Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis

Identification of bicalutamide resistance-related genes and prognosis prediction in patients with prostate cancer

Background

Prostate cancer (PCa) is the second most common type of cancer and the fifth leading cause of cancer-related death in men. Androgen deprivation therapy (ADT) has become the first-line therapy for inhibiting PCa progression; however, nearly all patients receiving ADT eventually progress to castrate-resistant prostate cancer. Therefore, this study aimed to identify hub genes related to bicalutamide resistance in PCa and provide new insights into endocrine therapy resistance.

Methods

The data were obtained from public databases. Weighted correlation network analysis was used to identify the gene modules related to bicalutamide resistance, and the relationship between the samples and disease-free survival was analyzed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed, and hub genes were identified. The LASSO algorithm was used to develop a bicalutamide resistance prognostic model in patients with PCa, which was then verified. Finally, we analyzed the tumor mutational heterogeneity and immune microenvironment in both groups.

Results

Two drug resistance gene modules were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that both modules are involved in RNA splicing. The protein-protein interaction network identified 10 hub genes in the brown module LUC7L3, SNRNP70, PRPF3, LUC7L, CLASRP, CLK1, CLK2, U2AF1L4, NXF1, and THOC1) and 13 in the yellow module (PNN, PPWD1, SRRM2, DHX35, DMTF1, SALL4, MTA1, HDAC7, PHC1, ACIN1, HNRNPH1, DDX17, and HDAC6). The prognostic model composed of RNF207, REC8, DFNB59, HOXA2, EPOR, PILRB, LSMEM1, TCIRG1, ABTB1, ZNF276, ZNF540, and DPY19L2 could effectively predict patient prognosis. Genomic analysis revealed that the high- and low-risk groups had different mutation maps. Immune infiltration analysis showed a statistically significant difference in immune infiltration between the high- and low-risk groups, and that the high-risk group may benefit from immunotherapy.

Conclusion

In this study, bicalutamide resistance genes and hub genes were identified in PCa, a risk model for predicting the prognosis of patients with PCa was constructed, and the tumor mutation heterogeneity and immune infiltration in high- and low-risk groups were analyzed. These findings offer new insights into ADT resistance targets and prognostic prediction in patients with PCa.

Single-cell sequencing analysis reveals gastric cancer microenvironment cells respond vastly different to oxidative stress

Gastric cancer is a common type of gastrointestinal malignant tumor in China. The mechanism of the development and progression of gastric cancer remains the continuing research focus. The tumor microenvironment plays an important role in the development and progression of tumors. The present study used single-cell sequencing data to characterize the microenvironment of gastric cancer, investigate the effects of oxidative stress on gastric cancer microenvironmental cells through the comparison between cancer tissue and normal tissue, and identify the key genes associated with gastric cancer patients' survival. The results showed that compared with normal gastric tissue, gastric cancer tissue had a decreased oxidative stress response, weaker oxidative detoxification ability, and increased oxidative stress-induced cell death. In the different types of single cells of gastric cancer microenvironment, the oxidative stress response of T cell was increased, the ability of oxidative detoxification was enhanced, and the oxidative stress-induced cell death was exacerbate. Mucous cell showed the same trend as gastric cancer cells: decreased oxidative stress response, weak oxidative detoxification ability, and weakened oxidative stress-induced cell death. Moreover, TRIM62, MET, and HBA1, which were significantly associated with oxidative stress, may be biomarkers for the prognosis of gastric cancer. High expression of TRIM62 indicated a good prognosis, while MET and HBA1 indicated a poor prognosis, which will be confirmed by further clinical studies.

Identification of a Novel Nomogram to Predict Progression Based on the Circadian Clock and Insights Into the Tumor Immune Microenvironment in Prostate Cancer

Background

Currently, the impact of the circadian rhythm on the tumorigenesis and progression of prostate cancer (PCA) has yet to be understood. In this study, we first established a novel nomogram to predict PCA progression based on circadian clock (CIC)-related genes and provided insights into the tumor immune microenvironment.

Methods

The TCGA and Genecards databases were used to identify potential candidate genes. Lasso and Cox regression analyses were applied to develop a CIC-related gene signature. The tumor immune microenvironment was evaluated through appropriate statistical methods and the GSCALite database.

Results

Ten genes were identified to construct a gene signature to predict progression probability for patients with PCA. Patients with high-risk scores were more prone to progress than those with low-risk scores (hazard ratio (HR): 4.11, 95% CI: 2.66-6.37; risk score cut-off: 1.194). CLOCK, PER (1, 2, 3), CRY2, NPAS2, RORA, and ARNTL showed a higher correlation with anti-oncogenes, while CSNK1D and CSNK1E presented a greater relationship with oncogenes. Overall, patients with higher risk scores showed lower mRNA expression of PER1, PER2, and CRY2 and higher expression of CSNK1E. In general, tumor samples presented higher infiltration levels of macrophages, T cells and myeloid dendritic cells than normal samples. In addition, tumor samples had higher immune scores, lower stroma scores and lower microenvironment scores than normal samples. Notably, patients with higher risk scores were associated with significantly lower levels of neutrophils, NK cells, T helper type 1, and mast cells. There was a positive correlation between the risk score and the tumor mutation burden (TMB) score, and patients with higher TMB scores were more prone to progress than those with lower TMB scores. Likewise, we observed similar results regarding the correlation between the microsatellite instability (MSI) score and the risk score and the impact of the MSI score on the progression-free interval. We observed that anti-oncogenes presented a significantly positive correlation with PD-L1, PD-L2, TIGIT and SIGLEC15, especially PD-L2.

Conclusion

We identified ten prognosis-related genes as a promising tool for risk stratification in PCA patients from the fresh perspective of CIC.

A Novel Cancer Stemness-Related Signature for Predicting Prognosis in Patients with Colon Adenocarcinoma

Objective

To explore the cancer stemness features and develop a novel cancer stemness-related prognostic signature for colon adenocarcinoma (COAD).

Methods

We downloaded the mRNA expression data and clinical data of COAD from TCGA database and GEO database. Stemness index, mRNAsi, was utilized to investigate cancer stemness features. Weighted gene coexpression network analysis (WGCNA) was used to identify cancer stemness-related genes. Univariate and multivariate Cox regression analyses were applied to construct a prognostic risk cancer stemness-related signature. We then performed internal and external validation. The relationship between cancer stemness and COAD immune microenvironment was investigated.

Results

COAD patients with higher mRNAsi score or EREG-mRNAsi score have significant longer overall survival (OS). We identified 483 differently expressed genes (DEGs) between the high and low mRNAsi score groups. We developed a cancer stemness-related signature using fifteen genes (including RAB31, COL6A3, COL5A2, CCDC80, ADAM12, VGLL3, ECM2, POSTN, DPYSL3, PCDH7, CRISPLD2, COLEC12, NRP2, ISLR, and CCDC8) for prognosis prediction of COAD. Low-risk score was associated with significantly preferable OS in comparison with high-risk score, and the area under the ROC curve (AUC) for OS prediction was 0.705. The prognostic signature was an independent predictor for OS of COAD. Macrophages, mast cells, and T helper cells were the vital infiltration immune cells, and APC costimulation and type II IFN response were the vital immune pathways in COAD.

Conclusions

We developed and validated a novel cancer stemness-related prognostic signature for COAD, which would contribute to understanding of molecular mechanism in COAD.

The Multifaceted Role of Aldehyde Dehydrogenases in Prostate Cancer Stem Cells

Cancer stem cells (CSCs) are the only tumor cells possessing self-renewal and differentiation properties, making them an engine of tumor progression and a source of tumor regrowth after treatment. Conventional therapies eliminate most non-CSCs, while CSCs often remain radiation and drug resistant, leading to tumor relapse and metastases. Thus, targeting CSCs might be a powerful tool to overcome tumor resistance and increase the efficiency of current cancer treatment strategies. The identification and isolation of the CSC population based on its high aldehyde dehydrogenase activity (ALDH) is widely accepted for prostate cancer (PCa) and many other solid tumors. In PCa, several ALDH genes contribute to the ALDH activity, which can be measured in the enzymatic assay by converting 4, 4-difluoro-4-bora-3a, 4a-diaza-s-indacene (BODIPY) aminoacetaldehyde (BAAA) into the fluorescent product BODIPY-aminoacetate (BAA). Although each ALDH isoform plays an individual role in PCa biology, their mutual functional interplay also contributes to PCa progression. Thus, ALDH proteins are markers and functional regulators of CSC properties, representing an attractive target for cancer treatment. In this review, we discuss the current state of research regarding the role of individual ALDH isoforms in PCa development and progression, their possible therapeutic targeting, and provide an outlook for the future advances in this field.

Matrix-assisted laser desorption ionization mass spectrometry profiling of plasma exosomes evaluates osteosarcoma metastasis

Osteosarcoma is the most common primary sarcoma of bone among adolescents, often characterized by early lung metastasis resulting in high mortality. Recently, exosomes have been used in liquid biopsy to monitor tumors. Herein, we used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile human plasma exosomes for the evaluation of osteosarcoma lung metastasis. Forty patients with osteosarcoma with (n = 20) or without (n = 20) lung metastasis as well as 12 heathy controls were recruited. Exosomes were isolated from human plasma for MALDI-TOF MS analysis. Multivariate statistical analyses were performed based on the MALDI-TOF mass spectra. The strategy can efficiently differentiate osteosarcomas from healthy controls and further discriminate osteosarcoma lung metastasis from non-lung metastasis. We identified seven exosomal proteins as potential biomarkers of osteosarcoma lung metastasis. The proposed method holds great promise to clinically diagnose osteosarcoma and monitor osteosarcoma lung metastasis.

Identification of docetaxel-related biomarkers for prostate cancer

Prostate cancer (PCa) which was the second commonly diagnosed malignancy, contributed to the top fifth carcinoma death in men. Nevertheless, the main chemotherapeutic agent docetaxel came to failure due to chemoresistance. Recently, increasing evidence suggested the importance of tumour microenvironment (TME) in PCa. The present study aimed to explore the specific TME in PCa and find biomarkers related to both immune infiltration and docetaxel. The docetaxel-specific genes and differential expression genes comparing PCa with normal control samples were derived using DESeq2 and zinbwave with GSE140440, TCGA and GTEx datasets. Immune-infiltration-related genes were identified using CIBERSORT and co-expression network analysis. Key genes related to both docetaxel and immune infiltrating in PCa, including nine genes, namely ZNF486, IFI6, TMOD2, HSPA4L, ITPR1, LRRC37A7P, APOC1, APOBEC3G, and ITGA2, were determined by overlapping above three gene sets. ITGA2 was then defined as the hub gene for its significant prognostic implications. Further validations conducted on Oncomine, GEO, TISIDB, MSigDB, and The Human Protein Atlas confirmed the docetaxel-specific and immune infiltrating characteristics of ITGA2. To sum up, our findings could provide a better understanding of immune infiltrating and docetaxel-resistance in PCa, mostly, ITGA2 could serve as potential prognosis biomarkers and targets for the combination of docetaxel.

Kinesin family member 18B regulates the proliferation and invasion of human prostate cancer cells

Expression of kinesin family member 18B (KIF18B), an ATPase with key roles in cell division, is deregulated in many cancers, but its involvement in prostate cancer (PCa) is unclear. Here, we investigated the expression and function of KIF18B in human PCa specimens and cell lines using bioinformatics analyses, immunohistochemical and immunofluorescence microscopy, and RT-qPCR and western blot analyses. KIF18B was overexpressed in PCa specimens compared with paracancerous tissues and was associated with poorer disease-free survival. In vitro, KIF18B knockdown in PCa cell lines promoted cell proliferation, migration, and invasion, and inhibited cell apoptosis, while KIF18B overexpression had the opposite effects. In a mouse xenograft model, KIF18B overexpression accelerated and promoted the growth of PCa tumors. Bioinformatics analysis of control and KIF18B-overexpressing PCa cells showed that genes involved in the PI3K–AKT–mTOR signaling pathway were significantly enriched among the differentially expressed genes. Consistent with this observation, we found that KIF18B overexpression activates the PI3K–AKT–mTOR signaling pathway in PCa cells both in vitro and in vivo. Collectively, our results suggest that KIF18B plays a crucial role in PCa via activation of the PI3K–AKT–mTOR signaling pathway, and raise the possibility that KIF18B could have utility as a novel biomarker for PCa.