Copy number variations and expression of MPDZ are prognostic biomarkers for clear cell renal cell carcinoma

MPDZ is associated with immune infiltration and regulates migration and invasion by switching YAP1 phosphorylation in colorectal cancer

BACKGROUND

Multiple PDZ Domain Crumbs Cell Polarity Complex Component (MPDZ) is involved in a few human cancers. However, the features and potential mechanisms of MPDZ in progression of colorectal cancer (CRC) remains unknown.

METHODS

The prognostic role of MPDZ in CRC was determined by Kaplan-Meier and univariate regression analysis. Enrichment analysis was performed to characterize crucial pathways of MPDZ. Immune infiltration and immunotherapeutic outcome were further evaluated. CCK8, EDU, transwell, and wound healing assay were used to assess the influence of MPDZ on pernicious performance of CRC cells. CD8+ T cells and CRC cells were co-cultured to explore the effect of MPDZ on the tumor microenvironment. qRT-PCR, western blot, immunoprecipitation (IP), and methylated RNA immunoprecipitation (me-RIP) were implemented in seeking for the potential mechanisms of MPDZ in CRC.

RESULTS

CRC patients with elevated MPDZ expression suffered from significantly worse prognosis. Enrichment analysis revealed that MPDZ involved in pathways related to metastasis and cell cycle in CRC. In addition, MPDZ expression were related to several immunoinhibitors and had the ability to predict immunotherapy response. Finally, in vitro assays demonstrated that MPDZ knockdown inhibited migration, invasion and immune evasion of CRC cells. Mechanistically, MPDZ knockdown enhanced YAP1 phosphorylation by increased LATS1 expression. Moreover, m6A-MPDZ mRNA may be recognized and degraded by m6A recognition protein YTHDF2.

CONCLUSIONS

MPDZ was critical for CRC development and could be a promising candidate for the treatment of CRC patients.

Construction of cuproptosis-related lncRNAs/mRNAs model and prognostic prediction of hepatocellular carcinoma

Cuproptosis is a recently reported novel form of cell death, which is involved in the regulation of tumor progression. However, the specific role of cuproprosis in hepatocellular carcinoma (HCC) development remains unclear. In this study, we comprehensively analyzed the effect of cuproprosis-related lncRNAs/mRNAs on the prognosis of HCC patients based on the RNA-Seq transcriptome data and clinical data. We identified 6 cuproprosis-related signatures by Cox and Lasso regression analysis, including 3 mRNAs (FBXO30, RNF2, MPDZ) and 3 lncRNAs (PICSAR, LINC00426, AL590705.3). In addition, we constructed a prognostic prediction model for HCC. Risk analysis, RT-qPCR, and Kaplan-Meier analysis showed that the expression of FBXO30, RNF2, AL590705.3 and PICSAR was elevated in HCC, while the expression of MPDZ and LINC00426 was suppressed which was associated with better overall survival. Furthermore, immune response analysis suggested that HCC with high-risk score might respond favorably to immunotherapy. Moreover, the potential drugs that HCC might be sensitive to were screened by drug sensitivity profiling analysis. Taken together, our findings provided important information for the prediction of the prognosis of HCC patients and the development of personalized targeted therapy.

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.

Somatic Copy Number Alterations and Associated Genes in Clear-Cell Renal-Cell Carcinoma in Brazilian Patients

Somatic copy number aberrations (CNAs) have been associated with clear-cell renal carcinoma (ccRCC) pathogenesis and are a potential source of new diagnostic, prognostic and therapeutic biomarkers. Recurrent CNAs include loss of chromosome arms 3p, 14q, 9p, and gains of 5q and 8q. Some of these regional CNAs are suspected of altering gene expression and could influence clinical outcomes. Despite many studies of CNAs in RCC, there are currently no descriptions of genomic copy number alterations in a Brazilian ccRCC cohort. This study was designed to evaluate the chromosomal profile of CNAs in Brazilian ccRCC tumors and explore clinical associations. A total of 92 ccRCC Brazilian patients that underwent nephrectomy at Barretos Cancer Hospital were analyzed for CNAs by array comparative genomic hybridization. Most patients in the cohort had early-stage localized disease. The most significant alterations were loss of 3p (87.3%), 14q (35.8%), 6q (29.3%), 9p (28.6%) and 10q (25.0%), and gains of 5q (59.7%), 7p (29.3%) and 16q (20.6%). Bioinformatics analysis revealed 19 genes mapping to CNA significant regions, including SETD2, BAP1, FLT4, PTEN, FGFR4 and NSD1. Moreover, gain of 5q34-q35.3 (FLT4 and NSD1) and loss of 6q23.2-q23.3 (MYB) and 9p21.3 (MLLT3) had gene expression levels that correlated with TCGA data and was also associated with advanced disease features, such as larger tumors, Fuhrman 3, metastasis at diagnosis and death. The loss of region 14q22.1 which encompasses the NIN gene was associated with poor overall survival. Overall, this study provides the first CNA landscape of Brazilian patients and pinpoints genomic regions and specific genes worthy of more detailed investigations. Our results highlight important genes that are associated with copy number changes involving large chromosomal regions that are potentially related to ccRCC tumorigenesis and disease biology for future clinical investigations.

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation) – to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

Identification of potential biomarkers for lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most predominant subtype of lung cancers and is one of the leading causes of cancer related mortality worldwide. Despite the advancements in the field of cancer diagnostics and therapeutics, detection at an early stage using reliable biomarkers is an unmet clinical need for a plethora of cancers, including LUAD, thus attributing to poor prognosis. In view of this, to identify potential biomarkers and therapeutic candidate genes, the expression of all known human genes was screened in the publicly available 'The Cancer Genome Atlas' (TCGA) samples of LUAD patients which resulted in the identification of overexpressed genes. Further analysis of these genes across various patient sample datasets revealed that ZNF687, ODR4, PBXIP1, PYGO2, METTL3, PIGM and RAD1 are consistently more highly expressed in LUAD. Higher expression of these genes either alone or in combination is correlated with poor survival of LUAD patients. Hence, in this study we propose that these identified genes could serve as potential candidates as gene signatures or biomarkers for LUAD that require further investigation in large cohorts of LUAD samples.

Comprehensive analysis of copy number variance and sensitivity to common targeted therapy in clear cell renal cell carcinoma: In silico analysis with in vitro validation

Background

Chromosomal rearrangements are common in clear cell renal cell carcinoma (ccRCC) and their roles in mediating sensitivity to tyrosine kinase inhibitors (TKIs) and mTOR inhibitors (mTORi) remain elusive.

Methods

We developed an in silico strategy by screening copy number variance (CNV) that was potentially related to TKI or mTORi sensitivity in ccRCC by reproducing the TCGA and GDSC datasets. Candidate genes should be both significantly prognostic and related to drug sensitivity or resistance, and were then validated in vitro.

Results

ADCYAP1 loss and GNAS gain were associated with sensitivity and resistance and to Cabozantinib, respectively. ACRBP gain and CTBP1 loss were associated with sensitivity and resistance and to Pazopanib, respectively. CDKN2A loss and SULT1A3 gain were associated with sensitivity and resistance and to Temsirolimus, respectively. CCNE1 gain was associated with resistance to Axitinib and LRP10 loss was associated with resistance to Sunitinib. Mutivariate analysis showed ADCYAP1, GNAS, and CCNE1 remained independently prognostic when adjusted for the rest.

Conclusion

Here we show CNVs of several genes that are associated with sensitivity and resistance to commonly used TKIs and mTORi in ccRCC. Further validation and functional analyses are therefore needed.

Copy number variation is highly correlated with differential gene expression: a pan-cancer study

BACKGROUND

Cancer is a heterogeneous disease with many genetic variations. Lines of evidence have shown copy number variations (CNVs) of certain genes are involved in development and progression of many cancers through the alterations of their gene expression levels on individual or several cancer types. However, it is not quite clear whether the correlation will be a general phenomenon across multiple cancer types.

METHODS

In this study we applied a bioinformatics approach integrating CNV and differential gene expression mathematically across 1025 cell lines and 9159 patient samples to detect their potential relationship.

RESULTS

Our results showed there is a close correlation between CNV and differential gene expression and the copy number displayed a positive linear influence on gene expression for the majority of genes, indicating that genetic variation generated a direct effect on gene transcriptional level. Another independent dataset is utilized to revalidate the relationship between copy number and expression level. Further analysis show genes with general positive linear influence on gene expression are clustered in certain disease-related pathways, which suggests the involvement of CNV in pathophysiology of diseases.

CONCLUSIONS

This study shows the close correlation between CNV and differential gene expression revealing the qualitative relationship between genetic variation and its downstream effect, especially for oncogenes and tumor suppressor genes. It is of a critical importance to elucidate the relationship between copy number variation and gene expression for prevention, diagnosis and treatment of cancer.