Triazole Modified Tetraiodothyroacetic Acid Conjugated to Polyethylene Glycol, a Thyrointegrin αvβ3 Antagonist as a Radio- and Chemo-Sensitizer in Pancreatic Cancer
<p>Flow chart of the study protocol for the in vivo and in vitro studies.</p> "> Figure 2
<p>Effect of P-bi-TAT on human pancreatic cancer (SUIT2-luc). (<b>A</b>) MTT assay analysis showed a dose-dependent inhibitory effect of P-bi-TAT (1, 3, 10, 30, and µM) on SUIT2-luc cell viability after 24 h of treatment. Data represent mean ± SEM, <span class="html-italic">n</span> = 3, * <span class="html-italic">p</span> < 0.01. (<b>B</b>) Effect of P-bi-TAT on SUIT2-luc xenografts in athymic mouse model. There was < 50% decrease in tumor weight in the animals treated with 3 or 10 mg/kg body weight P-bi-TAT doses compared to the untreated group. Data represent mean ± SEM, <span class="html-italic">n</span> = 6 per group, ** <span class="html-italic">p</span> < 0.001, NS = not significant. (<b>C</b>) P-bi-TAT exhibited a radio-sensitization effect and decreased human pancreatic cancer (SUIT2-luc) xenograft weight in athymic mice. SUIT2-luc cells were implanted s.c., as described in Methods, and mice were treated with P-bi-TAT (3 mg/kg body weight of animal) daily and with 1 or 5 Gy radiation doses. P-bi-TAT along with radiation decreased tumor weight significantly (<span class="html-italic">p</span> < 0.001) compared to P-bi-TAT or radiation alone. Data represent mean ± SEM, <span class="html-italic">n</span> = 6 per group, ** <span class="html-italic">p</span> < 0.001, NS = not significant. (<b>D</b>) Representative H & E sections of the SUIT2-luc xenografts showed reduction in viable cells (20× magnification) in P-bi-TAT treated tissues along with radiation (1 or 5 Gy) compared to untreated (control) and non-irradiated groups. (<b>E</b>) P-bi-TAT radio-sensitized SUIT2-luc xenograft tumors and reduced viable cells with P-bi-TAT treatment plus radiation (1 or 5 Gy) and increased radiation-induced necrosis significantly (* <span class="html-italic">p</span> < 0.01; ** <span class="html-italic">p</span> < 0.001) compared to P-bi-TAT or radiation alone. <span class="html-italic">n</span> = 6 per group. L = left tumor (without radiation); R = right tumor (with radiation).</p> "> Figure 3
<p>(<b>A</b>) P-bi-TAT acted as a chemo-sensitizer of 5 Fluorouracil (5FU) and reduced tumor bioluminescent signals in pancreatic cancer SUIT2-luc xenograft tumors. Mice with pancreatic xenografts were treated with P-bi-TAT (3 mg/kg body weight) alone or in combination with 5FU (10 mg/kg body weight), and there were 10 mice per group treated for 21 days. Five mice from each group were terminated after 21 days and tumor bioluminescent signals were imaged ex vivo with IVIS (ON treatment). There was decreased tumor bioluminescence in the combination treatment group with P-bi-TAT and 5FU compared to monotherapies. Treatment was discontinued for the remaining 5 mice in each group and monitored for another 14 days (ON + OFF treatment). Bioluminescent signals continued to decrease for 14 days in the P-bi-TAT and 5FU combination group after treatment discontinuation. There was an increase in signal intensity in 5FU monotherapy. Bioluminescent signals: red = live cells; blue/white = dead cells. IVIS bioluminescence signals were quantified for (<b>B</b>) ON treatment and (<b>C</b>) ON + OFF treatment and show the statistical significance of the P-bi-TAT monotherapy and P-bi-TAT and 5FU combination therapy compared to control (PBS) and 5FU monotherapy. (<b>D</b>) P-bi-TAT acted as a chemo-sensitizer of 5 Fluorouracil (5FU) and reduced pancreatic cancer SUIT2-luc xenograft tumor weight. Mice with pancreatic xenografts were treated with P-bi-TAT (3 mg/kg body weight) alone or in combination with 5FU (10 mg/kg body weight), and there were 10 mice per group and treated for 21 days (ON treatment). (<b>E</b>) Then, treatment was discontinued and monitored for another 14 days (ON + OFF treatment). P-bi-TAT had a continued chemo-sensitizing effect on 5FU and enhanced SUIT2-luc xenografts’ tumor weight reduction after discontinuation of the treatment for 14 days (ON + OFF). There was a significant (<span class="html-italic">p</span> < 0.001) tumor weight reduction due to the P-bi-TAT and 5FU combination therapy after discontinuation of treatment (ON + OFF) compared to monotherapy with 5FU alone. However, after withdrawal of the 5FU monotherapy, there was increased tumor weight in the ON + OFF group, indicating the regrowth of the tumor.</p> "> Figure 4
<p>Microarray analysis of SUIT2-luc cells after 24 h of treatment with P-bi-TAT (30 µM) and compared with untreated cells using Affymetrix Protocol for Clariom S Microarrays. Effects of the P-bi-TAT treatment on gene expression in human pancreatic carcinoma cells SUIT2-luc. A total of 59 signal transduction pathways were significantly affected by treatment of human pancreatic carcinoma cells with P-bi-TAT for 24 h (<a href="#biomedicines-10-00795-t002" class="html-table">Table 2</a>; <span class="html-italic">p</span> < 0.05 statistical significance cut-off; number of affected genes from 3 to 29). (<b>A</b>) Twenty-five signaling pathways (<span class="html-italic">p</span> < 0.05; at least 10 affected genes; range from 10 to 29 genes). (<b>B</b>) Forty signaling pathways (<span class="html-italic">p</span> < 0.05; at least 4 affected genes; range from 4 to 29 genes). (<b>C</b>) P-bi-TAT treatment interferes with gene expression of the naive pluripotency transcriptional network operating in human metastatic pancreatic carcinoma cells SUIT2. Note that highly ordered expression profiles of genes comprising naïve pluripotency transcriptional network of human preimplantation embryos are markedly distorted by the P-bi-TAT treatment. (<b>D</b>) Gene set enrichment analyses (GSEA) revealed significantly affected signaling pathways of potential mechanistic relevance highlighting biological functions of pancreatic cancer cells affected by the P-bi-TAT treatment. Complete descriptions of significantly enriched phenotypic records, associated genes, and statistical metrics are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Summaries S1 and S2; Supplementary Tables S1–S3</a>.</p> "> Figure 4 Cont.
<p>Microarray analysis of SUIT2-luc cells after 24 h of treatment with P-bi-TAT (30 µM) and compared with untreated cells using Affymetrix Protocol for Clariom S Microarrays. Effects of the P-bi-TAT treatment on gene expression in human pancreatic carcinoma cells SUIT2-luc. A total of 59 signal transduction pathways were significantly affected by treatment of human pancreatic carcinoma cells with P-bi-TAT for 24 h (<a href="#biomedicines-10-00795-t002" class="html-table">Table 2</a>; <span class="html-italic">p</span> < 0.05 statistical significance cut-off; number of affected genes from 3 to 29). (<b>A</b>) Twenty-five signaling pathways (<span class="html-italic">p</span> < 0.05; at least 10 affected genes; range from 10 to 29 genes). (<b>B</b>) Forty signaling pathways (<span class="html-italic">p</span> < 0.05; at least 4 affected genes; range from 4 to 29 genes). (<b>C</b>) P-bi-TAT treatment interferes with gene expression of the naive pluripotency transcriptional network operating in human metastatic pancreatic carcinoma cells SUIT2. Note that highly ordered expression profiles of genes comprising naïve pluripotency transcriptional network of human preimplantation embryos are markedly distorted by the P-bi-TAT treatment. (<b>D</b>) Gene set enrichment analyses (GSEA) revealed significantly affected signaling pathways of potential mechanistic relevance highlighting biological functions of pancreatic cancer cells affected by the P-bi-TAT treatment. Complete descriptions of significantly enriched phenotypic records, associated genes, and statistical metrics are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Summaries S1 and S2; Supplementary Tables S1–S3</a>.</p> "> Figure 5
<p>(<b>A</b>) Expression profiles and (<b>B</b>–<b>D</b>) GSEA of 517 human cancer-associated genes affected by P-bi-TAT treatment in human pancreatic carcinoma cells. Gene set enrichment analyses (GSEA) of P-bi-TAT target genes in SUIT2 human pancreatic cancer cells identified a gene expression signature comprising 517 DEGs (191 down-regulated and 326 up-regulated genes; Panel 4A; <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Table S6</a>), expression of which is altered in multiple types of human cancers. Panel 4B shows top 10 significantly enriched records of human diseases (DisGeNET database; a total of 1185 significant records) and oncogenic pathways (MSigDB Oncogenic Signatures database; a total of 41 significant records) identified by GSEA of 517 DEGs. Panel 4C shows clustergrams of top 30 significantly enriched records of oncogenic pathways signatures (MSigDB Oncogenic Signatures database) and human diseases (DisGeNET database). Panel 4D shows visualization of 1185 significantly enriched records (large blue colored dots; adjusted <span class="html-italic">p</span> value < 0.05) from the DisGeNET database of human diseases. Small grey colored dots depict records with no significant enrichments. Each dot represents a single gene set. Similar gene sets are clustered together, reflecting overlapping patterns of gene expression changes associated with different human disease states. Complete descriptions of all significantly enriched phenotypic records, associated P-bi-TAT target genes, and corresponding statistical metrics are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Table S6</a>.</p> "> Figure 5 Cont.
<p>(<b>A</b>) Expression profiles and (<b>B</b>–<b>D</b>) GSEA of 517 human cancer-associated genes affected by P-bi-TAT treatment in human pancreatic carcinoma cells. Gene set enrichment analyses (GSEA) of P-bi-TAT target genes in SUIT2 human pancreatic cancer cells identified a gene expression signature comprising 517 DEGs (191 down-regulated and 326 up-regulated genes; Panel 4A; <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Table S6</a>), expression of which is altered in multiple types of human cancers. Panel 4B shows top 10 significantly enriched records of human diseases (DisGeNET database; a total of 1185 significant records) and oncogenic pathways (MSigDB Oncogenic Signatures database; a total of 41 significant records) identified by GSEA of 517 DEGs. Panel 4C shows clustergrams of top 30 significantly enriched records of oncogenic pathways signatures (MSigDB Oncogenic Signatures database) and human diseases (DisGeNET database). Panel 4D shows visualization of 1185 significantly enriched records (large blue colored dots; adjusted <span class="html-italic">p</span> value < 0.05) from the DisGeNET database of human diseases. Small grey colored dots depict records with no significant enrichments. Each dot represents a single gene set. Similar gene sets are clustered together, reflecting overlapping patterns of gene expression changes associated with different human disease states. Complete descriptions of all significantly enriched phenotypic records, associated P-bi-TAT target genes, and corresponding statistical metrics are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Table S6</a>.</p> "> Figure 5 Cont.
<p>(<b>A</b>) Expression profiles and (<b>B</b>–<b>D</b>) GSEA of 517 human cancer-associated genes affected by P-bi-TAT treatment in human pancreatic carcinoma cells. Gene set enrichment analyses (GSEA) of P-bi-TAT target genes in SUIT2 human pancreatic cancer cells identified a gene expression signature comprising 517 DEGs (191 down-regulated and 326 up-regulated genes; Panel 4A; <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Table S6</a>), expression of which is altered in multiple types of human cancers. Panel 4B shows top 10 significantly enriched records of human diseases (DisGeNET database; a total of 1185 significant records) and oncogenic pathways (MSigDB Oncogenic Signatures database; a total of 41 significant records) identified by GSEA of 517 DEGs. Panel 4C shows clustergrams of top 30 significantly enriched records of oncogenic pathways signatures (MSigDB Oncogenic Signatures database) and human diseases (DisGeNET database). Panel 4D shows visualization of 1185 significantly enriched records (large blue colored dots; adjusted <span class="html-italic">p</span> value < 0.05) from the DisGeNET database of human diseases. Small grey colored dots depict records with no significant enrichments. Each dot represents a single gene set. Similar gene sets are clustered together, reflecting overlapping patterns of gene expression changes associated with different human disease states. Complete descriptions of all significantly enriched phenotypic records, associated P-bi-TAT target genes, and corresponding statistical metrics are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Table S6</a>.</p> "> Figure 5 Cont.
<p>(<b>A</b>) Expression profiles and (<b>B</b>–<b>D</b>) GSEA of 517 human cancer-associated genes affected by P-bi-TAT treatment in human pancreatic carcinoma cells. Gene set enrichment analyses (GSEA) of P-bi-TAT target genes in SUIT2 human pancreatic cancer cells identified a gene expression signature comprising 517 DEGs (191 down-regulated and 326 up-regulated genes; Panel 4A; <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Table S6</a>), expression of which is altered in multiple types of human cancers. Panel 4B shows top 10 significantly enriched records of human diseases (DisGeNET database; a total of 1185 significant records) and oncogenic pathways (MSigDB Oncogenic Signatures database; a total of 41 significant records) identified by GSEA of 517 DEGs. Panel 4C shows clustergrams of top 30 significantly enriched records of oncogenic pathways signatures (MSigDB Oncogenic Signatures database) and human diseases (DisGeNET database). Panel 4D shows visualization of 1185 significantly enriched records (large blue colored dots; adjusted <span class="html-italic">p</span> value < 0.05) from the DisGeNET database of human diseases. Small grey colored dots depict records with no significant enrichments. Each dot represents a single gene set. Similar gene sets are clustered together, reflecting overlapping patterns of gene expression changes associated with different human disease states. Complete descriptions of all significantly enriched phenotypic records, associated P-bi-TAT target genes, and corresponding statistical metrics are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Table S6</a>.</p> "> Figure 6
<p>(<b>A</b>) Gene ontology (GO) analyses of 517 human cancer-associated genes affected by P-bi-TAT treatment in human pancreatic carcinoma cells reveal potential mechanisms of anti-cancer activity. Complete records of the analyses are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Tables S6 and S7</a>. DEGs, differentially expressed genes. (<b>B</b>) GSEA of the auto-regulatory network of 70 transcription factors regulating expression of genes affected by P-bi-TAT treatment in human pancreatic carcinoma cells. DisGeNET database of human diseases. Complete records of the analyses are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Tables S6 and S7</a>. (<b>C</b>) shows a model of interconnected regulatory networks affected by the P-bi-TAT treatment in human metastatic pancreatic carcinoma cells.</p> "> Figure 6 Cont.
<p>(<b>A</b>) Gene ontology (GO) analyses of 517 human cancer-associated genes affected by P-bi-TAT treatment in human pancreatic carcinoma cells reveal potential mechanisms of anti-cancer activity. Complete records of the analyses are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Tables S6 and S7</a>. DEGs, differentially expressed genes. (<b>B</b>) GSEA of the auto-regulatory network of 70 transcription factors regulating expression of genes affected by P-bi-TAT treatment in human pancreatic carcinoma cells. DisGeNET database of human diseases. Complete records of the analyses are reported in <a href="#app1-biomedicines-10-00795" class="html-app">Supplementary Tables S6 and S7</a>. (<b>C</b>) shows a model of interconnected regulatory networks affected by the P-bi-TAT treatment in human metastatic pancreatic carcinoma cells.</p> "> Figure 7
<p>Summary of experimental and analytical protocols implemented during the investigation of therapeutic efficacy of P-bi-TAT on human pancreatic cancer.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Cancer Cell Line
2.2. Integrin αvβ3 Expression with Flow Cytometry
2.3. MTT Assay
2.4. Animal Studies
P-bi-TAT Radio-Sensitization Study
2.5. Cancer Cell Implantation
2.6. P-bi-TAT Chemo-Sensitization of 5-Fluorouracil (5FU)
2.7. Microarray Studies
2.8. Genome-Wide Gene Expression Profiling Analysis
2.9. Statistical Analysis
3. Results
3.1. Integrin αvβ3 Expression
3.2. Inhibitory Effect of P-bi-TAT on SUIT2-Luc Cells
3.3. P-bi-TAT Monotherapy and Tumor-Targeted Radiation
3.4. Chemo-Sensitizing Effect of P-bi-TAT on 5FU Therapy
3.5. Gene Expression Analysis
Overview of Mechanisms of Anti-Cancer Activities of the P-bi-TAT
3.6. Naïve Pluripotency Network Marked Majority of the P-bi-TAT Target Genes
3.7. Differential GSEA of Various Sub-Sets of the P-bi-TAT-Target Genes
3.8. Identification of Transcriptional Regulatory Networks Associated with P-bi-TAT Target Genes
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 2019, 69, 7–34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Seshacharyulu, P.; Baine, M.J.; Souchek, J.; Menning, M.; Kaur, S.; Yan, Y.; Ouellette, M.M.; Jain, M.; Lin, C.; Batra, S.K. Biological determinants of radioresistance and their remediation in pancreatic cancer. Biochim. Biophys. Acta Rev. Cancer 2017, 1868, 69–92. [Google Scholar] [CrossRef] [PubMed]
- Andrén-Sandberg, Å. Pancreatic cancer: Chemotherapy and radiotherapy. N. Am. J. Med. Sci. 2011, 3, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, Y.; Sun, X.-J.; Jiang, T.-H.; Mao, A.-W. Combined radiochemotherapy in patients with locally advanced pancreatic cancer: A meta-analysis. World J. Gastroenterol. 2013, 19, 7461–7471. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.-B.; Yang, Y.; Zhao, Y.-P.; Zhang, T.-P.; Liao, Q.; Shu, H. Recent studies of 5-fluorouracil resistance in pancreatic cancer. World J. Gastroenterol. 2014, 20, 15682–15690. [Google Scholar] [CrossRef]
- Kim, M.P.; Gallick, G.E. Gemcitabine Resistance in Pancreatic Cancer: Picking the Key Players. Clin. Cancer Res. 2008, 14, 1284–1285. [Google Scholar] [CrossRef] [Green Version]
- Willey, C.D.; Bonner, J.A. Chapter 4-Interaction of Chemotherapy and Radiation. In Clinical Radiation Oncology, 3rd ed.; Gunderson, L.L., Tepper, J.E., Eds.; W.B. Saunders: Philadelphia, PA, USA, 2012; pp. 65–82. [Google Scholar]
- Blomstrand, H.; Scheibling, U.; Bratthäll, C.; Green, H.; Elander, N.O. Real world evidence on gemcitabine and nab-paclitaxel combination chemotherapy in advanced pancreatic cancer. BMC Cancer 2019, 19, 40. [Google Scholar] [CrossRef]
- Adel, N. Current treatment landscape and emerging therapies for pancreatic cancer. Am. J. Manag. Care 2019, 25, S3–S10. [Google Scholar]
- Ji, X.; Lu, Y.; Tian, H.; Meng, X.; Wei, M.; Cho, W.C. Chemoresistance mechanisms of breast cancer and their countermeasures. Biomed. Pharmacother. 2019, 114, 108800. [Google Scholar] [CrossRef]
- Cheng, S.-Y.; Leonard, J.L.; Davis, P.J. Molecular aspects of thyroid hormone actions. Endocr. Rev. 2010, 31, 139–170. [Google Scholar] [CrossRef] [Green Version]
- Davis, P.J.; Glinsky, G.V.; Lin, H.Y.; Leith, J.T.; Hercbergs, A.; Tang, H.Y.; Ashur-Fabian, O.; Incerpi, S.; Mousa, S.A. Cancer cell gene expression modulated from plasma membrane integrin αvβ3 by thyroid hormone and nanoparticulate tetrac. Front. Endocrinol. 2014, 5, 240, Errantum in Front. Endocrinol. 2015, 6, 98. [Google Scholar]
- Davis, P.J.; Goglia, F.; Leonard, J.L. Nongenomic actions of thyroid hormone. Nat. Rev. Endocrinol. 2016, 12, 111–121. [Google Scholar] [CrossRef]
- Maubant, S.; Poulain, L.; Carreiras, F.; Staedel, C.; Gauduchon, P. Altered adhesion properties and alpha v integrin expression in a cisplatin-resistant human ovarian carcinoma cell line. Int. J. Cancer 2002, 97, 186–194. [Google Scholar] [CrossRef]
- Seguin, L.; Kato, S.; Franovic, A.; Camargo, M.F.; Lesperance, J.; Elliott, K.C.; Yebra, M.; Mielgo, A.; Lowy, A.M.; Husain, H.; et al. An integrin β(3)-KRAS-RalB complex drives tumour stemness and resistance to EGFR inhibition. Nat. Cell Biol. 2014, 16, 457–468. [Google Scholar] [CrossRef] [Green Version]
- Pan, B.; Guo, J.; Liao, Q.; Zhao, Y. β1 and β3 integrins in breast, prostate and pancreatic cancer: A novel implication. Oncol. Lett. 2018, 15, 5412–5416. [Google Scholar] [CrossRef]
- Wang, T.; Huang, J.; Vue, M.; Alavian, M.R.; Goel, H.L.; Altieri, D.C.; Languino, L.R.; FitzGerald, T.J. αvβ3 integrin mediates radioresistance of prostate cancer cells through regulation of survivin. Mol. Cancer Res. 2019, 17, 398–408. [Google Scholar] [CrossRef] [Green Version]
- Leith, J.T.; Mousa, S.A.; Hercbergs, A.; Lin, H.Y.; Davis, P.J. Radioresistance of cancer cells, integrin αvβ3 and thyroid hormone. Oncotarget 2018, 9, 37069–37075. [Google Scholar] [CrossRef] [Green Version]
- Albert, J.M.; Cao, C.; Geng, L.; Leavitt, L.; Hallahan, D.E.; Lu, B. Integrin αvβ3 antagonist cilengitide enhances efficacy of radiotherapy in endothelial cell and non-small-cell lung cancer models. Int. J. Radiat. Oncol. Biol. Phys. 2006, 65, 1536–1543. [Google Scholar] [CrossRef]
- Yalcin, M.; Lin, H.-Y.; Sudha, T.; Bharali, D.J.; Meng, R.; Tang, H.-Y.; Davis, F.B.; Stain, S.C.; Davis, P.J.; Mousa, S.A. Response of human pancreatic cancer cell xenografts to tetraiodothyroacetic acid nanoparticles. Horm. Cancer 2013, 4, 176–185. [Google Scholar] [CrossRef]
- Mousa, S.A.; Yalcin, M.; Bharali, D.J.; Meng, R.; Tang, H.-Y.; Lin, H.-Y.; Davis, F.B.; Davis, P.J. Tetraiodothyroacetic acid and its nanoformulation inhibit thyroid hormone stimulation of non-small cell lung cancer cells in vitro and its growth in xenografts. Lung Cancer 2012, 76, 39–45. [Google Scholar] [CrossRef]
- Hercbergs, A.H.; Lin, H.-Y.; Davis, F.B.; Davis, P.J.; Leith, J.T. Radiosensitization and production of DNA double-strand breaks in U87MG brain tumor cells induced by tetraiodothyroacetic acid (tetrac). Cell Cycle 2011, 10, 352–357. [Google Scholar] [CrossRef] [Green Version]
- Glinskii, A.B.; Glinsky, G.V.; Lin, H.-Y.; Tang, H.-Y.; Sun, M.; Davis, F.B.; Luidens, M.K.; Mousa, S.; Hercbergs, A.H.; Davis, P.J. Modification of survival pathway gene expression in human breast cancer cells by tetraiodothyroacetic acid (tetrac). Cell Cycle 2009, 8, 3562–3570. [Google Scholar] [CrossRef] [Green Version]
- Davis, P.J.; Davis, F.B.; Mousa, S.A.; Luidens, M.K.; Lin, H.-Y. Membrane receptor for thyroid hormone: Physiologic and pharmacologic implications. Annu. Rev. Pharmacol. Toxicol. 2011, 51, 99–115. [Google Scholar] [CrossRef]
- Rebbaa, A.; Chu, F.; Davis, F.B.; Davis, P.J.; Mousa, S.A. Novel function of the thyroid hormone analog tetraiodothyroacetic acid: A cancer chemosensitizing and anti-cancer agent. Angiogenesis 2008, 11, 269–276. [Google Scholar] [CrossRef]
- Chang, T.-C.; Chin, Y.-T.; Nana, A.W.; Wang, S.-H.; Liao, Y.-M.; Chen, Y.-R.; Shih, Y.-J.; Changou, C.A.; Yang, Y.-C.S.; Wang, K.; et al. Enhancement by nano-diamino-tetrac of antiproliferative action of gefitinib on colorectal cancer cells: Mediation by EGFR sialylation and PI3K activation. Horm. Cancer 2018, 9, 420–432. [Google Scholar] [CrossRef] [Green Version]
- Sudha, T.; Rehman, M.U.; Darwish, N.H.; Coskun, M.D.; Satti, J.A.; Davis, P.J.; Mousa, S.A. Nano-targeting of thyrointegrin αvβ3 receptor in solid tumors and impact of radiosensitization. Radiat. Res. 2021, 196, 375–385. [Google Scholar] [CrossRef]
- Rajabi, M.; Godugu, K.; Sudha, T.; Bharali, D.J.; Mousa, S.A. Triazole Modified tetraiodothyroacetic acid conjugated to polyethylene glycol: High affinity thyrointegrin αvβ3 antagonist with potent anticancer activities in glioblastoma multiforme. Bioconjug. Chem. 2019, 30, 3087–3097. [Google Scholar] [CrossRef]
- Aung, W.; Jin, Z.H.; Furukawa, T.; Claron, M.; Boturyn, D.; Sogawa, C.; Tsuji, A.B.; Wakizaka, H.; Fukurama, T.; Fujibayashy, Y.; et al. Micro-positron emission tomography/contrast-enhanced computed tomography imaging of orthotopic pancreatic tumor-bearing mice using the αvβ3 integrin tracer 64Cu-labeled cyclam-RAFT-c(-RGDfK-)4. Mol. Imaging 2013, 12, 376–387. [Google Scholar] [CrossRef] [PubMed]
- Turaga, R.C.; Sharma, M.; Mishra, F.; Krasinskas, A.; Yuan, Y.; Yang, J.J.; Wang, S.; Liu, C.; Li, S.; Loi, Z.R. Modulation of cancer-associated fibrotic stroma by An integrin α(v)β(3) targeting protein for pancreatic cancer treatment. Cell. Mol. Gastroenterol. Hepatol. 2021, 11, 161–179. [Google Scholar] [CrossRef] [PubMed]
- Goel, H.L.; Li, J.; Kogan, S.; Languino, L.R. Integrins in prostate cancer progression. Endocr. Relat. Cancer 2008, 15, 657–664. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schittenhelm, J.; Schwab, E.I.; Sperveslage, J.; Tatagiba, M.; Meyermann, R.; Fend, F.; Goodman, S.L.; Sipos, B. Longitudinal expression analysis of αv integrins in human gliomas reveals upregulation of integrin αvβ3 as a negative prognostic factor. J. Neuropathol. Exp. Neurol. 2013, 72, 194–210. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Davis, P.J.; Mousa, S.A.; Lin, H.-Y. Nongenomic actions of thyroid hormone: The integrin component. Physiol. Rev. 2021, 101, 319–352. [Google Scholar] [CrossRef] [PubMed]
- Godugu, K.; Sudha, T.; Davis, P.J.; Mousa, S.A. Nano diaminopropane tetrac and integrin αvβ3 expression in different cancer types: Anti-cancer efficacy and Safety. Cancer Treat. Res. Commun. 2021, 28, 100395. [Google Scholar] [CrossRef] [PubMed]
- Mousa, D.S.; El-Far, A.H.; Saddiq, A.A.; Sudha, T.; Mousa, S.A. Nanoformulated Bioactive compounds derived from different natural products combat pancreatic cancer cell proliferation. Int. J. Nanomed. 2020, 15, 2259–2268. [Google Scholar] [CrossRef] [Green Version]
- Sudha, T.; Bharali, D.J.; Sell, S.; Darwish, N.H.E.; Davis, P.J.; Mousa, S.A. Nanoparticulate tetrac inhibits growth and vascularity of glioblastoma xenografts. Horm. Cancer 2017, 8, 157–165. [Google Scholar] [CrossRef] [Green Version]
- Glinsky, G.V.; Glinskii, A.B.; Stephenson, A.J.; Hoffman, R.M.; Gerald, W.L. Gene expression profiling predicts clinical outcome of prostate cancer. J. Clin. Investig. 2004, 113, 913–923. [Google Scholar] [CrossRef]
- Glinsky, G.V.; Higashiyama, T.; Glinskii, A.B. Classification of human breast cancer using gene expression profiling as a component of the survival predictor algorithm. Clin. Cancer Res. 2004, 10, 2272–2283. [Google Scholar] [CrossRef] [Green Version]
- Glinsky, G.V.; Berezovska, O.; Glinskii, A.B. Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer. J. Clin. Investig. 2005, 115, 1503–1521. [Google Scholar] [CrossRef] [Green Version]
- Xie, Z.; Bailey, A.; Kuleshov, M.V.; Clarke, D.J.B.; Evangelista, J.E.; Jenkins, S.L.; Lachmann, A.; Wojciechowicz, M.L.; Kropiwnicki, E.; Jagodnik, K.M.; et al. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 2021, 1, e90. [Google Scholar] [CrossRef]
- Glinsky, G.V. Mechanistically distinct pathways of divergent regulatory DNA creation contribute to evolution of human-specific genomic regulatory networks driving phenotypic divergence of Homo sapiens. Genome Biol. Evol. 2016, 8, 2774–2788. [Google Scholar] [CrossRef] [Green Version]
- Glinsky, G.V. Activation of endogenous human stem cell-associated retroviruses (SCARs) and therapy-resistant phenotypes of malignant tumors. Cancer Lett. 2016, 376, 347–359. [Google Scholar] [CrossRef]
- Glinsky, G.V. Single cell genomics reveals activation signatures of endogenous SCAR’s networks in aneuploid human embryos and clinically intractable malignant tumors. Cancer Lett. 2016, 381, 176–193. [Google Scholar] [CrossRef]
- Glinsky, G.V. Contribution of transposable elements and distal enhancers to evolution of human-specific features of interphase chromatin architecture in embryonic stem cells. Chromosome Res. 2018, 26, 61–84. [Google Scholar] [CrossRef]
- Glinsky, G.; Barakat, T.S. The evolution of Great Apes has shaped the functional enhancers’ landscape in human embryonic stem cells. Stem Cell Res. 2019, 37, 101456. [Google Scholar] [CrossRef]
- Glinsky, G.V. A Catalogue of 59,732 human-specific regulatory sequences reveals unique-to-human regulatory patterns associated with virus-interacting proteins, pluripotency, and brain development. DNA Cell Biol. 2020, 39, 126–143. [Google Scholar] [CrossRef] [Green Version]
- Glinsky, G.V. Impacts of genomic networks governed by human-specific regulatory sequences and genetic loci harboring fixed human-specific neuro-regulatory single nucleotide mutations on phenotypic traits of modern humans. Chromosome Res. 2020, 28, 331–354. [Google Scholar] [CrossRef]
- Glinsky, G.V. Genomics-guided drawing of molecular and pathophysiological components of malignant regulatory signatures reveals a pivotal role in human diseases of stem cell-associated retroviral sequences and functionally-active hESC enhancers. Front. Oncol. 2021, 11, 638363. [Google Scholar] [CrossRef]
- Hercbergs, A. Clinical Implications and Impact of discovery of the thyroid hormone receptor on integrin αvβ3—A review. Front. Endocrinol. 2019, 10, 565. [Google Scholar] [CrossRef] [Green Version]
- Wang, F.; Chen, L.; Zhang, R.; Chen, Z.; Zhu, L. RGD peptide conjugated liposomal drug delivery system for enhance therapeutic efficacy in treating bone metastasis from prostate cancer. J. Control. Release 2014, 196, 222–233. [Google Scholar] [CrossRef]
- Christensen, S.; Van der Roest, B.; Besselink, N.; Janssen, R.; Boymans, S.; Martens, J.W.; Yaspo, M.-L.; Priestly, P.; Kujik, E.; Cuppen, E.; et al. 5-Fluorouracil treatment induces characteristic T > G mutations in human cancer. Nat. Commun. 2019, 10, 4571. [Google Scholar] [CrossRef] [Green Version]
- Su, C.-Y.; Li, J.-Q.; Zhang, L.-L.; Wang, H.; Wang, F.-H.; Tao, Y.-W.; Wang, Y.-Q.; Guo, Q.-R.; Li, J.-J.; Liu, Y.; et al. The biological functions and clinical applications of integrins in cancers. Front. Pharmacol. 2020, 11, 579068. [Google Scholar] [CrossRef]
- Hercbergs, A.; Johnson, R.E.; Ashur-Fabian, O.; Garfield, D.H.; Davis, P.J. Medically induced euthyroid hypothyroxinemia may extend survival in compassionate need cancer patients: An observational study. Oncologist 2014, 20, 72–76. [Google Scholar] [CrossRef] [Green Version]
- Uhlén, M.; Zhang, C.; Lee, S.; Sjöstedt, E.; Fagerberg, L.; Bidkhori, G.; Benfeitas, R.; Arif, M.; Liu, Z.; Edfors, F.; et al. A pathology atlas of the human cancer transcriptome. Science 2017, 357, 2507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moreno, M.; de Lange, P.; Lombardi, A.; Silvestri, E.; Lanni, A.; Goglia, F. Metabolic effects of thyroid hormone derivatives. Thyroid 2008, 18, 239–253. [Google Scholar] [CrossRef] [PubMed]
Pathways | Number of Genes | Up Regulated | Down Regulated | p-Value |
---|---|---|---|---|
GPCR ligand binding | 3 | 0 | 3 | 0.0000 |
Deubiquitination | 4 | 4 | 0 | 0.0000 |
Chromatin organization | 3 | 2 | 1 | 0.0000 |
Olfactory receptor activity | 6 | 1 | 5 | 0.0000 |
Processing of Capped Intron-Containing Pre-mRNA | 3 | 1 | 2 | 0.0000 |
Mitotic G2-G2/M phases | 3 | 1 | 2 | 0.0001 |
DNA IR-damage and cellular response via ATR | 17 | 13 | 4 | 0.0001 |
Transcriptional regulation by RUNX1 | 3 | 2 | 1 | 0.0003 |
GPCRs, Class A Rhodopsin-like | 5 | 2 | 3 | 0.0007 |
IL-6 signaling pathway | 9 | 7 | 2 | 0.0019 |
Benzo(a)pyrene metabolism | 4 | 4 | 0 | 0.0020 |
Assembly of the primary cilium | 5 | 4 | 1 | 0.0023 |
VEGFA-VEGFR2 Signaling Pathway | 29 | 14 | 15 | 0.0024 |
Focal Adhesion | 24 | 15 | 9 | 0.0031 |
Interactome of polycomb repressive complex 2 (PRC2) | 5 | 5 | 0 | 0.0032 |
Wnt Signaling Pathway and Pluripotency | 15 | 8 | 7 | 0.0039 |
Metabolism of carbohydrates | 4 | 2 | 2 | 0.0042 |
Oncostatin M Signaling Pathway | 11 | 5 | 6 | 0.0043 |
Gastric Cancer Network 2 | 7 | 7 | 0 | 0.0047 |
DNA Damage Response (only ATM dependent) | 15 | 10 | 5 | 0.0047 |
DNA IR-Double Strand Breaks (DSBs) and cellular response via ATM | 10 | 6 | 4 | 0.0050 |
Regulation of lipid metabolism by Peroxisome proliferator-activated receptor alpha (PPARalpha) | 3 | 2 | 1 | 0.0052 |
Signaling by VEGF | 3 | 1 | 2 | 0.0052 |
TCF dependent signaling in response to WNT | 9 | 4 | 5 | 0.0054 |
Brain-Derived Neurotrophic Factor (BDNF) signaling pathway | 19 | 9 | 10 | 0.0068 |
MAPK Signaling Pathway | 21 | 13 | 8 | 0.0077 |
Interleukin-11 Signaling Pathway | 8 | 4 | 4 | 0.0083 |
Androgen receptor signaling pathway | 13 | 9 | 4 | 0.0084 |
Integrin-mediated Cell Adhesion | 14 | 9 | 5 | 0.0085 |
Wnt Signaling in Kidney Disease | 7 | 4 | 3 | 0.0092 |
Wnt Signaling Pathway | 10 | 5 | 5 | 0.0123 |
Human Thyroid Stimulating Hormone (TSH) signaling pathway | 10 | 6 | 4 | 0.0123 |
Angiopoietin Like Protein 8 Regulatory Pathway | 17 | 13 | 4 | 0.0132 |
Hepatitis C and Hepatocellular Carcinoma | 9 | 6 | 3 | 0.0133 |
ESC Pluripotency Pathways | 15 | 10 | 5 | 0.0139 |
TGF-beta Signaling Pathway | 17 | 12 | 5 | 0.0139 |
Major pathway of rRNA processing in the nucleolus and cytosol | 5 | 3 | 2 | 0.0142 |
Insulin Signaling | 19 | 12 | 7 | 0.0170 |
EGF/EGFR Signaling Pathway | 19 | 10 | 9 | 0.0170 |
Prolactin Signaling Pathway | 11 | 6 | 5 | 0.0183 |
Cell surface interactions at the vascular wall | 3 | 1 | 2 | 0.0190 |
RNA Polymerase I, RNA Polymerase III, and Mitochondrial Transcription | 3 | 2 | 1 | 0.0194 |
Wnt Signaling Pathway Netpath | 8 | 3 | 5 | 0.0197 |
DNA Damage Response | 10 | 6 | 4 | 0.0238 |
Signaling of Hepatocyte Growth Factor Receptor | 6 | 3 | 3 | 0.0243 |
Apoptosis-related network due to altered Notch3 in ovarian cancer | 8 | 5 | 3 | 0.0244 |
Signaling by FGFR2 | 5 | 4 | 1 | 0.0257 |
HDR through Homologous Recombination (HR) or Single Strand Annealing (SSA) | 4 | 2 | 2 | 0.0267 |
Glucocorticoid Receptor Pathway | 10 | 5 | 5 | 0.0271 |
Endoderm Differentiation | 17 | 11 | 6 | 0.0281 |
Glycogen Metabolism | 6 | 3 | 3 | 0.0314 |
Corticotropin-releasing hormone signaling pathway | 12 | 5 | 7 | 0.0358 |
L1CAM interactions | 3 | 1 | 2 | 0.0359 |
G13 Signaling Pathway | 6 | 6 | 0 | 0.0398 |
Integrated Breast Cancer Pathway | 18 | 15 | 3 | 0.0441 |
MAPK6/MAPK4 signaling | 4 | 2 | 2 | 0.0474 |
Apoptotic Signaling Pathway | 11 | 7 | 4 | 0.0481 |
Hedgehog ‘on’ state | 3 | 2 | 1 | 0.0491 |
SUMOylation of DNA damage response and repair proteins | 3 | 3 | 0 | 0.0496 |
Database | 1386 P-bi-TAT Genes | 517 P-bi-TAT Genes | 70 TF Genes |
---|---|---|---|
Transcription Factor PPIs | 11 | 83 | 125 |
ARCHS4 TFs Coexpression in Human Tissues | 214 | 238 | 117 |
Enrichr Submissions TF-Gene Coocurrence | 1157 | 1538 | 1447 |
TF Perturbations Followed by Expression | 276 | 808 | 38 |
KEGG 2021 Human | 0 | 95 | 33 |
PPI Hub Proteins | 13 | 139 | 63 |
BioPlanet 2019 | 24 | 321 | 99 |
DisGeNET | 4 | 1185 | 1092 |
Jensen Disease database | 4 | 21 | 35 |
WikiPathways 2021 Human | 2 | 146 | 87 |
WikiPathways 2019 Mouse | 0 | 34 | 16 |
Panther 2016 | 4 | 21 | 3 |
NCI-Nature 2016 | 13 | 107 | 22 |
MSigDB Hallmark 2020 | 9 | 27 | 6 |
Reactome 2016 | 24 | 238 | 64 |
GO Biological Process 2018 | 12 | 313 | 162 |
GO Molecular Function 2018 | 10 | 70 | 63 |
MSigDB Oncogenic Signatures | 15 | 41 | 2 |
BioCarta 2016 | 2 | 66 | 16 |
Elsevier Pathway Collection | 3 | 431 | 256 |
Diseases | Overlap | p-Value | Adjusted p-Value | Odds Ratio | Combined Score |
---|---|---|---|---|---|
Breast Carcinoma | 398/4963 | 5.49 × 10−141 | 3.29 × 10−137 | 10.92964 | 3529.863 |
Malignant neoplasm of breast | 371/5054 | 5.47 × 10−112 | 1.64 × 10−108 | 8.030796 | 2057.408 |
Carcinogenesis | 286/4065 | 1.07 × 10−70 | 2.15 × 10−67 | 5.145025 | 828.9084 |
Malignant neoplasm of lung | 210/2449 | 1.25 × 10−61 | 1.88 × 10−58 | 5.268231 | 738.7838 |
Primary malignant neoplasm of lung | 199/2268 | 2.05 × 10−59 | 2.46 × 10−56 | 5.267008 | 711.7491 |
Malignant neoplasm of prostate | 238/3239 | 1.07 × 10−58 | 1.01 × 10−55 | 4.685076 | 625.3619 |
Carcinoma of lung | 207/2476 | 1.18 × 10−58 | 1.01 × 10−55 | 5.065892 | 675.7211 |
Neoplasm Metastasis | 258/3920 | 2.57 × 10−55 | 1.92 × 10−52 | 4.303636 | 540.9646 |
Squamous cell carcinoma | 173/1876 | 1.57 × 10−53 | 1.04 × 10−50 | 5.25055 | 638.4075 |
Mammary Neoplasms | 191/2387 | 2.78 × 10−50 | 1.67 × 10−47 | 4.612146 | 526.2717 |
Prostate carcinoma | 218/3145 | 3.95 × 10−48 | 2.15 × 10−45 | 4.123994 | 450.1387 |
Prostatic Neoplasms | 144/1554 | 8.61 × 10−44 | 4.30 × 10−41 | 4.9484 | 490.6863 |
Carcinoma of bladder | 123/1162 | 7.18 × 10−43 | 3.31 × 10−40 | 5.541769 | 537.7696 |
Bladder Neoplasm | 124/1217 | 1.73 × 10−41 | 7.41 × 10−39 | 5.308731 | 498.2656 |
Malignant neoplasm of urinary bladder | 120/1144 | 2.38 × 10−41 | 9.52 × 10−39 | 5.448776 | 509.6689 |
Malignant neoplasm of ovary | 155/2026 | 4.06 × 10−37 | 1.52 × 10−34 | 4.030492 | 337.7308 |
Colorectal Cancer | 204/3298 | 9.59 × 10−37 | 3.38 × 10−34 | 3.452375 | 286.3225 |
Colorectal Carcinoma | 190/2931 | 1.68 × 10−36 | 5.46 × 10−34 | 3.548985 | 292.343 |
Non−Small Cell Lung Carcinoma | 163/2243 | 1.73 × 10−36 | 5.46 × 10−34 | 3.852522 | 317.2388 |
Liver carcinoma | 213/3593 | 6.29 × 10−36 | 1.89 × 10−33 | 3.338075 | 270.5622 |
Tumor Progression | 154/2090 | 6.50 × 10−35 | 1.86 × 10−32 | 3.845135 | 302.6827 |
Ovarian Carcinoma | 157/2203 | 5.89 × 10−34 | 1.61 × 10−31 | 3.716749 | 284.384 |
Malignant neoplasm of stomach | 164/2398 | 1.73 × 10−33 | 4.51 × 10−31 | 3.587153 | 270.6057 |
Adenocarcinoma | 134/1712 | 1.32 × 10−32 | 3.30 × 10−30 | 3.969843 | 291.4102 |
Melanoma | 163/2454 | 9.86 × 10−32 | 2.34 × 10−29 | 3.455299 | 246.6867 |
Stomach Carcinoma | 160/2378 | 1.02 × 10−31 | 2.34 × 10−29 | 3.488645 | 248.964 |
Leukemia | 140/1941 | 2.03 × 10−30 | 4.51 × 10−28 | 3.645897 | 249.2702 |
Glioma | 148/2211 | 8.32 × 10−29 | 1.78 × 10−26 | 3.386759 | 218.9745 |
Lung Neoplasms | 103/1177 | 1.54 × 10−28 | 3.17 × 10−26 | 4.264448 | 273.1115 |
Colon Carcinoma | 142/2091 | 3.72 × 10−28 | 7.45 × 10−26 | 3.40664 | 215.1544 |
Pancreatic carcinoma | 132/1869 | 1.25 × 10−27 | 2.42 × 10−25 | 3.502788 | 216.9869 |
Solid Neoplasm | 84/840 | 4.13 × 10−27 | 7.74 × 10−25 | 4.805491 | 291.9395 |
Malignant tumor of colon | 134/2001 | 7.83 × 10−26 | 1.42 × 10−23 | 3.301179 | 190.8401 |
Glioblastoma | 131/1937 | 1.31 × 10−25 | 2.32 × 10−23 | 3.32181 | 190.312 |
Malignant neoplasm of pancreas | 127/1846 | 1.91 × 10−25 | 3.28 × 10−23 | 3.365147 | 191.5312 |
Ovarian neoplasm | 86/938 | 4.06 × 10−25 | 6.77 × 10−23 | 4.363327 | 245.0556 |
Leukemia, Myelocytic, Acute | 120/1703 | 7.48 × 10−25 | 1.21 × 10−22 | 3.417928 | 189.8723 |
Squamous cell carcinoma of the head and neck | 84/934 | 5.74 × 10−24 | 9.06 × 10−22 | 4.252607 | 227.577 |
Secondary malignant neoplasm of lymph node | 99/1271 | 1.62 × 10−23 | 2.50 × 10−21 | 3.700355 | 194.1775 |
Lymphoma | 100/1307 | 3.55 × 10−23 | 5.32 × 10−21 | 3.631097 | 187.699 |
Malignant Neoplasms | 105/1438 | 8.65 × 10−23 | 1.27 × 10−20 | 3.470073 | 176.2865 |
Central neuroblastoma | 112/1655 | 1.07 × 10−21 | 1.53 × 10−19 | 3.215285 | 155.2548 |
Renal Cell Carcinoma | 99/1348 | 1.24 × 10−21 | 1.73 × 10−19 | 3.457629 | 166.445 |
Neuroblastoma | 113/1698 | 2.56 × 10−21 | 3.49 × 10−19 | 3.158438 | 149.7545 |
Pancreatic Neoplasm | 66/665 | 3.67 × 10−21 | 4.89 × 10−19 | 4.613543 | 217.0849 |
Colorectal Neoplasms | 85/1073 | 1.28 × 10−20 | 1.67 × 10−18 | 3.683262 | 168.716 |
Brain Neoplasms | 64/646 | 1.70 × 10−20 | 2.17 × 10−18 | 4.588213 | 208.8636 |
Epithelial ovarian cancer | 94/1329 | 2.09 × 10−19 | 2.61 × 10−17 | 3.283491 | 141.2317 |
Cervi × carcinoma | 83/1105 | 1.04 × 10−18 | 1.28 × 10−16 | 3.45456 | 143.0298 |
Multiple Myeloma | 91/1312 | 3.13 × 10−18 | 3.75 × 10−16 | 3.194953 | 128.7784 |
Esophageal carcinoma | 62/685 | 6.41 × 10−18 | 7.54 × 10−16 | 4.125095 | 163.3051 |
Esophageal Neoplasms | 59/637 | 1.48 × 10−17 | 1.71 × 10−15 | 4.213426 | 163.2773 |
Pancreatic Ductal Adenocarcinoma | 62/701 | 1.98 × 10−17 | 2.24 × 10−15 | 4.018394 | 154.5595 |
Stomach Neoplasms | 68/835 | 3.54 × 10−17 | 3.93 × 10−15 | 3.69556 | 139.9903 |
Primary malignant neoplasm | 76/1032 | 1.12 × 10−16 | 1.22 × 10−14 | 3.339813 | 122.6713 |
Endometrial Carcinoma | 67/840 | 1.77 × 10−16 | 1.90 × 10−14 | 3.603766 | 130.7091 |
Glioblastoma Multiforme | 67/854 | 4.05 × 10−16 | 4.26 × 10−14 | 3.53701 | 125.362 |
Mesothelioma | 43/383 | 5.23 × 10−16 | 5.41 × 10−14 | 5.107651 | 179.7217 |
Chronic Lymphocytic Leukemia | 78/1120 | 9.40 × 10−16 | 9.56 × 10−14 | 3.144465 | 108.8003 |
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Sudha, T.; Godugu, K.; Glinsky, G.V.; Mousa, S.A. Triazole Modified Tetraiodothyroacetic Acid Conjugated to Polyethylene Glycol, a Thyrointegrin αvβ3 Antagonist as a Radio- and Chemo-Sensitizer in Pancreatic Cancer. Biomedicines 2022, 10, 795. https://doi.org/10.3390/biomedicines10040795
Sudha T, Godugu K, Glinsky GV, Mousa SA. Triazole Modified Tetraiodothyroacetic Acid Conjugated to Polyethylene Glycol, a Thyrointegrin αvβ3 Antagonist as a Radio- and Chemo-Sensitizer in Pancreatic Cancer. Biomedicines. 2022; 10(4):795. https://doi.org/10.3390/biomedicines10040795
Chicago/Turabian StyleSudha, Thangirala, Kavitha Godugu, Gennadi V. Glinsky, and Shaker A. Mousa. 2022. "Triazole Modified Tetraiodothyroacetic Acid Conjugated to Polyethylene Glycol, a Thyrointegrin αvβ3 Antagonist as a Radio- and Chemo-Sensitizer in Pancreatic Cancer" Biomedicines 10, no. 4: 795. https://doi.org/10.3390/biomedicines10040795
APA StyleSudha, T., Godugu, K., Glinsky, G. V., & Mousa, S. A. (2022). Triazole Modified Tetraiodothyroacetic Acid Conjugated to Polyethylene Glycol, a Thyrointegrin αvβ3 Antagonist as a Radio- and Chemo-Sensitizer in Pancreatic Cancer. Biomedicines, 10(4), 795. https://doi.org/10.3390/biomedicines10040795