Prognostic value of an immunohistochemical signature in patients with esophageal squamous cell carcinoma undergoing radical esophagectomy

Development and Validation of the Oxidative Stress Related lncRNAs for Prognosis in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell cancer (ESCC) is an aggressive disease associated with a poor prognosis. Long non-coding RNAs (lncRNAs) and oxidative stress play crucial roles in tumor progression. We aimed to identify an oxidative stress-related lncRNA signature that could predict the prognosis in ESCC. In the GSE53625 dataset, we identified 332 differentially expressed lncRNAs (DElncRNAs) between ESCC and control samples, out of which 174 were oxidative stress-related DElncRNAs. Subsequently, seven oxidative stress-related DElncRNAs (CCR5AS, LINC01749, PCDH9-AS1, TMEM220-AS1, KCNMA1-AS1, SNHG1, LINC01672) were selected based on univariate and LASSO Cox to build a prognostic risk model, and their expression was detected by RT-qPCR. The model exhibited an excellent ability for the prediction of overall survival (OS) and other clinicopathological traits using Kaplan-Meier (K-M) survival curves, receiver operating characteristic (ROC) curves, and the Wilcoxon test. Additionally, analysis of infiltrated immune cells and immune checkpoints indicated differences in immune status between the two risk groups. Finally, the in vitro experiments showed that PCDH9-AS1 overexpression inhibited proliferation ability and promoted apoptosis and oxidative stress levels in ESCC cells. In conclusion, our study demonstrated that a novel oxidative stress-related DElncRNA prognostic model performed favorably in predicting ESCC patient prognosis and benefits personalized clinical applications.

A demonstration based on multi-omics transcriptome sequencing data revealed disulfidptosis heterogeneity within the tumor microenvironment of esophageal squamous cell carcinoma

BACKGROUND

It is of great concern to identify prognostic signatures for the prediction and prediction of esophageal squamous cell carcinoma (ESCC), which is the lethal pathological type of malignancy.

METHOD

Bulk RNA sequencing and scRNA-seq data were retrieved from GSE53624, GSE53622, and GSE188900. Disulfidptosis-related differentially expressed genes (DEGs) were identified between disulfidptosis-high score and disulfidptosis-low score groups. Functional annotation of DEGs were analyzed by Gene Ontology (GO). Consistent clustering and co-expression modules were analyzed, and then constructed a risk score model via multivariate Cox regression analysis. Immune infiltration and immunotherapy response analyses were conducted based on risk score. qRT-PCR, colony formation assay, and flow cytometry analysis were conducted in KYSE-150 and TE-1 cell lines.

RESULTS

Seven genes (CD96, CXCL13, IL2RG, LY96, TPK1, ACAP1, and SOX17) were selected as marker genes. CD96 and SOX17 are independent prognostic signatures for ESCC patients, with a significant correlation with infiltrated immune cells. ESCC patients had worse response to nivolumab in the high-risk group. Through cellular experiments, we found that CD96 expression was associated with apoptosis and cell cycle ESCC cells.

CONCLUSION

In a word, the risk score based on disulfidptosis is associated with prognosis and the immune microenvironment, which may direct immunotherapy of ESCC. The key gene of risk score, namely CD96, plays a role in proliferation and apoptosis in ESCC. We offer an insight into the exploration of the genomic etiology of ESCC for its clinical management.

Construction of a prognostic model for radical esophagectomy based on immunohistochemical prognostic markers combined with clinicopathological factors

Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and lacks effective biomarkers to evaluate prognosis and treatment. Glycoprotein nonmetastatic melanoma protein B (GPNMB) is a protein highly expressed in ESCC tissues screened by isobaric tags for relative and absolute quantitation proteomics, which has significant prognostic value in a variety of malignant tumors, but its relationship with ESCC remains unclear. By immunohistochemical staining of 266 ESCC samples, we analyzed the relationship between GPNMB and ESCC. To explore how to improve the ability of ESCC prognostic assessment, we established a prognostic model of GPNMB and clinicopathological features. The results suggest that GPNMB expression is generally positive in ESCC tissues and is significantly associated with poorer differentiation, more advanced American Joint Council on Cancer (AJCC) stage, and higher tumor aggressiveness (P < .05). Multivariate Cox analysis indicated that GPNMB expression was an independent risk factor for ESCC patients. A total of 188 (70%) patients were randomly selected from the training cohort and the four variables were automatically screened by stepwise regression based on the AIC principle: GPNMB expression, nation, AJCC stage and nerve invasion. Through the weighted term, we calculate the risk score of each patient, and by drawing the receiver operating characteristic curve, we show that the model has good prognostic evaluation performance. The stability of the model was verified by test cohort. Conclusion: GPNMB is a prognostic marker consistent with the characteristics of tumor therapeutic targets. For the first time, we constructed a prognostic model combining immunohistochemical prognostic markers and clinicopathological features in ESCC, which showed higher prognostic efficacy than AJCC staging system in predicting the prognosis of ESCC patients in this region.

Cancer-Associated Stromal Fibroblast-Derived Transcriptomes Predict Poor Clinical Outcomes and Immunosuppression in Colon Cancer

Background: Previous studies revealed that colonic cancer-associated fibroblasts (CAFs) are associated with the modulation of the colon tumor microenvironment (TME). However, identification of key transcriptomes and their correlations with the survival prognosis, immunosuppression, tumor progression, and metastasis in colon cancer remains lacking. Methods: We used the GSE46824, GSE70468, GSE17536, GSE35602, and the cancer genome atlas (TCGA) colon adenocarcinoma (COAD) datasets for this study. We identified the differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, hub genes, and survival-associated genes in colon cancer. Finally, we investigated the correlation of key genes with the survival prognosis, immunosuppression, and metastasis. Results: We identified 246 common DEGs between the GSE46824 and GSE70468 datasets of colonic CAFs, which included 72 upregulated and 174 downregulated genes. The upregulated pathways are mainly involved with cancers and cellular signaling, and downregulated pathways are involved with immune regulation and cellular metabolism. The search tool for the retrieval of interacting genes (STRING)-based analysis identified 15 hub genes and 9 significant clusters in colonic CAFs. The upregulation of CTHRC1, PDGFC, PDLIM3, NTM, and SLC16A3 and downregulation of FBN2 are correlated with a shorter survival time in colon cancer. The CTHRC1, PDGFC, PDLIM3, and NTM genes are positively correlated with the infiltration of tumor-associated macrophages (TAM), macrophages, M2 macrophages, the regulatory T cells (Tregs), T cell exhaustion, and myeloid-derived suppressor cells (MDSCs), indicating the immunosuppressive roles of these transcriptomes in colon cancer. Moreover, the CTHRC1, PDGFC, PDLIM3, NTM, and SLC16A3 genes are gradually increased from normal tissue to the tumor and tumor to the metastatic tumor, and FBN2 showed the reverse pattern. Furthermore, the CTHRC1, FBN2, PDGFC, PDLIM3, and NTM genes are positively correlated with the metastatic scores in colon cancer. Then, we revealed that the expression value of CTHRC1, FBN2, PDGFC, PDLIM3, NTM, and SLC16A3 showed the diagnostic efficacy in colonic CAFs. Finally, the expression level of CTHRC1, PDGFC, and NTM genes are consistently altered in colon tumor stroma as well as in the higher CAFs-group of TCGA COAD patients. Conclusion: The identified colonic CAFs-derived key genes are positively correlated with survival prognosis, immunosuppression, tumor progression, and metastasis.

Targeting the LSD1-G9a-ER Stress Pathway as a Novel Therapeutic Strategy for Esophageal Squamous Cell Carcinoma

Despite recent advances in the management and treatment of esophageal squamous cell carcinoma (ESCC), the prognosis remains extremely poor, and current nonsurgical treatment options are limited. To identify new therapeutic targets, we screened a curated library of epigenetic compounds using a panel of cancer cell lines and found that coinhibiting the histone demethylase LSD1 and the histone methyltransferase G9a potently suppresses cell growth; similar results were obtained by knocking down both LSD1 and G9a expression. Importantly, we also found that inhibiting LSD1 and G9a significantly decreased tumor growth in a xenograft mouse model with ESCC cell lines. To examine the clinical relevance of these findings, we performed immunohistochemical analyses of microarray profiling data obtained from human esophageal squamous cancer tissues and found that both LSD1 and G9a are upregulated in cancer tissues compared to healthy tissues, and this increased expression was significantly correlated with poor prognosis. Mechanistically, we discovered that inhibiting LSD1 and G9a induces cell death via S-phase arrest and apoptosis, and cotargeting ER stress pathways increased this effect both in vitro and in vivo. Taken together, these findings provide compelling evidence that targeting LSD1, G9a, and ER stress-related pathways may serve as a viable therapeutic strategy for ESCC.

Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis

Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient's preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two questions: can a single checkup predict the need of active treatment?, and which attributes of VS development are important in decision making on active treatment? Using a machine-learning analysis of medical records of 93 patients, the objectives were addressed using two classification tasks: a time-independent case-based reasoning (CBR), where each medical record was treated as independent, and a personalized dynamic analysis (PDA), during which we analyzed the individual development of each patient's state in time. Using the CBR method we found that Koos classification of tumor size, speech reception threshold, and pure tone audiometry, collectively predict the need for active treatment with approximately 90% accuracy; in the PDA task, only the increase of Koos classification and VS size were sufficient. Our results indicate that VS treatment may be reliably predicted using only a small set of basic parameters, even without the knowledge of individual development, which may help to simplify VS treatment strategies, reduce the number of examinations, and increase cause effectiveness.

Development and Validation of a Nomogram based on cell growth-related Biomarkers for Oral Squamous Cell Carcinoma

Purpose: We aimed to develop a prognostic nomogram based on immunohistochemistry (IHC) biomarkers of patients with oral squamous cell carcinoma (OSCC). Methods: A total of 294 patients were enrolled in the study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was performed to develop a combined IHC score (IHCs) classifier. Results: Five biomarkers, specifically c-Met, Vimentin, HIF-2α, VEGF-c, and Bcl-2 were extracted. Then, an IHCs classifier was developed, and patients were stratified into high- and low-IHCs groups. In the training cohort, the 5-year overall survival (OS) was 62.1% in low-IHCs group and 28.2% in high-IHCs group (P<0.001). The 5-year OS was 68.6% for the low-IHCs group and 28.4% for the high-IHCs group in the validation cohort (P<0.001). The area under the ROC curve (AUROC) of the combination of the IHCs classifier and TNM stage was 0.746 (95% CI: 0.658-0.833) in the training cohort and 0.735 (95% CI: 0.651-0.818) in the validation cohort, respectively. Conclusions: The nomogram could effectively predict the prognosis for patients with OSCC and may be employed as a potential tool to guide the individual decision-making process.

Prognostic Implication of the m6A RNA Methylation Regulators in Rectal Cancer

N6-methyladenosine (m⁶A) is a very common and abundant RNA modifications occurring in nearly all types of RNAs. Although the dysregulated expression of m⁶A regulators is implicated in cancer progression, our understanding of the prognostic value of the m⁶A regulators in rectal cancer is still quite limited. In this study, we analyzed the RNA expression levels of the 17 m⁶A regulator genes of 95 rectal cancer and 10 normal rectal samples from the The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) dataset. Lasso regression analysis was conducted to build a prognostic model and calculate the risk score. The rectal cancer patients were then devided into the high-risk and low-risk groups according to the mean risk score. The prognostic value of the identified model was separately evaluated in the TCGA-READ and GSE87211 datasets. GSEA was conducted to analyze the functional difference of high-risk and low-risk rectal cancer patients. Our analysis revealed that rectal cancer patients with lower expression of YTHDC2 and METTL14 had a remarkable worse overall survival (P < 0.05). The prognostic value of the model was validated in GSE87211 datasets, with AUC = 0.612 for OS and AUC = 0.651 for RFS. Furthermore, the m⁶A modification-based risk score system is associated with activation of distinct signaling pathways, such as DNA repair, epithelial-mesenchymal transition, G₂M checkpoint and the MYC pathway, that may contribute to the progression of rectal cancer. In conclusion, our findings demonstrated that the m⁶A RNA methylation regulators, specifically YTHDC2 and METTL14, were significantly down-regulated and might be potential prognostic biomarkers in rectal cancer.

Artificial intelligence-based analysis for immunohistochemistry staining of immune checkpoints to predict resected non-small cell lung cancer survival and relapse

Background

Conventional analysis of single-plex chromogenic immunohistochemistry (IHC) focused on quantitative but spatial analysis. How immune checkpoints localization related to non-small cell lung cancer (NSCLC) prognosis remained unclear.

Methods

Here, we analyzed ten immune checkpoints on 1,859 tumor microarrays (TMAs) from 121 NSCLC patients and recruited an external cohort of 30 NSCLC patients with 214 whole-slide IHC. EfficientUnet was applied to segment tumor cells (TCs) and tumor-infiltrating lymphocytes (TILs), while ResNet was performed to extract prognostic features from IHC images.

Results

The features of galectin-9, OX40, OX40L, KIR2D, and KIR3D played an un-negatable contribution to overall survival (OS) and relapse-free survival (RFS) in the internal cohort, validated in public databases (GEPIA, HPA, and STRING). The IC-Score and Res-Score were two predictive models established by EfficientUnet and ResNet. Based on the IC-Score, Res-Score, and clinical features, the integrated score presented the highest AUC for OS and RFS, which could achieve 0.9 and 0.85 in the internal testing cohort. The robustness of Res-Score was validated in the external cohort (AUC: 0.80–0.87 for OS, and 0.83–0.94 for RFS). Additionally, the neutrophil-to-lymphocyte ratio (NLR) combined with the PD-1/PD-L1 signature established by EfficientUnet can be a predictor for RFS in the external cohort.

Conclusions

Overall, we established a reliable model to risk-stratify relapse and death in NSCLC with a generalization ability, which provided a convenient approach to spatial analysis of single-plex chromogenic IHC.

Prognostic Value of an Immunohistochemical Signature in Patients With Bladder Cancer Undergoing Radical Cystectomy

Background

This study aimed to assess the prognostic value of various diagnostic immunohistochemical (IHC) markers and develop an IHC-based classifier to predict the disease-free survival (DFS) of patients with bladder cancer undergoing radical cystectomy.

Methods

IHC was performed on tumor specimens from 366 patients with transitional cell bladder cancer. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to develop a multi-marker classifier for predicting DFS of patients with bladder cancer. The Kaplan-Meier estimate was performed to assess DFS, and unadjusted and adjusted Cox regression models were used to identify independent risk factors to predict DFS of patients with bladder cancer.

Results

Based on the LASSO Cox regression model, nine prognostic markers were identified in the training cohort. Patients were stratified into low- and high-risk groups using the IHC-based classifier. In the training cohort, the 10-year DFS was significantly better in low-risk patients (71%) compared with high-risk patients (18%) (p < 0.001); in the validation cohort, the 10-year DFS was 86% for the low-risk group and 20% for the high-risk group (p < 0.001). Multivariable Cox regression analyses showed that the high-risk group based on the classifier was associated with poorer DFS adjusted by clinicopathological characteristics. Finally, a nomogram comprising the classifier and clinicopathological factors was developed for clinical application.

Conclusion

The nine-IHC-based classifier is a reliable prognostic tool, which can eventually guide clinical decision making regarding treatment strategy and follow-up scheduling of bladder cancer.

Development and validation of a five-gene model to predict postoperative brain metastasis in operable lung adenocarcinoma

One of the most common sites of extra-thoracic distant metastasis of nonsmall-cell lung cancer is the brain. Our study was performed to discover genes associated with postoperative brain metastasis in operable lung adenocarcinoma (LUAD). RNA seq was performed in specimens of primary LUAD from seven patients with brain metastases and 45 patients without recurrence. Immunohistochemical (IHC) assays of the differentially expressed genes were conducted in 272 surgical-resected LUAD specimens. LASSO Cox regression was used to filter genes related to brain metastasis and construct brain metastasis score (BMS). GSE31210 and GSE50081 were used as validation datasets of the model. Gene Set Enrichment Analysis was performed in patients stratified by risk of brain metastasis in the TCGA database. Through the initial screening, eight genes (CDK1, KPNA2, KIF11, ASPM, CEP55, HJURP, TYMS and TTK) were selected for IHC analyses. The BMS based on protein expression levels of five genes (TYMS, CDK1, HJURP, CEP55 and KIF11) was highly predictive of brain metastasis in our cohort (12-month AUC: 0.791, 36-month AUC: 0.766, 60-month AUC: 0.812). The validation of BMS on overall survival of GSE31210 and GSE50081 also showed excellent predictive value (GSE31210, 12-month AUC: 0.682, 36-month AUC: 0.713, 60-month AUC: 0.762; GSE50081, 12-month AUC: 0.706, 36-month AUC: 0.700, 60-month AUC: 0.724). Further analyses showed high BMS was associated with pathways of cell cycle and DNA repair. A five-gene predictive model exhibits potential clinical utility for the prediction of postoperative brain metastasis and the individual management of patients with LUAD after radical resection.

Predictive value of intraoperative factors for complications after oesophagectomy

OBJECTIVES

Oesophagectomy for malignancy is a highly complex and difficult procedure associated with considerable postoperative complications. In this study, we aimed to identify the ability of an intraoperative factor (IPFs)-based classifier to predict complications after oesophagectomy.

METHODS

This retrospective review included 251 patients who underwent radical oesophagectomy from October 2015 to December 2017. Using the least absolute shrinkage and selection operator regression model, we extracted IPFs that were associated with postoperative morbidity and then built a classifier. Preoperative variables and the IPF-based classifier were analysed using univariable and multivariable logistic regression analysis. A nomogram to predict the risk of postoperative morbidity was constructed and validated using bootstrap resampling.

RESULTS

Following the least absolute shrinkage and selection operator regression analysis, we discovered that those 4 IPF (surgical approach, lowest heart rate, lowest mean arterial blood pressure and estimated blood loss) were associated with postoperative morbidity. After stratification into low-and high-risk groups with the IPF-based classifier, the differences in 30-day morbidity (7.2% vs 70.1%, P < 0.001, respectively) and mortality (0% vs 4.7%, P = 0.029, respectively) were found to be statistically significant. The multivariable analysis demonstrated that the IPF-based classifier was an independent risk factor for predicting postoperative morbidity for patients with oesophageal cancer. The performance of the nomogram was evaluated and proven to be clinically useful.

CONCLUSIONS

We demonstrated that an IPF-based nomogram could reliably predict the risk of postoperative morbidity. It has the potential to facilitate the individual perioperative management of patients with oesophageal cancer.

Development of a four-gene prognostic model for pancreatic cancer based on transcriptome dysregulation

We systematically developed a prognostic model for pancreatic cancer that was compatible across different transcriptomic platforms and patient cohorts. After performing quality control measures, we used seven microarray datasets and two RNA sequencing datasets to identify consistently dysregulated genes in pancreatic cancer patients. Weighted gene co-expression network analysis was performed to explore the associations between gene expression patterns and clinical features. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to construct a prognostic model. We tested the predictive power of the model by determining the area under the curve of the risk score for time-dependent survival. Most of the differentially expressed genes in pancreatic cancer were enriched in functions pertaining to the tumor immune microenvironment. The transcriptome profiles were found to be associated with overall survival, and four genes were identified as independent prognostic factors. A prognostic risk score was then proposed, which displayed moderate accuracy in the training and self-validation cohorts. Furthermore, patients in two independent microarray cohorts were successfully stratified into high- and low-risk prognostic groups. Thus, we constructed a reliable prognostic model for pancreatic cancer, which should be beneficial for clinical therapeutic decision-making.

Clinical-morphological profiles of esophageal carcinoma's main types

Aim: The purpose of the study was to define and then to compare particularly the morphological profiles of the main morphological types of esophageal carcinoma (EC). Patients, Materials and Methods: The studied group included 46 operated EC patients. Few parameters were clinical (gender and age). The rest of them described both gross and histological features of the entire group and of the two main histological types of carcinoma (lesion’ site, lateral extension, lesion dimensions, gross aspect, and histological type, and tumor grade, and stage). Stratification scales of cases were defined according to each parameter in order to compare the data and a statistical apparatus [Student’s t-test and χ² (chi-squared) test] was used. Results: The studied tumors were encountered mostly in mature adult and elderly men, usually in the lower segments of the esophagus. Many of them had between five and ten cm in the long diameter and produced stenosis. Most of them had infiltrating appearance combined often with protruding or/and ulcerated aspects. Usually, the tumors were poorly differentiated and in stage III. The two main histological types of EC showed different morphological profiles. Data from the literature revealed sometimes wide ranges of variation for the studied morphological parameters. Our results were within these ranges of variation. Conclusions: ECs proved to be aggressive and late diagnosed tumors in general, with distinct morphological and behavioral profiles for the two main histological types. Comparisons with literature data confirmed many of our observations regarding the clinical and morphological aspects of both ECs as a whole and its histological types.

Bioinformatics analysis of esophageal cancer unveils an integrated mRNA-lncRNA signature for predicting prognosis

Esophageal cancer (ESCA) carries a poor prognosis among gastrointestinal malignancies. The present study developed a signature based on mRNAs and long non-coding RNAs (lncRNAs) to predict prognosis in ESCA by using The Cancer Genome Atlas database. By using least absolute shrinkage and selection operator penalized regression, a set of RNAs (three mRNAs and two lncRNAs) was identified and used to build a risk score system of ESCA prognosis, which was used to stratify patients having considerable diverse survival in the training set [hazard ratio (HR), 3.932; 95% CI, 1.555–9.944; P<0.002] into high- and low-risk groups. The authentication of the results was achieved through the test set (HR, 3.150; 95% CI, 1.113–8.918; P<0.02) and the entire set (HR, 3.181; 95% CI, 1.686–6.006; P<0.0002). The results from multivariate Cox proportional hazard regression analysis in the entire set suggested that the prognostic significance of this signature may be independent of patients' clinicopathological characteristics. Furthermore, this signature was associated with several molecular signaling pathways of cancer according to Gene Set Enrichment Analysis. In addition, a nomogram was built and the risk score and TNM stage were integrated to estimate the 1- and 3-year overall survival rates. The results from the present study demonstrated that the integrated mRNA-lncRNA signature may be considered as a novel biomarker for the prognosis of ESCA.

Patterns and risks of postoperative recurrence in completely resected EGFR-mutant non-small cell lung cancer: prognostic significance of routine immunohistochemical markers

Background

Recent studies indicate that EGFR-mutant non-small cell lung cancer (NSCLC) is a heterogeneous disease with varying prognosis. In order to design an optimized surveillance strategy and identify potential candidates for adjuvant therapy, the patterns and risks of postoperative recurrence in completely resected EGFR-mutant NSCLC should be investigated, which are currently largely unknown.

Methods

Consecutive patients with curatively resected EGFR-positive NSCLC receiving standard adjuvant chemotherapy without EGFR tyrosine kinase inhibitors (TKI), with or without adjuvant radiotherapy, from January 2007 to December 2017 in our cancer center, were retrospectively reviewed. Prognostic significance of ten routine immunohistochemical (IHC) markers were examined.

Results

After a median follow-up of 32 (range, 5-122) months, disease recurrence occurred in 197 (37.1%) of the 531 enrolled patients. The frequencies of thoracic recurrence, brain recurrence, bone recurrence, abdominal recurrence and neck recurrence, were 69.0%, 20.8%, 20.8%, 7.1% and 6.6%, respectively. Using the Cox regression model, tumor size, Ki67, CK20, and N stage were identified as independent predictors of overall recurrence. A nomogram predicting the 1-, 2-, and 3-year cumulative rate of overall recurrence was then developed and internally validated, with a bias-corrected C-index of 0.723 (95% CI, 0.675 to 0.771) and a small extent of "over-fitting" (0.8%). Risk factors of site-specific recurrence were also discovered. Additionally, using competing risk analyses, N stage, lymphovascular invasion (LVI) and CK5/6 were found as independent predictors of loco-regional recurrence. Among patients with N2-positive disease (n=91), adjuvant radiotherapy tended to prolong disease free survival (DFS) (P=0.067), but not overall survival (OS) (P=0.271).

Conclusions

This study provides the proof of concept of using routine IHC markers, along with common clinical-pathological parameters, in predicting postoperative recurrence among completely resected EGFR-mutant NSCLC. Adjuvant radiotherapy may improve DFS, but hard to prolong OS in N2-positive EGFR-mutant NSCLC without further biomarker-guided patients' selection.

Genome-wide ChIP-seq data with a transcriptome analysis reveals the groups of genes regulated by histone demethylase LSD1 inhibition in esophageal squamous cell carcinoma cells

Expression of genes is controlled by histone modification, histone acetylation and methylation, but abnormalities of these modifications have been observed in carcinogenesis and cancer development. The effect of the lysine-specific histone demethylase 1 (LSD1) inhibitor, a demethylating enzyme of histones, is thought to be caused by controlling the expression of genes. The aim of the present study is to elucidate the efficacies of the LSD1 inhibitor on the gene expression of esophageal cancer cell lines using chromatin immunoprecipitation (ChIP)-Seq. A comprehensive analysis of gene expression changes in esophageal squamous cell carcinoma (ESCC) cell lines induced by the LSD1 inhibitor NCL1 was clarified via analysis using microarray. In addition, ChIP-seq analysis was conducted using a SimpleChIP plus Enzymatic Chromatin IP kit. NCL1 strongly suppressed the proliferation of T.Tn and TE2 cells, which are ESCC cell lines, and further induced apoptosis. According to the combinatory analysis of ChIP-seq and microarray, 17 genes were upregulated, and 16 genes were downregulated in both cell lines. The comprehensive gene expression study performed in the present study is considered to be useful for analyzing the mechanism of the antitumor effect of the LSD1 inhibitor in patients with ESCC.