Combined Biomarker System Predicts Prognosis in Patients with Metastatic Oral Squamous Cell Carcinoma
<p>Quantification of marker protein expression across different regions of interest (ROIs) (oral mucosa; center of primary OSCC; invasive front, IF; and local lymph node metastasis, LNM) annotated with the pairwise comparison results. Histoscore (H-score) values are reported. The score is derived by adding 3× the percentage of tumor cells with strong staining, 2× those with moderate staining, and 1× those with weak staining. This generates a score range from 0 (all tumor cells negative) to 300 (all tumor cells strongly positive). (<b>A</b>) Cx43 expression; (<b>B</b>) EMMPRIN expression; (<b>C</b>) E-cadherin expression; (<b>D</b>) vimentin expression.</p> "> Figure 2
<p>Representative illustration of immunohistochemical evaluation in primary OSCC tissue sample and its corresponding lymph node metastasis (LNM) based on Cx43 staining. (<b>A</b>) Overview of primary OSCC with individual ROIs. The upper right corner illustrates the transition to adjacent healthy oral mucosa (ROI 1–3). (<b>B</b>) Enlarged view of ROI 2 (OM), (<b>C</b>) ROI 6 = primary OSCC, and (<b>D</b>) ROI 9 = invasive front (IF). (<b>E</b>) Enlarged view of LNM. Scale bar, 4 mm and 200 μm.</p> "> Figure 3
<p>Representative illustration of immunohistochemical evaluation in primary OSCC tissue sample and corresponding lymph node metastasis (LNM) based on EMMPRIN staining. (<b>A</b>) Overview of primary OSCC with individual ROIs. The upper right corner illustrates the transition to adjacent healthy oral mucosa (ROI 1–3). (<b>B</b>) Enlarged view of ROI 2 (OM), (<b>C</b>) ROI 6 = primary OSCC, and (<b>D</b>) ROI 9 = invasive front (IF). (<b>E</b>) Enlarged view of LNM. Scale bar, 4 mm and 200 μm.</p> "> Figure 4
<p>Representative illustration of immunohistochemical evaluation in primary OSCC tissue sample and corresponding lymph node metastasis (LNM) based on E-cadherin staining. (<b>A</b>) Overview of primary OSCC with individual ROIs. The upper right corner shows the transition to adjacent healthy oral mucosa (ROI 1–3). (<b>B</b>) Enlarged view of ROI 2 (OM), (<b>C</b>) ROI 6 = primary OSCC, and (<b>D</b>) ROI 9 = invasive front (IF). (<b>E</b>) Enlarged view of LNM. Scale bar, 4 mm and 200 μm.</p> "> Figure 5
<p>Representative illustration of immunohistochemical evaluation in primary OSCC tissue sample and corresponding lymph node metastasis (LNM) based on vimentin staining. (<b>A</b>) Overview of primary OSCC with individual ROIs. The upper right corner illustrates the transition to adjacent healthy oral mucosa (ROI 1–3). (<b>B</b>) Enlarged view of ROI 2 (OM), (<b>C</b>) ROI 6 = primary OSCC, and (<b>D</b>) ROI 9 = invasive front (IF). (<b>E</b>) Enlarged view of LNM. Scale bar, 4 mm and 200 μm.</p> "> Figure 6
<p>Differences between ‘neighboring’ ROIs (OM vs. OSCC, OSCC vs. IF, IF vs. LNM, IF vs. OM) were estimated and compared between proteins. This plot illustrates the expected marginal means of these differences (red dots) with 95% confidence intervals (red bars). The provided <span class="html-italic">p</span>-values originate from contrast tests adjusted for multiple comparisons using Holm’s procedure.</p> "> Figure 7
<p>Survival curves for DFS after prediction by multivariable Cox regression model when split at the optimal cutoff. <span class="html-italic">p</span>-value from log-rank test.</p> "> Figure 8
<p>Survival curves for OS after prediction by multivariable Cox regression model when split at the optimal cutoff. <span class="html-italic">p</span>-value from log-rank test.</p> ">
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patients
2.2. Tissue Sample Processing and Semiautomated Semiquantitative Immunohistochemical Analysis
2.3. Statistical Analysis
3. Results
3.1. Patients’ Clinical Baseline Characteristics
3.2. Marker Protein Expression in Different Regions of Interest (ROIs)
3.2.1. Connexin 43 (Cx43)
3.2.2. EMMPRIN
3.2.3. E-Cadherin
3.2.4. Vimentin
3.3. Analysis of Independent Marker Protein Expression
3.4. Patient Prognosis Prediction
3.4.1. Disease-Free Survival (DFS)
3.4.2. Overall Survival (OS)
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
- Hoene, G.; Gruber, R.M.; Leonhard, J.J.; Wiechens, B.; Schminke, B.; Kauffmann, P.; Schliephake, H.; Brockmeyer, P. Combined quality of life and posttraumatic growth evaluation during follow-up care of patients suffering from oral squamous cell carcinoma. Mol. Clin. Oncol. 2021, 15, 189. [Google Scholar] [CrossRef] [PubMed]
- Stone, R.C.; Pastar, I.; Ojeh, N.; Chen, V.; Liu, S.; Garzon, K.I.; Tomic-Canic, M. Epithelial-mesenchymal transition in tissue repair and fibrosis. Cell Tissue Res. 2016, 365, 495–506. [Google Scholar] [CrossRef] [PubMed]
- Goldmann, O.; Medina, E. The expanding world of extracellular traps: Not only neutrophils but much more. Front. Immunol. 2012, 3, 420. [Google Scholar] [CrossRef]
- Zhang, Y.; Weinberg, R.A. Epithelial-to-mesenchymal transition in cancer: Complexity and opportunities. Front. Med. 2018, 12, 361–373. [Google Scholar] [CrossRef] [PubMed]
- Peitzsch, C.; Tyutyunnykova, A.; Pantel, K.; Dubrovska, A. Cancer stem cells: The root of tumor recurrence and metastases. Semin. Cancer Biol. 2017, 44, 10–24. [Google Scholar] [CrossRef]
- Fiore, A.; Ribeiro, P.F.; Bruni-Cardoso, A. Sleeping Beauty and the Microenvironment Enchantment: Microenvironmental Regulation of the Proliferation-Quiescence Decision in Normal Tissues and in Cancer Development. Front. Cell Dev. Biol. 2018, 6, 59. [Google Scholar] [CrossRef]
- Gao, X.L.; Zhang, M.; Tang, Y.L.; Liang, X.H. Cancer cell dormancy: Mechanisms and implications of cancer recurrence and metastasis. Onco Targets Ther. 2017, 10, 5219–5228. [Google Scholar] [CrossRef]
- Aasen, T.; Johnstone, S.; Vidal-Brime, L.; Lynn, K.S.; Koval, M. Connexins: Synthesis, Post-Translational Modifications, and Trafficking in Health and Disease. Int. J. Mol. Sci. 2018, 19, 1296. [Google Scholar] [CrossRef]
- Solan, J.L.; Lampe, P.D. Spatio-temporal regulation of connexin43 phosphorylation and gap junction dynamics. Biochim. Biophys. Acta Biomembr. 2018, 1860, 83–90. [Google Scholar] [CrossRef] [PubMed]
- Brockmeyer, P.; Jung, K.; Perske, C.; Schliephake, H.; Hemmerlein, B. Membrane connexin 43 acts as an independent prognostic marker in oral squamous cell carcinoma. Int. J. Oncol. 2014, 45, 273–281. [Google Scholar] [CrossRef]
- Loewenstein, W.R.; Penn, R.D. Intercellular communication and tissue growth: II. Tissue regeneration. J. Cell Biol. 1967, 33, 235–242. [Google Scholar] [CrossRef] [PubMed]
- Tittarelli, A.; Guerrero, I.; Tempio, F.; Gleisner, M.A.; Avalos, I.; Sabanegh, S.; Ortiz, C.; Michea, L.; Lopez, M.N.; Mendoza-Naranjo, A.; et al. Overexpression of connexin 43 reduces melanoma proliferative and metastatic capacity. Br. J. Cancer 2015, 113, 259–267. [Google Scholar] [CrossRef] [PubMed]
- Caillou, B.; Talbot, M.; Weyemi, U.; Pioche-Durieu, C.; Al Ghuzlan, A.; Bidart, J.M.; Chouaib, S.; Schlumberger, M.; Dupuy, C. Tumor-associated macrophages (TAMs) form an interconnected cellular supportive network in anaplastic thyroid carcinoma. PLoS ONE 2011, 6, e22567. [Google Scholar] [CrossRef]
- Cronier, L.; Crespin, S.; Strale, P.O.; Defamie, N.; Mesnil, M. Gap junctions and cancer: New functions for an old story. Antioxid. Redox Signal. 2009, 11, 323–338. [Google Scholar] [CrossRef]
- Mulkearns-Hubert, E.E.; Reizes, O.; Lathia, J.D. Connexins in Cancer: Jekyll or Hyde? Biomolecules 2020, 10, 1654. [Google Scholar] [CrossRef]
- Naus, C.C.; Laird, D.W. Implications and challenges of connexin connections to cancer. Nat. Rev. Cancer 2010, 10, 435–441. [Google Scholar] [CrossRef] [PubMed]
- Chevallier, D.; Carette, D.; Segretain, D.; Gilleron, J.; Pointis, G. Connexin 43 a check-point component of cell proliferation implicated in a wide range of human testis diseases. Cell. Mol. Life Sci. 2013, 70, 1207–1220. [Google Scholar] [CrossRef]
- Ghosh, S.; Kumar, A.; Chandna, S. Connexin-43 downregulation in G2/M phase enriched tumour cells causes extensive low-dose hyper-radiosensitivity (HRS) associated with mitochondrial apoptotic events. Cancer Lett. 2015, 363, 46–59. [Google Scholar] [CrossRef] [PubMed]
- Kameritsch, P.; Pogoda, K.; Pohl, U. Channel-independent influence of connexin 43 on cell migration. Biochim. Biophys. Acta 2012, 1818, 1993–2001. [Google Scholar] [CrossRef] [PubMed]
- Kazan, J.M.; El-Saghir, J.; Saliba, J.; Shaito, A.; Jalaleddine, N.; El-Hajjar, L.; Al-Ghadban, S.; Yehia, L.; Zibara, K.; El-Sabban, M. Cx43 Expression Correlates with Breast Cancer Metastasis in MDA-MB-231 Cells In Vitro, In a Mouse Xenograft Model and in Human Breast Cancer Tissues. Cancers 2019, 11, 460. [Google Scholar] [CrossRef]
- Ke, Q.; Li, L.; Cai, B.; Liu, C.; Yang, Y.; Gao, Y.; Huang, W.; Yuan, X.; Wang, T.; Zhang, Q.; et al. Connexin 43 is involved in the generation of human-induced pluripotent stem cells. Hum. Mol. Genet. 2013, 22, 2221–2233. [Google Scholar] [CrossRef]
- Amit-Cohen, B.C.; Rahat, M.M.; Rahat, M.A. Tumor cell-macrophage interactions increase angiogenesis through secretion of EMMPRIN. Front. Physiol. 2013, 4, 178. [Google Scholar] [CrossRef]
- Grass, G.D.; Toole, B.P. How, with whom and when: An overview of CD147-mediated regulatory networks influencing matrix metalloproteinase activity. Biosci. Rep. 2015, 36, e00283. [Google Scholar] [CrossRef]
- Walter, M.; Simanovich, E.; Brod, V.; Lahat, N.; Bitterman, H.; Rahat, M.A. An epitope-specific novel anti-EMMPRIN polyclonal antibody inhibits tumor progression. Oncoimmunology 2016, 5, e1078056. [Google Scholar] [CrossRef] [PubMed]
- Cai, H.; Li, J.; Zhang, Y.; Liao, Y.; Zhu, Y.; Wang, C.; Hou, J. LDHA Promotes Oral Squamous Cell Carcinoma Progression Through Facilitating Glycolysis and Epithelial-Mesenchymal Transition. Front. Oncol. 2019, 9, 1446. [Google Scholar] [CrossRef] [PubMed]
- Grass, G.D.; Dai, L.; Qin, Z.; Parsons, C.; Toole, B.P. CD147: Regulator of hyaluronan signaling in invasiveness and chemoresistance. Adv. Cancer Res. 2014, 123, 351–373. [Google Scholar] [CrossRef] [PubMed]
- Wu, X.; Qiao, B.; Liu, Q.; Zhang, W. Upregulation of extracellular matrix metalloproteinase inducer promotes hypoxia-induced epithelial-mesenchymal transition in esophageal cancer. Mol. Med. Rep. 2015, 12, 7419–7424. [Google Scholar] [CrossRef]
- Bankhead, P.; Loughrey, M.B.; Fernandez, J.A.; Dombrowski, Y.; McArt, D.G.; Dunne, P.D.; McQuaid, S.; Gray, R.T.; Murray, L.J.; Coleman, H.G.; et al. QuPath: Open source software for digital pathology image analysis. Sci. Rep. 2017, 7, 16878. [Google Scholar] [CrossRef] [PubMed]
- Team, R.C. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013. [Google Scholar]
- Bates, D.; Mächler, M.; Bolker, B.; Walker, S. Fitting Linear Mixed-Effects Models Usinglme4. J. Stat. Softw. 2015, 67, 1–48. [Google Scholar] [CrossRef]
- Russell, L. Emmeans: Estimated Marginal Means, aka Least-Squares Means, R package version 1.4. 3.01; The University of Iowa: Iowa City, IA, USA, 2019. [Google Scholar]
- Miguel, A.F.; Mello, F.W.; Melo, G.; Rivero, E.R. Association between immunohistochemical expression of matrix metalloproteinases and metastasis in oral squamous cell carcinoma: Systematic review and meta-analysis. Head Neck 2020, 42, 569–584. [Google Scholar] [CrossRef] [PubMed]
- Loh, C.-Y.; Chai, J.Y.; Tang, T.F.; Wong, W.F.; Sethi, G.; Shanmugam, M.K.; Chong, P.P.; Looi, C.Y. The E-cadherin and N-cadherin switch in epithelial-to-mesenchymal transition: Signaling, therapeutic implications, and challenges. Cells 2019, 8, 1118. [Google Scholar] [CrossRef] [PubMed]
- Yamakawa, N.; Kirita, T.; Umeda, M.; Yanamoto, S.; Ota, Y.; Otsuru, M.; Okura, M.; Kurita, H.; Yamada, S.i.; Hasegawa, T. Tumor budding and adjacent tissue at the invasive front correlate with delayed neck metastasis in clinical early-stage tongue squamous cell carcinoma. J. Surg. Oncol. 2019, 119, 370–378. [Google Scholar] [CrossRef]
- Rajshri, R. Emmprin (CD147) Expression in Oral Submucous Fibrosis and in Oral Squamous Cell Carcinoma: A Potential Predictor; Madha Dental College: Chennai, India, 2022. [Google Scholar]
- Min, A.; Xiong, H.; Wang, W.; Hu, X.; Wang, C.; Mao, T.; Yang, L.; Huang, D.; Xia, K.; Su, T. CD147 promotes proliferation and migration of oral cancer cells by inhibiting junctions between E-cadherin and β-catenin. J. Oral Pathol. Med. 2020, 49, 1019–1029. [Google Scholar] [CrossRef] [PubMed]
- Usman, S.; Waseem, N.H.; Nguyen, T.K.N.; Mohsin, S.; Jamal, A.; Teh, M.-T.; Waseem, A. Vimentin is at the heart of epithelial mesenchymal transition (EMT) mediated metastasis. Cancers 2021, 13, 4985. [Google Scholar] [CrossRef]
- Feigelman, G.; Simanovich, E.; Brockmeyer, P.; Rahat, M.A. Knocking-Down CD147/EMMPRIN Expression in CT26 Colon Carcinoma Forces the Cells into Cellular and Angiogenic Dormancy That Can Be Reversed by Interactions with Macrophages. Biomedicines 2023, 11, 768. [Google Scholar] [CrossRef] [PubMed]
Antigen | Antibody | Pretreatment | Detection Method | Source |
---|---|---|---|---|
E-cadherin | Mouse, monoclonal, clone NCH-38, RTU | HIER (pH 9) | Dako EnVision FLEX | Agilent Dako (IR05961-2) |
Vimentin | Mouse, monoclonal, clone V9, RTU | HIER (pH 9) | Dako EnVision FLEX | Agilent Dako (IR63061-2) |
Cx43 | Rabbit, monoclonal, clone EPR21153, 1:500 | HIER (pH 6) | Dako EnVision FLEX | Abcam (ab217676) |
EMMPRIN | Mouse, monoclonal, clone 8D6, 1:100 | HIER (pH 6) | Dako EnVision FLEX | Abcam (ab194401) |
N | Sex | Age | OSCC Localization | pT | pN | pM | AJCC Stage | G | Dead | OS (Years) | Recurrence | DFS [Months] |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | F | 76 | Cheek | 1 | 0 | 0 | I | 2 | + | 3 | - | 24 |
2 | M | 55 | Tongue | 1 | 0 | 0 | I | 1 | - | 4 | + | 12 |
3 | M | 57 | Gum | 2 | 2 | 0 | IV | 2 | + | 2 | - | 22 |
4 | M | 89 | Tongue | 2 | 2 | 0 | IV | 2 | + | 2 | + | 0 |
5 | M | 78 | Gum | 2 | 2 | 0 | IV | 2 | + | 1 | + | 8 |
6 | F | 78 | Gum | 2 | 0 | 0 | II | 2 | - | 4 | + | 6 |
7 | M | 44 | Floor of mouth | 1 | 0 | 0 | I | 2 | - | 3 | + | 4 |
8 | M | 56 | Palate | 2 | 0 | 0 | II | 2 | - | 4 | - | 52 |
9 | M | 82 | Palate | 4 | 1 | 0 | IV | 2 | + | 1 | + | 6 |
10 | F | 65 | Floor of mouth | 2 | 0 | 0 | II | 2 | - | 4 | - | 34 |
11 | M | 58 | Floor of mouth | 1 | 0 | 0 | I | 2 | - | 5 | + | 26 |
12 | F | 84 | Gum | 2 | 0 | 0 | II | 2 | + | 1 | + | 4 |
13 | M | 63 | Gum | 3 | 0 | 0 | III | 2 | + | 2 | + | 5 |
14 | M | 45 | Floor of mouth | 4 | 2 | 0 | IV | 2 | + | 1 | - | 4 |
15 | F | 62 | Inside of lips | 3 | 2 | 0 | IV | 2 | + | 1 | + | 8 |
16 | M | 79 | Floor of mouth | 4 | 1 | 0 | IV | 2 | + | 1 | - | 9 |
17 | M | 74 | Tongue | 1 | 1 | 0 | III | 2 | - | 5 | - | 39 |
18 | F | 70 | Gum | 4 | 1 | 0 | IV | 2 | - | 5 | + | 2 |
19 | F | 49 | Palate | 4 | 2 | 0 | IV | 2 | + | 2 | - | 19 |
20 | M | 66 | Floor of mouth | 2 | 0 | 0 | II | 3 | - | 4 | + | 13 |
21 | F | 82 | Gum | 4 | 0 | 0 | IV | 2 | - | 3 | - | 35 |
22 | F | 63 | Gum | 4 | 2 | 0 | IV | 2 | + | 2 | + | 8 |
23 | M | 70 | Floor of mouth | 2 | 0 | 0 | II | 1 | - | 5 | - | 45 |
24 | F | 79 | Floor of mouth | 2 | 0 | 0 | II | 2 | + | 4 | + | 48 |
N | OM | OSCC | IF | LNM |
---|---|---|---|---|
1 | 66 | 162 | 128 | |
2 | 170 | 182 | 132 | |
3 | 3 | 165 | 166 | 72 |
4 | 1 | 30 | 10 | 175 |
5 | 1 | 133 | 100 | 82 |
6 | 40 | 233 | 106 | |
7 | 38 | 115 | 89 | |
8 | 6 | 142 | 44 | |
9 | 3 | 191 | 115 | |
10 | 35 | 61 | 43 | |
11 | 1 | 69 | 57 | |
12 | 0 | 175 | 88 | |
13 | 0 | 0 | 0 | |
14 | 3 | 43 | 0 | 18 |
15 | 16 | 175 | 60 | 166 |
16 | 0 | 0 | 0 | 80 |
17 | 38 | 231 | 18 | 118 |
18 | 0 | 32 | 11 | 152 |
19 | 0 | 31 | 0 | 182 |
20 | 8 | 0 | 8 | |
21 | 0 | 188 | 76 | |
22 | 137 | 274 | 194 | 193 |
23 | 0 | 2 | 4 | |
24 | 25 | 20 | 30 |
N | OM | OSCC | IF | LNM |
---|---|---|---|---|
1 | 19 | 195 | 237 | |
2 | 43 | 36 | 73 | |
3 | 47 | 119 | 213 | 206 |
4 | 9 | 40 | 75 | 0 |
5 | 0 | 141 | 256 | 151 |
6 | 150 | 213 | 274 | |
7 | 73 | 81 | 146 | |
8 | 16 | 180 | 192 | |
9 | 6 | 160 | 225 | |
10 | 8 | 45 | 95 | |
11 | 43 | 102 | 150 | |
12 | 41 | 54 | 107 | |
13 | 0 | 2 | 10 | |
14 | 11 | 33 | 36 | 70 |
15 | 0 | 0 | 0 | 30 |
16 | 1 | 193 | 78 | 219 |
17 | 72 | 110 | 229 | 0 |
18 | 46 | 27 | 41 | 129 |
19 | 28 | 57 | 59 | 110 |
20 | 0 | 52 | 242 | |
21 | 0 | 157 | 224 | |
22 | 0 | 267 | 291 | 0 |
23 | 28 | 86 | 153 | |
24 | 0 | 77 | 198 |
N | OM | OSCC | IF | LNM |
---|---|---|---|---|
1 | 174 | 171 | 81 | |
2 | 102 | 107 | 17 | |
3 | 112 | 79 | 23 | 128 |
4 | 62 | 104 | 122 | 201 |
5 | 92 | 134 | 78 | 97 |
6 | 61 | 31 | 7 | |
7 | 244 | 217 | 113 | |
8 | 151 | 183 | 94 | |
9 | 109 | 118 | 40 | |
10 | 153 | 121 | 31 | |
11 | 117 | 85 | 82 | |
12 | 166 | 179 | 66 | |
13 | 49 | 65 | 66 | |
14 | 123 | 235 | 180 | 145 |
15 | 68 | 169 | 96 | 100 |
16 | 134 | 87 | 20 | 250 |
17 | 67 | 73 | 1 | 197 |
18 | 42 | 44 | 28 | 147 |
19 | 178 | 70 | 60 | 192 |
20 | 92 | 73 | 10 | |
21 | 107 | 136 | 116 | |
22 | 160 | 109 | 83 | 286 |
23 | 17 | 110 | 0 | |
24 | 18 | 8 | 0 |
N | OM | OSCC | IF | LNM |
---|---|---|---|---|
1 | 27 | 27 | 94 | |
2 | 27 | 29 | 74 | |
3 | 45 | 134 | 189 | 125 |
4 | 30 | 49 | 76 | 84 |
5 | 25 | 57 | 96 | 113 |
6 | 12 | 38 | 114 | |
7 | 21 | 41 | 67 | |
8 | 22 | 39 | 73 | |
9 | 15 | 96 | 154 | |
10 | 17 | 38 | 102 | |
11 | 20 | 41 | 48 | |
12 | 25 | 79 | 108 | |
13 | 21 | 43 | 65 | |
14 | 9 | 12 | 13 | 50 |
15 | 1 | 16 | 193 | 53 |
16 | 41 | 60 | 104 | 33 |
17 | 80 | 54 | 76 | 112 |
18 | 47 | 54 | 86 | 65 |
19 | 69 | 47 | 65 | 37 |
20 | 25 | 29 | 94 | |
21 | 22 | 19 | 34 | |
22 | 19 | 14 | 28 | 10 |
23 | 100 | 69 | 174 | |
24 | 44 | 66 | 193 |
Variable | Level | N | HR | 95% CI | p-Value |
---|---|---|---|---|---|
Age | ≤45 | 2 | |||
>45 | 22 | 0.11 | [0.02; 0.69] | 0.018 | |
Adjuvant therapy | No | 14 | |||
Yes | 10 | 3.9 | [1.40; 11.0] | 0.01 | |
ΔE-cad (OM-IF) | ≤9 | 20 | |||
>9 | 4 | 6 | [1.70; 22.0] | 0.006 | |
Cx43 (IF) | ≤75.7 | 15 | |||
>75.7 | 9 | 2.7 | [1.0; 7.20] | 0.045 | |
EMMPRIN (IF) | ≤146 | 11 | |||
>146 | 13 | 0.3 | [0.11; 0.79] | 0.014 | |
ΔEMMPRIN (OM-IF) | ≤77 | 10 | |||
>77 | 14 | 0.15 | [0.05; 0.47] | 0.001 |
Variable | Level | N | HR | 95% CI | p-Value |
---|---|---|---|---|---|
EMMPRIN (OSCC) | ≤40 | 6 | |||
>40 | 18 | 0.16 | [0.04; 0.56] | 0.004 | |
EMMPRIN (IF) | ≤146 | 11 | |||
>146 | 13 | 0.27 | [0.09; 0.77] | 0.014 | |
ΔEMMPRIN (OM-IF) | ≤77 | 10 | |||
>77 | 14 | 0.13 | [0.04; 0.46] | 0.001 | |
ΔE-cad (OM-IF) | ≤9 | 20 | |||
>9 | 4 | 5.2 | [1.24; 21.7] | 0.024 | |
Cx43 (IF) | ≤75.7 | 15 | |||
>75.7 | 9 | 2.7 | [1.00; 7.10] | 0.05 |
Variable | N | HR | 95% CI | p-Value |
---|---|---|---|---|
ΔEMMPRIN (OM-IF) | 24 | 0.99 | [0.99; 1.00] | 0.019 |
ΔE-cad (OM-IF) | 24 | 1.01 | [1.00; 1.00] | 0.254 |
Cx43 (IF) | 24 | 1.01 | [1.00; 1.00] | 0.107 |
Variable | Level | N | HR | 95% CI | p-Value |
---|---|---|---|---|---|
pT | pT ≤ 2 | 15 | |||
pT > 2 | 9 | 3.4 | [1.10; 10.0] | 0.032 | |
pN | N− | 13 | |||
N+ | 11 | 5.2 | [1.50; 17.0] | 0.008 | |
Adjuvant therapy | No | 14 | |||
Yes | 10 | 6.8 | [1.90; 24.0] | 0.003 | |
AJCC stage | ≤2 | 11 | |||
>2 | 13 | 5.3 | [1.40; 20.0] | 0.014 | |
ΔE-cad (IF-LNM) | ≤−78.8 | 7 | |||
>−78.8 | 3 | 13 | [1.20; 128.0] | 0.032 | |
ΔE-cad (OM-IF) | ≤9 | 20 | |||
>9 | 4 | 4.8 | [1.30; 17.0] | 0.016 | |
Vim (OM) | ≤9 | 2 | |||
>9 | 22 | 0.1 | [0.02; 0.59] | 0.011 | |
Vim (OSCC) | ≤54 | 17 | |||
>54 | 7 | 3.4 | [1.10; 10.0] | 0.028 | |
Δvim (OM-OSCC) | ≤−32.4 | 4 | |||
>−32.4 | 20 | 0.14 | [0.04; 0.52] | 0.003 | |
Δvim (OM-IF) | ≤102 | 20 | |||
>102 | 4 | 3.3 | [1.0; 11.0] | 0.047 | |
Cx43 (IF) | ≤0 | 4 | |||
>0 | 20 | 0.21 | [0.06; 0.74] | 0.016 | |
ΔCx43 (OM-IF) | ≤75.7 | 20 | |||
>75.7 | 4 | 7.1 | [1.90; 26.0] | 0.003 | |
EMMPRIN (OM) | ≤13.5 | 12 | |||
>13.5 | 12 | 0.29 | [0.09; 0.94] | 0.039 | |
EMMPRIN (IF) | ≤36 | 3 | |||
>36 | 21 | 0.19 | [0.05; 0.75] | 0.018 | |
ΔEMMPRIN (OSCC-IF) | ≤−12 | 19 | |||
>−12 | 5 | 6.4 | [1.80; 22.0] | 0.003 |
Variable | Level | N | HR | 95% CI | p-Value |
---|---|---|---|---|---|
ΔEMMPRIN (OSCC-IF) | ≤−12 | 19 | |||
>−12 | 5 | 6.9 | [1.40; 34.5] | 0.018 | |
Δvim (OM-OSCC) | ≤−32.4 | 4 | |||
ΔCx43 (OM-IF) | >−32.4 | 20 | 0.21 | [0.06; 0.81] | 0.024 |
≤75.7 | 20 | ||||
>75.7 | 4 | 4.7 | [1.23; 18.0] | 0.024 |
Variable | N | HR | 95% CI | p-Value |
---|---|---|---|---|
ΔEMMPRIN (OSCC-IF) | 24 | 1.02 | [1.00; 1.00] | 0.023 |
Δvim (OM-OSCC) | 24 | 0.98 | [0.95; 1.00] | 0.240 |
ΔCx43 (OM-IF) | 24 | 1.01 | [0.99; 1.00] | 0.603 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Khromov, T.; Fischer, L.; Leha, A.; Bremmer, F.; Fischer, A.; Schliephake, H.; Rahat, M.A.; Brockmeyer, P. Combined Biomarker System Predicts Prognosis in Patients with Metastatic Oral Squamous Cell Carcinoma. Cancers 2023, 15, 4924. https://doi.org/10.3390/cancers15204924
Khromov T, Fischer L, Leha A, Bremmer F, Fischer A, Schliephake H, Rahat MA, Brockmeyer P. Combined Biomarker System Predicts Prognosis in Patients with Metastatic Oral Squamous Cell Carcinoma. Cancers. 2023; 15(20):4924. https://doi.org/10.3390/cancers15204924
Chicago/Turabian StyleKhromov, Tatjana, Lucas Fischer, Andreas Leha, Felix Bremmer, Andreas Fischer, Henning Schliephake, Michal Amit Rahat, and Phillipp Brockmeyer. 2023. "Combined Biomarker System Predicts Prognosis in Patients with Metastatic Oral Squamous Cell Carcinoma" Cancers 15, no. 20: 4924. https://doi.org/10.3390/cancers15204924