Identification of copy number alterations in colon cancer from analysis of amplicon-based next generation sequencing data

Insights into the Clinical, Biological and Therapeutic Impact of Copy Number Alteration in Cancer

Copy number alterations (CNAs), resulting from the gain or loss of genetic material from as little as 50 base pairs or as big as entire chromosome(s), have been associated with many congenital diseases, de novo syndromes and cancer. It is established that CNAs disturb the dosage of genomic regions including enhancers/promoters, long non-coding RNA and gene(s) among others, ultimately leading to an altered balance of key cellular functions. In cancer, CNAs have been associated with almost all steps of the disease: predisposition, initiation, development, maintenance, response to treatment, resistance, and relapse. Therefore, understanding how specific CNAs contribute to tumourigenesis may provide prognostic insight and ultimately lead to the development of new therapeutic approaches to improve patient outcomes. In this review, we provide a snapshot of what is currently known about CNAs and cancer, incorporating topics regarding their detection, clinical impact, origin, and nature, and discuss the integration of innovative genetic engineering strategies, to highlight the potential for targeting CNAs using novel, dosage-sensitive and less toxic therapies for CNA-driven cancer.

Multi-omics prognostic signatures of IPO11 mRNA expression and clinical outcomes in colorectal cancer using bioinformatics approaches

The most prevalent malignant illness of the gastrointestinal system, colorectal cancer, is the third most prevalent cancer in males and the second most prevalent cancer in women. Importin-11 is a protein that acts as a regulator of cancer cell proliferation in colorectal tumours by conveying β -catenin to the cell nucleus. However, the IPO11 gene was found to encode a protein called Importin-11, which functions as a nucleus importer for the cell. As a result, preventing β -catenin from entering the nucleus requires blocking Importin-11. As a result, we conducted a multi-omics investigation to assess IPO11 gene potential as a therapeutic biomarker for human colorectal cancer (CC). Oncomine, GEPIA2, immunohisto-chemistry, and UALCAN databases were used to analyses the mRNA expression profiles of IPO11 in CC. The investigation has yielded clear evidence of the increase of IPO11 expression in CC subtypes, as indicated by the data acquired. Analysing CC research from the cBioPortal database, the study discovered three new missense mutations in the importin-11 protein sequence at a frequency of 0.00-1.50% copy number changes. Additionally, the Kaplan-Meier plots demonstrated a strong connection concerning IPO11 downregulation and a poorer CC patient survival rate. The co-expressed gene profile of IPO11 was likewise associated with the onset of CC. IPO11 co-expressed gene profile was also linked to CC development. Moreover, the correlation analysis using bc-GenExMiner and the UCSC Xena server identified KIF2A as the most positively co-expressed gene. The study found that KIF2A and its co-expressed genes were involved in a wide variety of cancer progression pathways using the Enrichr database. Cumulatively, this result will not only provide new information about the expression of IPO11 associated with CC progression and patient survival, but could also serve as a therapeutic biomarker for treating CC in a significant and worthwhile manner.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00259-2.

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

Background

The increasing incidence of gastrointestinal stromal tumors (GISTs) has led to the discovery of more novel prognostic markers. We aim to establish an unsupervised prognostic model for the early prediction of the prognosis of future patients with GISTs and to guide clinical treatment.

Methods

We downloaded the GISTs dataset through the cBioPortal website. We extracted clinical information and pathological information, including the microsatellite instability (MSI) score, fraction genome altered (FGA) score, tumor mutational burden (TMB), and copy number alteration burden (CNAB), of patients with GISTs. For survival analysis, we used univariate Cox regression to analyze the contribution of each factor to prognosis and calculated a hazard ratio (HR) and 95% confidence interval (95% CI). For clustering groupings, we used the t-distributed stochastic neighbor embedding (t-SNE) method for data dimensionality reduction. Subsequently, the k-means method was used for clustering analysis.

Results

A total of 395 individuals were included in the study. After dimensionality reduction with t-SNE, all patients were divided into two subgroups. Cluster 1 had worse OS than cluster 2 (HR=3.45, 95% CI, 2.22-5.56, P<0.001). The median MSI score of cluster 1 was 1.09, and the median MSI score of cluster 2 was 0.24, which were significantly different (P<0.001). The FGA score of cluster 1 was 0.28, which was higher than that of cluster 2 (P<0.001). In addition, both the TMB and CNAB of cluster 1 were higher than those of cluster 2, and the P values were less than 0.001.

Conclusion

Based on the CNA of GISTs, patients can be divided into high-risk and low-risk groups. The high-risk group had a higher MSI score, FGA score, TMB and CNAB than the low-risk group. In addition, we established a prognostic nomogram based on the CNA and clinicopathological characteristics of patients with GISTs.

Colorectal cancer concurrent gene signature based on coherent patterns between genomic and transcriptional alterations

Background

The aim of the study was to enhance colorectal cancer prognostication by integrating single nucleotide polymorphism (SNP) and gene expression (GE) microarrays for genomic and transcriptional alteration detection; genes with concurrent gains and losses were used to develop a prognostic signature.

Methods

The discovery dataset comprised 32 Taiwanese colorectal cancer patients, of which 31 were assayed for GE and copy number variations (CNVs) with Illumina Human HT-12 BeadChip v4.0 and Omni 25 BeadChip v1.1. Concurrent gains and losses were declared if coherent manners were observed between GE and SNP arrays. Concurrent genes were also identified in The Cancer Genome Atlas Project (TCGA) as the secondary discovery dataset (n = 345).

Results

The “universal” concurrent genes, which were the combination of z-transformed correlation coefficients, contained 4022 genes. Candidate genes were evaluated within each of the 10 public domain microarray datasets, and 1655 (2000 probe sets) were prognostic in at least one study. Consensus across all datasets was used to build a risk predictive model, while distinct relapse-free/overall survival patterns between defined risk groups were observed among four out of five training datasets. The predictive accuracy of recurrence, metastasis, or death was between 61 and 86% (cross-validation area under the receiver operating characteristic (ROC) curve: 0.548-0.833) from five independent validation studies.

Conclusion

The colorectal cancer concurrent gene signature is prognostic in terms of recurrence, metastasis, or mortality among 1746 patients. Genes with coherent patterns between genomic and transcriptional contexts are more likely to provide prognostication for colorectal cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09627-9.

Copy Number Alterations as Novel Biomarkers and Therapeutic Targets in Colorectal Cancer

In colorectal cancer, somatic mutations have played an important role as prognostic and predictive biomarkers, with some also functioning as therapeutic targets. Another genetic aberration that has shown significance in colorectal cancer is copy number alterations (CNAs). CNAs occur when a change to the DNA structure propagates gain/amplification or loss/deletion in sections of DNA, which can often lead to changes in protein expression. Multiple techniques have been developed to detect CNAs, including comparative genomic hybridization with microarray, low pass whole genome sequencing, and digital droplet PCR. In this review, we summarize key findings in the literature regarding the role of CNAs in the pathogenesis of colorectal cancer, from adenoma to carcinoma to distant metastasis, and discuss the roles of CNAs as prognostic and predictive biomarkers in colorectal cancer.

Altered SERCA Expression in Breast Cancer

Background and Objectives: Calcium (Ca²⁺) signaling is critical for the normal functioning of various cellular activities. However, abnormal changes in cellular Ca²⁺ can contribute to pathological conditions, including various types of cancer. The maintenance of intracellular Ca²⁺ levels is achieved through tightly regulated processes that help maintain Ca²⁺ homeostasis. Several types of regulatory proteins are involved in controlling intracellular Ca²⁺ levels, including the sarco/endoplasmic reticulum (SR/ER) Ca²⁺ ATPase pump (SERCA), which maintains Ca²⁺ levels released from the SR/ER. In total, three ATPase SR/ER Ca²⁺-transporting (ATP2A) 1-3 genes exist, which encode for several isoforms whose expression profiles are tissue-specific. Recently, it has become clear that abnormal SERCA expression and activity are associated with various types of cancer, including breast cancer. Breast carcinomas represent 40% of all cancer types that affect women, with a wide variety of pathological and clinical conditions. Materials and methods: Using cBioPortal breast cancer patient data, Kaplan–Meier plots demonstrated that high ATP2A1 and ATP2A3 expression was associated with reduced patient survival. Results: The present study found significantly different SERCA specific-type expressions in a series of breast cancer cell lines. Moreover, bioinformatics analysis indicated that ATP2A1 and ATP2A3 expression was highly altered in patients with breast cancer. Conclusion: Overall, the present data suggest that SERCA gene-specific expressioncan possibly be considered as a crucial target for the control of breast cancer development and progression.

Identification and Characterization of the Copy Number Dosage-Sensitive Genes in Colorectal Cancer

Dosage effect is one of the common mechanisms of somatic copy number alteration in the development of colorectal cancer, yet the roles of dosage-sensitive genes (DSGs) in colorectal cancer (CRC) remain to be characterized more deeply. In this study, we developed a five-step pipeline to identify DSGs and analyzed their characterization in CRC. Results showed that our pipeline performed better than existing methods, and the result was significantly overlapped between solid tumor and cell line. We also found that the top five DSGs (PSMF1, RAF1, PTPRA, MKRN2, and ELP3) were associated with the progression of CRC. By analyzing the characterization, DSGs were enriched in driver genes and they drove sub-pathways of CRC. In addition, immune-related DSGs are associated with CRC progression. Our results also showed that the CRC samples affected by high microsatellites have fewer DSGs, but a higher overlap with DSGs in microsatellite low instability and microsatellite stable samples. In addition, we applied DSGs to identify potential drug targets, with the results showing that 22 amplified DSGs were more sensitive to four drugs. In conclusion, DSGs play an important role in CRC, and our pipeline is effective to identify them.

Identification of Prognostic Dosage-Sensitive Genes in Colorectal Cancer Based on Multi-Omics

Several studies have already identified the prognostic markers in colorectal cancer (CRC) based on somatic copy number alteration (SCNA). However, very little information is available regarding their value as a prognostic marker. Gene dosage effect is one important mechanism of copy number and dosage-sensitive genes are more likely to behave like driver genes. In this work, we propose a new pipeline to identify the dosage-sensitive prognostic genes in CRC. The RNAseq data, the somatic copy number of CRC from TCGA were assayed to screen out the SCNAs. Wilcoxon rank-sum test was used to identify the differentially expressed genes in alteration samples with |SCNA| > 0.3. Cox-regression was used to find the candidate prognostic genes. An iterative algorithm was built to identify the stable prognostic genes. Finally, the Pearson correlation coefficient was calculated between gene expression and SCNA as the dosage effect score. The cell line data from CCLE was used to test the consistency of the dosage effect. The differential co-expression network was built to discover their function in CRC. A total of six amplified genes (NDUFB4, WDR5B, IQCB1, KPNA1, GTF2E1, and SEC22A) were found to be associated with poor prognosis. They demonstrate a stable prognostic classification in more than 50% threshold of SCNA. The average dosage effect score was 0.5918 ± 0.066, 0.5978 ± 0.082 in TCGA and CCLE, respectively. They also show great stability in different data sets. In the differential co-expression network, these six genes have the top degree and are connected to the driver and tumor suppressor genes. Function enrichment analysis revealed that gene NDUFB4 and GTF2E1 affect cancer-related functions such as transmembrane transport and transformation factors. In conclusion, the pipeline for identifying the prognostic dosage-sensitive genes in CRC was proved to be stable and reliable.

Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method

Melanoma is one of the most malignant types of skin cancer. However, the efficacy and utility of available drug therapies for melanoma are limited. The objective of the present study was to identify potential genes associated with melanoma progression and to explore approved therapeutic drugs that target these genes. Weighted gene co-expression network analysis was used to construct a gene co-expression network, explore the associations between genes and clinical characteristics and identify potential biomarkers. Gene expression profiles of the GSE65904 dataset were obtained from the Gene Expression Omnibus database. RNA-sequencing data and clinical information associated with melanoma obtained from The Cancer Genome Atlas were used for biomarker validation. A total of 15 modules were identified through average linkage hierarchical clustering. In the two significant modules, three network hub genes associated with melanoma prognosis were identified: C-X-C motif chemokine receptor 4 (CXCR4), interleukin 7 receptor (IL7R) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG). The receiver operating characteristic curve indicated that the mRNA levels of these genes exhibited excellent prognostic efficiency for primary and metastatic tumor tissues. In addition, the proximity between candidate genes associated with melanoma progression and drug targets obtained from DrugBank was calculated in the protein interaction network, and the top 15 drugs that may be suitable for treating melanoma were identified. In summary, co-expression network analysis led to the selection of CXCR4, IL7R and PIK3CG for further basic and clinical research on melanoma. Utilizing a network-based method, 15 drugs that exhibited potential for the treatment of melanoma were identified.

Graph Algorithms for Condensing and Consolidating Gene Set Analysis Results

Gene set analysis plays a critical role in the functional interpretation of omics data. Although this is typically done for one omics experiment at a time, there is an increasing need to combine gene set analysis results from multiple experiments performed on the same or different omics platforms, such as in multi-omics studies. Integrating results from multiple experiments is challenging, and annotation redundancy between gene sets further obscures clear conclusions. We propose to use a weighted set cover algorithm to reduce redundancy of gene sets identified in a single experiment. Next, we use affinity propagation to consolidate similar gene sets identified from multiple experiments into clusters and to automatically determine the most representative gene set for each cluster. Using three examples from over representation analysis and gene set enrichment analysis, we showed that weighted set cover outperformed a previously published set cover method and reduced the number of gene sets by 52-77%. Focusing on overlapping genes between the list of input genes and the enriched gene sets in over-representation analysis and leading-edge genes in gene set enrichment analysis further reduced the number of gene sets. A use case combining enrichment analysis results from RNA-Seq and proteomics data comparing basal and luminal A breast cancer samples highlighted the known difference in proliferation and DNA damage response. Finally, we used these algorithms for a pan-cancer survival analysis. Our analysis clearly revealed prognosis-related pathways common to multiple cancer types or specific to individual cancer types, as well as pathways associated with prognosis in different directions in different cancer types. We implemented these two algorithms in an R package, Sumer, which generates tables and static and interactive plots for exploration and publication. Sumer is publicly available at https://github.com/bzhanglab/sumer.

CDK8 as a therapeutic target for cancers and recent developments in discovery of CDK8 inhibitors

Cyclin-dependent kinases 8 (CDK8) regulates transcriptional process via associating with the mediator complex or phosphorylating transcription factors (TF). Overexpression of CDK8 has been observed in various cancers. It mediates aberrant activation of Wnt/β-catenin signaling pathway, which is initially recognized and best studied in colorectal cancer (CRC). CDK8 acts as an oncogene and represents a potential target for developing novel CDK8 inhibitors in cancer therapeutics. However, other study has revealed its contrary role. The function of CDK8 is context dependent. Even so, a variety of potent and selective CDK8 inhibitors have been discovered after crystal structures were resolved in two states (active or inactive). In this review, we summarize co-crystal structures, biological mechanisms, dysregulation in cancers and recent progress in the field of CDK8 inhibitors, trying to offer an outlook of CDK8 inhibitors in cancer therapy in future.