Patterns of immune infiltration in lung adenocarcinoma revealed a prognosis-associated microRNA-mast cells network

Identification and validation of RB1 as an immune-related prognostic signature based on tumor mutation burdens in bladder cancer

Bladder cancer (BCa) is one of the most common malignant tumors in the urinary system. Developing effective prognostic gene and exploring the immune cells that affect the prognosis of tumor are required. Full transcriptome data ( n = 433), clinical information ( n = 581) and mutation sequencing ( n = 412) were obtained from The Cancer Genome Atlas and independent mutation sequencing data of 101 samples were acquired from International Cancer Genome Consortium. Statistical processing was conducted using R packages. Gene biologically functional research was performed with gene set enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes database. Twenty-two types of immune cell infiltration were assessed and calculated in 398 samples of BCa. Furthermore, the expression of immune-related prognostic signature was verified. The relationship between prognostic gene and immune cells was explored preliminarily. Tumor mutation burdens of mutant-type groups were higher than wild-type groups of 19 genes, except for FGFR3 and CREBBP. Kaplan-Meier analysis showed that high frequency of retinoblastomal 1 (RB1) mutation led to poor prognosis of BCa patients and was an independent prognostic factor ( P = 0.004; HR = 1.776). Proportions and correlation of 22 types of immune cells in 433 samples were determined. We found that RB1 expression decreased in BCa validated through quantitative PCR and immunohistochemistry. In addition, regulatory T cells (Tregs) were detected as a negatively correlated type of immune cell to mutation of RB1, whereas fluorescence costaining showed that Foxp3 expression of Tregs infiltration was negatively related to the expression of RB1. Mutation of RB1 can be identified as an independent prognostic predictor of BCa, and it may suppress the infiltration of Tregs in BCa tissues, increasing the incidence of tumor immune escape.

Prognostic Value of lncRNA DRAIC and miR-3940-3p in Lung Adenocarcinoma and Their Effect on Lung Adenocarcinoma Cell Progression

Purpose

Lung adenocarcinoma (LUAD) is a most common malignant tumor, even worse for diseases with relatively poor prognosis. Non-coding RNAs have the potential to be biomarkers for the prognosis of various cancers. LncRNA DRAIC and miR-3940-3p have been screened as dysregulated RNAs in LUAD. The clinical significance and biological function of lncRNA DRAIC and miR-3940-3p in LUAD were assessed in this study.

Patients and Methods

A total of 122 cases of LUAD patients with complete clinical information were enrolled. The expression levels of lncRNA DRAIC and miR-3940-3p were determined via RT-qPCR in LUAD tissues and cells. The relationship between lncRNA DRAIC or miR-3940-3p expression and the clinicopathological features of patients was analyzed based on the Pearson Chi-square test. For the prognostic value, the Kaplan–Meier plot and multi-variate Cox proportional regression analysis were introduced. Finally, the effect of lnc DRAIC and miR-3940-3p on the LUAD cellular function was investigated by CCK-8 and Transwell assay.

Results

lnc DRAIC was upregulated in LUAD tissues and cells, but miR-3940-3p was downregulated. Both of them showed significant associations with and TNM stage, lymph node metastasis, and a poor prognosis. Lnc-DRAIC and miR-3940-3p have the potential as independent prognostic factors for LUAD. Furthermore, the inhibition of lnc DRAIC can inhibit cell proliferation, migration, and invasion of LUAD partly as a ceRNA of miR-3940-3p.

Conclusion

lncRNA DRAIC/miR-3940-3p axis may be involved in the progression of LUAD and can be developed to promising prognostic factors, which may provide new insights into the treatment of LUAD.

Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma

Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes. Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples. Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages. Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient’s quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear.

Methods: We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts.” Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism.

Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [area under curves (AUCs) of 1, 3, and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747, and 0.772; AUCs of 1, 3, and 5-year survivals in nomogram based on immune cells: 0.603, 0.642, and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells.

Conclusion: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

Development and validation of m6A regulators' prognostic significance for endometrial cancer

BACKGROUND

Endometrial cancer (EC) is the sixth most common cancer in women globally. It has been found that the expression levels of m6A regulators can be potentially used for prognostic stratification in some cancers, but the role of m6A regulators in EC prognosis remains unclear.

METHODS

The data of 584 EC samples were downloaded from The Cancer Genome Atlas and the mRNA expression profiles of 20 m6A regulators were analyzed, followed by functional enrichment analysis, immune infiltration analysis, and least absolute shrinkage and selection operator method-COX regression analysis.

RESULTS

The mRNA expression levels of 20 m6A regulators were significantly different between cancer samples across different grades. The 548 EC samples could be clearly divided into 2 clusters. Kaplan-Meier survival analysis proved that these two groups had highly different overall survival probabilities. Besides, the univariate regression analysis further reserved eight genes related to overall survival from the 20 m6A regulators. We established a prognostic signature including two genes, that is, IGF2BP1 and YTHDF3, that showed a strong ability for stratifying prognostically different EC patients. We identified 3239 differentially expressed genes between the high- and low-risk groups, involving in multiple biological processes and signaling pathways. Meanwhile, 6 differentially infiltrated immune cell types between the high- and low-risk groups could effectively distinguish the high- and low-risk EC groups. The expressions of immune checkpoints were different between high- and low-risk EC patients.

CONCLUSION

We first report the prognostic role of m6A regulators in EC, which should contribute to a better understanding of the underlying mechanisms of EC pathogenesis and progression.