一般口演
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非小細胞性肺癌と高悪性度神経内分泌腫瘍の鑑別診断におけるStathmin 1染色の有用性 演題番号 : O1-6
1:群馬大学・総合外科、2:群馬大学附属病院・病理診断科、3:群馬大学・病態腫瘍薬理学
Objective: Stathmin 1 (STMN1) is a major cytosolic phosphoprotein that regulates microtubule dynamics and is associated with malignant phenotypes in lung cancer. The aim of this study is to evaluate the diagnostic value of STMN1 protein expression assessed by immunohistochemistry (IHC) for differentiating non-small cell lung cancer (NSCLC) and high-grade lung neuroendocrine tumor (HGNET, including small cell carcinoma [SCLC] and large cell neuroendocrine carcinoma [LCNEC])
Patients and Methods: STMN1 protein expression was assessed by IHC in both surgically resected and biopsy specimens of adenocarcinoma (AD, 304 and 29 cases, respectively), squamous cell carcinoma (SCC, 103 and 28 cases, respectively), large cell carcinoma (LA, 7 and 6 cases, respectively) SCLC (17 and 26 cases, respectively) and LCNEC patients (17 and 17 cases, respectively). We analyzed the STMN1 mRNA expression levels between NSCLC and HGNET using reverse transcription-polymerase chain reaction (RT-PCR). Results: In AD, low expression group included 139 cases (46%), intermediate expression group included 162 cases (53 %), and high expression group included 3 cases (1%), In SCC, low expression group included 15 cases (15%), intermediate expression group included 86 cases (83%), and high expression group included 2 cases (2%). In LA, low expression group 1 case (14%), intermediate expression group 5 cases (71%), and high expression group 1case (14%). In LCNEC and SCLC, all cases were classified in high expression group (100%). In addition, the pathological re-review of six NSCLC cases classified as high expression group revealed that neuroendocrine features were observed in all cases. In biopsy specimens, all cases of HGNET were also classified in high expression group. By RT-PCR, the STMN1 expression of HGNET tissues was significantly higher than that of NSCLC tissues (p<0.001). Conclusion: STMN1 expression may be a useful diagnostic marker for differentiating NSCLC and HGNET. |
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