, Gittleman H, de Blank PM, Finlay JL, Gurney JG, McKean-Cowdin R, Stearns DS, Wolff JE, Liu M, Wolinsky Y, et al: American Brain Tumor Association Adolescent and Young Adult Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2008–2012. Neuro Oncol 2016, 18 Suppl 1:i1-i50.
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW: The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathologica 2016, 131:803–820.
Ross JL, Vega JV, Plant A, MacDonald TJ, Becher OJ, Hambardzumyan D: Tumour immune landscape of paediatric high-grade gliomas. Brain 2021, 144:2594–2609.
Vitucci M, Hayes DN, Miller CR: Gene expression profiling of gliomas: merging genomic and histopathological classification for personalised therapy. British Journal of Cancer 2011, 104:545–553.
Hanahan D: Hallmarks of Cancer: New Dimensions. Cancer Discov 2022, 12:31–46.
Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100:57–70.
Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. Cell 2011, 144:646–674.
Sepulveda-Sanchez JM, Gil-Gil M, Alonso-Garcia M, Vaz Salgado MA, Vicente E, Mesia Barroso C, Rodriguez Sanchez A, Duran G, De Las Penas R, Munoz-Langa J, et al: Phase II Trial of Palbociclib in Recurrent Retinoblastoma-Positive Anaplastic Oligodendroglioma: A Study from the Spanish Group for Research in Neuro-Oncology (GEINO). Target Oncol 2020, 15:613–622.
DeWire M, Fuller C, Hummel TR, Chow LML, Salloum R, de Blank P, Pater L, Lawson S, Zhu X, Dexheimer P, et al: A phase I/II study of ribociclib following radiation therapy in children with newly diagnosed diffuse intrinsic pontine glioma (DIPG). J Neurooncol 2020, 149:511–522.
Jiang Y, Zhou C, Gao Q, Yin ZQ, Wang J, Mu H, Yan J: FAM64A Promotes Osteosarcoma Cell Growth and Metastasis and Is Mediated by miR-493. J Oncol 2020, 2020:2518297.
Hashimoto K, Kodama A, Honda T, Hanashima A, Ujihara Y, Murayama T, Nishimatsu SI, Mohri S: Fam64a is a novel cell cycle promoter of hypoxic fetal cardiomyocytes in mice. Sci Rep 2017, 7:4486.
Yao ZC, Zheng XC, Lu ST, He ZX, Miao YT, Huang HH, Chu XW, Cai CQ, Zou F: Knockdown of FAM64A suppresses proliferation and migration of breast cancer cells. Breast Cancer 2019, 26:835–845.
Zhang C, Han Y, Huang H, Min L, Qu L, Shou C: Integrated analysis of expression profiling data identifies three genes in correlation with poor prognosis of triple-negative breast cancer. Int J Oncol 2014, 44:2025–2033.
Zhou Y, Ou L, Xu J, Yuan H, Luo J, Shi B, Li X, Yang S, Wang Y: FAM64A is an androgen receptor-regulated feedback tumor promoter in prostate cancer. Cell Death Dis 2021, 12:668.
Mizuno K, Tanigawa K, Nohata N, Misono S, Okada R, Asai S, Moriya S, Suetsugu T, Inoue H, Seki N: FAM64A: A Novel Oncogenic Target of Lung Adenocarcinoma Regulated by Both Strands of miR-99a (miR-99a-5p and miR-99a-3p). Cells 2020, 9.
Qiu X, Qin F: FAM64A antagonizes tumor suppressive effects of miR-610 in neuroblastoma in vitro. J Neurosurg Sci 2021.
Zhang J, Qian L, Wu JQ, Lu D, Yuan HY, Li WJ, Ying XM, Hu SF: Up-regulation of FAM64A promotes epithelial-to-mesenchymal transition and enhances stemness features in breast cancer cells. Biochemical and Biophysical Research Communications 2019, 513:472–478.
Xu ZS, Zhang HX, Li WW, Ran Y, Liu TT, Xiong MG, Li QL, Wang SY, Wu M, Shu HB, et al: FAM64A positively regulates STAT3 activity to promote Th17 differentiation and colitis-associated carcinogenesis. Proc Natl Acad Sci U S A 2019, 116:10447–10452.
Neftel C, Laffy J, Filbin MG, Hara T, Shore ME, Rahme GJ, Richman AR, Silverbush D, Shaw ML, Hebert CM, et al: An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. Cell 2019, 178:835–849 e821.
He L, Liu Y, Lai W, Tian H, Chen L, Xie L, Liu Z: DNA sensors, crucial receptors to resist pathogens, are deregulated in colorectal cancer and associated with initiation and progression of the disease. J Cancer 2020, 11:893–905.
Stanic J, Carta M, Eberini I, Pelucchi S, Marcello E, Genazzani AA, Racca C, Mulle C, Di Luca M, Gardoni F: Rabphilin 3A retains NMDA receptors at synaptic sites through interaction with GluN2A/PSD-95 complex. Nat Commun 2015, 6:10181.
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R: Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 2018, 36:411–420.
Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S: Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 2019, 16:1289–1296.
Zheng R, Wan C, Mei S, Qin Q, Wu Q, Sun H, Chen CH, Brown M, Zhang X, Meyer CA, Liu XS: Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis. Nucleic Acids Res 2019, 47:D729-D735.
Geeleher P, Cox N, Huang RS: pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014, 9:e107468.
Zhu XL, Yang H, Zhang MY, Wu XW, Jiang L, Liu XC, Lv K: YTHDC1-mediated VPS25 regulates cell cycle by targeting JAK-STAT signaling in human glioma cells. Cancer Cell International 2021, 21.
Wen NY, Guo BF, Zheng HW, Xu LB, Liang H, Wang Q, Wang D, Chen XY, Zhang SN, Li Y, Zhang L: Bromodomain inhibitor jq1 induces cell cycle arrest and apoptosis of glioma stem cells through the VEGF/PI3K/AKT signaling pathway. International Journal of Oncology 2019, 55:879–895.
Bronner SM, Merrick KA, Murray J, Salphati L, Moffat JG, Pang J, Sneeringer CJ, Dompe N, Cyr P, Purkey H, et al: Design of a brain-penetrant CDK4/6 inhibitor for glioblastoma. Bioorg Med Chem Lett 2019, 29:2294–2301.
Jiao Y, Fu Z, Li YQ, Zhang W, Liu YH: Aberrant FAM64A mRNA expression is an independent predictor of poor survival in pancreatic cancer. Plos One 2019, 14.
Kaminska B, Cyranowski S: Recent Advances in Understanding Mechanisms of TGF Beta Signaling and Its Role in Glioma Pathogenesis. Adv Exp Med Biol 2020, 1202:179–201.
Kaminska B, Kocyk M, Kijewska M: TGF beta signaling and its role in glioma pathogenesis. Adv Exp Med Biol 2013, 986:171–187.
Ueshima E, Fujimori M, Kodama H, Felsen D, Chen J, Durack JC, Solomon SB, Coleman JA, Srimathveeravalli G: Macrophage-secreted TGF-beta1 contributes to fibroblast activation and ureteral stricture after ablation injury. Am J Physiol Renal Physiol 2019, 317:F52-F64.
Song G, Yang R, Zhang Q, Chen L, Huang D, Zeng J, Yang C, Zhang T: TGF-beta Secretion by M2 Macrophages Induces Glial Scar Formation by Activating Astrocytes In Vitro. J Mol Neurosci 2019, 69:324–332.
Gieryng A, Pszczolkowska D, Walentynowicz KA, Rajan WD, Kaminska B: Immune microenvironment of gliomas. Lab Invest 2017, 97:498–518.
Murciano-Goroff YR, Warner AB, Wolchok JD: The future of cancer immunotherapy: microenvironment-targeting combinations. Cell Research 2020, 30:507–519.
Wang L, Han HQ, Wang ZH, Shi LT, Yang M, Qin YR: Targeting the Microenvironment in Esophageal Cancer. Frontiers in Cell and Developmental Biology 2021, 9.
Almotlak AA, Farooqui M, Soloff AC, Siegfried JM, Stabile LP: Targeting the ER beta/HER Oncogenic Network in KRAS Mutant Lung Cancer Modulates the Tumor Microenvironment and Is Synergistic with Sequential Immunotherapy. International Journal of Molecular Sciences 2022, 23.
Sreenivasan L, Li LV, Leclair P, Lim CJ: Targeting the gp130/STAT3 Axis Attenuates Tumor Microenvironment Mediated Chemoresistance in Group 3 Medulloblastoma Cells. Cells 2022, 11.
Derynck R, Turley SJ, Akhurst RJ: TGFbeta biology in cancer progression and immunotherapy. Nat Rev Clin Oncol 2021, 18:9–34.
Joseph JV, Balasubramaniyan V, Walenkamp A, Kruyt FA: TGF-beta as a therapeutic target in high grade gliomas - promises and challenges. Biochem Pharmacol 2013, 85:478–485.
Han J, Alvarez-Breckenridge CA, Wang QE, Yu J: TGF-beta signaling and its targeting for glioma treatment. Am J Cancer Res 2015, 5:945–955.
Papachristodoulou A, Silginer M, Weller M, Schneider H, Hasenbach K, Janicot M, Roth P: Therapeutic Targeting of TGFbeta Ligands in Glioblastoma Using Novel Antisense Oligonucleotides Reduces the Growth of Experimental Gliomas. Clin Cancer Res 2019, 25:7189–7201.
Pezuk JA, Brassesco MS, Morales AG, de Oliveira JC, de Paula Queiroz RG, Machado HR, Carlotti CG, Jr., Neder L, Scrideli CA, Tone LG: Polo-like kinase 1 inhibition causes decreased proliferation by cell cycle arrest, leading to cell death in glioblastoma. Cancer Gene Ther 2013, 20:499–506.
Daneman R, Prat A: The blood-brain barrier. Cold Spring Harb Perspect Biol 2015, 7:a020412.
Gratchev A: TGF-beta signalling in tumour associated macrophages. Immunobiology 2017, 222:75–81.

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Cell cycle-related FAM64A could be activated by TGF-β signaling to promote glioma progression

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