Identification of genomic functional hotspots with copy number alteration in liver cancer

Screening of differentially expressed genes and identification of AMACR as a prognostic marker in prostate cancer

Prostate cancer, the second most common cancer found in male over the world, was estimated to have 191,930 new cases and 33,330 deaths in 2020 in the United States. Prostate cancer is very common in male, about 12.1% of men will acquire this cancer in their lifetime, and a higher risk was reported in older men and African American men. Gene deregulations have been found to be extensively associated with cancer development. To gain further insight into how gene deregulation affects prostate cancer, we analysed three gene profiling datasets of prostate cancer from Gene Expression Omnibus (GEO) applying bioinformatic tools in our study. Firstly, we identified common differently expressed genes (DEGs) shared by the three gene profiling datasets, constructed protein-protein interaction network and determined top 10 hub genes. Further DEGs validation in TCGA and Human Protein Atlas Database identified AMACR as the core gene. We then analysed the role of AMACR in prostate cancer cell lines and found that AMACR-knockdown resulted in the decreased cell proliferation and increased apoptosis. These results suggest an oncogenic role of AMACR in prostate cancer, and it could be a potential biomarker for the diagnosis of prostate cancer.

Hypoxia Molecular Characterization in Hepatocellular Carcinoma Identifies One Risk Signature and Two Nomograms for Clinical Management

Hypoxia is a universal feature in the tumor microenvironment (TME). Nonetheless, the heterogeneous hypoxia patterns of TME have still not been elucidated in hepatocellular carcinoma (HCC). Using consensus clustering algorithm and public datasets, we identified heterogeneous hypoxia subtypes. We also revealed the specific biological and clinical characteristics via bioinformatic methods. The principal component analysis algorithm was employed to develop a hypoxia-associated risk score (HARS). We identified the two hypoxia subtypes: low hypoxia pattern (C1) and high hypoxia pattern (C2). C1 was less sensitive to immunotherapy compared to C2, consistent with the lack of immune cells and immune checkpoints (ICPs) in C1, whereas C2 was the opposite. C2 displayed worse prognosis and higher sensitivity to obatoclax relative to C1, while C1 was more sensitive to sorafenib. The two subtypes also demonstrated subtype-specific genomic variations including mutation, copy number alteration, and methylation. Moreover, we developed and validated a risk signature: HARS, which had excellent performance for predicting prognosis and immunotherapy. We revealed two hypoxia subtypes with distinct biological and clinical characteristics in HCC, which enhanced the understanding of hypoxia pattern. The risk signature was a promising biomarker for predicting prognosis and immunotherapy.

Immune microenvironment of triple-negative breast cancer in African-American and Caucasian women

PURPOSE

African-American (AA) patients with triple-negative breast cancer (TNBC) are less likely to achieve pathologic complete response from neoadjuvant chemotherapy and have poorer prognosis than Caucasian patients with TNBC, suggesting potential biological differences by race. Immune infiltration is the most consistent predictive marker for chemotherapy response and improved prognosis in TNBC. In this study, we test the hypothesis that the immune microenvironment differs between AA and Caucasian patients.

METHODS

RNA-seq expression data were obtained from The Cancer Genome Atlas (TCGA) database for 162 AA and 697 Caucasian breast cancers. Estrogen receptor (ER)-positive, human epidermal growth factor receptor-2 (HER2)-positive, and TNBC subtypes were included in the analyses. Tumor infiltrating lymphocyte (TIL) counts, immunomodulatory scores, and molecular subtypes were obtained from prior publications for a subset of the TNBC cases. Differences in immune cell distributions and immune functions, measured through gene expression and TIL counts, as well as neoantigen, somatic mutation, amplification, and deletion loads, were compared by race and tumor subtype.

RESULTS

Immune metagene analysis demonstrated marginal immune attenuation in AA TNBC relative to Caucasian TNBC that did not reach statistical significance. The distributions of immune cell populations, lymphocyte infiltration, molecular subtypes, and genomic aberrations between AA and Caucasian subtypes were also not significantly different. The MHC1 metagene demonstrated increased expression in AA ER-positive cancers relative to Caucasian ER-positive cancers.

CONCLUSIONS

This study suggests that the immunological differences between AA and Caucasian breast cancers represented by TCGA data are subtle, if they exist at all. We observed no consistent racial differences in immune gene expression or TIL counts in TNBC by race. However, this study cannot rule out small differences in immune cell subtype distribution and activity status that may not be apparent in bulk RNA analysis.

Identification of differentially expressed molecular functions associated with breast cancer using Gibbs sampling

The aim of the present study was to identify differentially expressed molecular functions (DEMFs) for breast cancer using the Gibbs sampling approach. Molecular functions (MFs) were obtained on the basis of the Bayesian Approach for Geneset Selection package. Subsequently, MFs were converted into Markov chains (MCs) prior to calculating their probabilities, utilizing the MC Monte Carlo algorithm. DEMFs were identified with probabilities ≥0.8 and the gene compositions were studied. Finally, a co-expression network was constructed via the empirical Bayes method and a pathway enrichment analysis of genes in DEMFs was performed. A total of 396 MFs were identified and all transformed to MCs. With the threshold, 2 DEMFs (structural molecule activity and protein heterodimerization activity) were obtained. The DEMFs were comprised of 297 genes, 259 of which were mapped to the co-expression network. These 297 genes were identified to be enriched in 10 pathways, and ribosome was the most significant pathway. The results of the present study revealed 2 DEMFs (structural molecule activity and protein heterodimerization activity) which may be associated with the pathological molecular mechanisms underlying breast cancer, based on Gibbs sampling.

Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer

The presence of tumor-infiltrating lymphocytes (TIL) is a favorable prognostic factor in breast cancer, but what drives immune infiltration remains unknown. Here we examine if clonal heterogeneity, total mutation load, neoantigen load, copy number variations (CNV), gene- or pathway-level somatic mutations, or germline polymorphisms (SNP) are associated with immune metagene expression in breast cancer subtypes. Thirteen published immune metagenes correlated separately with genomic metrics in the three major breast cancer subtypes. We analyzed RNA-Seq, DNA copy number, mutation and germline SNP data of 627 ER⁺, 207 HER2⁺, and 191 triple-negative (TNBC) cancers from The Cancer Genome Atlas. P-values were adjusted for multiple comparisons, and permutation testing was used to assess false discovery rates. Increased immune metagene expression associated significantly with lower clonal heterogeneity estimated by MATH score in all subtypes and with a trend for lower overall mutation, neoantigen, and CNV loads in TNBC and HER2⁺ cancers. In ER⁺ cancers, mutation load, neoantigen load, and CNV load weakly but positively associated with immune infiltration, which reached significance for overall mutation load only. No highly recurrent single gene or pathway level mutations associated with immune infiltration. High immune gene expression and lower clonal heterogeneity in TNBC and HER2⁺ cancers suggest an immune pruning effect and equilibrium between immune surveillance and clonal expansion. Thus, immune checkpoint inhibitors may tip the balance in favor of immune surveillance in these cancers. Cancer Res; 77(12); 3317-24. ©2017 AACR.