Assessment of Quality Outcomes and the Learning Curve for Robot-Assisted Anatomical Lung Resections.

Background: To determine the perioperative quality assessment results and learning curves for robot-assisted anatomical lung resection. Methods: We analyzed the data of the initial 400 patients who underwent lobectomies or segmentectomies by 1 surgeon from January 2020 to November 2021. The learning curve was analyzed using cumulative sum analysis. Results: The surgical experience was divided into an initial phase (1st-40th procedures), a transition phase (41st-131st procedures), and a proficient phase (132nd procedure onward). The operative time showed a conspicuously continuous improvement over the 400 consecutive patients. After the 120th procedure, there were significant improvements in the rate of persistent air leakage (11.7% versus 3.9%; P = .003), chest tube duration (3.92 ± 1.91 versus 2.99 ± 1.31, P = .00), and postoperative hospital stay (6.22 ± 2.02 versus 4.93 ± 1.44, P = .00). Conclusions: In conclusion, 40 patients were necessary to pass the learning curve, and technical proficiency with favorable perioperative outcomes was achieved after 120-130 patients.

Identification of immunotherapy biomarkers for improving the clinical outcome of homologous recombination deficiency patients with lung adenocarcinoma.

Homologous recombination deficiency (HRD) is a common molecular signature of genomic instability and has been shown to be a biomarker for targeted therapies. However, there is a lack of studies on the role of HRD changes in lung adenocarcinoma (LUAD) transcriptomics. HRD scores were determined using single nucleotide polymorphism (SNP) array data from LUAD patients from The Cancer Genome Atlas (TCGA) database. Transcriptional data from patients with different scores were analyzed to identify biomarkers associated with HRD. Candidate biomarkers were validated using Gene Expression Omnibus (GEO)-sourced datasets and an immunotherapy cohort. According to the bulk transcriptome and clinical characteristics of 912 LUAD patients and Single-cell RNA-seq of 9 LUAD patients from TCGA and GEO databases, we observed increased MS4A6A expression in HRD tumors; high MS4A6A expression predicted improved survival outcomes. Furthermore, a comprehensive analysis of the tumor immune microenvironment (TIME) revealed a positive correlation between MS4A6A expression and neoantigen loading and immune cell infiltration. Additionally, the immunotherapy cohort confirmed the possibility of using MS4A6A as a biomarker. Collectively, we suggest that MS4A6A is associated with HRD and provide a new perspective toward identifying promising biomarkers for immunotherapy.

Immunogenic cell death-based prognostic model for predicting the response to immunotherapy and common therapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a malignant tumor in the respiratory system. The efficacy of current treatment modalities varies greatly, and individualization is evident. Therefore, finding biomarkers for predicting treatment prognosis and providing reference and guidance for formulating treatment options is urgent. Cancer immunotherapy has made distinct progress in the past decades and has a significant effect on LUAD. Immunogenic Cell Death (ICD) can reshape the tumor's immune microenvironment, contributing to immunotherapy. Thus, exploring ICD biomarkers to construct a prognostic model might help individualized treatments. We used a lung adenocarcinoma (LUAD) dataset to identify ICD-related differentially expressed genes (DEGs). Then, these DEGs were clustered and divided into subgroups. We also performed variance analysis in different dimensions. Further, we established and validated a prognostic model by LASSO Cox regression analysis. The risk score in this model was used to evaluate prognostic differences by survival analysis. The treatment prognosis of various therapies were also predicted. LUAD samples were divided into two subgroups. The ICD-high subgroup was related to an immune-hot phenotype more sensitive to immunotherapy. The prognostic model was constructed based on six ICD-related DEGs. We found that high-risk score patients responded better to immunotherapy. The ICD prognostic model was validated as a standalone factor to evaluate the ICD subtype of individual LUAD patients, which might contribute to more effective therapies.

Bioinformatics analysis and single-cell RNA sequencing: elucidating the ubiquitination pathways and key enzymes in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer associated with high mortality rates. We aimed to utilize single-cell multiomics analysis to identify the key molecules involved in ubiquitination modification, which plays a role in LUAD development and progression. Methods: We use a systematic approach to analyze LUAD-related single-cell and bulk transcriptome datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Single-cell RNA sequencing (scRNA-seq) data were normalized, clustered, and annotated with the Seurat package in R. InferCNV was used to distinguish malignant from epithelial cells, and AUCell evaluated the area under the curve (AUC) score of ubiquitination-related enzymes. Survival and differential analyses identified significant molecular markers associated with ubiquitination. PSMD14 expression was confirmed using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot assays, and its knockdown cell lines were assessed for effects on cellular processes and tumor formation in mice. PSMD14's interacting proteins were predicted, and its impact on AGR2 protein half-life and ubiquitination was evaluated. Rescue experiments involving PSMD14 overexpression and AGR2 silencing assessed their impact on malignant behaviors. Results: By means of single-cell sequencing analysis, we probed the ubiquitination modification landscape in the LUAD microenvironment. Malignant cells had elevated scores for enzymes and ubiquitin-binding domains compared to normal epithelial cells, with 53 ubiquitination-related molecules showing prognostic disparities. FGR, PSMD14, and ZBTB16 were identified as genes with prognostic significance, with PSMD14 showing higher expression in epithelial and malignant cells. Two missense mutation sites were identified in PSMD14, which had a high copy number amplification ratio and positive correlation with messenger RNA (mRNA) expression. PSMD14 expression and tumor stage were found to be independent prognostic factors, and interfering with PSMD14 expression reduced the malignant behavior of LUAD cells. PSMD14 was found to bind to AGR2 protein and reduce its ubiquitination, leading to increased AGR2 stability. Knockdown of AGR2 inhibited the enhancement of cell viability, invasion, and migration resulting from PSMD14 overexpression. Conclusions: This study examined ubiquitination modifications in LUAD using sequencing data, identifying PSMD14's critical role in malignancy regulation and its potential as a prognostic and therapeutic biomarker. These insights enhance understanding of LUAD mechanisms and treatment.

An anoikis-based gene signature for predicting prognosis in malignant pleural mesothelioma and revealing immune infiltration.

INTRODUCTION: Malignant pleural mesothelioma (MPM) is an aggressive, treatment-resistant tumor. Anoikis is a particular type of programmed apoptosis brought on by the separation of cell-cell or extracellular matrix (ECM). Anoikis has been recognized as a crucial element in the development of tumors. However, few studies have comprehensively examined the role of anoikis-related genes (ARGs) in malignant mesothelioma. METHODS: ARGs were gathered from the GeneCard database and the Harmonizome portals. We obtained differentially expressed genes (DEGs) using the GEO database. Univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm were utilized to select ARGs associated with the prognosis of MPM. We then developed a risk model, and time-dependent receiver operating characteristic (ROC) analysis and calibration curves were employed to confirm the ability of the model. The patients were divided into various subgroups using consensus clustering analysis. Based on the median risk score, patients were divided into low- and high-risk groups. Functional analysis and immune cell infiltration analysis were conducted to estimate molecular mechanisms and the immune infiltration landscape of patients. Finally, drug sensitivity analysis and tumor microenvironment landscape were further explored. RESULTS: A novel risk model was constructed based on the six ARGs. The patients were successfully divided into two subgroups by consensus clustering analysis, with a striking difference in the prognosis and landscape of immune infiltration. The Kaplan-Meier survival analysis indicated that the OS rate of the low-risk group was significantly higher than the high-risk group. Functional analysis, immune cell infiltration analysis, and drug sensitivity analysis showed that high- and low-risk groups had different immune statuses and drug sensitivity. CONCLUSIONS: In summary, we developed a novel risk model to predict MPM prognosis based on six selected ARGs, which could broaden comprehension of personalized and precise therapy approaches for MPM.

Development and validation of a nomogram for predicting overall survival of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy.

INTRODUCTION: Currently, the prognosis of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy is poor. The goal of this research was to develop and validate a novel nomogram for exactly predicting the overall survival (OS) of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. METHODS: The data applied in our research were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. We divided selected data into a training cohort and a validation cohort using R software, with a ratio of 7:3. Univariate Cox regression and multivariate Cox regression were utilized to select significant variables to build the nomogram. To validate our nomogram, calibration curves, receiver operating characteristic curves (ROC), decision curve analysis (DCA), and Kaplan-Meier survival curves were employed. The nomogram model was also compared with the tumor-node-metastasis (TNM) staging system by utilizing net reclassification index (NRI) and integrated discrimination improvement (IDI). RESULTS: Eight variables-age, sex, operative type, LN removed number, chemotherapy, AJCC stage, M stage, histology-were statistically significant in the multivariate Cox regression analysis and were selected to develop our nomogram. Based on ROC curves, calibration curves, and DCA analysis, our novel nomogram demonstrated good predictive accuracy and clinical utility. Using Kaplan-Meier (KM) survival curves and log-rank tests, the risk stratification system was able to stratify patients based on their estimated mortality risk. The nomogram performed better than the TNM staging system based on the NRI and IDI indexes. CONCLUSIONS: We developed and validated a nomogram to predict prognosis of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. Using this nomogram, clinicians may find this nomogram useful in predicting OS of targeted patients and making more appropriate treatment decisions.

Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer.

Objectives: The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients. Methods: After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively. Results: Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS. Conclusions: Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients.

Gliotoxin Induced Ferroptosis by Downregulating SUV39H1 Expression in Esophageal Cancer Cells.

BACKGROUND: Gliotoxin, a secondary metabolite isolated from marine-derived Aspergillus fumigatus, has demonstrated anti-tumor properties in several cancers. Ferroptosis, a recently discovered type of programmed cell death that depends on the accumulation of iron and lipid peroxides, participates in the occurrence and development of various diseases, including cancer. A recent patent, US20200383943, has suggested that the promotion of ferroptosis is a method of cancer treatment. Therefore, the development of drugs that induce ferroptosis in cancer cells would constitute a novel therapeutic approach. OBJECTIVE: Gliotoxin is a natural compound which has exhibited anti-tumor properties in multiple cancers, however, studies of the effect of gliotoxin on esophageal cancer are lacking. Although cancer treatment has shown great progress, including traditional surgery, chemotherapy, radiotherapy, and immunotherapy, the prognosis of esophageal cancer is still poor. Therefore, the development of new treatment approaches for esophageal cancer is necessary. METHODS: The effects of gliotoxin on esophageal cancer cells were determined by functional assays, such as CCK-8, wound healing and transwell assays. We used online tools to predict the target genes of gliotoxin, followed by further verification using Western blotting assays. To assess the role of gliotxin in inducing ferroptosis in esophageal cancer, we detected characteristics associated with ferroptosis including ROS, MDA, GSH and Fe2+. RESULTS: Using online tools SEA and SwissTargetPrediction, we predicted that SUV39H1 was the gliotoxin target gene. Furthermore, in esophageal cancer tissues, SUV39H1 was expressed at higher levels than in normal tissues, while in patients with Esophageal Squamous Cell Carcinoma (ESCC), high expression levels of SUV39H1 indicated a poor prognosis. In vitro, we observed that gliotoxin increased ESCC cell death and inhibited cell migration. We treated ESCC cells with pan-caspase inhibitor Z-VAD-FMK or ferroptosis inhibitors, including Fer-1 and DFO. Our results showed that Fer-1 and DFO reduced the toxic effects of gliotoxin, while Z-VAD-FMK did not. Furthermore, gliotoxin treatment reduced tumor weight and volume in the xenograft tumor mouse model. CONCLUSION: In summary, our findings indicate that gliotoxin downregulated SUV39H1 expression in ESCC cells and induced ferroptosis, suggesting a novel natural therapy for ESSC.

Identification of tumor antigens and immune subtypes in lung squamous cell carcinoma for mRNA vaccine development.

Background: Cancer vaccines are therapies that activate the patient's own immune system by inoculating the patient with cancer-specific antigens to identify and clear cancer cells. Messenger ribonucleic acid (mRNA) vaccines have received much attention because of their ease of synthesis, relative affordability, and long-term safety, but have not been studied in lung squamous cell carcinoma (LUSC). Thus, the identification of tumor antigens is necessary to facilitate the development of mRNA vaccines for LUSC. Methods: Genes with significant copy number amplification, the presence of gene mutations in LUSC, and genes associated with survival were identified as potential tumor antigens. Subsequently, the genes significantly correlated with the level of infiltration of 3 antigen-presenting cells in LUSC were identified as LUSC tumor antigens. Unsupervised clustering and immune profiling analysis were used to identify potential suitable patients for mRNA vaccines. Lastly, drug sensitivity analysis was used to explore individualized treatment options for mRNA vaccines suitable and unsuitable patients. Results: In this study, among the highly mutated genes in LUSC, we found that bone morphogenetic protein 5 (BMP5) and claudin 5 (CLDN5) expression were positively correlated with antigen-presenting cell infiltration, which indicates that these 2 genes have potential for development as mRNA cancer vaccines. Further, the immune landscape analysis identified different immune subtypes. Our findings provide a reference for the development of treatment strategies and the prediction of treatment responsiveness. Conclusions: Overall, our study provides a new perspective on the development of mRNA vaccines for LUSC and highlights the importance of individualized therapy.

Identification of a prognostic index system and tumor immune infiltration characterization for lung adenocarcinoma based on mRNA molecular of pyroptosis.

Background and purpose: Pyroptosis is a form of programmed cell death, which plays an important role in tumorigenesis, progression, and regulation of the tumor microenvironment. It can affect lung adenocarcinoma (LUAD) progression. This study aimed to construct a pyroptosis-related mRNA prognostic index (PRMPI) for LUAD and clarify the tumor microenvironment infiltration characterization of LUAD. Materials and methods: We performed a univariate Cox regression analysis for pyroptosis-related mRNAs in the TCGA cohort. Then, we used LASSO Cox regression to establish a PRMPI. The quantitative real time polymerase chain reaction (qRT-PCR) was used to quantify the relative expression of pyroptosis-related mRNAs. The CPTAC cohort was used to confirm the stability and wide applicability of the PRMPI. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the tumor microenvironment infiltration characterization. Results: A total of 36 pyroptosis-related mRNAs were identified. The PRMPI was established based on five pyroptosis-related mRNAs. The expression patterns of these mRNAs were verified in LUAD samples from our medical center by qRT-PCR. High-PRMPI patients had worse overall survival than low-PRMPI patients. The result was validated in the CPTAC cohort. The comprehensive analysis indicated that the high-PRMPI patients exhibited lower immune activity, more aggressive immunophenotype, lower expression of immune checkpoint molecule, higher TP53 mutation rate, and higher tumor stemness than low-PRMPI patients. Low-PRMPI patients may be more sensitive to immunotherapy, while high-PRMPI patients may benefit more from chemotherapy and targeted therapy. Conclusions: The PRMPI may be a promising biomarker to predict the prognosis, tumor microenvironment infiltration characterization, and the response to adjuvant therapy in LUAD.

Identification of lactate metabolism-related subtypes and development of a lactate-related prognostic indicator of lung adenocarcinoma.

Background: Increasing evidence supports that lactate plays an important role in tumor proliferation, invasion and within the tumor microenvironment (TME). This is particularly relevant in lung adenocarcinoma (LUAD). Therefore, there is a current need to investigate lactate metabolism in LUAD patients and how lactate metabolism is affected by different therapies. Methods: Data from LUAD patients were collected from The Cancer Genome Atlas (TCGA) and patients were divided into two subtypes according to 12 lactate metabolism-related genes to explore the effect of lactate metabolism in LUAD. We established a lactate-related prognostic indicator (LRPI) based on different gene expression profiles. Subsequently, we investigated associations between this LRPI and patient survival, molecular characteristics and response to therapy. Some analyses were conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results: The two LUAD subtypes exhibited different levels of lactate metabolism, in which patients that displayed high lactate metabolism also had a worse prognosis and a poorer immune environment. Indeed, LRPI was shown to accurately predict the prognosis of LUAD patients. Patients with a high LRPI showed a poor prognosis coupled with high sensitivity to chemotherapy using GDSC data. Meanwhile, these patients exhibited a high responsiveness to immunotherapy in TMB (Tumor mutation burden) and TIDE (Tumor Immune Dysfunction and Exclusion) analyses. Conclusion: We validated the effect of lactate metabolism on the prognosis of LUAD patients and established a promising biomarker. LRPI can predict LUAD patient survival, molecular characteristics and response to therapy, which can aid the individualized treatment of LUAD patients.

Multi-omics analysis to identify lung squamous carcinoma lactate metabolism-related subtypes and establish related index to predict prognosis and guide immunotherapy.

Lung squamous carcinoma (LUSC) is a malignant tumor of the respiratory system with highly heterogeneous characteristics. Lactate is the main product of aerobic glycolysis during the metabolic reprogramming of tumors. There is growing evidence that lactate metabolic processes have a broad and sophisticated impact on tumor phenotypic plasticity and tumor microenvironment (TME). However, the pattern of lactate metabolism in patients with LUSC and its impact on TME, phenotype, prognosis, and treatment have not been fully elucidated. In this study, we identified two subtypes with different lactate metabolism patterns in LUSC by non-negative matrix factorization and explored their multi-omics features. We observed that lactate metabolism levels in LUSC extensively influenced tumor immune infiltration patterns, adaptation to the hypoxia environment, and energy metabolic reprogramming. Subsequently, we constructed the lactate metabolism-related prognostic index (LMRPI) using Cox stepwise regression analysis. LMRPI showed excellent stability and accuracy, and based on the median value of LMRPI, LUAD were divided into two subgroups. The two subgroups have different patterns of immune infiltration and somatic mutations. Meanwhile, the two subgroups had different responsiveness to immune checkpoint inhibitor (ICI) therapies and different sensitivity to various chemotherapeutic and molecular targeting agents. In conclusion, we defined two subtypes with different lactate metabolism patterns in LUSC and extensively characterized their multi-omics profile. Furthermore, we developed LMRPI that predicts the prognosis of LUSC patients while also predicting their response to various adjuvant therapies, including immunotherapy, to guide their individualized treatment.

A bioinformatics-based immune-related prognostic index for lung adenocarcinoma that predicts patient response to immunotherapy and common treatments.

Background: There is increasing evidence of the effectiveness of immune checkpoint blockade (ICB) therapy for the treatment of lung adenocarcinoma (LUAD). However, the benefits of ICB therapy vary among LUAD patients. Due to the research dimension, existing biomarkers, such as programmed death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB), could not reflect the complex tumor environment, and had low prediction accuracy of ICB. Therefore, we aimed to uncover a prognostic biomarker that could also predict whether a patient would benefit from ICB therapy and other common treatments from multiple dimensions, so as to improve the prediction accuracy of pre-treatment patients. Methods: Based on the LUAD dataset retrieved from The Cancer Genome Atlas (TCGA) database, 50 immune-related hub genes were identified using weighted gene co-expression network analysis and univariate Cox regression analyses. An immune-related gene prognostic index (IRGPI) was constructed using a Cox proportional-hazards model based on 15 genes and validated using GSE72094 dataset. We tested its prognostic accuracy by Kaplan-Meier (K-M) survival curves of the two datasets and assessed its predictive power by comparing area under curve (AUC) of IRGPI with existing biomarkers. Subsequently, we analyzed the molecular and immune characteristics, and evaluated the benefits of ICB by PD-L1 expression and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, predicted the inhibitory concentration 50 of common treatments drugs for two IRGPI score-related subgroups. Results: Patients in the IRGPI-high subgroup had lower overall survival (OS) than patients in the IRGPI-low subgroup in K-M survival curve in two cohorts. And IRGPI has AUC values of 0.715, 0.724, and 0.743 in 1, 2, and 3 years, respectively. A higher tumor mutation burden and PD-L1 expression and the tumor microenvironment (TME) landscape demonstrated that IRGPI-high subgroup patients may respond better to ICB therapy. Genomics of Drug Sensitivity in Cancer (GDSC) analysis indicated that the IRGPI-high subgroup showed greater sensitivity to chemotherapy. Conclusions: IRGPI is a prospective biomarker for evaluating whether a patient will benefit from ICB therapy and other treatments, and distinguishing patients with different molecular and immune characteristics.

Corrigendum: Hinokiflavone Inhibits Growth of Esophageal Squamous Cancer By Inducing Apoptosis via Regulation of the PI3K/AKT/mTOR Signaling Pathway.

[This corrects the article DOI: 10.3389/fonc.2022.833719.].

Identification of Tumor Antigens and Immune Subtypes of Esophageal Squamous Cell Carcinoma for mRNA Vaccine Development.

Purpose: The applicability of mRNA vaccines against esophageal squamous cell carcinoma (ESCC) remains unclear. Here, we identified potential antigens for developing mRNA vaccines against ESCC and characterized immune subtypes to select appropriate patients for vaccination. Methods: RNA-seq, genetic alteration data, and corresponding clinical information of ESCC patients were obtained from the Cancer Genome Atlas (TCGA) database. The RNA-seq data of normal esophageal tissue were obtained from the Genotype-Tissue Expression (GTEx) database. Potential tumor antigens were screened by analyzing differentially expressed and mutated genes and potential antigens with significant differences in prognosis were screened using the Kaplan-Meier method. The proportion of immune cell infiltration in the tumor microenvironment was estimated using CIBERSORT and MCPcounter, and the correlation of potential antigens with antigen-presenting cells and major histocompatibility complex class II was analyzed. Subsequently, immune subtypes were constructed using consensus clustering analysis and characterized by single-sample gene set enrichment analysis and weighted gene co-expression network analysis (WGCNA). The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to analyze the drug sensitivity of different immune subtypes. Results: Four overexpressed and mutated tumor antigens associated with antigen presentation and poor prognosis were identified in ESCC, including NLRC5, FCRL4, TMEM229B, and LCP2. By consensus clustering, we identified two immune-associated ESCC subtypes, immune subtype 1 (IS1) and immune subtype 2 (IS2); the prognosis of the two subtypes was statistically different. In addition, the two immune subtypes had distinctly different cellular, molecular, and clinical characteristics. IS1 patients have a distinct immune "hot" phenotype with strong immune tolerance, whereas patients with IS2 have an immune "cold" phenotype. Differential expression of immune checkpoints and immunogenic cell death modulators was observed between the different immune subtypes. Finally, we found that IS1 and IS2 patients showed different drug sensitivities to common anti-tumor drugs, possibly facilitating the development of individualized treatment regimens for patients. Conclusion: NLRC5, LCP2, TMEM229B, and FCRL4 are potential antigens for ESCC mRNA vaccines, and such vaccines may be more suitable for IS2 patients. This study provides a theoretical basis for mRNA vaccines against ESCC, by identifying the critical characteristics to predict ESCC prognosis and select suitable patients for vaccination.

Mitochondrial Homeostasis-Related lncRNAs are Potential Biomarkers for Predicting Prognosis and Immune Response in Lung Adenocarcinoma.

The prognosis of the most common histological subtype of lung cancer, lung adenocarcinoma (LUAD), is relatively poor. Mitochondrial homeostasis depends to a great extent on the coordination between mitophagy and mitochondrial biogenesis, the deregulation of which causes various human diseases, including cancer. There is accumulating evidence that long noncoding RNAs (lncRNAs) are critical in predicting the prognosis and immune response in carcinoma. Therefore, it is critical to discern lncRNAs related to mitochondrial homeostasis in LUAD patients. In this study, we identified mitochondrial homeostasis-related lncRNAs (MHRlncRNAs) by coexpression analysis. In order to construct a prognostic signature composed of three MHRlncRNAs, univariate and multivariate Cox regression analyses were performed. Kaplan-Meier analysis, stratification analysis, principal component analysis (PCA), receiver operating characteristic (ROC) curve, gene set enrichment analysis (GSEA), and nomogram were applied to evaluate and optimize the risk model. Subsequently, we identified the mitochondrial homeostasis-related lncRNA signature (MHLncSig) as an independent predictive factor of prognosis. Based on the LUAD subtypes regrouped by this risk model, we further investigated the underlying tumor microenvironment, tumor mutation burden, and immune landscape behind different risk groups. Likewise, individualized immunotherapeutic strategies and candidate compounds were screened to aim at different risk subtypes of LUAD patients. Finally, we validated the expression trends of lncRNAs included in the risk model using quantitative real-time polymerase chain reaction (qRT-PCR) assays. The established MHLncSig may be a promising tool for predicting the prognosis and guiding individualized treatment in LUAD.

Erratum: Serum Prohibitin 2/PHB2 in Parkin-Mediated Mitophagy: A Potential Therapeutic Target for Non-Small Cell Lung Carcinoma.

The authors requested replacing Figure 2 as there was an error. The details of this error are as follows: Representative picture of transwell migration assay, A549 control group (Figure 2E). Representative picture of wound healing assay, 24h, A549 negative control group (Figure 2C). The above pictures were repeated within their own group (control group and negative control group). Representative picture of wound healing assay, 0h, A549 si-PHB2 group (Figure 2C). The authors used the wrong picture during data handling. Changes do not influence the results of the paper. In the original experiment, H1299 and A549 cells were divided into 4 groups (Control, si-PHB2, +PHB2, and negative control). Transwell migration assay and wound healing assay were performed 5 times. In addition, these results have been repeated by another research group (PHB2 promotes tumorigenesis via RACK1 in non-small cell lung cancer, Theranostics 2021, Vol. 11, Issue 7). Reference: Han Zhang, Chuntong Yin, Xin Liu, Xue Bai, Lei Wang, Honglin Xu, Jin Ju, Linyou Zhang. Prohibitin 2/PHB2 in Parkin-Mediated Mitophagy: A Potential Therapeutic Target for Non-Small Cell Lung Carcinoma.  Med Sci Monit. 2020; 26: e923227. DOI: 10.12659/MSM.923227.

The MLR, NLR, PLR and D-dimer are associated with clinical outcome in lung cancer patients treated with surgery.

OBJECTIVE: The study objective was to investigate the use of peripheral blood biomarkers as predictors of patient survival. The aim of this study was to identify the baseline peripheral blood biomarkers associated with clinical outcome in patients with early lung cancer (stage I-II) treated with surgery. METHODS: We included and analysed data from 376 patients with early-stage lung cancer who underwent a standard lobectomy. Univariate and multivariate Cox regression analyses were performed on all patients to assess the relationships between progression-free survival (PFS) and overall survival (OS) and the peripheral blood biomarker metrics measured before surgical treatment. The peripheral blood parameters included monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and dimeric fibrin fragment D (D-dimer). RESULTS: After univariate Cox regression analysis, low MLR, low NLR, low PLR and low D-dimer values were significantly associated with both better OS and PFS (all p < 0.05). In multivariate Cox regression analysis, a low MLR was significantly and independently associated with both better overall survival and better progression-free survival (both p <0.05). A low D-dimer level was significantly and independently associated with better overall survival (p <0.05). Furthermore, the categorization of patients according to the number of factors with favourable results revealed that those without favourable results had significantly worse outcomes than that of those patients with at least one. CONCLUSION: A baseline signature of low MLR, low NLR, low PLR, and low D-dimer values was associated with a better survival outcome for patients treated with surgery. Patients with more favourable results had better survival outcomes.

Identification of Tumor Antigens and Immune Subtypes in Lung Adenocarcinoma for mRNA Vaccine Development.

Cancer vaccines are emerging as a viable strategy for cancer treatment. In the current study, we screened for genes associated with the prognosis of patients with lung adenocarcinoma and positively correlated with antigen-presenting cell infiltration and identified KLRG1 and CBFA2T3 as potential tumor antigens for mRNA vaccines in lung adenocarcinoma (LUAD). Further analyses of immune subtypes revealed that patients with early-stage LUAD, high immune cell infiltration, high immune checkpoint expression, and low tumor mutation burden might benefit from mRNA vaccination. Moreover, we identified four biomarkers that can be used to assess mRNA vaccination suitability. We also identified potentially sensitive anti-cancer drugs for populations not suitable for vaccination by means of anti-cancer drug susceptibility prediction. Overall, we provided a new perspective for mRNA vaccine treatment strategies for LUAD and emphasized the importance of precise and personalized treatments.