LPAR1, Correlated With Immune Infiltrates, Is a Potential Prognostic Biomarker in Prostate Cancer

Identification of a five-miRNA signature as a novel potential prognostic biomarker in patients with nasopharyngeal carcinoma

Background

MicroRNAs (miRNAs) are involved in the prognosis of nasopharyngeal carcinoma (NPC). This study used clinical data and expression data of miRNAs to develop a prognostic survival signature for NPC patients to detect high-risk subject.

Results

We identified 160 differentially expressed miRNAs using RNA-Seq data from the GEO database. Cox regression model consisting of hsa-miR-26a, hsa-let-7e, hsa-miR-647, hsa-miR-30e, and hsa-miR-93 was constructed by the least absolute contraction and selection operator (LASSO) in the training set. All the patients were classified into high-risk or low-risk groups by the optimal cutoff value of the 5-miRNA signature risk score, and the two risk groups demonstrated significant different survival. The 5-miRNA signature showed high predictive and prognostic accuracies. The results were further confirmed in validation and external validation set. Results from multivariate Cox regression analysis validated 5-miRNA signature as an independent prognostic factor. A total of 13 target genes were predicted to be the target genes of miRNA target genes. Both PPI analysis and KEGG analysis networks were closely related to tumor signaling pathways. The prognostic model of mRNAs constructed using data from the dataset GSE102349 had higher AUCs of the target genes and higher immune infiltration scores of the low-risk groups. The mRNA prognostic model also performed well on the independent immunotherapy dataset Imvigor210.

Conclusions

This study constructed a novel 5-miRNA signature for prognostic prediction of the survival of NPC patients and may be useful for individualized treatment of NPC patients.

ARID1A Is a Prognostic Biomarker and Associated with Immune Infiltrates in Hepatocellular Carcinoma

OBJECTIVE

ARID1A has been discovered as a potential cancer biomarker. But its role in hepatocellular carcinoma (HCC) is subject to considerable dispute.

METHODS

The relationship between ARID1A and clinical factors was investigated. Clinicopathological variables related to overall survival in HCC subjects were identified using Cox and Kaplan-Meier studies. The connection between immune infiltrating cells and ARID1A expression was investigated using the tumor Genome Atlas (TCGA) dataset for gene set enrichment analysis (GSEA). Finally, a cell experiment was used to confirm it.

RESULTS

The gender and cancer topography (T) categorization of HCC were linked to increased ARID1A expression. Participants with advanced levels of ARID1A expression had a worse prognosis than someone with lower levels. ARID1A was shown to be a risk indicator of overall survival on its own. ARID1A expression is inversely proportional to immune cell infiltration. In vitro, decreasing ARID1A expression substantially slowed the cell cycle and decreased HCC cell proliferation, migration, and invasion.

CONCLUSION

The expression of ARID1A could be used to predict the outcome of HCC. It is closely related to tumor immune cell infiltration.

Methylation and Expression of the Exercise-Related TLR1 Gene Is Associated With Low Grade Glioma Prognosis and Outcome

Background: Exercise improves function, reduces disability, maintains independence, and improves quality of life for low-grade glioma (LGG) patients. Exercise can also improve the effectiveness of cancer treatment. The goal of this research was to find potential exercise related genes that may be used to predict exercise levels and may be used as a biomarker for cancer outcomes.

Methods: The GSE111551 database was thoroughly examined in this research, and the resulting conclusion of exercise-related genes was reached. The protein interaction network (PPI) was used to examine the differentially expressed genes (DEGs). Then the exercise-related gene TLR1 was chosen. The expression, methylation degree, prognosis, and immune relevance of TLR1 were investigated using bioinformatics. In addition, we verified the role of TLR1 in Glioma cell lines.

Results: LGG patients with reduced TLR1 expression and hypermethylation had a better overall survival (OS) and progression free survival (PFS), using the TCGA database. Low TLR1 expression and hypermethylation of TLR1 were found to be independent biomarkers for OS using Cox regression. Furthermore, the CGGA database was used to confirm the prognostic function of TLR1 in this cancer. Finally, most methylation sites of TLR1 were strongly correlated with immune infiltration and immune checkpoint. Then, reducing TLR1 expression substantially slowed the cell cycle and decreased LGG cell proliferation, emigration, and infiltration in vitro.

Conclusions: Exercise-related gene TLR1 has the potential to be a useful prognostic biomarker, and it is thought to be involved in immune cell infiltration and immunotherapy in LGG.

Inflammation-Related Long Non-Coding RNA Signature Predicts the Prognosis of Gastric Carcinoma

Background: Gastric carcinoma (GC) is a molecularly and phenotypically highly heterogeneous disease, making the prognostic prediction challenging. On the other hand, Inflammation as part of the active cross-talk between the tumor and the host in the tumor or its microenvironment could affect prognosis.

Method: We established a prognostic multi lncRNAs signature that could better predict the prognosis of GC patients based on inflammation-related differentially expressed lncRNAs in GC.

Results: We identified 10 differently expressed lncRNAs related to inflammation associated with GC prognosis. Kaplan-Meier survival analysis demonstrated that high-risk inflammation-related lncRNAs signature was related to poor prognosis of GC. Moreover, the inflammation-related lncRNAs signature had an AUC of 0.788, proving their utility in predicting GC prognosis. Indeed, our risk signature is more precise in predicting the prognosis of GC patients than traditional clinicopathological manifestations. Immune and tumor-related pathways for individuals in the low and high-risk groups were further revealed by GSEA. Moreover, TCGA based analysis revealed significant differences in HLA, MHC class-I, cytolytic activity, parainflammation, co-stimulation of APC, type II INF response, and type I INF response between the two risk groups. Immune checkpoints revealed CD86, TNFSF18, CD200, and LAIR1 were differently expressed between lowand high-risk groups.

Conclusion: A novel inflammation-related lncRNAs (AC015660.1, LINC01094, AL512506.1, AC124067.2, AC016737.1, AL136115.1, AP000695.1, AC104695.3, LINC00449, AC090772.1) signature may provide insight into the new therapies and prognosis prediction for GC patients.

Immune and Stromal Related Genes in Colon Cancer: Analysis of Tumor Microenvironment Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) Databases

BACKGROUND

The incidence of colon cancer is among the top three in the world. The tumor microenvironment plays an important role in the occurrence and development of colon cancer. Stromal cells and immune cells are the main components of the tumor microenvironment.

METHODS

Our study detected genes which affected the infiltration of stromal, immune cells and the way they affected the prognosis of colon cancer patients.

RESULTS

We found that the colon's immune system had a special way to affect the tumor microenvironment. Moderate infiltration of stromal and immune cells were proved to be important protective factors for colon cancer patients, which has not been found in other tumors. C3, C5, CXCL12, GNAI1, LPAR1, PENK, PYY, SAA1 and SST were the differential expression hub genes of moderate stromal and immune score group. They had a more significant correlation with tumor purity and infiltration of B cells, CD8⁺ T cells, CD4⁺ T cells, macrophage, neutrophil, democratic cells. The proteins encoded by C3, C5, CXCL12, GNAI1, PENK, PYY, SST were detected in colon cancer cells.

CONCLUSION

These genes had the potential to become markers to predict the prognosis of patients with colon cancer.

Natural Products for the Management of Castration-Resistant Prostate Cancer: Special Focus on Nanoparticles Based Studies

Prostate cancer is the most common type of cancer among men and the second most frequent cause of cancer-related mortality around the world. The progression of advanced prostate cancer to castration-resistant prostate cancer (CRPC) plays a major role in disease-associated morbidity and mortality, posing a significant therapeutic challenge. Resistance has been associated with the activation of androgen receptors via several mechanisms, including alternative dehydroepiandrosterone biosynthetic pathways, other androgen receptor activator molecules, oncogenes, and carcinogenic signaling pathways. Tumor microenvironment plays a critical role not only in the cancer progression but also in the drug resistance. Numerous natural products have shown major potential against particular or multiple resistance pathways as shown by in vitro and in vivo studies. However, their efficacy in clinical trials has been undermined by their unfavorable pharmacological properties (hydrophobic molecules, instability, low pharmacokinetic profile, poor water solubility, and high excretion rate). Nanoparticle formulations can provide a way out of the stalemate, employing targeted drug delivery, improved pharmacokinetic drug profile, and transportation of diagnostic and therapeutic agents via otherwise impermeable biological barriers. This review compiles the available evidence regarding the use of natural products for the management of CRPC with a focus on nanoparticle formulations. PubMed and Google Scholar search engines were used for preclinical studies, while ClinicalTrials.gov and PubMed were searched for clinical studies. The results of our study suggest the efficacy of natural compounds such as curcumin, resveratrol, apigenin, quercetin, fisetin, luteolin, kaempferol, genistein, berberine, ursolic acid, eugenol, gingerol, and ellagic acid against several mechanisms leading to castration resistance in preclinical studies, but fail to set the disease under control in clinical studies. Nanoparticle formulations of curcumin and quercetin seem to increase their potential in clinical settings. Using nanoparticles based on betulinic acid, capsaicin, sintokamide A, niphatenones A and B, as well as atraric acid seems promising but needs to be verified with preclinical and clinical studies.

A five-gene methylation signature predicts overall survival of patients with clear cell renal cell carcinoma

BACKGROUND

In this study, we aimed to screen methylation signatures associated with the prognosis of patients with clear cell renal cell carcinoma (ccRCC).

METHODS

Gene expression and methylation profiles of ccRCC patients were downloaded from publicly available databases, and differentially expressed genes (DEGs)-differentially methylated genes (DMGs) were obtained. Subsequently, gene set enrichment and transcription factor (TF) regulatory network analyses were performed. In addition, a prognostic model was constructed and the relationship between disease progression and immunity was analyzed.

RESULTS

A total of 23 common DEGs-DMGs were analyzed, among which 14 DEGs-DMGs were obtained with a cutoff value of PCC < 0 and p < 0.05. The enrichment analysis showed that the 14 DEGs-DMGs were enriched in three GO terms and three KEGG pathways. In addition, a total of six TFs were shown to be associated with the 14 DEGs-DMGs, including RP58, SOX9, NF-κB65, ATF6, OCT, and IK2. A prognostic model using five optimized DEGs-DMGs which efficiently predicted survival was constructed and validated using the GSE105288 dataset. Additionally, four types of immune cells (NK cells, macrophages, neutrophils, and cancer-associated fibroblasts), as well as ESTIMATE, immune, and stromal scores were found to be significantly correlated with ccRCC progression (normal, primary, and metastasis) in addition to the five optimized DEGs-DMGs.

CONCLUSION

A five-gene methylation signature with the predictive ability for ccRCC prognosis was investigated in this study, consisting of CCNB2, CDKN1C, CTSH, E2F2, and ERMP1. In addition, potential targets for methylation-mediated immunotherapy were highlighted.

Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients

Background

Not only glycolysis but also lncRNAs play a significant role in the growth, proliferation, invasion and metastasis of of ovarian cancer (OC). However, researches about glycolysis -related lncRNAs (GRLs) remain unclear in OC. Herein, we first constructed a GRL-based risk model for patients with OC.

Methods

The processed RNA sequencing (RNA-seq) profiles with clinicopathological data were downloaded from TCGA and glycolysis-related genes (GRGs) were obtained from MSigDB. Pearson correlation coefficient between glycolysis-related genes (GRGs) and annotated lncRNAs (|r| > 0.4 and p < 0.05) were calculated to identify GRLs. After screening prognostic GRLs, a risk model based on five GRLs was constructed using Univariate and Cox regression. The identified risk model was validated by two validation sets. Further, the differences in clinicopathology, biological function, hypoxia score, immune microenvironment, immune checkpoint, immune checkpoint blockade, chemotherapy drug sensitivity, N6-methyladenosine (m6A) regulators, and ferroptosis-related genes between risk groups were explored by abundant algorithms. Finally, we established networks based on co-expression, ceRNA, cis and trans interaction.

Results

A total of 535 GRLs were gained and 35 GRLs with significant prognostic value were identified. The prognostic signature containing five GRLs was constructed and validated and can predict prognosis. The nomogram proved the accuracy of the model for predicting prognosis. After computing hypoxia score of each sample by ssGSEA, we found patients with higher risk scores exhibited higher hypoxia score and high hypoxia score was a risk factor. It was revealed that a total of 21 microenvironment cells (such as Central memory CD4 T cell, Neutrophil, Regulatory T cell and so on) and Stromal score had significant differences between the two groups. Four immune checkpoint genes (CD274, LAG3, VTCN1, and CD47) showed disparate expression levels in the two groups. Besides, 16 m6A regulators and 126 ferroptosis-related genes were expressed higher in the low-risk group. GSEA revealed that the risk groups were associated with tumor-related pathways. The two risk groups were confirmed to be sensitive to several chemotherapeutic agents and patients in the low-risk group were more sensitive to ICB therapy. The networks based on co-expression, ceRNA, cis and trans interaction provided insights into the regulatory mechanisms of GRLs.

Conclusions

Our identified and validated risk model based on five GRLs is an independent prognostic factor for OC patients. Through comprehensive analyses, findings of our study uncovered potential biomarker and therapeutic target for the risk model based on the GRLs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00881-2.

Development and validation of ferroptosis-related lncRNAs signature for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) with high heterogeneity is one of the most frequent malignant tumors throughout the world. However, there is no research to establish a ferroptosis-related lncRNAs (FRlncRNAs) signature for the patients with HCC. Therefore, this study was designed to establish a novel FRlncRNAs signature to predict the survival of patients with HCC.

Method

The expression profiles of lncRNAs were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. FRlncRNAs co-expressed with ferroptosis-related genes were utilized to establish a signature. Cox regression was used to construct a novel three FRlncRNAs signature in the TCGA cohort, which was verified in the GEO validation cohort.

Results

Three differently expressed FRlncRNAs significantly associated with prognosis of HCC were identified, which composed a novel FRlncRNAs signature. According to the FRlncRNAs signature, the patients with HCC could be divided into low- and high-risk groups. Patients with HCC in the high-risk group displayed shorter overall survival (OS) contrasted with those in the low-risk group (P < 0.001 in TCGA cohort and P = 0.045 in GEO cohort). This signature could serve as a significantly independent predictor in Cox regression (multivariate HR > 1, P < 0.001), which was verified to a certain extent in the GEO cohort (univariate HR > 1, P < 0.05). Meanwhile, it was also a useful tool in predicting survival among each stratum of gender, age, grade, stage, and etiology,etc. This signature was connected with immune cell infiltration (i.e., Macrophage, Myeloid dendritic cell, and Neutrophil cell, etc.) and immune checkpoint blockade targets (PD-1, CTLA-4, and TIM-3).

Conclusion

The three FRlncRNAs might be potential therapeutic targets for patients, and their signature could be utilized for prognostic prediction in HCC.

Identification of P2RY13 as an immune-related prognostic biomarker in lung adenocarcinoma: A public database-based retrospective study

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of non-small cell lung cancer (NSCLC).

Methods

In the present study, the gene matrixes of LUAD were downloaded from The Cancer Genome Atlas to infer immune and stromal scores with the ‘Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data’ (ESTIMATE) algorithm and identified immune-related differentially expressed genes (DEGs) between the high- and low-stromal/immune score groups. Next, all DEGs were subjected to univariate Cox regression and survival analyses to screen out prognostic biomarkers in the tumor microenvironment (TME), and were validated in the Gene Expression Omnibus database. Single-sample gene set enrichment analysis (ssGSEA) was performed to assess the level of tumor-infiltrating immune cells (TIICs) and immune functions, and GSEA was used to identified pathways altered by prognostic biomarkers.

Results

Survival analysis showed that LUAD in the high-immune and stromal score group had a better clinical prognosis. A total of 303 immune-related DEGs were detected. Univariate Cox regression and survival analyses revealed that P2Y purinoceptor 13 (P2RY13) was a favorable factor for the prognosis of LUAD. ssGSEA and Spearman correlation analysis demonstrated that P2RY13 was highly correlated with various TIICs and immune functions. Several immune-associated pathways were enriched between the high- and low-expression P2RY13 groups.

Conclusion

P2RY13 may be a potential prognostic indicator and is highly associated with the TME in LUAD. However, further experimental studies are required to validate the present findings.

The role of ferroptosis in breast cancer patients: a comprehensive analysis

Breast cancer (BC) affects the breast tissue and is the second most common cause of mortalities among women. Ferroptosis is an iron-dependent cell death mode that is characterized by intracellular accumulation of reactive oxygen species (ROS). We constructed a prognostic multigene signature based on ferroptosis-associated differentially expressed genes (DEGs). Moreover, we comprehensively analyzed the role of ferroptosis-associated miRNAs, lncRNAs, and immune responses. A total of 259 ferroptosis-related genes were extracted. KEGG function analysis of these genes revealed that they were mainly enriched in the HIF-1 signaling pathway, NOD-like receptor signaling pathway, central carbon metabolism in cancer, and PPAR signaling pathway. Fifteen differentially expressed genes (ALOX15, ALOX15B, ANO6, BRD4, CISD1, DRD5, FLT3, G6PD, IFNG, NGB, NOS2, PROM2, SLC1A4, SLC38A1, and TP63) were selected as independent prognostic factors for BC patients. Moreover, T cell functions, including the CCR score, immune checkpoint, cytolytic activity, HLA, inflammation promotion, para-inflammation, T cell co-stimulation, T cell co-inhibition, and type II INF responses were significantly different between the low-risk and high-risk groups of the TCGA cohort. Immune checkpoints between the two groups revealed that the expressions of PDCD-1 (PD-1), CTLA4, LAG3, TNFSF4/14, TNFRSF4/8/9/14/18/25, and IDO1/2 among others were significantly different. A total of 1185 ferroptosis-related lncRNAs and 219 ferroptosis-related miRNAs were also included in this study. From the online database, we identified novel ferroptosis-related biomarkers for breast cancer prognosis. The findings of this study provide new insights into the development of new reliable and accurate cancer treatment options.

Identification of a ferroptosis-related gene signature (FRGS) for predicting clinical outcome in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer. Ferroptosis, an oxidative, iron-dependent form of necrotic cell death, is highly associated with tumorigenesis and cancer progression. However, the prognostic value of ferroptosis progress in LUAD was still rarely be investigated.

Methods

Herein, we collected three mRNA expression profiles and 85 ferroptosis-related genes from public databases. The “limma” package was used to identify ferroptosis-related differentially expressed genes (DEGs). Univariate Cox regression analysis and LASSO regression analysis were applied to screen and develop a ferroptosis-related gene signature (FRGS) and a formula to calculate the risk score. Multivariate Cox regression analysis was implemented to determine independent prognostic predictors of overall survival (OS). The area under the receiver operating characteristic curve (AUC) and calibration plot were used to evaluate the predictive accuracy of the FRGS and nomogram.

Results

We developed a FRGS with five genes (CYBB, CISD1, FADD, SAT2, VDAC2). The AUC of the FRGS in TCGA cohort was 0.777 at 1-year, 0.721 at 3-year and 0.725 at 5-year, significantly superior to the AUC of TNM stage (1-year: 0.701, 3-year: 0.691, 5-year: 0.686). A similar phenomenon was observed in GEO cohort 1 and 2. Multivariate Cox regression analysis indicted TNM stage and risk score were independent prognostic predictors. Finally, we built a nomogram with TNM stage and FRGS, the AUCs of which markedly higher than that of FRGS or TNM stage alone.

Conclusion

We constructed a prognostic FRGS with five ferroptosis-related genes and a nomogram for predicting the 1-, 3- and 5-year survival rate of LUAD patients, which may provide a new understanding of the prognostic value of ferroptosis progress in LUAD and will benefit prognosis assessment of LUAD patients.

Ferroptosis-Related Long Non-Coding RNA signature predicts the prognosis of Head and neck squamous cell carcinoma

Background: Head and neck squamous cell carcinoma (HNSCC) are head and neck cancers. On the other hand, ferroptosis is a novel iron-dependent and ROS reliant type of cell death observed various disease conditions.

Method: We constructed a prognostic multilncRNA signature based on ferroptosis-related differentially expressed lncRNAs in HNSCC.

Results: We identified 25 differently expressed lncRNAs associated with prognosis of HNSCC. Kaplan-Meier analyses revealed the high-risk lncRNAs signature associated with poor prognosis of HNSCC. Moreover, the AUC of the lncRNAs signature was 0.782, underscoring their utility in prediction HNSCC prognosis. Indeed, our risk assessment model was superior to traditional clinicopathological features in predicting HNSCC prognosis. GSEA revealed the immune and tumor-related pathways in the low risk group individuals. Moreover, TCGA revealed T cell functions including cytolytic activity, HLA, regulation of inflammationp, co-stimulation, co-inhibition and coordination of type II INF response were significantly different between the low-risk and high-risk groups. Immune checkpoints such as PDCD-1 (PD-1), CTLA4 and LAG3, were also expressed differently between the two risk groups.

Conclusion: A novel ferroptosis-related lncRNAs signature impacts on the prognosis of HNSCC.