MicroRNA-mediated high expression of PDIA3 was correlated with poor prognosis of patients with LUAD.

Lung cancer, especially lung adenocarcinoma (LUAD) as the most common subtype has threatened the health of people. Even though more and more patients diagnosed as LUAD could be treated efficiently or even cured, a spilt of patients still suffer from disease. Here, on the basis of previous research, we firstly performed the mRNA expression of PDIA3 in pan-cancer, and differential expression between tumor and normal groups was followed. We further analyzed the survival difference and ultimately the expression of PDIA3 in LUAD was selected as our current study. Next, we investigated the mRNA and protein expression of PDIA3 from online databases and performed qRT-PCR and western blotting to verify the outcomes. We still analyzed the correlation between the expression of PDIA3 and clinicopathologic parameters and predicted the potential signal pathways as well as the possible upstream molecular of PDIA3. Considering the correlation of PDIA3 and immune infiltration, related analysis of PDIA3 and immune biomarkers along with PD-1/PD-L1, CTLA-4 were made. We clarified the expression of PDIA3 was upregulated in LUAD and its oncogenic role may be played through tumor infiltration. Thus targeting PDIA3 and immune checkpoint could enhance the efficacy of immunotherapy on patients with LUAD.

Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs.

Background: Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study the long non-coding RNAs (lncRNAs) that regulate cuprotosis to identify molecular subtypes and predict survival of LUAD. Methods: The expression profile, clinical, and mutation data of LUAD were downloaded from The Cancer Genome Atlas (TCGA), and the "ConsensusClusterPlus" package was used to cluster LUADs based on cuprotosis-related lncRNAs (CR-lncRNAs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were used to construct a prognostic model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used for assessing immune cells infiltration and immune function. The tumor microenvironment (TME) score was calculated by ESTIMATE, and the tumor mutational burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) were used to evaluate the efficacy of immunotherapy. Results: Firstly, 501 CR-lncRNAs were identified based on the co-expression relationship of 19 cuprotosis genes. And univariate Cox further obtained 34 prognosis-related CR-lncRNAs. The unsupervised consensus clustering divided LUAD samples into cluster A and cluster B, and showed cluster A had better prognosis, more immune cells infiltration, stronger immune function, and a higher TME score. Subsequently, we used Lasso Cox regression to construct a prognostic model, and univariate and multivariate Cox analyses showed the risk score could be an independent prognostic indicator. Immune cells infiltration, immune function, and TME score were increased markedly in the low-risk group, while TMB and TIDE suggested the efficacy of immunotherapy might be increased in high-risk group. Conclusions: Our research identified two new molecular subtypes and constructed a novel prognostic model of LUAD which could provide new direction for its diagnosis, treatment, and prognosis.

RAB11FIP1: An Indicator for Tumor Immune Microenvironment and Prognosis of Lung Adenocarcinoma from a Comprehensive Analysis of Bioinformatics.

Lung adenocarcinoma (LUAD) was the first one all over the world. RAB11FIP1 was found to be expressed differently in a critical way among different cancers. However, the prognostic value and immune infiltration of RAB11FIP1 expression in LUAD are unclear. In this study, the expression of RAB11FIP1 in LUAD was investigated in the Oncomine, TCGA, GEO, and UALCAN databases. Kaplan-Meier analysis was chosen to compare the association between RAB11FIP1 expression and overall survival (OS) in LUAD patients. The dataset of TCGA was used to analyze the pertinence between RAB11FIP1 and clinicpathological factors. GO, KEGG, and network analysis of protein-protein interactions (PPI) were conducted to investigate the potential mechanism of RAB11FIP1. In the end, the relevance of RAB11FIP1 to cancer-immune infiltrates was investigated. RAB11FIP1 was found to be down-regulated by tumors compared with adjacent normal tissue in multiple LUAD cohorts. RAB11FIP1 is an independent prognostic factor in lung adenocarcinoma. There was a high correlation between low RAB11FIP1 in tumors and worse OS in LUAD. Functional network analysis suggested that RAB11FIP1 was associated with multiple pathways. Besides, the expression of RAB11FIP1 was closely related to the infiltration levels of B cell, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. RAB11FIP1 expression in LUAD occurred with a variety of immune markers. Our findings suggest that RAB11FIP1 is related to prognosis and immune infiltrates in LUAD.

Prognostic model of lung adenocarcinoma constructed by the CENPA complex genes is closely related to immune infiltration.

BACKGROUND: Lung adenocarcinoma (LUAD) is still one of the primary malignant diseases leading to higher mortality worldwide. It has been previously reported that multiple genes in the CENPA-nucleosome associated complex (NAC) complex in lung cancer can be used as prognostic markers; however, there is lack of comprehensive research on the CENPA-NAC complex. METHODS: The hub genes of lung cancer were obtained by analyzing multiple gene expression omnibus (GEO) lung cancer datasets. The key genes of the CENPA-NAC complex in the evolution of LUAD were identified according to lung cancer data obtained from The Cancer Genome Atlas (TCGA) database, and the key genes were constructed as a survival prognostic model. The relationship between the model and immune cell infiltration was studied by the Tumor Immune Estimation Resource (TIMER) and single-sample gene set enrichment analysis (ssGSEA) studies.Droplet Digital polymerase chain reaction (ddPCR) was used to verify the effectiveness of the prognostic model to predict survival using clinical samples. RESULTS: A comprehensive study showed that CENPA, CENPH, CENPM, CENPN and CENPU were key genes in the development and evolution of LUAD. The constructed survival prognosis model was an independent risk factor for LUAD and can be used to assess the survival of LUAD patients. The risk score was closely related to the infiltration of multiple immune cells. The independent cohorts GSE31210 and GSE50081 further confirmed the validity of the prognostic model, and finally, the model was validated with clinical samples. CONCLUSIONS: In conclusion, the results of the present study showed that CENPA, CENPH, CENPM, CENPN, and CENPU are a group of potential prognostic markers in LUAD. The constructed model has been confirmed to be applicable in the clinical setting in evaluating the survival of patients with LUAD, and providing more evidence on immunotherapy for LUAD.

Characteristic of molecular subtypes in lung adenocarcinoma based on m6A RNA methylation modification and immune microenvironment.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky. METHODS: The gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model. RESULT: Consensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094). CONCLUSION: Our study provides a robust signature for predicting patients' prognosis, which might be helpful for therapeutic strategies discovery of LUAD.

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer.

BACKGROUND: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA. METHODS: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan-Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves. RESULTS: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules. CONCLUSION: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.

NEIL3 may act as a potential prognostic biomarker for lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death. This study aimed to develop and validate reliable prognostic biomarkers and signature. METHODS: Differentially expressed genes were identified based on three Gene Expression Omnibus (GEO) datasets. Based on 1052 samples' data from our cohort, GEO and The Cancer Genome Atlas, we explored the relationship of clinicopathological features and NEIL3 expression to determine clinical effect of NEIL3 in LUAD. Western blotting (22 pairs of tumor and normal tissues), Real-time quantitative PCR (19 pairs of tumor and normal tissues), and immunohistochemical analyses (406-tumor tissues subjected to microarray) were conducted. TIMER and ImmuCellAI analyzed relationship between NEIL3 expression and the abundance of tumor-infiltrating immune cells in LUAD. The co-expressed-gene prognostic signature was established based on the Cox regression analysis. RESULTS: This study identified 502 common differentially expressed genes and confirmed that NEIL3 was significantly overexpressed in LUAD samples (P < 0.001). Increased NEIL3 expression was related to advanced stage, larger tumor size and poor overall survival (p < 0.001) in three LUAD cohorts. The proportions of natural T regulatory cells and induced T regulatory cells increased in the high NEIL3 group, whereas those of B cells, Th17 cells and dendritic cells decreased. Gene set enrichment analysis indicated that NEIL3 may activate cell cycle progression and P53 signaling pathway, leading to poor outcomes. We identified nine prognosis-associated hub genes among 370 genes co-expressed with NEIL3. A 10-gene prognostic signature including NEIL3 and nine key co-expressed genes was constructed. Higher risk-score was correlated with more advanced stage, larger tumor size and worse outcome (p < 0.05). Finally, the signature was verified in test cohort (GSE50081) with superior diagnostic accuracy. CONCLUSIONS: This study suggested that NEIL3 has the potential to be an immune-related therapeutic target and an independent predictor of LUAD prognosis. We also developed a prognostic signature for LUAD with a precise diagnostic accuracy.

Prognostic biomarker TUBA1C is correlated to immune cell infiltration in the tumor microenvironment of lung adenocarcinoma.

BACKGROUND: TUBA1C is a microtubule component that is involved in a variety of cancers. Our main objective was to investigate TUBA1C expression, its prognostic value, its potential biological functions, and its impact on the immune system of patients with lung adenocarcinoma (LUAD). METHODS: The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA) and Immunohistochemistry Analysis were used to analyze TUBA1C expression, its clinicopathology, overall survival (OS), and disease-free survival (DFS) in LUAD patients. We also determined the correlation between TUBA1C and tumor-infiltrating immune cells (TIICs) by using CIBERSORT and GEPIA databases. To determine the expression of TUBA1C in LUAD, we analyzed a collection of immune infiltration levels and cumulative survival of LUAD tissues in TIMER database. By using UALCAN, STRING, and GeneMANIA databases, we investigated the protein-coding genes related to TUBA1C and its co-expression genes in LUAD tissues. Gene set enrichment analysis (GSEA) was performed by using the TCGA dataset. RESULTS: The mRNA and the protein expression of TUBA1C were found to be up-regulated in LUAD tissues. The univariate analysis indicated that an increased expression of TUBA1C was significantly correlated to the following parameters: age, stage, and lymph node metastasis. An over-expression of TUBA1C was associated with a poor prognosis of LUAD. In TIMER and CIBERSORT databases, we found that TUBA1C is correlated with 13 types of TIICs: activated B cell, activated CD4 T cell, central memory CD4 T cell, effector memory CD8 T cell, eosinophils, immature B cell, gamma-delta T cell, immature dendritic cell, mast cell, memory B cell, natural killer T cell, regulatory T cell, and type 2T helper cell. By performing GSEA, we found that TUBA1C is closely correlated to cell cycle, p53 signaling pathway, glycolysis, and gluconeogenesis. CONCLUSIONS: Our findings indicate that TUBA1C is associated with TIICs in tumor microenvironment. Therefore, it serves as a novel prognostic biomarker and a target for future treatment methods of LUAD.

Mining database for the expression and clinical significance of STAT family in head and neck squamous cell carcinomas.

BACKGROUND: Head and neck squamous cell carcinomas (HNSC) are among the most common malignant tumors with high incidence, relapse, and mortality rate. STAT proteins are implicated in various biological processes, including cell proliferation, metastasis, and immune regulation. METHOD: Various bioinformatics tools were used to explore the role of the STAT family in HNSC. RESULT: The mRNA levels of STAT1/2/4/5A/6 were significantly upregulated in HNSC tissues. The levels of STAT1/2/4/5A/6 could be used for the detection of HNSC. HNSC patients with a high level of STAT5A had a poor overall survival and relapse-free survival. A moderate to high correlation was obtained between the STAT family and HNSC. Genetic alteration revealed that STAT1/2/3/4/5A/5B/6 were altered in 6%, 5%, 7%, 8%, 6%, 6%, and 4% of the queried TCGA HNSC samples, respectively. Immune infiltrations analysis suggested a significant association between STAT5A expression and abundance of specific immune cells. Further, copy number alteration of STAT5A could certainly inhibit infiltration level. Moreover, a close correlation was obtained between STAT5A level and the expression of immune markers in HNSC. Several kinase targets and transcription factor targets of STAT5A in HNSC were also identified. Enrichment analysis suggested that STAT5A and co-expression genes were mainly responsible for adaptive immune response, T cell activation, cytokine-cytokine receptor interaction, chemokine signaling pathway, cell-adhesion molecules, and ribosome and RNA transport. CONCLUSION: Our results provided additional data for the expression and clinical significance of the STAT family in HNSC, and further study should be performed to verify these.

Correlation between prognostic indicator AHNAK2 and immune infiltrates in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is among the most aggressive malignant tumors in humans. Although AHNAK nucleoprotein 2 (AHNAK2) is considered a new oncogene, the function of the AHNAK2 in LUAD remains unknown. METHODS: Oncomine, Tumor Immune Estimation Resource (TIMER), and Human Protein Atlas databases were used to investigate AHNAK2 expression in LUAD. Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter databases were employed to elucidate the relationship between AHNAK2 and survival time. Data of The Cancer Genome Atlas were downloaded to analyze the correlation between AHNAK2 and clinicopathological parameters. We then immunohistochemically stained tissue chips to further confirm the correlation and conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network analyses to explore the possible functional mechanism of AHNAK2. Finally, we investigated the relationship between AHNAK2 and tumor infiltrating immune cells (TIICs). RESULTS: AHNAK2 gene was significantly overexpressed in LUAD tumor tissues and an independent prognostic indicator of LUAD patients. The expression of AHNAK2 was related to disease stage, differentiation, tumor size and lymph node metastasis. We found AHNAK2 expression was mainly positively correlated with cell adhesion-related pathways and negatively correlated with oxidative phosphorylation and amino acid metabolism. AHNAK2 expression was also negatively correlated with activated B cell, activated CD8 + T cell, and immature B cell infiltrates and positively correlated with central memory CD4 + T cell, tumor-associated macrophage, M1 macrophage, and M2 macrophage infiltrates. CONCLUSION: Our findings provide strong evidence of AHNAK2 expression as a prognostic indicator related to TIICs in LUAD.