Fibroblast-based FN1CFD contributes to parathyroid cancer identification using integrated analysis

doi:10.21203/rs.3.rs-3786420/v1

Download PDF

Research Article

Fibroblast-based FN1CFD contributes to parathyroid cancer identification using integrated analysis

https://doi.org/10.21203/rs.3.rs-3786420/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Parathyroid cancer (PC) is a highly aggressive cancer with high relapse and metastasis rate. However, there still lack effective preoperative and intraoperative ways to discriminate between PC and parathyroid adenoma (PA). The intrinsic biological alternations that lead to PC still unclear. Here, we performed integrated analysis on the transcriptomic data of parathyroid tumors. To identify pivotal genes associated with PC diagnosis, we integrated the immune infiltration analysis, gene differential analysis and ROC analysis, and discovered that fibroblast expressed gene FN1 and CFD (FC score) was of great importance. The cancer diagnostic value of FC score was also validated in pan-cancer data covering the TCGA bulk tumor and single cell data. Cell communication and cancer hallmark correlation analysis showed that FC score was strongly associated with EMT process, which was the key step for tumor invasion and metastasis. Our results suggest the tumor fibroblast phenotype defined by FC score contributed to PC identification.

parathyroid cancer

fibroblast

FN1

CFD

Primary hyperparathyroidism (PHPT) is one of the common endocrine diseases [1], which can result in hypercalcemia, reiterative urinary calculus, osteoporosis, and other systemic symptoms. It is reported that parathyroid adenoma (PA) accounts for more than 80% of PHPT, while parathyroid cancer (PC) only comprises 0.5-5%[2, 3]. Although the incidence of PC was rare, its high recurrence rate after initial surgery, easy metastasis, and poor prognosis characteristics could be of great harm. Identification of PC is critical for surgery treatment options and follow-up management. However, the pathological mechanisms that determine PCs remain unclear, which makes PC diagnosis difficult.

It has been reported that tumors with server hypercalcemia, younger patient age, and larger tumor size help the PC diagnosis[4–7]. However, increasing studies reported PC also found in smaller tumors and patients without severe hypercalcemia [8]. The gold standard that determines PC diagnosis is local invasion or distant metastasis[2, 9]. It is well-known that fibroblasts play a critical role in promoting both tumor invasion and metastasis. However, cancer-associated fibroblast (CAF) was highly heterogeneous. Recently, single-cell RNA sequencing (scRNA-seq) studies have classified fibroblasts into myofibroblast (mCAF), inflammatory fibroblast (iCAF), antigen-presented fibroblast (apCAF), and normal fibroblast (NF)[10–12]. The phenotype of fibroblast cells was flexible and influenced by the cellular effector medium. For example, under the transforming growth factor (TGF)-β enriched TME, fibroblast cells are often presented as mCAF. The phenotype of mCAF, iCAF, and NF could also be identified in parathyroid tumors. However, its role in deciding PC initiation or diagnosis was still unknown.

In this study, we first explored the infiltration pattern of TME cells between PAs and PCs and found fibroblasts or the related genes help PC diagnosis. Further investigation on fibroblast-related genes revealed two distinct gene clusters and FN1 and CFD were identified as representative genes. We then defined a FN1-CFD score (FC score) to successfully separate PCs from PAs. The diagnostic role of the FC score can also extend to more than 10 cancer types in the TCGA pan-cancer cohort. Further analysis revealed that FC score may influence cancer cell invasiveness through the epithelial-mesenchymal transition (EMT) process.

1. Parathyroid carcinoma tissue collection and procession

The parathyroid samples enrolled in this study was collected from public resource or the Beijing Chaoyang Hospital (BJCY), Beijing, China. The BJCY cohort consisted of two parts: BJCY.1 and BJCY.2. The BJCY.1 cohort included 3 PA and 3 PC samples, collected from October 2017 to September 2020[13], and the remaining 8 PC tissues in BJCY.2 cohort were collected from April 2019 to July 2022, and all subject to Illumina HiSeq sequencing platform. Among the PC patients, 4 PC tissues were harvested from primary lesions with initial surgery in the Beijing Chaoyang hospital and 7 patients received extended en bloc reoperation after tumor recurrence. All patients did not receive chemotherapy or radiotherapy before surgery and did not suffer MEN1 or other system diseases. Clinical information data including patient age, serum calcium, PTH level, etc. was collected (Table S1).

Tissue samples were transferred to RNAlater^TM solution after tumor resection and stored at -80℃. Fresh tissue was subjected to total RNA isolation. Raw sequencing data was mapped to the hg38 genome and gene expression level was quantified in log2(TPM+1) form.

2. Public data acquisition

Public parathyroid transcription data was collected from GEO (accession number: GSE83421) and we enrolled 25 PA samples in our study[14]. PA samples in the GSE83421 cohort and the BJCY data were integrated by applying combat to adjust the batch effect (Fig. S1A, Fig.S1B). FC score was calculated as follows:

FC score = Exp. FN1 – Exp. CFD

Exp. means the gene expression level in log2(TPM + 1).

To evaluate the effect of FC score on cancer diagnosis, we downloaded the TCGA pan-cancer data from UCSC Xena (https://xena.ucsc.edu/), and cancer cohorts with less than 10 adjacent tissues were excluded. For scRNA-seq data, we downloaded the raw count data from GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi) under the accession number GSE210347. This pan-cancer single-cell data included 11 cancer types covering thyroid, gastric, colorectal, etc., and the fibroblasts were separated to explore the expression pattern of FN1 and CFD.

3. Immune and stromal gene collection

For the marker genes of the immune and stromal cells, the top significant difference expressed genes concluded from scRNA-seq data were collected[11, 15]. The TME gene sets from R package IOBR[16] and MCP-counter[17] were incorporated. To comprehensively clarify the role of CAF, we manually collected signature genes of iCAF and mCAF[18]. The following genes were seen as markers of parathyroid parenchyma cells: PTH, CHGA, RARRES2, PVALB, CISD1, CASR, GCM2, KLCASR, VDR, LRP2, EPCAM, SLC17A5[19-21]. The collected genes were rearranged and can be found in TableS2.

4. Bioinformatic analysis of bulk data

Immune and stromal infiltration levels were estimated using the R package MCP-counter[17]. Gene differential analysis and GO enrichment analysis were conducted at clusterProfiler[22]. For the ligand-receptor interaction analysis, we first downloaded the ligand-receptor links from NicheNet[23]. Ligand-receptor pairs that positively correlated with FN1/CFD were considered potential cellular interactions. Data visualization was performed through ggplot2, circlize[24], and ComplexHeatmap[25] R package.

5. Statistical analysis

All the statistical analysis was performed at the R statistic platform (R version 4.03). Wilcoxon-test was applied to test the difference between the two groups. A P-value less than 0.5 was considered as statistical significance and the Benjamini-Hochberg method was used to adjust the p-value for multiple testing.

1. Fibroblast-related genes are the major difference between PA and PC

To comprehensively investigate the TME alternations, we first explored the expression pattern of the TME compartment-dominant genes in 39 parathyroid tumors pooling BJCY.1, BJCY.2 and GSE83421 cohort. The expression heatmap showed that TME genes were highly distinct between PA and PC samples, especially the stromal cell compartment (Fig.1A). More importantly, among the top 10 ranked genes based on AUC value, 6 genes, namely SERPINA1, INSR, FN1, CXCL14, CFD and CD9, were fibroblast associated genes (Fig. 1A). Next, we evaluated the TME infiltration level. We found that immune cells including NK cells, CD8T cells, T cells, and B lineages were significantly enriched in PAs. Fibroblasts were the only cells that upregulated in PCs despite the P-value being 0.071 (Fig. 1B). We also illustrated PCs expressed a high level of fibroblast-related genes (Fig. 1C) and GO biological process enrichment analysis revealed that PCs upregulated genes were mainly associated with extracellular matrix/structure organization and cell-substrate adhesion (Fig.1D). These results indicated that fibroblast related genes are major contribution in PC diagnosis.

2. FN1 and CFD contribute to PC identification

Because fibroblast cells were highly heterogeneous with distinct cell clusters[18, 26, 27], we next focus on their functional status in parathyroid tumors. The expression level of these DEGs and fibroblast-related genes identified 2 distinct gene clusters (Fig. 2A). It is worth noting that iCAF markers like CFD, CXCL12, DPT, AGTR1 and IGF1 were enriched in PA tumors, while mCAF or vCAF markers like MMP11, POSTN, GJA4, and RGS5 and pan-CAF markers COL1A1 and FAP were upregulated in PCs (Fig.2B) [18, 28]. To identify representative genes of the above 2 gene clusters, we integrated the AUC and fold change values and finally, FN1 and CFD were selected as representative markers (Fig.2C). As expected, FN1 was mainly expressed by CAF and CFD was enriched in adipogenic CAF and NF cluster according to the pan-cancer scRNA-seq data (https://gist-fgl.github.io/sc-caf-atlas/). The expression pattern of FN1 and CFD showed significant exclusive and FC score marked a big difference between PA and PC (Fig. 2D). The exclusive pattern between FN1 and CFD was also validated in the fibroblasts from the pan-cancer scRNA-seq data (Fig. 2E). FN1 exhibited high expression level in cancers; on the contrary, CFD was enriched in normal or cancer adjacent tissue (Fig. 2E). We also examine the distinguish capacity of FC score in pan-cancer using RNA-seq data in TCGA. Among the identified 15 cancers, the FC score in 10 cancers including thyroid carcinoma is obvious (Fig. S2A). However, the FC score did not show a diagnosis role in kidney clear cell carcinoma, kidney papillary cell carcinoma, liver hepatocellular carcinoma, etc., which might be because of low infiltration level (Fig. S2B, Fig.S2C). Taken together, these data suggested that FN1 and CFD were highly exclusive in distinct fibroblast states, which also played an important role in cancer diagnosis.

3. FC score highly correlated with the EMT process

The TME compartments were closely related through soluble factors or direct contact, we then next focused on cytokine and ligand-receptor (LR) interactions to assess the influence of FC score on the PC microenvironment. We have identified multiple FN1/CFD-related LR links in PC but failed in PA samples. Many FN1-related ligand-receptor interactions were links associated with angiogenesis, and metastasis like TGFB2:TGFBR1[29, 30], and FN1:ITGAV [31]. While CFD-associated LR pairs were concerned with CCR5 and LPAR1. The widely reported ligand of CCR5 was CCL5, and functions in recruiting cDC1, Treg, and CD8T cells and mediating tumor-TAM interactions[32, 33]. LPAR1 was positively correlated with immune infiltration level in prostate cancer[34] (Fig. 3A). In the cytokines and cytokine receptors analysis, we have found chemokines (INHBA, TGFB2, VEGFA) associated with EMT showed a positive relation with FC score. Similarly, enrichment analysis of the cancer hallmark gene set indicated that EMT, NOTCH signaling, and inflammatory response related signature was enriched in patients with high FC score (Fig. 3C). Next, we implemented the immune cell infiltration correlation analysis and revealed the proportions of CD8⁺T, fibroblast and endothelial cells were increased in high FC score patients (Fig. 3D). And the EMT canonical like TWIST1, SNAI2 and cytotoxic CD8B, PRF1 were positively correlated with FC score, although these marker genes did not reach the significant level (Fig. S3).

4. Relevance of FC score to clinical metrics

PHPT was first discovered by hypercalcemia and increased levels of PTH. Therefore, we examine whether the FC score was associated with these clinical parameters. Correlation analysis indicated that FC score did not relate with the level of serum calcium, PTH level, 25-dihydroxyvitamin D (25OHD), phosphorus (P), alkaline phosphatase (ALP) and creatinine (CREA) in blood before surgery (Fig. 4).

PC is a rare endocrine tumor with a high rate of postoperative recurrence and distant metastasis if patients did not receive the standard surgical operation at the initial treatment. For PC tissues with complete excision in the initial surgery or received recur surgery with the en bloc resection of parathyroid lesions with ipsilateral hemithyroidectomy, the recurrence rate can be reduced compared with the local excision group[4, 35]. However, recent PC postoperative diagnosis relies on diagnostician experience, and there were no reliable preoperative and intraoperative ways to diagnose PC until now.

CAF can remodel the TME to promote cancer invasion and metastasis, and invasiveness is a key factor in PC diagnosis. However, CAF is a heterogenous cell population with high plasticity and transformed to a specific phenotype under distinct TME conditions. Consistent with CAF identified in thyroid cancer, colorectal cancer, liver cancer, etc., CAF in parathyroid tumors can be divided into mCAF and iCAF[21, 36]. mCAF was characterized by high expression of POSTN, COL1A1, DCN and functioned as extracellular media to promote cancer cell invasion and metastasis. While the iCAF cells were featured by high expression of chemokine and inflammatory cytokines like IL-6, LIF, CXCL12, etc. We have reported that iCAF marker genes including CFD, CXCL12, and IGF1 were enriched in PA samples and mCAF marker genes were highly expressed in PC samples. However, it was reported that the iCAF were mainly observed enriched in PC samples in one sc-RNAseq data incorporating 2 PA and 5 PA samples[19]. This opposite conclusion might result from the small cohort data, indicating the role of iCAF in parathyroid tumors needs further investigation.

Gene FN1 encodes glycoprotein and has been demonstrated as an extracellular matrix protein, involved in cell adhesion and migration process[37–39]. The adverse role of FN1 in promoting the invasion and metastasis ability of cancer cells has been verified in vivo and in vitro[39]. Also, recent sc-RNAseq analysis reported that FN1 bridged the crosstalk between macrophage[40] or tumor epithelial cell[41] and fibroblast, indicating that FN1 was a pivotal medium that controls the TME cell-cell network. CFD, a member of the serine peptidases family, previously functioned through the complement pathway. Its role in regulating collagen type I expression and fibroblast migration has been reported recently[42]. However, the role of FN1 and CFD in cancer diagnosis, especially the parathyroid tumors, were not fully evaluated.

In this study, by integrated analysis of the PA and PC transcriptome data, we identified a fibroblast-based FC score that can successfully separate PC from PA. Furthermore, we also found that the cancer diagnosis role of FC score was significant in more than 10 cancers, which proved that the fibroblasts were critical in promoting cancer occurrence. Still, this study had some limitations. Because PC is a rare endocrine cancer with less than 1% of PHPT patients[8], this study only enrolled 28 PA and 11 PC samples and the diagnosis role of FC score needs to be validated in a larger and independent cohort. Second, due to the lack of a PC cell line, our study did not prove the tumor initiation role of FC score in vivo and in vitro.

CAF: cancer associated fibroblast; apCAF: antigen-presented fibroblast; iCAF: inflammatory fibroblast; mCAF: myo-fibroblast; NF: normal fibroblast; EMT: epithelial–mesenchymal transition; FC score: FN1-CFD score; PA: parathyroid adenoma; PC: parathyroid cancer; PHPT: primary hyperparathyroidism; TME: tumor microenvironment;

Acknowledgments

The authors would like to express their deepest appreciation to all patients enrolled in this study for their selfless dedication and thank John A Olson Jr and his team for their kindly sharing the transcriptome data of 25 PAs publicly.

Author contributions

Bojun Wei, Teng Zhao, and Qian Wang conceived of and designed this study. Teng Zhao. and Qian Wang generate the transcriptome data of the BJCY cohort. Rongfang Shen and Wenjing Yang performed the data analysis and Rongfang Shen wrote the original draft. Xing Liu, Jiacheng Wang, Daling Feng, Li Zhao, Yuting Wang, Jian Huang, and Hong Shen provided help for the tissue collection. All authors read and approved the final manuscript.

Funding

This work was supported by the Beijing Municipal Administration of Hospitals Incubating Program (PX2023009) and the Golden Seed Fund Project, Beijing Chaoyang Hospital, Capital Medical University (CYJZ202230).

Availability of data and materials

Public PA data and pan-cancer scRNA-seq data are accessible through the GEO database (https:// www.ncbi.nlm.nih.gov/geo) under accession numbers GSE83421 and GSE210347. The cohort TCGA pan-cancer data was downloaded through the UCSC Xena browser (https://xenabrowser.net). The BJCY cohort data are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

The experimental procedures involved in this study were approved by the Ethics Committee of Beijing Chaoyang Hospital, and written informed consent was provided by the patients. Consent for publication

Not applicable.

Competing interests

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

M.D. Walker and S.J. Silverberg, Nat Rev Endocrinol 14, 115-125 (2018) doi: 10.1038/nrendo.2017.104
J.P. Rodrigo, J.C. Hernandez-Prera, G.W. Randolph, M.E. Zafereo, D.M. Hartl, C.E. Silver, C. Suarez, R.P. Owen, C.R. Bradford, A.A. Makitie, A.R. Shaha, J.A. Bishop, A. Rinaldo and A. Ferlito, Cancer Treat Rev 86, 102012 (2020) doi: 10.1016/j.ctrv.2020.102012
C. Sadler, K.W. Gow, E.A. Beierle, J.J. Doski, M. Langer, J.G. Nuchtern, S.A. Vasudevan and M. Goldfarb, Surgery 156, 1622-1629; discussion 1629-1630 (2014) doi: 10.1016/j.surg.2014.08.069
S. Xue, H. Chen, C. Lv, X. Shen, J. Ding, J. Liu and X. Chen, Clin Endocrinol (Oxf) 85, 29-36 (2016) doi: 10.1111/cen.13055
J.H. Bae, H.J. Choi, Y. Lee, M.K. Moon, Y.J. Park, C.S. Shin, D.J. Park, H.C. Jang, S.Y. Kim and S.W. Kim, J Korean Med Sci 27, 890-895 (2012) doi: 10.3346/jkms.2012.27.8.890
Q. Chen, H. Kaji, R. Nomura, H. Sowa, M. Yamauchi, T. Tsukamoto, T. Yamaguchi, A. Kobayashi, T. Sugimoto and K. Chihara, Endocr J 50, 527-534 (2003) doi: 10.1507/endocrj.50.527
C.E. Quinn, J. Healy, A.H. Lebastchi, T.C. Brown, J.E. Stein, M.L. Prasad, G.G. Callender, T. Carling and R. Udelsman, J Am Coll Surg 220, 1054-1062 (2015) doi: 10.1016/j.jamcollsurg.2014.10.007
P. Roser, B.M. Leca, C. Coelho, K.M. Schulte, J. Gilbert, E.E. Drakou, C. Kosmas, L. Ling Chuah, H. Wassati, A.D. Miras, J. Crane, S.J.B. Aylwin, A.B. Grossman and G.K. Dimitriadis, Endocr Relat Cancer 30, (2023) doi: 10.1530/ERC-22-0287
L.A. Erickson, O. Mete, C.C. Juhlin, A. Perren and A.J. Gill, Endocr Pathol 33, 64-89 (2022) doi: 10.1007/s12022-022-09709-1
Z. Fang, Q. Meng, J. Xu, W. Wang, B. Zhang, J. Liu, C. Liang, J. Hua, Y. Zhao, X. Yu and S. Shi, Cancer Commun (Lond) 43, 3-41 (2023) doi: 10.1002/cac2.12392
H. Luo, X. Xia, L.B. Huang, H. An, M. Cao, G.D. Kim, H.N. Chen, W.H. Zhang, Y. Shu, X. Kong, Z. Ren, P.H. Li, Y. Liu, H. Tang, R. Sun, C. Li, B. Bai, W. Jia, Y. Liu, W. Zhang, L. Yang, Y. Peng, L. Dai, H. Hu, Y. Jiang, Y. Hu, J. Zhu, H. Jiang, Z. Li, C. Caulin, J. Park and H. Xu, Nat Commun 13, 6619 (2022) doi: 10.1038/s41467-022-34395-2
J.M. Houthuijzen, R. de Bruijn, E. van der Burg, A.P. Drenth, E. Wientjens, T. Filipovic, E. Bullock, C.S. Brambillasca, E.M. Pulver, M. Nieuwland, I. de Rink, F. van Diepen, S. Klarenbeek, R. Kerkhoven, V.G. Brunton, C. Scheele, M.C. Boelens and J. Jonkers, Nat Commun 14, 183 (2023) doi: 10.1038/s41467-023-35793-w
Q. Wang, J. Wang, Y. Xin, Z. He, X. Zhou, X. Liu, T. Zhao, L. He, H. Shen, M. Jin and B. Wei, Endocr Connect 11, (2022) doi: 10.1530/EC-21-0605
N. Balenga, P. Azimzadeh, J.A. Hogue, P.N. Staats, Y. Shi, J. Koh, H. Dressman and J.A. Olson, Jr., J Bone Miner Res 32, 654-666 (2017) doi: 10.1002/jbmr.3023
R. Bill, P. Wirapati, M. Messemaker, W. Roh, B. Zitti, F. Duval, M. Kiss, J.C. Park, T.M. Saal, J. Hoelzl, D. Tarussio, F. Benedetti, S. Tissot, L. Kandalaft, M. Varrone, G. Ciriello, T.A. McKee, Y. Monnier, M. Mermod, E.M. Blaum, I. Gushterova, A.L.K. Gonye, N. Hacohen, G. Getz, T.R. Mempel, A.M. Klein, R. Weissleder, W.C. Faquin, P.M. Sadow, D. Lin, S.I. Pai, M. Sade-Feldman and M.J. Pittet, Science 381, 515-524 (2023) doi: 10.1126/science.ade2292
D. Zeng, Z. Ye, R. Shen, G. Yu, J. Wu, Y. Xiong, R. Zhou, W. Qiu, N. Huang, L. Sun, X. Li, J. Bin, Y. Liao, M. Shi and W. Liao, Front Immunol 12, 687975 (2021) doi: 10.3389/fimmu.2021.687975
E. Becht, N.A. Giraldo, L. Lacroix, B. Buttard, N. Elarouci, F. Petitprez, J. Selves, P. Laurent-Puig, C. Sautes-Fridman, W.H. Fridman and A. de Reynies, Genome Biol 17, 218 (2016) doi: 10.1186/s13059-016-1070-5
E. Elyada, M. Bolisetty, P. Laise, W.F. Flynn, E.T. Courtois, R.A. Burkhart, J.A. Teinor, P. Belleau, G. Biffi, M.S. Lucito, S. Sivajothi, T.D. Armstrong, D.D. Engle, K.H. Yu, Y. Hao, C.L. Wolfgang, Y. Park, J. Preall, E.M. Jaffee, A. Califano, P. Robson and D.A. Tuveson, Cancer Discov 9, 1102-1123 (2019) doi: 10.1158/2159-8290.CD-19-0094
L. Chen, C. Shan, S. Xu, X. Deng, B. Guo, Q. Jiao, L. Zhang, Y. Fan, H. Yue and Z. Zhang, J Bone Miner Res 38, 994-1005 (2023) doi: 10.1002/jbmr.4824
S. Lu, X. Chen, M. Gong, S. Chen, J. Zhang, X. Zhang, C. Wu, A. Cui and X. Jiang, Front Endocrinol (Lausanne) 14, 1165890 (2023) doi: 10.3389/fendo.2023.1165890
X. Zhang, Y. Hu, M. Cui, M. Wang, X. Li, Y. Zhang, S. Yang, S. Hua, M. Shen and Q. Liao, Endocr Relat Cancer 30, (2023) doi: 10.1530/ERC-22-0325
G. Yu, L.G. Wang, Y. Han and Q.Y. He, OMICS 16, 284-287 (2012) doi: 10.1089/omi.2011.0118
R. Browaeys, W. Saelens and Y. Saeys, Nat Methods 17, 159-162 (2020) doi: 10.1038/s41592-019-0667-5
Z. Gu, L. Gu, R. Eils, M. Schlesner and B. Brors, Bioinformatics 30, 2811-2812 (2014) doi: 10.1093/bioinformatics/btu393
Z. Gu, R. Eils and M. Schlesner, Bioinformatics 32, 2847-2849 (2016) doi: 10.1093/bioinformatics/btw313
Z. Chen, L. Zhou, L. Liu, Y. Hou, M. Xiong, Y. Yang, J. Hu and K. Chen, Nat Commun 11, 5077 (2020) doi: 10.1038/s41467-020-18916-5
E. Minna, S. Brich, K. Todoerti, S. Pilotti, P. Collini, E. Bonaldi, P. Romeo, L. Cecco, M. Dugo, F. Perrone, A. Busico, A. Vingiani, I. Bersani, A. Anichini, R. Mortarini, A. Neri, G. Pruneri, A. Greco and M.G. Borrello, Cancers (Basel) 12, (2020) doi: 10.3390/cancers12010112
M. Zhang, H. Yang, L. Wan, Z. Wang, H. Wang, C. Ge, Y. Liu, Y. Hao, D. Zhang, G. Shi, Y. Gong, Y. Ni, C. Wang, Y. Zhang, J. Xi, S. Wang, L. Shi, L. Zhang, W. Yue, X. Pei, B. Liu and X. Yan, J Hepatol 73, 1118-1130 (2020) doi: 10.1016/j.jhep.2020.05.039
A. Calon, E. Espinet, S. Palomo-Ponce, D.V. Tauriello, M. Iglesias, M.V. Cespedes, M. Sevillano, C. Nadal, P. Jung, X.H. Zhang, D. Byrom, A. Riera, D. Rossell, R. Mangues, J. Massague, E. Sancho and E. Batlle, Cancer Cell 22, 571-584 (2012) doi: 10.1016/j.ccr.2012.08.013
B.A. Teicher, Pharmacol Ther 217, 107666 (2021) doi: 10.1016/j.pharmthera.2020.107666
T.N. Wu, C.K. Chen, I.C. Liu, L.S. Wu and A.T. Cheng, Sci Rep 9, 5298 (2019) doi: 10.1038/s41598-019-41689-x
M. Miao, E. De Clercq and G. Li, Expert Opin Drug Metab Toxicol 16, 11-30 (2020) doi: 10.1080/17425255.2020.1711884
K. Kohli, V.G. Pillarisetty and T.S. Kim, Cancer Gene Ther 29, 10-21 (2022) doi: 10.1038/s41417-021-00303-x
J. Shi, D. Jiang, S. Yang, X. Zhang, J. Wang, Y. Liu, Y. Sun, Y. Lu and K. Yang, Front Oncol 10, 846 (2020) doi: 10.3389/fonc.2020.00846
B. Wei, T. Zhao, H. Shen, M. Jin, Q. Zhou, X. Liu, J. Wang and Q. Wang, Ann Surg Oncol 29, 1208-1215 (2022) doi: 10.1245/s10434-021-10962-7
C. Verdelli, L. Avagliano, P. Creo, V. Guarnieri, A. Scillitani, L. Vicentini, G.B. Steffano, E. Beretta, L. Soldati, E. Costa, A. Spada, G.P. Bulfamante and S. Corbetta, Endocr Relat Cancer 22, 87-98 (2015) doi: 10.1530/ERC-14-0161
C. Ucaryilmaz Metin and G. Ozcan, BMC Cancer 22, 692 (2022) doi: 10.1186/s12885-022-09736-5
H. Zhang, X. Chen, P. Xue, X. Ma, J. Li and J. Zhang, Gene 769, 145253 (2021) doi: 10.1016/j.gene.2020.145253
X. Huo, S. Ma, C. Wang, L. Song, B. Yao, S. Zhu, P. Li, L. Wang, Z. Wu and K. Wang, Clin Transl Med 13, e1429 (2023) doi: 10.1002/ctm2.1429
K. Hoeft, G.J.L. Schaefer, H. Kim, D. Schumacher, T. Bleckwehl, Q. Long, B.M. Klinkhammer, F. Peisker, L. Koch, J. Nagai, M. Halder, S. Ziegler, E. Liehn, C. Kuppe, J. Kranz, S. Menzel, I. Costa, A. Wahida, P. Boor, R.K. Schneider, S. Hayat and R. Kramann, Cell Rep 42, 112131 (2023) doi: 10.1016/j.celrep.2023.112131
X. Wang, C. Liu, J. Chen, L. Chen, X. Ren, M. Hou, X. Cui, Y. Jiang, E. Liu, Y. Zong, A. Duan, X. Fu, W. Yu, X. Zhao, Z. Yang, Y. Zhang, J. Fu and H. Wang, Cell Discov 8, 101 (2022) doi: 10.1038/s41421-022-00445-8
J. Chen, J. Wang, D.A. Hart, Z. Zhou, P.W. Ackermann and A.S. Ahmed, Front Immunol 14, 1225957 (2023) doi: 10.3389/fimmu.2023.1225957

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Fibroblast-based FN1CFD contributes to parathyroid cancer identification using integrated analysis

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussions

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1