Level of Melanotransferrin in Tissue and Sera Serves as a Prognostic Marker of Gastric Cancer

The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma

Due to the high heterogeneity, lung adenocarcinoma (LUAD) cannot be distinguished into precise molecular subtypes, thereby resulting in poor therapeutic effect and low 5-year survival rate clinically. Although the tumor stemness score (mRNAsi) has been shown to accurately characterize the similarity index of cancer stem cells (CSCs), whether mRNAsi can serve as an effective molecular typing tool for LUAD isn't reported to date. In this study, we first demonstrate that mRNAsi is significantly correlated with the prognosis and disease degree of LUAD patients, i.e., the higher the mRNAsi, the worse the prognosis and the higher the disease degree. Second, we identify 449 mRNAsi-related genes based on both weighted gene co-expression network analysis (WGCNA) and univariate regression analysis. Third, our results display that 449 mRNAsi-related genes can accurately distinguish the LUAD patients into two molecular subtypes: ms-H subtype (with high mRNAsi) and ms-L subtype (with low mRNAsi), particularly the ms-H subtype has a worse prognosis. Remarkably, significant differences in clinical characteristics, immune microenvironment, and somatic mutation exist between the two molecular subtypes, which might lead to the poorer prognosis of the ms-H subtype patients than that of the ms-L subtype ones. Finally, we establish a prognostic model containing 8 mRNAsi-related genes, which can effectively predict the survival rate of LUAD patients. Taken together, our work provides the first molecular subtype related to mRNAsi in LUAD, and reveals that these two molecular subtypes, the prognostic model and marker genes may have important clinical value for effectively monitoring and treating LUAD patients.

Isoform switching leads to downregulation of cytokine producing genes in estrogen receptor positive breast cancer

Objective: Estrogen receptor breast cancer (BC) is characterized by the expression of estrogen receptors. It is the most common cancer among women, with an incidence rate of 2.26 million cases worldwide. The aim of this study was to identify differentially expressed genes and isoform switching between estrogen receptor positive and triple negative BC samples. Methods: The data were collected from ArrayExpress, followed by preprocessing and subsequent mapping from HISAT2. Read quantification was performed by StringTie, and then R package ballgown was used to perform differential expression analysis. Functional enrichment analysis was conducted using Enrichr, and then immune genes were shortlisted based on the ScType marker database. Isoform switch analysis was also performed using the IsoformSwitchAnalyzeR package. Results: A total of 9,771 differentially expressed genes were identified, of which 86 were upregulated and 117 were downregulated. Six genes were identified as mainly associated with estrogen receptor positive BC, while a novel set of ten genes were found which have not previously been reported in estrogen receptor positive BC. Furthermore, alternative splicing and subsequent isoform usage in the immune system related genes were determined. Conclusion: This study identified the differential usage of isoforms in the immune system related genes in cancer cells that suggest immunosuppression due to the dysregulation of CXCR chemokine receptor binding, iron ion binding, and cytokine activity.

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis

Background

Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively.

Materials and methods

LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay.

Results

A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues.

Conclusion

Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

The E2F1/MELTF axis fosters the progression of lung adenocarcinoma by regulating the Notch signaling pathway

BACKGROUND

Lung adenocarcinoma (LUAD) represents the predominant subtype of lung cancer. MELTF, an oncogene, exhibits high expression in various cancer tissues. Nevertheless, the precise role of MELTF in the progression of LUAD remains enigmatic. This work was devised to investigate the effect of MELTF on LUAD progression and its underlying mechanism.

METHODS

mRNA expression data of LUAD were from The Cancer Genome Atlas database, and the enrichment pathway of MELTF was analyzed. The upstream transcription factors of MELTF were predicted, and the correlation between MELTF and E2F1 as well as the expression of the two in LUAD tissues were dissected by bioinformatics. The expression of MELTF and E2F1 in LUAD tissues and cells was assayed by qRT-PCR. Effects of MELTF/E2F1 on proliferation, migration, and invasion of LUAD cells were tested by CCK-8, colony formation, and Transwell assays. The binding relationship between E2F1 and MELTF was estimated by dual-luciferase reporter gene assay and ChIP assay. Western blot was utilized to assay the expression of Notch signaling pathway-related proteins in different treatment groups.

RESULTS

Bioinformatics analysis and qRT-PCR results exhibited high expression of E2F1 and MELTF in LUAD tissues and cells, respectively. Dual-luciferase reporter gene assay and ChIP assay ascertained the binding of E2F1 to MELTF. MELTF was ascertained to enrich the Notch signaling pathway by bioinformatics means. In cell experiments, MELTF was shown to foster the malignant progression of LUAD cells and promoted the expression of NOTCH1 and HES1 proteins, but RO4929097 offset the effect of MELTF on cells. Rescue assay confirmed that E2F1 activated MELTF to promote LUAD progression via the Notch signaling pathway.

CONCLUSION

Together, our outcomes demonstrated that E2F1 fostered LUAD progression by activating MELTF via the Notch signaling activity. Hence, MELTF emerged as a feasible target for treating LUAD.

MFI2 upregulation promotes malignant progression through EGF/FAK signaling in oral cavity squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the predominant histological type of the head and neck squamous cell carcinoma (HNSCC). By comparing the differentially expressed genes (DEGs) in OSCC-TCGA patients with copy number variations (CNVs) that we identify in OSCC-OncoScan dataset, we herein identified 37 dysregulated candidate genes. Among these potential candidate genes, 26 have been previously reported as dysregulated proteins or genes in HNSCC. Among 11 novel candidates, the overall survival analysis revealed that melanotransferrin (MFI2) is the most significant prognostic molecular in OSCC-TCGA patients. Another independent Taiwanese cohort confirmed that higher MFI2 transcript levels were significantly associated with poor prognosis. Mechanistically, we found that knockdown of MFI2 reduced cell viability, migration and invasion via modulating EGF/FAK signaling in OSCC cells. Collectively, our results support a mechanistic understanding of a novel role for MFI2 in promoting cell invasiveness in OSCC.

Gamma-aminobutyric Acid Type A Receptor Subunit Delta as a Potential Therapeutic Target in Gastric Cancer

BACKGROUND

Novel therapeutic targets are needed to improve the poor prognosis of patients with advanced gastric cancer. The aim of this study was to identify a novel therapeutic target for the treatment of GC and to investigate the potential therapeutic value of an antibody raised against the target.

METHODS

We identified gamma-aminobutyric acid type A receptor subunit delta as a candidate therapeutic target by differential transcriptome analysis of metastatic GC tissue and adjacent nontumor tissues. GABRD mRNA levels were analyzed in 230 pairs of gastric tissue by quantitative reverse-transcription polymerase chain reaction. GABRD function was assessed in proliferation, invasion, and apoptosis assays in human GC cell lines expressing control or GABRD-targeting small interfering RNA (siRNA). Mouse anti-human polyclonal GABRD antibodies were generated and assessed for inhibition of GC cell growth in vitro and in a mouse xenograft model of peritoneal GC dissemination.

RESULTS

High GABRD mRNA expression level in primary human GC tissue was associated with poor prognosis. Expression of siGABRD in GC cell lines significantly decreased cell proliferation and invasion and increased apoptosis compared with control siRNA expression. Anti-GABRD polyclonal antibodies inhibited GC cell proliferation in vitro and decreased peritoneal tumor nodule size in the mouse xenograft model.

CONCLUSION

We identified GABRD as novel regulator of GC cell growth and function. GABRD is upregulated in GC tissue and is associated with poor prognosis, suggesting that it may be a potential therapeutic target for GC.

Correlation of Tryptophan Metabolic Pathway with Immune Activation and Chemosensitivity in Patients with Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer with high malignancy and easy metastasis in the early stage. In this study, we aimed to figure out the role of tryptophan metabolic pathway in LUAD prognosis and treatment. Different molecular subtypes were constructed based on tryptophan metabolism-related genes. Significant prognostic genes and clinical prognostic characteristics, immune infiltration level, and pathway activity in different subtypes were determined by algorithms, such as the least absolute shrinkage and selection operator (Lasso), CIBERSORT, Tumor Immune Dysfunction and Exclusion (TIDE), and gene set enrichment analysis (GSEA). The effect of different gene mutation types on the prognosis of patients with LUAD was explored. The clinical prognosis model was constructed and its reliability was verified. Of the 40 genes in the tryptophan metabolism pathway, 13 had significant prognostic significance. Based on these 13 genes, three molecular subtypes (C1, C2, and C3) were established. Among them, C1 had the worst prognosis and the lowest enrichment score of tryptophan metabolism. At the same time, C1 had the most genetic variation, the highest level of immune infiltration, and significantly activated pathways related to tumor development. The high-risk and low-risk groups had significant differences in prognosis, immune infiltration and pathway enrichment, which was consistent with the results of subtype analysis. Mutation in tryptophan metabolism-related genes leads to abnormal tryptophan metabolism, immune deficiency, and activation of cancer-promoting pathways. This results in high malignancy, poor prognosis, and failure of traditional clinical treatments. Through the establishment of risk score (RS) clinical prognosis model, we determined that RS could reliably predict the prognosis of patients with LUAD.

A novel metabolic-immune related signature predicts prognosis and immunotherapy response in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is one of the most frequent types of lung cancer, with a high mortality and recurrence rate. This study aimed to design a RiskScore to predict the prognosis and immunotherapy response of LUAD patients due to a lack of metabolic and immune-related prognostic models.

Methods

To identify prognostic genes and generate a RiskScore, we conducted differential gene expression analysis, bulk survival analysis, Lasso regression analysis, and univariate and multivariate Cox regression analysis using TCGA-LUAD as a training subset. GSE31210 and GSE50081 were used as validation subsets to validate the constructed RiskScore. Following that, we explored the connection between RiskScore and clinicopathological characteristics, immune cells infiltration, and immunotherapy. In addition, we investigated into RiskScore's biological roles and constructed a Nomogram model.

Results

A RiskScore was identified consisting of five genes (DKK1, CCL20, NPAS2, GNPNAT1 and MELTF). In the RiskScore-high group, LUAD patients showed decreased overall survival rates and shorter progression-free survival. Multiple clinicopathological characteristics and immune cells infiltration in TME, in particular, have been linked to RiskScore. Of note, RiskScore-related genes have been implicated to substance metabolism, carcinogenesis, and immunological pathways, among other things. Finally, the C-index of the RiskScore-based Nomogram model was 0.804 (95% CI: 0.783-0.825), and time-dependent ROC predicted probabilities of 1-, 3- and 5-year survival for LUAD patients were 0.850, 0.848 and 0.825, respectively.

Conclusion

The RiskScore, which integrated metabolic and immunological features with DKK1, CCL20, NPAS2, GNPNAT1, and MELTF, could reliably predict prognosis and immunotherapy response in LUAD patients. Moreover, the RiskScore-based Nomogram model had a promising clinical application.

Tumor immune microenvironment of self-identified African American and non-African American triple negative breast cancer

Differences in the tumor immune microenvironment may result in differences in prognosis and response to treatment in cancer patients. We hypothesized that differences in the tumor immune microenvironment may exist between African American (AA) and NonAA patients, due to ancestry-related or socioeconomic factors, that may partially explain differences in clinical outcomes. We analyzed clinically matched triple-negative breast cancer (TNBC) tissues from self-identified AA and NonAA patients and found that stromal TILs, PD-L1 IHC-positivity, mRNA expression of immune-related pathways, and immunotherapy response predictive signatures were significantly higher in AA samples (p < 0.05; Fisher's Exact Test, Mann-Whitney Test, Permutation Test). Cancer biology and metabolism pathways, TAM-M2, and Immune Exclusion were significantly higher in NonAA samples (p < 0.05; Permutation Test, Mann-Whitney Test). There were no differences in somatic tumor mutation burden. Overall, there is greater immune infiltration and inflammation in AA TNBC and these differences may impact response to immune checkpoint inhibitors and other therapeutic agents that modulate the immune microenvironment.

A Lipid Metabolism-Based Seven-Gene Signature Correlates with the Clinical Outcome of Lung Adenocarcinoma

Background. Herein, we tried to develop a prognostic prediction model for patients with LUAD based on the expression profiles of lipid metabolism-related genes (LMRGs). Methods. Molecular subtypes were identified by non-negative matrix factorization (NMF) clustering. The overall survival (OS) predictive gene signature was developed and validated internally and externally based on online data sets. Time-dependent receiver operating characteristic (ROC) curve, Kaplan–Meier curve, nomogram, restricted mean survival time (EMST), and decision curve analysis (DCA) were used to assess the performance of the gene signature. Results. We identified three molecular subtypes in LUAD with distinct characteristics on immune cells infiltration and clinical outcomes. Moreover, we confirmed a seven-gene signature as an independent prognostic factor for patients with LUAD. Calibration and DCA analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. In addition, the nomogram showed higher robustness and clinical usability compared with four previously reported prognostic gene signatures. Conclusions. Findings in the present study shed new light on the characteristics of lipid metabolism within LUAD, and the established seven-gene signature can be utilized as a new prognostic marker for predicting survival in patients with LUAD.

Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso-Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.

A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining

Background

Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD.

Method

RNA expression profile and clinical data from 522 LUAD patients were accessed and downloaded from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were extracted and analyzed between normal tissues and LUAD samples. Then, a 14-DEG signature was developed and identified for the survival prediction in LUAD patients by means of univariate and multivariate Cox regression analyses. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential biological functions and pathways of these DEGs.

Results

Twenty-two out of 5924 DEGs in the TCGA dataset were screened and associated with the overall survival (OS) of LUAD patients. 14CID="C008" value=" "DEGs were finally selected and included in our development and validation model by risk score analysis. The ROC analysis indicated that the specificity and sensitivity of this profile signature were high. Further functional enrichment analyses indicated that these DEGs might regulate genes that affect the function of release of sequestered calcium ion into cytosol and pathways that associated with vibrio cholerae infection.

Conclusion

Our study developed a novel 14-DEG signature providing more efficient and persuasive prognostic information beyond conventional clinicopathological factors for survival prediction of LUAD patients.

Identification of a four-gene panel predicting overall survival for lung adenocarcinoma

Background

Lung cancer is the most frequently diagnosed carcinoma and the leading cause of cancer-related mortality. Although molecular targeted therapy and immunotherapy have made great progress, the overall survival (OS) is still poor due to a lack of accurate and available prognostic biomarkers. Therefore, in this study we aimed to establish a multiple-gene panel predicting OS for lung adenocarcinoma.

Methods

We obtained the mRNA expression and clinical data of lung adenocarcinoma (LUAD) from TCGA database for further integrated bioinformatic analysis. Lasso regression and Cox regression were performed to establish a prognosis model based on a multi-gene panel. A nomogram based on this model was constructed. The receiver operating characteristic (ROC) curve and the Kaplan–Meier curve were used to assess the predicted capacity of the model. The prognosis value of the multi-gene panel was further validated in TCGA-LUAD patients with EGFR, KRAS and TP53 mutation and a dataset from GEO. Gene set enrichment analysis (GSEA) was performed to explore potential biological mechanisms of a novel prognostic gene signature.

Results

A four-gene panel (including DKK1, GNG7, LDHA, MELTF) was established for LUAD prognostic indicator. The ROC curve revealed good predicted performance in both test cohort (AUC = 0.740) and validation cohort (AUC = 0.752). Each patient was calculated a risk score according to the model based on the four-gene panel. The results showed that the risk score was an independent prognostic factor, and the high-risk group had a worse OS compared with the low-risk group. The nomogram based on this model showed good prediction performance. The four-gene panel was still good predictors for OS in LUAD patients with TP53 and KRAS mutations. GSEA revealed that the four genes may be significantly related to the metabolism of genetic material, especially the regulation of cell cycle pathway.

Conclusion

Our study proposed a novel four-gene panel to predict the OS of LUAD, which may contribute to predicting prognosis accurately and making the clinical decisions of individual therapy for LUAD patients.