The close interaction between hypoxia-related proteins and metastasis in pancarcinomas

Abstract

Many primary-tumor subregions exhibit low levels of molecular oxygen and restricted access to nutrients due to poor vascularization in the tissue, phenomenon known as hypoxia. Hypoxic tumors are able to regulate the expression of certain genes and signaling molecules in the microenvironment that shift it towards a more aggressive phenotype. The transcriptional landscape of the tumor favors malignant transformation of neighboring cells and their migration to distant sites. Herein, we focused on identifying key proteins that participate in the signaling crossroads between hypoxic environment and metastasis progression that remain poorly defined. To shed light on these mechanisms, we performed an integrated multi-omics analysis encompassing genomic/transcriptomic alterations of hypoxia-related genes and Buffa hypoxia scores across 17 pancarcinomas taken from the PanCancer Atlas project from The Cancer Genome Atlas consortium, protein-protein interactome network, shortest paths from hypoxia-related proteins to metastatic and angiogenic phenotypes, and drugs involved in current clinical trials to treat the metastatic disease. As results, we identified 30 hypoxia-related proteins highly involved in metastasis and angiogenesis. This set of proteins, validated with the MSK-MET Project, could represent key targets for developing therapies. The upregulation of mRNA was the most prevalent alteration in all cancer types. The highest frequencies of genomic/transcriptomic alterations and hypoxia score belonged to tumor stage 4 and positive metastatic status in all pancarcinomas. The most significantly associated signaling pathways were HIF-1, PI3K-Akt, thyroid hormone, ErbB, FoxO, mTOR, insulin, MAPK, Ras, AMPK, and VEGF. The interactome network revealed high-confidence interactions among hypoxic and metastatic proteins. The analysis of shortest paths revealed several ways to spread metastasis and angiogenesis from hypoxic proteins. Lastly, we identified 23 drugs enrolled in clinical trials focused on metastatic disease treatment. Six of them were involved in advanced-stage clinical trials: aflibercept, bevacizumab, cetuximab, erlotinib, ipatasertib, and panitumumab.

Figure 1

Panoramic landscape of genomic and transcriptomic alterations across pancarcinomas from PCA-TCGA. (A) OncoPrint (mRNA high, mRNA low, CNV amplification, CNV deep deletion, putative driver mutation and fusion genes) of the most altered hypoxia-related genes. (B) Ranking of the most altered hypoxia-related genes per alteration type. TCGA: The Cancer Genome Atlas; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma and endocervical carcinoma; CRC: colorectal adenocarcinoma; ESCA: esophageal carcinoma; HNSC: head and neck squamous cell carcinoma; KIRC: kidney renal clear cell carcinoma; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; MESO: mesothelioma; PAAD: pancreatic adenocarcinoma; PRAD: prostate adenocarcinoma; SKCM: skin cutaneous melanoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; CNV: copy number variant.

Figure 2

Frequency of genomic and transcriptomic alterations per PCA-TCGA type. (A) Mean frequency per alteration type and significant Bonferroni correction (P < 0.001) of mRNA upregulation, CNV amplification, putative driver mutation, CNV deep deletion, mRNA downregulation, and fusion gene in comparison with other alterations. (B) Ranking of the most altered pancarcinomas from PCA-TCGA according to the mean frequency of alterations, and its validation with a pairwise map of significant Bonferroni correction across PCA-TCGA. (C) Ranking of the most altered pancarcinomas per alteration type. TCGA: The Cancer Genome Atlas; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma and endocervical carcinoma; CRC: colorectal adenocarcinoma; ESCA: esophageal carcinoma; HNSC: head and neck squamous cell carcinoma; KIRC: kidney renal clear cell carcinoma; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; MESO: mesothelioma; PAAD: pancreatic adenocarcinoma; PRAD: prostate adenocarcinoma; SKCM: skin cutaneous melanoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; CNV: copy number variant.

Figure 3

Tumor stages and metastatic status. (A) Mean frequency of genomic and transcriptomic alterations per tumor stage across 17 pancarcinomas from PCA-TCGA. (B) Mean frequency of each alteration type per tumor stage. (C) Mean frequency of genomic and transcriptomic alterations per metastatic status, and its validation through the Mann–Whitney U test (P < 0.001). CNV: copy number variant.

Figure 4

Hypoxia score. (A) Hypoxia score mean across 13 PCA-TCGA types, and its validation with a pairwise map of significant Bonferroni correction (P < 0.001) across. (B) Mean frequency of hypoxia score per tumor stage, and its validation with the Bonferroni correction test. (C) Mean frequency of hypoxia score per metastatic status, and its validation with the Mann–Whitney U test (P < 0.001). TCGA: The Cancer Genome Atlas; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma and endocervical carcinoma; CRC: colorectal adenocarcinoma; HNSC: head and neck squamous cell carcinoma; KIRC: kidney renal clear cell carcinoma; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; PAAD: pancreatic adenocarcinoma; PRAD: prostate adenocarcinoma; SKCM: skin cutaneous melanoma; THCA: thyroid carcinoma; HS: hypoxia score.

Figure 5

Protein–protein interactome network encompassed by hypoxia-related proteins and metastatic proteins. Network of 108 nodes with at least one high confidence interaction (cutoff = 0.9). Darkest nodes represent proteins with the highest degrees of centrality and a mean of degree of centrality of 11.2.

Figure 6

Cell overview of pathways with the shortest distance score from hypoxia-related proteins to the metastatic phenotype. HRP: hypoxia-related proteins.

Figure 7

Integration of multi-omics approaches and functional enrichment analysis. (A) Venn diagram shows 30 hypoxic/metastatic/angiogenic proteins significantly expressed in the PCA-TCGA, the protein–protein interactome network, and the shortest paths to cancer hallmark phenotypes (metastasis and angiogenesis). (B) Circos plot showing that several hypoxia-related proteins promote or suppress cancer hallmark phenotypes. (C) Manhattan plot of the functional enrichment analysis showing the most significant GO: biological processes related to hypoxia and cell migration, and the most significant signaling pathways with a Benjamini–Hochberg FDR q < 0.001. FDR: false discovery rate; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Figure 8

Validation of the essential HRGs through the MSK-MET project. (A) Comparison of overall survival between 18,446 patients with alterations in the 30 essential HRGs highly involved in metastasis and 7213 unaltered patients, showing a log rank test P < 0.001. (B) Percentage of samples with alterations in the 30 essential HRGs and its respective metastatic site. CNS: Central nervous system; LN: lymph node; UT: urothelial; HRG: hypoxia-related genes; CI: coefficient intervals; MSK-MET: Memorial Sloan Kettering—Metastatic Events and Tropisms.

Figure 9

Overview of clinical trials of drugs focused on metastasis. (A) Percentage of clinical trials per cancer type. (B) Hypoxia-related proteins with highest number of clinical trials on metastasis. (C) Phase of clinical trials where drugs are involved. (D) Target class. (E) Type of drugs. (F) Sankey plot showing the therapeutic targets, cancer types, and drugs involved in clinical trials.