Identification of Novel Diagnostic Biomarkers in Prostate Adenocarcinoma Based on the Stromal-Immune Score and Analysis of the WGCNA and ceRNA Network

Identification of a glycolysis-related miRNA Signature for Predicting Breast cancer Survival

Breast cancer (BC) is a common type of cancer and has a poor prognosis. In this study, we collected the mRNA and miRNA expression profiles of BC patients were obtained from The Cancer Genome Atlas (TCGA) to explore a novel prognostic strategy for BC patients using bioinformatics tools. We found that six glycolysis-related miRNAs (GRmiRs, including hsa-mir-1247, hsa-mir148b, hsa-mir-133a-2, has-mir-1307, hsa-mir-195 and hsa-mir-1258) were correlated with prognosis of BC samples. The risk score model was established based on 6 prognosis-associated GRmiRs. The outcome of high risk group was significantly poorer. Cox regression analysis showed that risk score was an independent prognostic factor. Differentially expressed genes identified between high and low risk groups were mainly enriched in inflammation and immune-related signaling pathways. The proportion of infiltration of 12 kinds of immune cells in high and low risk groups were significantly different. Risk score was closely associated with many immune indexes. Multiple DEGRGs and miRNAs were associated with drugs. In conclusion, glycolysis-related miRNA signature effectively predicts BC prognosis.

Identification of Hypoxia and Mitochondrial-related Gene Signature and Prediction of Prognostic Model in Lung Adenocarcinoma

Background: The correlation between hypoxia and tumor development is widely acknowledged. Meanwhile, the foremost organelle affected by hypoxia is mitochondria. This study aims to determine whether they possess prognostic characteristics in lung adenocarcinoma (LUAD). For this purpose, a bioinformatics analysis was conducted to assess hypoxia and mitochondrial scores related genes, resulting in the successful establishment of a prognostic model. Methods: Using the single sample Gene Set Enrichment Analysis algorithm, the hypoxia and mitochondrial scores were computed. Differential expression analysis and weighted correlation network analysis were employed to identify genes associated with hypoxia and mitochondrial scores. Prognosis-related genes were obtained through univariate Cox regression, followed by the establishment of a prognostic model using least absolute shrinkage and selection operator Cox regression. Two independent validation datasets were utilized to verify the accuracy of the prognostic model using receiver operating characteristic and calibration curves. Additionally, a nomogram was employed to illustrate the clinical significance of this study. Results: 318 differentially expressed genes associated with hypoxia and mitochondrial scores were identified for the construction of a prognostic model. The prognostic model based on 16 genes, including PKM, S100A16, RRAS, TUBA4A, PKP3, KCTD12, LPGAT1, ITPRID2, MZT2A, LIFR, PTPRM, LATS2, PDIK1L, GORAB, PCDH7, and CPED1, demonstrates good predictive accuracy for LUAD prognosis. Furthermore, tumor microenvironments analysis and drug sensitivity analysis indicate an association between risk scores and certain immune cells, and a higher risk scores suggesting improved chemotherapy efficacy. Conclusion: The research established a prognostic model consisting of 16 genes, and a nomogram was developed to accurately predict the prognosis of LUAD patients. These findings may contribute to guiding clinical decision-making and treatment selection for patients with LUAD, ultimately leading to improved treatment outcomes.

Transcriptomic analysis identifies CYP27A1 as a diagnostic marker for the prognosis and immunity in lung adenocarcinoma

BACKGROUND

The association between lipid metabolism disorder and carcinogenesis is well-established, but there is limited research on the connection between lipid metabolism-related genes (LRGs) and lung adenocarcinoma (LUAD). The objective of our research was to identify LRGs as the potential biomarkers for prognosis and assess their impact on immune cell infiltration in LUAD.

METHODS

We identified novel prognostic LRGs for LUAD patients via the bioinformatics analysis. CYP27A1 expression level was systematically evaluated via various databases, such as TCGA, UALCAN, and TIMER. Subsequently, LinkedOmics was utilized to perform the CYP27A1 co-expression network and GSEA. ssGSEA was conducted to assess the association between infiltration of immune cells and CYP27A1 expression. CYP27A1's expression level was validated by qRT-PCR analysis.

RESULTS

CYP27A1 expression was decreased in LUAD. Reduced CYP27A1 expression was linked to unfavorable prognosis in LUAD. Univariate and multivariate analyses indicated that CYP27A1 was an independent prognostic biomarker for LUAD patients. GSEA results revealed a positive correlation between CYP27A1 expression and immune-related pathways. Furthermore, CYP27A1 expression was positively correlated with the infiltration levels of most immune cells.

CONCLUSION

CYP27A1 is a potential biomarker for LUAD patients, and our findings provided a novel perspective to develop the prognostic marker for LUAD patients.

Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma

BACKGROUND

The current molecular classifications cannot completely explain the polarized malignant biological behavior of low-grade gliomas (LGGs), especially for tumor recurrence. Therefore, we tried to identify suspicious hub genes related to tumor recurrence in LGGs.

METHODS

In this study, we constructed a gene-miRNA-lncRNA co-expression network for LGGs by a weighted gene co-expression network analysis (WGCNA). GDCRNATools and the WGCNA R package were mainly used in data analysis.

RESULTS

Sequencing data from 502 LGG patients were analyzed in this study. Compared with recurrent glioma tissues, we identified 774 differentially expressed (DE) mRNAs, 49 DE miRNAs, and 129 DE lncRNAs in primary LGGs and ultimately determined that the expression of MKLN1 was related to tumor recurrence in LGG.

CONCLUSION

This study identified the potential biomarkers for the pathogenesis and recurrence of LGGs and proposed that MKLN1 could be a potential therapeutic target.

hsa-mir-133a-2 promotes the proliferation and invasion of cervical cancer cells by targeting the LAMB3-mediated PI3K/ATK pathway

OBJECTIVE

Cervical cancer, one of the common types of malignant tumors progressed in women, is on the rise in developing countries. Numerous previous studies have demonstrated that hsa-mir-133a-2 miRNA is abnormally expressed in cervical cancer cells. However, its fundamental mechanism in cervical cancer needs to be further clarified. Our study set out to investigate the effect of hsa-mir-133a-2 on the phenotypes of cervical cancer cells as well as any potential molecular processes involved in the proliferation and invasion of cervical cancer cells.

METHODS

The Cancer Genome Atlas-cervical squamous cell carcinoma and endocervical adenocarcinoma(TCGA-CESC) was adopted in order to verify the expression of hsa-mir-133a-2 in cervical cancer tissues and to identify its potential targets. The interaction between Laminin subunit beta-3(LAMB3) and hsa-mir-133a-2 was verified by TargetScan database as well as Luciferase reporter assay. The Cell Counting Kit-8 (CCK8) and transwell methods were utilized to assess the influence of hsa-mir-133a-2 on the proliferation and invasion characteristics of cervical cancer cells. We studied the role that hsa-mir-133a-2 plays in cervical cancer progression through Kyoto Encyclopedia of Genes and Genomes(KEGG) analysis as well as Western Blot (WB) experiment.

RESULTS

Down-regulation of hsa-mir-133a-2 was detected in cervical cancer tissues. It directly targeted LAMB3 and negatively regulated LAMB3 expression. The overexpression of hsa-mir-133a-2 has a significant inhibiting effect on cervical cancer cell proliferation and invasion. The overexpression of hsa-mir-133a-2 significantly inhibits the proliferation and invasion of cervical cancer cells. Moreover, the LAMB3 was able to up-regulate the phosphorylation levels of AKT and phosphatidylinositol 3-kinase (PI3K) protein in cervical cancer cells. hsa-mir-133a-2 could also modulate the PI3K/AKT signaling pathway by targeting LAMB3.

CONCLUSION

hsa-mir-133a-2 inhibits cervical cancer cell proliferation and invasion by indirectly regulating the PI3K/AKT signaling pathway, providing us with a new clinical treatment strategy for cervical cancer.

Prognostic biomarkers of pancreatic cancer identified based on a competing endogenous RNA regulatory network

BACKGROUND

Pancreatic cancer is an insidious and heterogeneous malignancy with poor prognosis that is often locally unresectable. Therefore, determining the underlying mechanisms and effective prognostic indicators of pancreatic cancer may help optimize clinical management. This study was conducted to develop a prognostic model for pancreatic cancer based on a competing endogenous RNA (ceRNA) network.

METHODS

We obtained transcriptomic data and corresponding clinicopathological information of pancreatic cancer samples from The Cancer Genome Atlas (TCGA) database (training set). Based on the ceRNA interaction network, we screened candidate genes to build prediction models. Univariate Cox regression analysis was performed to screen for genes associated with prognosis, and least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive model. A receiver operating characteristic (ROC) curve was drawn, and the C-index was calculated to evaluate the accuracy of the prediction model. Furthermore, we downloaded transcriptomic data and related clinical information of pancreatic cancer samples from the Gene Expression Omnibus database (validation set) to evaluate the robustness of our prediction model.

RESULTS

Eight genes (ANLN, FHDC1, LY6D, SMAD6, ACKR4, RAB27B, AUNIP, and GPRIN3) were used to construct the prediction model, which was confirmed as an independent predictor for evaluating the prognosis of patients with pancreatic cancer through univariate and multivariate Cox regression analysis. By plotting the decision curve, we found that the risk score model is an independent predictor has the greatest impact on survival compared to pathological stage and targeted molecular therapy.

CONCLUSIONS

An eight-gene prediction model was constructed for effectively and independently predicting the prognosis of patients with pancreatic cancer. These eight genes identified show potential as diagnostic and therapeutic targets.

Serum multi-cytokines screening identifies TRAIL and IL-10 as probable new biomarkers for prostate health index diagnostic utility adjustment in grey zone aggressive prostate cancer detection: A single-center data in China

Objective

To identify less invasive and easily applicable serum cytokine-derived biomarkers which contribute to the diagnostic utility and risk assessment ability of the prostate health index (PHI) based multivariable model in grey zone aggressive prostate cancer (AG PCa) early detection.

Methods

Serum 45 cytokines screening was performed in a small training cohort consisting of 10 sera by Luminex liquid array-based multiplexed immunoassays and identified TRAIL and IL-10 as new biomarkers for PHI diagnostic utility adjustment for further validation with a multivariable predictive model in a cohort including 79 aggressive prostate cancer patients and 209 benign prostatic hyperplasia or indolent PCa patients within the PSA grey zone.

Results

TRAIL and IL-10 were identified as potential serum biomarkers for AG PCa detection by the result of multi-cytokines screening in the univariate analysis, while multivariable logistic regression confirmed the AUC of the full risk predictive model (0.915) including tPSA, fPSA, PHI, TRAIL, and IL-10 was higher than various diagnostic strategies. DCA suggested a superior net benefit and indicated a good discriminative ability of the full risk model consistently with the result of the nomogram.

Conclusion

We suggest a significant advantage for the PHI-based multivariate combinations of serum TRAIL and IL-10 comparing to PHI or other serum-derived biomarkers alone in the detection and risk stratification of grey zone AG PCa.

SIRGs score may be a predictor of prognosis and immunotherapy response for esophagogastric junction adenocarcinoma

Background

Esophagogastric junction adenocarcinoma (EGJA) is a special malignant tumor with unknown biological behavior. PD-1 checkpoint inhibitors have been recommended as first-line treatment for advanced EGJA patients. However, the biomarkers for predicting immunotherapy response remain controversial.

Methods

We identified stromal immune-related genes (SIRGs) by ESTIMATE from the TCGA-EGJA dataset and constructed a signature score. In addition, survival analysis was performed in both the TCGA cohort and GEO cohort. Subsequently, we explored the differences in tumor-infiltrating immune cells, immune subtypes, immune-related functions, tumor mutation burden (TMB), immune checkpoint gene expression, immunophenoscore (IPS) between the high SIRGs score and low SIRGs score groups. Finally, two validation cohorts of patients who had accepted immunotherapy was used to verify the value of SIRGs score in predicting immunotherapy response.

Results

Eight of the SIRGs were selected by LASSO regression to construct a signature score (SIRGs score). Univariate and multivariate analyses in the TCGA and GEO cohort suggested that SIRGs score was an independent risk factor for the overall survival (OS) and it could increase the accuracy of clinical prediction models for survival. However, in the high SIRGs score group, patients had more immune cell infiltration, more active immune-related functions, higher immune checkpoint gene expression and higher IPS-PD1 and IPS-PD1-CTLA4 scores, which indicate a better response to immunotherapy. The external validation illustrated that high SIRGs score was significantly associated with immunotherapy response and immune checkpoint inhibitors (ICIs) can improve OS in patients with high SIRGs score.

Conclusion

The SIRGs score may be a predictor of the prognosis and immune-therapy response for esophagogastric junction adenocarcinoma.