Diagnosis Of Lung Squamous Cell Carcinoma Research Articles

New vision of HookEfficientNet deep neural network: Intelligent histopathological recognition system of non-small cell lung cancer

BackgroundEfficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical for subsequent targeted therapy and immunotherapy. Since the advent of whole slide images (WSIs), the transition from traditional histopathology to digital pathology has aroused the application of convolutional neural networks (CNNs) in histopathological recognition and diagnosis. HookNet can make full use of macroscopic and microscopic information for pathological diagnosis, but it cannot integrate other excellent CNN structures. The new version of HookEfficientNet is based on a combination of HookNet structure and EfficientNet that performs well in the recognition of general objects. Here, a high-precision artificial intelligence-guided histopathological recognition system was established by HookEfficientNet to provide a basis for the intelligent differential diagnosis of NSCLC. MethodsA total of 216 WSIs of lung adenocarcinoma (LUAD) and 192 WSIs of lung squamous cell carcinoma (LUSC) were recruited from the First Affiliated Hospital of Zhengzhou University. Deep learning methods based on HookEfficientNet, HookNet and EfficientNet B4–B6 were developed and compared with each other using area under the curve (AUC) and the Youden index. Temperature scaling was used to calibrate the heatmap and highlight the cancer region of interest. Four pathologists of different levels blindly reviewed 108 WSIs of LUAD and LUSC, and the diagnostic results were compared with the various deep learning models. ResultsThe HookEfficientNet model outperformed HookNet and EfficientNet B4–B6. After temperature scaling, the HookEfficientNet model achieved AUCs of 0.973, 0.980, and 0.989 and Youden index values of 0.863, 0.899, and 0.922 for LUAD, LUSC and normal lung tissue, respectively, in the testing set. The accuracy of the model was better than the average accuracy from experienced pathologists, and the model was superior to pathologists in the diagnosis of LUSC. ConclusionsHookEfficientNet can effectively recognize LUAD and LUSC with performance superior to that of senior pathologists, especially for LUSC. The model has great potential to facilitate the application of deep learning-assisted histopathological diagnosis for LUAD and LUSC in the future.

Pre-clinical lung squamous cell carcinoma mouse models to identify novel biomarkers and therapeutic interventions.

Primary lung carcinoma or lung cancer (LC) is classified into small-cell or non-small-cell (NSCLC) lung carcinoma. Lung squamous cell carcinoma (LSCC) is the second most common subtype of NSCLC responsible for 30% of all LCs, and its survival remains low with only 24% of patients living for five years or longer post-diagnosis primarily due to the advanced stage of tumors at the time of diagnosis. The pathogenesis of LSCC is still poorly understood and has hampered the development of effective diagnostics and therapies. This review highlights the known risk factors, genetic and epigenetic alterations, miRNA biomarkers linked to the development and diagnosis of LSCC and the lack of therapeutic strategies to target specifically LSCC. We will also discuss existing animal models of LSCC including carcinogen induced, transgenic and xenograft mouse models, and their advantages and limitations along with the chemopreventive studies and molecular studies conducted using them. The importance of developing new and improved mouse models will also be discussed that will provide further insights into the initiation and progression of LSCC, and enable the identification of new biomarkers and therapeutic targets.

MiR-139-5p and miR-451a as a Diagnostic Biomarker in LUSC.

Lung squamous cell carcinoma (LUSC) is a type of lung cancer that originates from segmental or subsegmental bronchial mucosa. There is evidence that miRNA plays an important role in the occurrence and progression of tumors. In this study, plasma samples of patients with early LUSC and healthy volunteers were subjected to miRNA sequencing, and the levels of differentially expressed miRNAs (DEMs) in LUSC tissues were analyzed using R language. Cox regression and Kaplan-Meier (K-M) survival curve analyses were performed to determine the relationship between DEMs and prognosis in LUSC, and PCR method was verified for the plasma expression level of DEMs in patients with LUSC. The levels of CYFRA21-1 and SCC-Ag in plasma were measured, and area under curve (AUC) was used to evaluate the diagnostic value of the DEMs. A total of 21 DEMs were screened out by sequencing. The expression levels of DEMs in tissue samples in the TCGA database were analyzed, and four DEMs with consistent expression levels were further screened from plasma and tissue samples. Regression analysis and K-M curve were performed to select two DEMs (miR-139-5p, miR-451a) that were correlated with the prognosis. PCR verification results showed that the levels of miR-451a and miR-139-5p were low in patients, and the level of miR-139-5p in late stages III & IV with the patients of LUSC was higher than that in stages I & II. The AUC values of the four indicators (SCC-Ag, CYFRA21-1, miR-451a and miR-139-5p) in the diagnosis of LUSC, early and late cases were 0.884, 0.935 and 0.778, respectively. The detection of miR-139-5p and miR-451a levels in plasma has a certain potential in the non-invasive diagnosis, especially in patients with early stages of LUSC.

CHMP4C regulates lung squamous carcinogenesis and progression through cell cycle pathway.

BackgroundLung cancer is a common kind of human malignancies. Lung squamous cell carcinoma (LUSC) is a key subtype of lung cancer. Cell cycle plays an important role in the development and occurrence of LUSC, however, there is still a lack of cell cycle-related genes in LUSC diagnosis and prediction of prognosis.MethodsWe identified differentially expressed genes (DEGs) with “limma” package in R software, and determined the biomarkers of LUSC in diagnosing by performing receiver operating characteristic (ROC) curve analysis, the biomarker effectiveness in diagnosing LUSC was assessed by performing five-fold cross-validation with logistic regression. Kaplan-Meier plot and the nomogram assessed the relationship between the biomarker and patient survival, and WB and qRT-PCR detected the biomarker expression in cells and tissues. Flow cytometry detects the role of the biomarker in the cell cycle.ResultsIntegration analysis with The Cancer Genome Atlas (TCGA) database obtained a unique gene related to cell cycle in LUSC (Charged multivesicular body protein 4C, CHMP4C), and the protein of CHMP4C was highly expressed in LUSC tissues. ROC analysis indicated that CHMP4C was a biomarker for the diagnosis of LUSC. Bioinformatic analysis indicated that CHMP4C might be associated with cell cycle in LUSC. CHMP4C knockdown resulted in S-phase arrest of cells with LUSC. According to the survival rate analysis, CHMP4C overexpression indicated poor prognosis in patients with LUSC.ConclusionsCHMP4C regulates the proliferation process of tumor cells through the cell cycle. It can be used as a potential diagnostic and prognostic biomarker for LUSC.

Frequent Mutations in p53, CDKN2A, PTEN Genes in Lung Squamous Cell Carcinoma: A Next Generation Sequencing Study

Abstract Introduction/Objective Targeted therapies have been successfully used for the treatment of lung adenocarcinoma but have not been implemented in the treatment of lung squamous cell carcinoma (SqCC). In order to better understand the underlying biology of SqCC, we present comprehensive Next Generation Sequencing (NGS) data via Cancerplex from SqCC. We have observed frequent mutations in p53, CDKN2A, PTEN, CDKN2B, and TGFBR2 genes together with few new rare gene mutations. Methods Twenty-one patients with diagnosis of Lung SqCC have been selected for Cancerplex assay (Kew, Inc., Waltham, MA). Formalin-fixed tissues from these patients were used for the assay. Neoplastic tissues with tumor content higher than 20% were micro-dissected from the blocks and NGS was performed with at least 50 ng DNA content and with a limit of detection of 10% mutant alleles. The depth of coverage was 500, and the size of the targeted region was 2.8 Mb. Results were compared with published databases. Results We have observed p53 mutations in 100% cases. p53 gene mutations included single point mutations with various coding protein mutations as well as splice variants. Mutations in CDKN2A is found in 47.6% of cases; PTEN mutations in 33.3% of cases; CDKN2B mutations in 28.6% of cases, and TGFBR2 in 28.6% of cases, which has been reported as actionable variants and actionable copy number variants. Conclusion Previous literatures have provided evidence that SqCC arising in different anatomical sites share common genomic mutation patterns. Our data partially supports this finding. We demonstrated p53, CDKN2A and PTEN are among the most commonly mutated genes in lung SqCC, while a few other genes mutations does not fit in this pattern. Apparently more studies need to be done to provide a more clear genetic landscape of SqCC, which would facilitate the development of targeted therapies.

Nomograms to Predict Survival in Patients with Lung Squamous Cell Cancer: A Population-Based Study

This study aimed to identify risk factors affecting cancer-specific survival (CSS) and overall survival (OS) in patients with lung squamous cell carcinoma (LSCC) and to develop nomograms for prognostic prediction in these patients. Patients who received an LSCC diagnosis between 2007 and 2013 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic effect of each variable on survival was evaluated with Cox regression and Kaplan-Meier analysis, and nomograms were developed to predict 3-, 5-, and 7-year CSS and OS rates. Data from 23,004 patients with LSCC were analyzed. Nomograms were first developed by using variables that were significantly associated with CSS and OS and then validated by using an internal bootstrap resampling approach, which showed that they had a sufficient level of discrimination, according to the C-index. The nomograms satisfactorily predicted 3-, 5-, and 7-year CSS and OS rates for patients with LSCC.

Predicting the Lung Squamous Cell Carcinoma Diagnosis and Prognosis Markers by Unique DNA Methylation and Gene Expression Profiles.

The early diagnosis of lung squamous cell carcinoma (LUSC) is difficult, causing an unsatisfactory prognosis. Therefore, the 5-year survival rate of LUSC is poor. This study aimed at screening the potential diagnostic and prognostic markers for LUSC. The data of LUSC gene expression profiles and DNA methylation were obtained from The Cancer Genome Atlas (TCGA) database; the differentially expressed genes (DEGs) and the differentially methylated genes (DMGs) were screened out by an independent t-test and Benjamini/Hochberg methods. Further, the classifiers of the gene expression and DNA methylation markers in LUSC were constructed. After that, diagnostic and prognostic markers in LUSC were analyzed by the protein-protein interaction (PPI) network. The DEGs and the DMGs from TCGA database of LUSC were screened out. After strict filtration, we identified three potential DMGs (POU domain, class 4, transcription factor 2 [POU4F2], EN1, single-minded homolog 1 [SIM1]) for early diagnosis and seven potential DEGs (G-protein coupled receptor 78 [GPR78], PCDHA5, myosin binding protein H [MYBPH], RTL3, KIAA0408, HSD3B2, PCDHA12) for prognosis of LUSC. The tumor-normal tissue classification model and prognosis model were validated in two independent datasets. In addition, the PPI network was constructed, including three DMGs and the five DEGs (GPR78, MYBPH, KIAA0408, HSD3B2, PCDHA12) of the seven DEGs. The potential DMGs (POU4F2, EN1, SIM1) and DEGs (GPR78, MYBPH, KIAA0408, HSD3B2, PCDHA12) for the diagnosis and prognosis of LUSC identified in this article are expected to be further applied in clinical practice of the treatment of LUSC.

Identification of Specific Candidate Diagnostic Biomarkers for Lung Squamous Cell Carcinoma Based on Methylation.

DNA methylation abnormalities are frequent events in early tumors. DNA methylation is relatively stable over time and can be detected in blood. Therefore, DNA methylation has a great potential to become an early diagnostic biomarker of cancers. To find potential diagnostic markers for lung squamous cell carcinoma (LUSC), a method for identifying LUSC-specific candidate diagnostic markers was proposed. We screened 6 LUSC-specific CpGs by comparing the methylation profiles of 172 samples from LUSC patients, 42 normal lung samples, 1306 samples from patients with other cancers, which was collected from The Cancer Genome Atlas (TCGA) database, and 184 normal blood samples, which was collected from Gene Expression Omnibus (GEO) database. A support vector machine model was built based on the methylation levels of the candidate diagnostic biomarkers, and we optimized the model by sixfold cross-validation. The combination of six sites achieved 93%-99% sensitivity in predicting LUSC, 100% specificity in excluding normal samples, and 99.55% specificity in excluding non-LUSC samples. In addition, a diagnostic model was established by using six LUSC-specific biomarkers, and the sensitivity and specificity of LUSC stage I samples were 95.2% and 99.4%. At the same time, genes for six LUSC-specific CpGs localization are closely related to cancer occurrence, which indicates that six LUSC-specific CpGs can be used as candidate biomarkers for LUSC diagnosis. Overall, our study provides promising biomarkers for the diagnosis of LUSC.

Carcinoma of Pulmonary Squamous Cells with Small Intestine Metastasis in a Cat

Background: Primary lung tumors in cats are mainly classified into adenocarcinoma arising from peripheral pulmonary epithelium and, in a smaller number of cases, into adenosquamous or squamous carcinoma, bronchioloalveolar carcinoma, and carcinoid tumor. There are few reports in the international literature describing lung cancer metastasis to visceral organs in cats, especially with regard to squamous tumors. This report describes the clinical pathological, histological, and immunohistochemical findings of lung squamous cell carcinoma with metastasis to small intestine in a cat.Case:A female, Siamese, unneutered, adult cat presented with an unhealed ulcerative crusted lesion on the left ear, previously diagnosed as squamous cell carcinoma (SCC). Six months later, the cat returned presenting with increased volume in the left pelvic limb and claudication due to severe osteolysis in metatarsal and tarsal bones, as shown in radiographic examinations. The limb was amputated forty day later, but it did not undergo histological examination. The cat died fourteen days after amputation and underwent necropsy. Macroscopically, significant changes were observed in the small intestine, including serous segmental necrotizing and hemorrhagic lesion with luminal stenosis and corrugation of mucous surface, which was covered with a fibrinopurulent pseudomembrane. Palpation of lung parenchyma showed small firm nodules. There was an ulcerative crusted lesion on the left ear. Histopatological examination revealed infiltration of pleomorphic neoplastic epithelial cells disrupting the architecture of small intestine and lung, with pronounced anisocyitosis and anisokaryosis. The neoplastic cells had indistinct borders; their cytoplasm was eosinophilic and pleomorphic and varied from moderate to abundant in amount; and their nuclei were markedly pleomorphic and large, with up to two evident nucleoli and salt-and-paper chromatin. Additionally, numerous keratin pearls and remarkable squamous differentiation were observed. Immunohistochemistry (IHC) was performed with the streptavidin-biotin-peroxidase method using anti-pan-cytokeratin and antivimentin antibodies. Neoplastic cells exhibited strong and uniform cytoplasmatic immunoreactivity for pan-cyitokeratin. Vimentin expression was absent in neoplastic cells but present in the connective tissue associated with the neoplasm. Discussion: Although there are some studies on primary lung squamous cell carcinoma, to the authors’ knowledge, this is the second report describing the metastasis of this tumor to small intestine in a cat. The diagnosis of lung SCC in a cat with metastasis to small intestine was confirmed by histopathological assessment and IHC. Although the amputated limb had not undergone histopathological examination, osteolytic lesions in metatarsal and tarsal bones suggested metastasis from primary lung tumors. The lack of involvement of head lymph nodes shows that cutaneous SCC was not related to lung tumors, because metastasis of this carcinoma always involves regional lymph nodes. Paleness of oral and ocular mucosae suggested anemia or paraneoplastic syndrome, which could not be confirmed because the animal was debilitated at presentation and eventually died. IHC confirmed the epithelial origin of the tumor, as evidenced by the presence of epithelial cells and keratin pearls in the small intestine, thus proving the existence of metastasis.

Lung cancer diagnosis with quantitative DIC microscopy and a deep convolutional neural network.

We present a study on lung squamous cell carcinoma diagnosis using quantitative TI-DIC microscopy and a deep convolutional neural network (DCNN). The 2-D phase map of unstained tissue sections is first retrieved from through-focus differential interference contrast (DIC) images based on the transport of intensity equation (TIE). The spatially resolved optical properties are then computed from the 2-D phase map via the scattering-phase theorem. The scattering coefficient ( ) and the reduced scattering coefficient ( ) are found to increase whereas the anisotropy factor (g) is found to decrease with cancer. A DCNN classifier is developed afterwards to classify the tissue using either the DIC images or 2-D optical property maps of , and g. The DCNN classifier with the optical property maps exhibits high accuracy, significantly outperforming the same DCNN classifier on the DIC images. The label-free quantitative phase microscopy together with deep learning may emerge as a promising approach for in situ rapid cancer diagnosis.

Assessment of Seven Clinical Tumor Markers in Diagnosis of Non-Small-Cell Lung Cancer.

Background The correlation between tumor markers (TM) and TNM stage of non-small-cell lung cancer (NSCLC) has not been widely reported. Methods TM levels (CEA, CA125, CA15-3, CA19-9, CA72-4, CYFRA21-1, and SCC-Ag) in 424 cases of lung adenocarcinoma (LAC), 166 cases of lung squamous cell carcinoma (LSCC), and 103 cases of benign chest disease (BCD) were analyzed before treatment. Results By Kendall's tau-b correlation analysis, CEA, CA125, CA15-3, CA19-9, CA72-4, CYFRA21-1, and SCC-Ag were correlated with T stage of LAC (r = 0.235, p < 0.05; r = 0.298, p < 0.05; r = 0.254, p < 0.05; r = 0.063, p < 0.05; r = 0.080, p < 0.05; r = 0.268, p < 0.05; and r = 0.080, p < 0.05). CEA, CA125, CA15-3, CA19-9, CA72-4, and CYFRA21-1 were correlated with N stage of LAC (r = 0.356, p < 0.05; r = 0.415, p < 0.05; r = 0.340, p < 0.05; r = 0.117, p < 0.05; r = 0.175, p < 0.05; and r = 0.351, p < 0.05). CEA, CA125, CA15-3, CA19-9, CA72-4, and CYFRA21-1 were correlated with M stage of LAC (r = 0.365, p < 0.05; r = 0.353, p < 0.05; r = 0.293, p < 0.05; r = 0.135, p < 0.05; r = 0.169, p < 0.05; and r = 0.312, p < 0.05). CA125, CYFRA21-1, and SCC-Ag were correlated with T stage of LSCC (r = 0.202, p < 0.05; r = 0.233, p < 0.05; and r = 0.099, p < 0.05). CA125 and CYFRA21-1 were correlated with N stage of LSCC (r = 0.178, p < 0.05 and r = 0.284, p < 0.05). CA125, CA15-3, and CYFRA21-1 were correlated with M stage of LSCC (r = 0.214, p < 0.05; r = 0.152, p < 0.05; and r = 0.213, p < 0.05). Combining hazard ratio, AUC, sensitivity, specificity, NPV, and PPV, it was concluded that CEA and CYFRA21-1were the most related TM of LAC. SCC-Ag and CYFRA21-1 were the most related TM of LSCC. Conclusions CEA combined with CYFRA21-1 contributed to auxiliary diagnosis of LAC. CYFRA21-1 combined with SCC-Ag contributed to auxiliary diagnosis of LSCC. CEA, CA125, CA15-3, CA19-9, CA72-4, and CYFRA21-1 were correlated with primary tissue and metastasis of LAC. CA125 and CYFRA21-1 were correlated with primary tissue and metastasis of LSCC.

Targeted Tissue and Cell-Free Tumor DNA Sequencing of Advanced Lung Squamous-Cell Carcinoma Reveals Clinically Significant Prevalence of Actionable Alterations

BackgroundMajor guidelines do not recommend routine molecular profiling of lung squamous-cell carcinoma (LUSC) because the prevalence of actionable alterations is thought to be low. Increased utilization of next-generation sequencing (NGS), particularly with cell-free circulating tumor DNA, facilitates reevaluation of this premise. Patients and MethodsWe retrospectively evaluated the prevalence of actionable alterations in 2 distinct LUSC cohorts totaling 492 patients. A total of 410 consecutive patients with stage 3B or 4 LUSC were tested with a targeted cell-free circulating DNA NGS assay, and 82 patients with LUSC of any stage were tested with a tissue NGS cancer panel. ResultsIn the overall cohort, 467 patients (94.9%) had a diagnosis of LUSC, and 25 patients (5.1%) had mixed histology with a squamous component. A total of 10.5% of the LUSC subgroup had somatic alterations with therapeutic relevance, including in EGFR (2.8%), ALK/ROS1 (1.3%), BRAF (1.5%), and MET amplification or exon 14 skipping (5.1%). Sixteen percent of patients with mixed histology had an actionable alteration. In the LUSC subgroup, 3 evaluable patients were treated with targeted therapy for an actionable alteration; all of them experienced partial response. ConclusionIn this large, real-world LUSC cohort, we observed a clinically significant prevalence of actionable alterations. Accurate local histopathologic assessment in advanced-stage LUSC can be challenging. Further evaluation of the genomic landscape in this setting is warranted to potentially identify underappreciated treatment options.

Identification of four plasma microRNAs as potential biomarkers in the diagnosis of male lung squamous cell carcinoma patients in China.

Dysregulated microRNAs (miRNAs) in the plasma of patients with lung squamous cell carcinoma (LSCC) might serve as biomarkers for LSCC diagnosis. The expression of miRNAs was performed using quantitative reverse transcription–polymerase chain reaction (qRT‐PCR) on the basis of Exiqon panels in the initial screening phase including three male LSCC pool samples and one normal control (NC) pool sample (per 10 samples were pooled as one pool sample). After the training (32 LSCC vs. 31 NCs), the testing (55 LSCC vs. 55 NCs), and the external validation (15 LSCC vs. 15 NCs) stages via qRT‐PCR, a four‐miRNA signature (miR‐181a‐5p, miR‐21‐5p, miR‐106a‐5p, and miR‐93‐5p) was identified for LSCC detection. Areas under the receiver operating characteristic (ROC) curve (AUC) of the four‐miRNA panel for the training, the testing, and the external validation phases were 0.795, 0.827, and 0.914, respectively. Then, the four miRNAs were explored in LSCC tissue samples (23 LSCC vs. 23 NCs), and their expression was significantly up‐regulated. However, none of the four miRNAs found significantly up‐regulated in plasma exosomes expect miR‐93‐5p with borderline significance (16 LSCC vs. 16 NCs). In summary, our study established a four‐miRNA peripheral plasma signature, which contributed to diagnosing male LSCC patients in China to a certain degree.

Long noncoding RNA SFTA1P promoted apoptosis and increased cisplatin chemosensitivity via regulating the hnRNP-U-GADD45A axis in lung squamous cell carcinoma.

Chemotherapeutic insensitivity remains one of the major obstacles in clinical treatment of lung squamous cell carcinoma (LSCC). Recently, increasing evidence has suggested that long non-coding RNAs (lncRNAs) promote tumorigenesis in many cancer types. However, the potential biological roles and regulatory mechanisms of lncRNAs in response to cisplatin treatment are poorly understood. Here, we found that lncRNA SFTA1P (surfactant associated 1, pseudogene), highly expressed in lung, was down-regulated in LSCC tissues and could be induced upon cisplatin treatment in LSCC cells. Elevated SFTA1P induced apoptosis and enhanced the sensitivity to cisplatin of LSCC cells. We further identified that hnRNP-U (heterogeneous nuclear ribonucleoprotein U) was down-regulated in LSCCs and positively correlated with patients’ poor prognosis as well as SFTA1P. Mechanistic studies revealed that SFTA1P could up-regulate hnRNP-U expression. In addition, we identified that hnRNP-U enhanced cisplatin-induced apoptosis through up-regulation of GADD45A, high expression of which was correlated with good prognosis in LSCC patients. Our findings demonstrated that SFTA1P might serve as a useful biomarker for LSCC diagnosis and a predictor for cisplatin chemotherapy response in patients with LSCC.

A three-microRNA signature for lung squamous cell carcinoma diagnosis in Chinese male patients.

Various studies have demonstrated the diagnostic value of microRNA (miRNA) for lung cancer, but miRNA signatures varied between different subtypes. Whether serum miRNAs could be used as biomarkers in lung squamous cell carcinoma (SCC) remains unknown. Using quantitative real-time polymerase chain reaction (qRT-PCR) based Exiqon panel, 38 differentially expressed miRNAs were identified from 3 male lung SCC pool samples and 1 normal control (NC) pool in the initial screening phase. After the training (24 SCC VS. 15 NCs), testing (44 SCC VS. 57 NCs) and external validation (34 SCC VS. 36 NCs VS. 10 pulmonary hamartoma) processes via qRT-PCR, we identified a three-miRNA panel ((miR-106a-5p, miR-20a-5p and miR-93-5p) to be a potential diagnostic marker for male lung SCC patients. The areas under the receiver operating characteristic (ROC) curve of the three-miRNA panel for the training, testing and validation phases were 0.969, 0.881 and 0.954 respectively. In addition, this signature could also differentiate lung SCC from pulmonary hamartoma (AUC=0.900). The 3 miRNAs were consistently up-regulated in lung SCC tissues (23 SCC VS. 23 NCs) and serum exosomes (17 SCC VS. 24 NCs). Moreover, expression of the 3 miRNAs was decreased in arterial serum (n = 3). In conclusion, we established a three-miRNA signature in the peripheral serum with considerable clinical value in the diagnosis of male lung SCC patients.

Double staining: diagnostic utility in non-small cell lung carcinoma in the era of tissue conservation

In an era of precision medicine distinguishing pulmonary squamous cell carcinoma (SQCC) from adenocarcinoma (ADC) is vital for treatment. Immunohistochemical (IHC) staining for p40, p63 and Cytokeratin 5 (CK5) are useful for SQCC, while TTF-1 and Napsin-A can be used for confirming ADC. Fine needle aspiration (FNA) cell blocks (CB) have limited tissue, hence, double IHC staining is helpful for tissue conservation for molecular analysis. Thirty six confirmed lung SQCC and 45 ADC CB were selected for IHC. Double staining was performed with p40/CK5 and p63/CK5 on all SQCC, and with TTF-1/Napsin-A on all ADC. Results were positive if at least 5% of malignant cells were immunoreactive for the antigen. P40/CK5 had (92%) sensitivity, (100%) specificity, (100%) positive predictive value (PPV), (91%) negative predictive value (NPV) and an overall diagnostic accuracy of (96%). By contrast, P63/CK5 double stains showed (92%) sensitivity, (80%) specificity, (85%) PPV, (89%) NPV and (86%) overall diagnostic accuracy, respectively. TTF-1/Napsin A staining for ADC showed a sensitivity of 80%, specificity of 96%, PPV of 97%, NPV of 71% and accuracy of 85%. P40/CK5 double stain has higher specificity, PPV, NPV, and overall accuracy than P63/CK5 double stain in the diagnosis of lung SQCC. TTF-1/Napsin-A double staining is a valuable marker with high specificity, PPV, and diagnostic accuracy in diagnosing lung ADC. The usage of P40/CK5 and TTF-1/Napsin-A as a panel can be recommended for characterizing non-small cell carcinoma (NSCC) of the lung and for conserving tissue for molecular testing.

RBMS3 is a tumor suppressor gene that acts as a favorable prognostic marker in lung squamous cell carcinoma.

Recent research indicates that RBMS3 may act as a tumor suppressor gene (TSG) in nasopharyngeal carcinoma (NPC) and esophageal squamous cell carcinoma (ESCC). It has been reported that RBMS3 directly binds to the promoter region of c-Myc in ESCC and that β-catenin from both whole cell extracts and nuclear fractionation was significantly downregulated in RBMS3-transfected NPC cells compared to control cells. The aim of this study was to evaluate the clinical significance of the RBMS3 gene expression in relation to the expression of Wnt pathway components in patients with lung squamous cell carcinoma (LSCC). RBMS3, c-Myc and cytoplasmic β-catenin were detected in 39.76, 56.63 and 89.16% of 83 LSCC samples by immunohistochemistry, respectively, in 83 primary LSCC samples. Semiquantitative reverse transcriptase-polymerase chain reaction and Western blotting demonstrated decreased RBMS3 mRNA and expression in 33.33% (10/30) and 36.67% (11/30) tumor tissues, respectively. Statistical correlation analysis showed RBMS3 to be negatively correlated with c-Myc (r=-0.384, p<0.001) and not correlated with cytoplasmic β-catenin in the LSCC samples. Multivariate Cox proportional hazards model analysis showed that the combined marker RBMS3/c-Myc was an independent prognostic indicator of overall survival (p=0.001; HR 3.470; IC 95%, 1.652-7.290), and c-Myc was a prognostic indicator of disease-free survival (p<0.001; HR 3.182; IC 95%, 1.961-8.920). RBMS3 is a novel TSG in LSCC, and its downregulation facilitates development and progression of LSCC. Therefore, it is suggested that Rbms3 as a tumor marker may play an important role in diagnosis of LSCC.

A 5-MicroRNA Signature for Lung Squamous Cell Carcinoma Diagnosis and hsa-miR-31 for Prognosis

Recent studies have suggested that microRNA biomarkers could be useful for stratifying lung cancer subtypes, but microRNA signatures varied between different populations. Squamous cell carcinoma (SCC) is one major subtype of lung cancer that urgently needs biomarkers to aid patient management. Here, we undertook the first comprehensive investigation on microRNA in Chinese SCC patients. MicroRNA expression was measured in cancerous and noncancerous tissue pairs strictly collected from Chinese SCC patients (stages I-III), who had not been treated with chemotherapy or radiotherapy prior to surgery. The molecular targets of proposed microRNA were further examined. We identified a 5-microRNA classifier (hsa-miR-210, hsa-miR-182, hsa-miR-486-5p, hsa-miR-30a, and hsa-miR-140-3p) that could distinguish SCC from normal lung tissues. The classifier had an accuracy of 94.1% in a training cohort (34 patients) and 96.2% in a test cohort (26 patients). We also showed that high expression of hsa-miR-31 was associated with poor survival in these 60 SCC patients by Kaplan-Meier analysis (P = 0.007), by univariate Cox analysis (P = 0.011), and by multivariate Cox analysis (P = 0.011). This association was independently validated in a separate cohort of 88 SCC patients (P = 0.008, 0.011, and 0.003 in Kaplan-Meier analysis, univariate Cox analysis, and multivariate Cox analysis, respectively). We then determined that the tumor suppressor DICER1 is a target of hsa-miR-31. Expression of hsa-miR-31 in a human lung cancer cell line repressed DICER1 activity but not PPP2R2A or LATS2. Our results identified a new diagnostic microRNA classifier for SCC among Chinese patients and a new prognostic biomarker, hsa-miR-31.