DriverDBv3: a multi-omics database for cancer driver gene research

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

EMP1 correlated with cancer progression and immune characteristics in pan-cancer and ovarian cancer

This study examines the prognostic role and immunological relevance of EMP1 (epithelial membrane protein-1) in a pan-cancer analysis, with a focus on ovarian cancer. Utilizing data from TCGA, CCLE, and GTEx databases, we assessed EMP1 mRNA expression and its correlation with tumor progression, prognosis, and immune microenvironment across various cancers. Our results indicate that EMP1 expression is significantly associated with poor prognosis in multiple cancer types, including ovarian, bladder, testicular, pancreatic, breast, brain, and uveal melanoma. Immune-related analyses reveal a positive correlation between EMP1 and immune cell infiltration, particularly neutrophils, macrophages, and dendritic cells, as well as high expression of immune checkpoint such as CD274, HAVCR2, IL10, PDCD1LG2, and TGFB1 in most tumors. In vivo experiments confirm that EMP1 promotes ovarian cancer cell proliferation, metastasis, and invasion. In conclusion, EMP1 emerges as a potential prognostic biomarker and therapeutic target in various cancers, particularly ovarian cancer, due to its influence on tumor progression and immune cell dynamics. Further research is warranted to elucidate the precise mechanisms of EMP1 in cancer biology and to translate these findings into clinical applications.

Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy

BACKGROUND

Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers.

METHODS

We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach. In addition to minimizing the risk of immune-related adverse events by selecting antigens based on their expression profile in tumor biopsies and healthy tissues, we incorporated a network analysis-derived antigen indispensability index based on computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model relying on peptide physicochemical characteristics.

RESULTS

In a model study of uveal melanoma, Human Leukocyte Antigen (HLA) docking simulations and experimental quantification of the peptide-major histocompatibility complex binding affinities confirmed that our approach discriminates between high-binding and low-binding affinity peptides with a performance similar to that of established methodologies. Blinded validation experiments with autologous T-cells yielded peptide stimulation-induced interferon-γ secretion and cytotoxic activity despite high interdonor variability. Dissecting the score contribution of the tested antigens revealed that peptides with the potential to induce cytotoxicity but unsuitable due to potential tissue damage or instability of expression were properly discarded by the computational pipeline.

CONCLUSIONS

In this study, we demonstrate the feasibility of the de novo computational selection of antigens with the capacity to induce an anti-tumor immune response and a predicted low risk of tissue damage. On translation to the clinic, our pipeline supports fast turn-around validation, for example, for adoptive T-cell transfer preparations, in both generalized and personalized antigen-directed immunotherapy settings.

Characterization of zinc finger protein 536, a neuroendocrine regulator, using pan-cancer analysis

BACKGROUND

Previous studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear.

METHODS

ZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR.

RESULTS

Genetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers.

CONCLUSIONS

Our results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.

Gigaxonin Suppresses Epithelial-to-Mesenchymal Transition of Human Cancer Through Downregulation of Snail

Gigaxonin is an E3 ubiquitin ligase that plays a role in cytoskeletal stability. Its role in cancer is not yet clearly understood. Our previous studies of head and neck cancer had identified gigaxonin interacting with p16 for NFκB ubiquitination. To explore its role in cancer cell growth suppression, we analyzed normal and tumor DNA from cervical and head and neck cancers. There was a higher frequency of exon 8 SNP (c.1293 C>T, rs2608555) in the tumor (46% vs. 25% normal, P = 0.011) pointing to a relationship to cancer. Comparison of primary tumor with recurrence and metastasis did not reveal a statistical significance. Two cervical cancer cell lines, ME180 and HT3 harboring exon 8 SNP and showing T allele expression correlated with higher gigaxonin expression, reduced in vitro cell growth and enhanced cisplatin sensitivity in comparison with C allele expressing cancer cell lines. Loss of gigaxonin expression in ME180 cells through CRISPR-Cas9 or siRNA led to aggressive cancer cell growth including increased migration and Matrigel invasion. The in vitro cell growth phenotypes were reversed with re-expression of gigaxonin. Suppression of cell growth correlated with reduced Snail and increased e-cadherin expression. Mouse tail vein injection studies showed increased lung metastasis of cells with low gigaxonin expression and reduced metastasis with reexpression of gigaxonin. We have found an association between C allele expression and RNA instability and absence of multimeric protein formation. From our results, we conclude that gigaxonin expression is associated with suppression of epithelial-mesenchymal transition through inhibition of Snail.

SIGNIFICANCE

Our results suggest that GAN gene exon 8 SNP T allele expression correlates with higher gigaxonin expression and suppression of aggressive cancer cell growth. There is downregulation of Snail and upregulation of e-cadherin through NFκB ubiquitination. We hypothesize that exon 8 T allele and gigaxonin expression could serve as diagnostic markers of suppression of aggressive growth of head and neck cancer.

Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome

Singleton or low-frequency driver mutations are challenging to identify. We present a domain driver mutation estimator (DOME) to identify rare candidate driver mutations. DOME analyzes positions analogous to known statistical hotspots and resistant mutations in combination with their functional and biochemical residue context as determined by protein structures and somatic mutation propensity within conserved PFAM domains, integrating the CADD scoring scheme. Benchmarked against seven other tools, DOME exhibited superior or comparable accuracy compared to all evaluated tools in the prediction of functional cancer drivers, with the exception of one tool. DOME identified a unique set of 32 917 high-confidence predicted driver mutations from the analysis of whole proteome missense variants within domain boundaries across 1331 genes, including 1192 noncancer gene census genes, emphasizing its unique place in cancer genome analysis. Additionally, analysis of 8799 TCGA (The Cancer Genome Atlas) and in-house tumor samples revealed 847 potential driver mutations, with mutations in tyrosine kinase members forming the dominant burden, underscoring its higher significance in cancer. Overall, DOME complements current approaches for identifying novel, low-frequency drivers and resistant mutations in personalized therapy.

Inhibition of ADAM9 promotes the selective degradation of KRAS and sensitizes pancreatic cancers to chemotherapy

Kirsten rat sarcoma virus (KRAS) signaling drives pancreatic ductal adenocarcinoma (PDAC) malignancy, which is an unmet clinical need. Here, we identify a disintegrin and metalloproteinase domain (ADAM)9 as a modulator of PDAC progression via stabilization of wild-type and mutant KRAS proteins. Mechanistically, ADAM9 loss increases the interaction of KRAS with plasminogen activator inhibitor 1 (PAI-1), which functions as a selective autophagy receptor in conjunction with light chain 3 (LC3), triggering lysosomal degradation of KRAS. Suppression of ADAM9 by a small-molecule inhibitor restricts disease progression in spontaneous models, and combination with gemcitabine elicits dramatic regression of patient-derived tumors. Our findings provide a promising strategy to target the KRAS signaling cascade and demonstrate a potential modality to enhance sensitivity to chemotherapy in PDAC.

Comparative study on genomic and epigenomic profiles of retinoblastoma or tuberous sclerosis complex via nanopore sequencing and a joint screening framework

Recurrence and extraocular metastasis in advanced intraocular retinoblastoma (RB) are still major obstacles for successful treatment of Chinese children. Tuberous sclerosis complex (TSC) is a very rare, multisystemic genetic disorder characterized by hamartomatous growth. In this study, we aimed to compare genomic and epigenomic profiles with human RB or TSC using recently developed nanopore sequencing, and to identify disease-associated variations or genes. Peripheral blood samples were collected from either RB or RB/TSC patients plus their normal siblings, followed by nanopore sequencing and identification of disease-specific structural variations (SVs) and differentially methylated regions (DMRs) by a systematic biology strategy named as multiomics-based joint screening framework. In total, 316 RB- and 1295 TSC-unique SVs were identified, as well as 1072 RB- and 1114 TSC-associated DMRs, respectively. We eventually identified 6 key genes for RB for further functional validation. Knockdown of CDK19 with specific siRNAs significantly inhibited Y79 cellular proliferation and increased sensitivity to carboplatin, whereas downregulation of AHNAK2 promoted the cell growth as well as drug resistance. Those two genes might serve as potential diagnostic markers or therapeutic targets of RB. The systematic biology strategy combined with functional validation might be an effective approach for rare pediatric malignances with limited samples and challenging collection process.

Genome-wide quantification of copy-number aberration impact on gene expression in ovarian high-grade serous carcinoma

Copy-number alterations (CNAs) are a hallmark of cancer and can regulate cancer cell states via altered gene expression values. Herein, we have developed a copy-number impact (CNI) analysis method that quantifies the degree to which a gene expression value is impacted by CNAs and leveraged this analysis at the pathway level. Our results show that a high CNA is not necessarily reflected at the gene expression level, and our method is capable of detecting genes and pathways whose activity is strongly influenced by CNAs. Furthermore, the CNI analysis enables unbiased categorization of CNA categories, such as deletions and amplifications. We identified six CNI-driven pathways associated with poor treatment response in ovarian high-grade serous carcinoma (HGSC), which we found to be the most CNA-driven cancer across 14 cancer types. The key driver in most of these pathways was amplified wild-type KRAS, which we validated functionally using CRISPR modulation. Our results suggest that wild-type KRAS amplification is a driver of chemotherapy resistance in HGSC and may serve as a potential treatment target.

Dysregulated ANLN reveals immune cell landscape and promotes carcinogenesis by regulating the PI3K/Akt/mTOR pathway in clear cell renal cell carcinoma

Background

Abnormal anillin (ANLN) expression has been observed in multiple tumours and is closely associated with patient prognosis and clinical features. In this study, we systematically elucidated the clinical significance and biological roles of ANLN in patients with clear cell renal cell carcinoma (ccRCC).

Methods

We obtained transcriptome and clinical data of patients with ccRCC from public databases. Multi-omics data and clinical samples were combined to analyse the correlation between ANLN expression and the clinical characteristics of patients with renal cancer. Additionally, the immune cell landscape of ANLN expression was evaluated using different immune algorithms in the tumour microenvironment. The tumour-promoting potential of ANLN was confirmed using in vitro assays, including CCK8 and Transwell assays.

Results

Bioinformatics analysis showed that ANLN is over-expressed in patients with ccRCC, as validated by clinical samples. Publicly available clinical data suggest that high ANLN expression may indicate poor outcomes in patients with ccRCC. Moreover, biological function analysis revealed a marked enrichment of the cell cycle and PI3K-Akt pathways. The distribution of immune cells, particularly M2 macrophages, differed in patients with ccRCC. Furthermore, ANLN silencing inhibited the proliferation, migration, and invasion of renal cancer cells in vitro. After ANLN expression was knocked down in 786-O cells, the protein levels of important PI3K signalling pathway components, including PI3K, Akt, and mTOR, drastically decreased.

Conclusions

These findings suggest that ANLN is dysregulated in renal cancer tissues and promotes tumour progression by activating the PI3K/Akt/mTOR signalling pathway.

N-of-one differential gene expression without control samples using a deep generative model

Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

IL23R as an indicator of immune infiltration and poor prognosis in intrahepatic cholangiocarcinoma: a bioinformatics analysis

Background

Although the incidence of intrahepatic cholangiocarcinoma (CHOL) is low, the prognosis is very poor. The expression level of interleukin 23 receptor (IL23R) is linked to the occurrence and development of cancers. This study aimed to identify the role of IL23R in CHOL using bioinformatics tools and experimental validation.

Methods

Circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) datasets were obtained from the Gene Expression Omnibus (GEO) database, and R software was used for data analysis and visualization. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to conduct functional enrichment analysis, which was verified with gene set enrichment analysis software. Clinical data were obtained from The Cancer Genome Atlas (TCGA), and survival analyses were performed using the DriverDBv3 database and the Gene Expression Profiling Interactive Analysis website. The TIMER2.0 database provided us for immune cell infiltration analysis results of IL23R. Real-time quantitative polymerase chain reaction (RT-qPCR) was used for IL23R expression verification.

Results

Differentially expressed (DE) mRNAs were enriched in phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, immune-related tumor microenvironment (TME), and amino acid metabolism, etc. In addition, expression of IL23R was associated with immune infiltration-related cells. Furthermore, a circRNA-miRNA-IL23R network and a IL23R protein-protein interaction network were established. Most importantly, IL23R, as a prognostic gene, was found to have a low expression in CHOL.

Conclusions

A circRNA-miRNA-IL23R network was identified, and it was found that IL23R may be a prognostic and immune-related biomarker in CHOL, which is worthy of further exploration.

The discovery of promising candidate biomarkers in kidney renal clear cell carcinoma: evidence from the in-depth analysis of high-throughput data

Kidney renal clear cell carcinoma (KIRC) is the most prevalent subtype of renal tumor. The underlying mechanisms governing KIRC initiation and progression are less known. The present study aimed to reveal novel hub genes associated with the initiation and progression of KIRC, which may be utilized as novel molecular biomarkers and therapeutic targets for the treatment of KIRC. The GSE6344 dataset from the Gene Expression Omnibus (GEO) database was integrated to identify differentially expressed genes (DEGs) using the limma package. Then, hub genes were identified and UALCAN, GEPIA, OncoDB, DriverDBv3, GENT2, and HPA databases were employed for the expression, survival, and methylation analyses. cBioPortal tool was used to investigate the genetic alterations, while CancerSEA, TIMER, DAVID, ENCORI, DrugBank, and GSCAlite were utilized to explore a few more hub gene-associated parameters. Finally, targeted bisulfite sequencing (bisulfite-seq), and RT-qPCR techniques were used to validate the expression and methylation level of the hub genes using Human RCC cell line 786-O, A-498, and normal renal tubular epithelial cell line HK-2. In total, 7299 DEGs were found between KIRC and normal samples in the GSE6344 dataset. Using STRING and Cytohubba analysis, four hub genes including VEGFA (vascular endothelial growth factor), ALB (Albumin), ENO2 (enolase 2), and CAVI1 (Caveolin 1) were selected as the hub genes. Further, it was validated through extensive analysis of TCGA datasets that these VEGA, ENO2, and CAV1 hub genes were significantly up-regulated, while ALB was significantly down-regulated in KIRC samples compared to controls. The dysregulation of these genes was found to be associated with the overall survival (OS) of the KIRC patients. Moreover, this study also revealed some novel links between VEGA, ALB, ENO2, and CAV1 expression and genetic alterations, promoter methylation status, immune cell infiltration, miRNAs, gene enrichment terms, and various chemotherapeutic drugs. The present study revealed a panel of four hub genes, which contributed to improving our understanding of the underlying molecular mechanisms of KIRC development and can be utilized as promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.

DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker

We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.

Circulating tumour DNA alterations: emerging biomarker in head and neck squamous cell carcinoma

Head and Neck cancers (HNC) are a heterogeneous group of upper aero-digestive tract cancer and account for 931,922 new cases and 467,125 deaths worldwide. About 90% of these cancers are of squamous cell origin (HNSCC). HNSCC is associated with excessive tobacco and alcohol consumption and infection with oncogenic viruses. Genotyping tumour tissue to guide clinical decision-making is becoming common practice in modern oncology, but in the management of patients with HNSCC, cytopathology or histopathology of tumour tissue remains the mainstream for diagnosis and treatment planning. Due to tumour heterogeneity and the lack of access to tumour due to its anatomical location, alternative methods to evaluate tumour activities are urgently needed. Liquid biopsy approaches can overcome issues such as tumour heterogeneity, which is associated with the analysis of small tissue biopsy. In addition, liquid biopsy offers repeat biopsy sampling, even for patients with tumours with access limitations. Liquid biopsy refers to biomarkers found in body fluids, traditionally blood, that can be sampled to provide clinically valuable information on both the patient and their underlying malignancy. To date, the majority of liquid biopsy research has focused on blood-based biomarkers, such as circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), and circulating microRNA. In this review, we will focus on ctDNA as a biomarker in HNSCC because of its robustness, its presence in many body fluids, adaptability to existing clinical laboratory-based technology platforms, and ease of collection and transportation. We will discuss mechanisms of ctDNA release into circulation, technological advances in the analysis of ctDNA, ctDNA as a biomarker in HNSCC management, and some of the challenges associated with translating ctDNA into clinical and future perspectives. ctDNA provides a minimally invasive method for HNSCC prognosis and disease surveillance and will pave the way in the future for personalized medicine, thereby significantly improving outcomes and reducing healthcare costs.

Mutational landscape of cancer-driver genes across human cancers

The genetic mutations that contribute to the transformation of healthy cells into cancerous cells have been the subject of extensive research. The molecular aberrations that lead to cancer development are often characterised by gain-of-function or loss-of-function mutations in a variety of oncogenes and tumour suppressor genes. In this study, we investigate the genomic sequences of 20,331 primary tumours representing 41 distinct human cancer types to identify and catalogue the driver mutations present in 727 known cancer genes. Our findings reveal significant variations in the frequency of cancer gene mutations across different cancer types and highlight the frequent involvement of tumour suppressor genes (94%), oncogenes (93%), transcription factors (72%), kinases (64%), cell surface receptors (63%), and phosphatases (22%), in cancer. Additionally, our analysis reveals that cancer gene mutations are predominantly co-occurring rather than exclusive in all types of cancer. Notably, we discover that patients with tumours displaying different combinations of gene mutation patterns tend to exhibit variable survival outcomes. These findings provide new insights into the genetic landscape of cancer and bring us closer to a comprehensive understanding of the underlying mechanisms driving the development of various forms of cancer.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in Triple Negative Breast Cancer

Breast cancers exhibit substantial transcriptional heterogeneity, posing a significant challenge to the prediction of treatment response and prognostication of outcomes. Especially, translation of TNBC subtypes to the clinic remains a work in progress, in part because of a lack of clear transcriptional signatures distinguishing the subtypes. Our recent network-based approach, PathExt, demonstrates that global transcriptional changes in a disease context are likely mediated by a small number of key genes, and these mediators may better reflect functional or translationally relevant heterogeneity. We apply PathExt to 1059 BRCA tumors and 112 healthy control samples across 4 subtypes to identify frequent, key-mediator genes in each BRCA subtype. Compared to conventional differential expression analysis, PathExt-identified genes (1) exhibit greater concordance across tumors, revealing shared as well as BRCA subtype-specific biological processes, (2) better recapitulate BRCA-associated genes in multiple benchmarks, and (3) exhibit greater dependency scores in BRCA subtype-specific cancer cell lines. Single cell transcriptomes of BRCA subtype tumors reveal a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified TNBC subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target top novel genes potentially mediating drug resistance. Overall, PathExt applied to breast cancer refines previous views of gene expression heterogeneity and identifies potential mediators of TNBC subtypes, including potential therapeutic targets.

Genomic landscape and gene expression profiles of feline oral squamous cell carcinoma

Feline oral squamous cell carcinoma (FOSCC) is a cancer of the squamous cell lining in the oral cavity and represents up to 80% of all oral cancers in cats, with a poor prognosis. We have used whole exome sequencing (WES) and RNA sequencing of the tumor to discover somatic mutations and gene expression changes that may be associated with FOSCC occurrence. FOSCC offers a potential comparative model to study human head and neck squamous cell carcinoma (HNSCC) due to its similar spontaneous formation, and morphological and histological features. In this first study using WES to identify somatic mutations in feline cancer, we have identified tumor-associated gene mutations in six cats with FOSCC and found some overlap with identified recurrently mutated genes observed in HNSCC. Four samples each had mutations in TP53, a common mutation in all cancers, but each was unique. Mutations in other cellular growth control genes were also found such as KAT2B and ARID1A. Enrichment analysis of FOSCC gene expression profiles suggests a molecular similarity to human OSCC as well, including alterations in epithelial to mesenchymal transition and IL6/JAK/STAT pathways. In this preliminary study, we present exome and transcriptome results that further our understanding of FOSCC.

In silico study of novel niclosamide derivatives, SARS-CoV-2 nonstructural proteins catalytic residue-targeting small molecules drug candidates

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-mediated coronavirus disease 2019 (COVID-19) infection remains a global pandemic and health emergency with overwhelming social and economic impacts throughout the world. Therapeutics for COVID-19 are limited to only remdesivir; therefore, there is a need for combined, multidisciplinary efforts to develop new therapeutic molecules and explore the effectiveness of existing drugs against SARS-CoV-2. In the present study, we reported eight (SCOV-L-02, SCOV-L-09, SCOV-L-10, SCOV-L-11, SCOV-L-15, SCOV-L-18, SCOV-L-22, and SCOV-L-23) novel structurally related small-molecule derivatives of niclosamide (SCOV-L series) for their targeting potential against angiotensin-converting enzyme-2 (ACE2), type II transmembrane serine protease (TMPRSS2), and SARS-COV-2 nonstructural proteins (NSPs) including NSP5 (3CLpro), NSP3 (PLpro), and RdRp. Our correlation analysis suggested that ACE2 and TMPRSS2 modulate host immune response via regulation of immune-infiltrating cells at the site of tissue/organs entries. In addition, we identified some TMPRSS2 and ACE2 microRNAs target regulatory networks in SARS-CoV-2 infection and thus open up a new window for microRNAs-based therapy for the treatment of SARS-CoV-2 infection. Our in vitro study revealed that with the exception of SCOV-L-11 and SCOV-L-23 which were non-active, the SCOV-L series exhibited strict antiproliferative activities and non-cytotoxic effects against ACE2- and TMPRSS2-expressing cells. Our molecular docking for the analysis of receptor-ligand interactions revealed that SCOV-L series demonstrated high ligand binding efficacies (at higher levels than clinical drugs) against the ACE2, TMPRSS2, and SARS-COV-2 NSPs. SCOV-L-18, SCOV-L-15, and SCOV-L-09 were particularly found to exhibit strong binding affinities with three key SARS-CoV-2's proteins: 3CLpro, PLpro, and RdRp. These compounds bind to the several catalytic residues of the proteins, and satisfied the criteria of drug-like candidates, having good adsorption, distribution, metabolism, excretion, and toxicity (ADMET) pharmacokinetic profile. Altogether, the present study suggests the therapeutic potential of SCOV-L series for preventing and managing SARs-COV-2 infection and are currently under detailed investigation in our lab.

Multi-omics and experimental analysis unveil theragnostic value and immunological roles of inner membrane mitochondrial protein (IMMT) in breast cancer

BACKGROUND

The inner membrane mitochondrial protein (IMMT) is a central unit of the mitochondrial contact site and cristae organizing system (MICOS). While researchers continue to demonstrate the physiological function of IMMT in regulating mitochondrial dynamics and preserving mitochondrial structural integrity, the roles of IMMT in clinicopathology, the tumor immune microenvironment (TIME), and precision oncology in breast cancer (BC) remain unclear.

METHODS

Multi-omics analysis was used here to evaluate the diagnostic and prognostic value of IMMT. Web applications aimed at analyzing the whole tumor tissue, single cells, and spatial transcriptomics were used to examine the relationship of IMMT with TIME. Gene set enrichment analysis (GSEA) was employed to determine the primary biological impact of IMMT. Experimental verification using siRNA knockdown and clinical specimens of BC patients confirmed the mechanisms behind IMMT on BC cells and the clinical significance, respectively. Potent drugs were identified by accessing the data repositories of CRISPR-based drug screenings.

RESULTS

High IMMT expression served as an independent diagnostic biomarker, correlated with advanced clinical status, and indicated a poor relapse-free survival (RFS) rate for patients with BC. Although, the contents of Th1, Th2, MSC, macrophages, basophil, CD4 + T cell and B cell, and TMB levels counteracted the prognostic significance. Single-cell level and whole-tissue level analyses revealed that high IMMT was associated with an immunosuppressive TIME. GSEA identified IMMT perturbation as involved in cell cycle progression and mitochondrial antioxidant defenses. Experimental knockdown of IMMT impeded the migration and viability of BC cells, arrested the cell cycle, disturbed mitochondrial function, and increased the ROS level and lipid peroxidation. The clinical values of IMMT were amenable to ethnic Chinese BC patients, and can be extrapolated to some other cancer types. Furthermore, we discovered that pyridostatin acted as a potent drug candidate in BC cells harboring an elevated IMMT expression.

CONCLUSION

This study combined a multi-omics survey with experimental verification to reveal the novel clinical significance of IMMT in BC, demonstrating its role in TIME, cancer cell growth and mitochondrial fitness, and identified pyridostatin as a promising drug candidate for the development of precision medicine.

Comprehensive analysis reveals CCDC60 as a potential biomarker correlated with prognosis and immune infiltration of head and neck squamous cell carcinoma

Background

Coiled-coil domain containing 60 (CCDC60) is a member of the CCDC family, which participates in the progression of many types of cancer. However, the prognostic value of CCDC60 in head and neck squamous cell carcinoma (HNSC) and its function in tumor immunity remain unclear.

Methods

CCDC60 expression and its prognostic potential in HNSC were evaluated by bioinformatics approaches, which was validated in human HNSC samples. Genetic alteration analysis of CCDC60 and the underlying biological function of CCDC60 related co-expressed genes in HNSC were analyzed. The impact of CCDC60 on the regulation of immune infiltration in HNSC was comprehensively investigated. In vitro, a series of functional assays on CCDC60 were performed in HNSC cells.

Results

Our study has indicated that compared with the adjacent normal tissues, CCDC60 expression was considerably downregulated in HNSC tissues. High CCDC60 expression was connected with favorable outcome of HNSC patients, and its prognostic significance was examined by distinct clinical characteristics. We identified the CCDC60-related co-expression genes, which were mainly enriched in the NOD-like receptor signaling pathway associated with the inhibition of tumor growth, leading to a better prognosis of HNSC patients. In vitro, CCDC60 overexpression significantly inhibited the growth, migration and invasiveness but regulated cell cycle progression, and promoted cell adhesion of Fadu and Cal27 cells. Additionally, high CCDC60 expression had strong connections with the infiltrating levels of immune cells, immune marker sets, immunomodulators and chemokines in HNSC, suggesting that targeting CCDC60 could be a promising strategy to enhance the efficacy of immunotherapy for HNSC patients.

Conclusion

Tumor suppressor CCDC60 may be identified as a prognostic and immune-related indicator in HNSC, which had the potential functions in regulating the immune infiltration of HNSC and improving the response to immunotherapy for HNSC patients.

A New View of Activating Mutations in Cancer

A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.

TLR3 serves as a novel diagnostic and prognostic biomarker and is closely correlated with immune microenvironment in three types of cancer

Background: Toll-like receptor 3 (TLR3) plays an important role in both innate and adaptive immunity, but the prognostic value of TLR3 in heterogeneous tumors and the correlations between TLR3 expression and immune infiltration of heterogeneous tumors remain unclear. Methods: We investigated the expression of TLR3 in a variety of tumors and focused on the diagnostic and prognostic values of TLR3 in kidney renal clear cell carcinoma (KIRC), pancreatic adenocarcinoma (PAAD) and brain lower grade glioma (LGG) by GEPIA, DriverDBv3, UALCAN, TIMER, LinkedOmics, STRING, GeneMANIA and FunRich, as well as the possible mechanisms of TLR3 affecting tumor prognosis were discussed. Additionally, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to validate TLR3 expression in early KIRC. We also compared the expression of TLR3 in the plasma of early KIRC patients and normal controls by enzyme linked immunosorbent assay (ELISA). Results: TLR3 expression was significantly different in multiple tumors compared with paracancerous nontumor tissues. Elevated expression of TLR3 contributed to the prolonged survival outcome in KIRC patients. Suppressed expression of TLR3 contributed to the prolonged survival outcome in LGG and PAAD patients. Moreover, TLR3 was significantly elevated in stage1, grade1 and N0 of KIRC. The expression and function of TLR3 in KIRC, LGG and PAAD were closely related to tumor immune microenvironment. TRAF6 was a key gene in the interactions between TLR3 and its interacting genes. Finally, the results of RT-qPCR and ELISA indicated that TLR3 expression levels were significantly raised in renal tissue and plasma of early KIRC patients. Conclusion: TLR3 has the potential to be a diagnostic biomarker of KIRC, LGG and PAAD as well as a biomarker for evaluating the prognosis of KIRC, LGG and PAAD, particularly for the early diagnosis of KIRC. TLR3 affects tumors mainly by acting on the immune microenvironment of KIRC, LGG and PAAD. These findings could lead to new insights into the immunotherapeutic targets for KIRC, LGG, and PAAD.

Patient-Derived Organoids from Colorectal Cancer with Paired Liver Metastasis Reveal Tumor Heterogeneity and Predict Response to Chemotherapy

There is no effective method to predict chemotherapy response and postoperative prognosis of colorectal cancer liver metastasis (CRLM) patients. Patient-derived organoid (PDO) has become an important preclinical model. Herein, a living biobank with 50 CRLM organoids derived from primary tumors and paired liver metastatic lesions is successfully constructed. CRLM PDOs from the multiomics levels (histopathology, genome, transcriptome and single-cell sequencing) are comprehensively analyzed and confirmed that this organoid platform for CRLM could capture intra- and interpatient heterogeneity. The chemosensitivity data in vitro reveal the potential value of clinical application for PDOs to predict chemotherapy response (FOLFOX or FOLFIRI) and clinical prognosis of CRLM patients. Taken together, CRLM PDOs can be utilized to deliver a potential application for personalized medicine.

Multi-Omics Analysis Reveals Clinical Value and Possible Mechanisms of ATAD1 Down-Regulation in Human Prostate Adenocarcinoma

Prostate adenocarcinoma (PRAD) is the most common histological subtype of prostate cancer. Post-treatment biochemical recurrence is a challenging issue. ATAD1 (ATPase Family AAA Domain Containing 1) plays a vital role in mitochondrial proteostasis and apoptosis activity, while its clinical value in PRAD and its impact on the tumor microenvironment (TME) remain unanswered. In this study, we aimed to investigate the clinical value and possible mechanisms of ATAD1 in PRAD via multi-omics analysis. Using cBioPortal, we confirmed that ATAD1 alteration was associated with gene expression and unfavorable DFS. Deep deletion predominantly occurred in PRAD. By integrating DriverDBv3 and GEPIA2, we noted ATAD1 downregulation in PRAD tissues compared to normal tissues, associated with unfavorable DFS in PRAD patients. DNA repair genes ATM, PARP1and BRCA2 had positive associations with ATAD1 expression. We found that the generalization value of ATAD1 could be applied to other cancers such as KIRC and UCEC. In addition, LinkedOmics identified that the functional involvement of ATAD1 participates in mitochondrial structure and cell cycle progression. Using TIMER analysis, we demonstrated that ATAD1 downregulation correlated with an immunosuppressive TME. Furthermore, we accessed a GSE55062 dataset on UALCAN and discovered the involvement of ERG-mediated transcriptional repression on ATAD1 downregulation. Cross-association screening of shATAD1 efficacy vs. altered mRNAs identified 190 perturbed mRNAs. Then, functional enrichment analysis using the Metascape omics tool recognized that shATAD1-perturbed mRNAs are primarily in charge of the activation of Wnt/β-catenin pathway and lipid metabolic processes. In conclusion, multi-omics results reveal that ATAD1 downregulation is a clinical biomarker for pathological diagnosis and prognosis for patients with PRAD. Reduced ATAD1 may be involved in the enhanced activity of mitochondria and cell cycle, as well as possibly shaping an immunosuppressive TME. ERG serves as an upstream transcriptional repressor of ATAD1. Downstream mechanisms of ATAD1 are involved in Wnt/β-catenin pathway and lipid metabolic processes.

The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors

Metastatic prostate cancer remains a major clinical challenge and metastatic lesions are highly heterogeneous and difficult to biopsy. Liquid biopsy provides opportunities to gain insights into the underlying biology. Here, using the highly sensitive enrichment-based sequencing technology, we provide analysis of 60 and 175 plasma DNA methylomes from patients with localized and metastatic prostate cancer, respectively. We show that the cell-free DNA methylome can capture variations beyond the tumor. A global hypermethylation in metastatic samples is observed, coupled with hypomethylation in the pericentromeric regions. Hypermethylation at the promoter of a glucocorticoid receptor gene NR3C1 is associated with a decreased immune signature. The cell-free DNA methylome is reflective of clinical outcomes and can distinguish different disease types with 0.989 prediction accuracy. Finally, we show the ability of predicting copy number alterations from the data, providing opportunities for joint genetic and epigenetic analysis on limited biological samples.

CmirC: an integrated database of clustered miRNAs co-localized with copy number variations in cancer

Genomic rearrangements and copy number variations (CNVs) are the major regulators of clustered microRNAs (miRNAs) expression. Several clustered miRNAs are harbored in and around chromosome fragile sites (CFSs) and cancer-associated genomic hotspots. Aberrant expression of such clusters can lead to oncogenic or tumor suppressor activities. Here, we developed CmirC (Clustered miRNAs co-localized with CNVs), a comprehensive database of clustered miRNAs co-localized with CNV regions. The database consists of 481 clustered miRNAs co-localized with CNVs and their expression patterns in 35 cancer types of the TCGA. The portal also provides information on CFSs, miRNA cluster candidates, genomic coordinates, target gene networks, and gene functionality. The web portal is integrated with advanced tools such as JBrowse, NCBI-BLAST, GeneSCF, visNetwork, and NetworkD3 to help the researchers in data analysis, visualization, and browsing. This portal provides a promising avenue for integrated data analytics and offers additional evidence for the complex regulation of clustered miRNAs in cancer. The web portal is freely accessible at http://slsdb.manipal.edu/cmirclust to explore clinically significant miRNAs.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

A comprehensive pancancer analysis reveals the potential value of RAR-related orphan receptor C (RORC) for cancer immunotherapy

Background: RAR-related orphan receptor C (RORC) plays an important role in autoimmune responses and inflammation. However, its function in cancer immunity is still unclear. Its potential value in cancer immunotherapy (CIT) needs to be further studied.

Methods: Expression and clinical data for 33 cancers were obtained from UCSC-Xena. The correlation between RORC expression and clinical parameters was analyzed using the limma software package to assess the prognostic value of RORC. Timer2.0 and DriverDBv3 were used to analyze the RORC mutation and methylation profiles. RORC-associated signaling pathways were identified by GSEA. The correlations of RORC expression with tumor microenvironment factors were further assessed, including immune cell infiltration (obtained by CIBERSORT) and immunomodulators (in pancancer datasets from the Tumor-Immune System Interactions and Drug Bank [TISIDB] database). In addition, the correlations of RORC with four CIT biomarkers (tumor mutational burden, microsatellite instability, programmed death ligand-1, and mismatch repair) were explored. Furthermore, three CIT cohorts (GSE67501, GSE168204, and IMvigor210) from the Gene Expression Omnibus database and a previously published study were used to determine the association between RORC expression and CIT response.

Results: RORC was differentially expressed in many tumor tissues relative to normal tissues (20/33). In a small number of cancers, RORC expression was correlated with age (7/33), sex (4/33), and tumor stage (9/33). Furthermore, RORC expression showed prognostic value in many cancers, especially in kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), and mesothelioma (MESO). The mutation rate of RORC in most cancer types was low, while RORC was hypermethylated or hypomethylated in multiple cancers. RORC was associated with a variety of biological processes and signal transduction pathways in various cancers. Furthermore, RORC was strongly correlated with immune cell infiltration, immunomodulators, and CIT biomarkers. However, no significant association was found between RORC and CIT response in the three CIT cohorts.

Conclusion Our findings revealed the potential immunotherapeutic value of RORC for various cancers and provides preliminary evidence for the application of RORC in CIT.

Identification of Warning Transition Points from Hepatitis B to Hepatocellular Carcinoma Based on Mutation Accumulation for the Early Diagnosis and Potential Drug Treatment of HBV-HCC

The accumulation of multiple genetic mutations is essential during the occurrence and development of hepatocellular carcinoma induced by hepatitis B (HBV-HCC), but understanding their cooperative effects and identifying the warning transition point from hepatitis B to HCC are challenges. In the genomic analysis of somatic mutations of the patient with HBV-HCC in a patient-specific protein-protein interaction (ps-PPI) network, we find mutation influence can propagate along the ps-PPI network. Therefore, in the article, we got the mutation cluster as a new research unit using the Random Walks with Restarts algorithm that is used to describe the efficient boundary of mutation influences. The connection of mutation cluster leads to dysregulation of signaling pathways corresponding to HCC, while dysregulated signaling pathways accumulate gradually and experience a process from quantitative to qualitative changes including a critical mutation cluster called transition point (TP) from hepatitis B to HCC. Moreover, two subtypes of HCC patients with different prognosis and their corresponding biological and clinical characteristics were identified according to TP. The poor prognosis HCC subtype was associated with significant metabolic pathway dysregulation and lower immune cell infiltration, while we also identified several preventive drugs to block the transformation of hepatitis B to hepatocellular carcinoma. The network-level study integrated multiomics data not only showed the sequence of multiple somatic mutations and their cooperative effect but also identified the warning transition point in HCC tumorigenesis for each patient. Our study provides new insight into exploring the cooperative molecular mechanism of chronic inflammatory malignancy in the liver and lays the foundation for the development of new approaches for early prediction and diagnosis of hepatocellular carcinoma and personalized targeted therapy.

Combined Analysis of BSA-Seq Based Mapping, RNA-Seq, and Metabolomic Unraveled Candidate Genes Associated with Panicle Grain Number in Rice (Oryza sativa L.)

Rice grain yield is a complex and highly variable quantitative trait consisting of several key components, including the grain weight, the effective panicles per unit area, and the grain number per panicle (GNPP). The GNPP is a significant contributor to grain yield controlled by multiple genes (QTL) and is crucial for improvement. Attempts have been made to find genes for this trait, which has always been a challenging and arduous task through conventional methods. We combined a BSA analysis, RNA profiling, and a metabolome analysis in the present study to identify new candidate genes involved in the GNPP. The F₂ population from crossing R4233 (high GNPP) and Ce679 (low GNPP) revealed a frequency distribution fitting two segregated genes. Three pools, including low, middle, and high GNPP, were constructed and a BSA analysis revealed six candidate regions spanning 5.38 Mb, containing 739 annotated genes. Further, a conjunctive analysis of BSA-Seq and RNA-Seq showed 31 differentially expressed genes (DEGs) in the candidate intervals. Subsequently, a metabolome analysis showed 1024 metabolites, with 71 significantly enriched, including 44 up and 27 downregulated in Ce679 vs. R4233. A KEGG enrichment analysis of these 31 DEGs and 71 differentially enriched metabolites (DEMs) showed two genes, Os12g0102100 and Os01g0580500, significantly enriched in the metabolic pathways’ biosynthesis of secondary metabolites, cysteine and methionine metabolism, and fatty acid biosynthesis. Os12g0102100, which encodes for the alcohol dehydrogenase superfamily and a zinc-containing protein, is a novel gene whose contribution to the GNPP is not yet elucidated. This gene coding for mitochondrial trans-2-enoyl-CoA reductase is involved in the biosynthesis of myristic acid, also known as tetradecanoic acid. The Os01g0580500 coding for the enzyme 1-aminoclopropane-1-carboxylate oxidase (OsACO7) is responsible for the final step of the ethylene biosynthesis pathway through the conversion of 1-aminocyclopropane-1-carboxylic acid (ACC) into ethylene. Unlike Os12g0102100, this gene was significantly upregulated in R4233, downregulated in Ce679, and significantly enriched in two of the three metabolite pathways. This result pointed out that these two genes are responsible for the difference in the GNPP in the two cultivars, which has never been identified. Further validation studies may disclose the physiological mechanisms through which they regulate the GNPP in rice.

Bioinformatics-Driven Identification of p62 as A Crucial Oncogene in Liver Cancer

Liver hepatocellular carcinoma (LIHC) is the major form of liver cancer that is the fourth most common cause of cancer death worldwide. It has been reported that the multifunctional protein p62 (also known as SQSTM1) plays a cancer-promoting role in LIHC, but the detailed mechanisms underlying p62 interaction with LIHC remains unclear. To gain a comprehensive understanding of p62 interaction with LIHC in clinical settings, we performed bioinformatic analyses using various online algorithms derived from high throughput profiling. Our results indicate that p62 expression is significantly upregulated, partially due to its promoter demethylation, rather than p62 gene mutation, in LIHC. Mutation of TP53, CTNNB1, or ALB significantly correlates with, and mutation of AXIN1 reversely correlates with, the p62 expression level. Its upregulation occurs as early as liver cirrhosis, and go through all stages of the carcinogenesis. HCV infection makes a significant contribution to p62 upregulation in LIHC. We further identified p62-associated molecular signatures in LIHC, including many genes that are involved in antioxidant stress and metabolism, such as SRX1 and TXNRD1. Regarding to the clinical outcome, p62 expression level reversely correlates with the survival of LIHC patients (p<0.01). Importantly, we experimentally validated that p62 depletion in liver cancer cell lines downregulates the expression of SRX1 and TXNRD1 at both transcriptional and translational levels, and reduces cell proliferation. As the potential mechanisms underlying the tumor-promoting role of p62, we show that p62 upregulation is remarkably associated with reprogramming of pathways mediated by p53, Wnt/β-catenin, and Keap1-NRF2, which are crucial for oncogenesis in many contexts. Our findings provide a comprehensive insight into the interaction between p62 and LIHC, offering valuable information for understanding of LIHC pathogenesis.

Integrated computational analysis reveals HOX genes cluster as oncogenic drivers in head and neck squamous cell carcinoma

Alterations in homeobox (HOX) gene expression are involved in the progression of several cancer types including head and neck squamous cell carcinoma (HNSCC). However, regulation of the entire HOX cluster in the pathophysiology of HNSCC is still elusive. By using different comprehensive databases, we have identified the significance of differentially expressed HOX genes (DEHGs) in stage stratification and HPV status in the cancer genome atlas (TCGA)-HNSCC datasets. The genetic and epigenetic alterations, druggable genes, their associated functional pathways and their possible association with cancer hallmarks were identified. We have performed extensive analysis to identify the target genes of DEHGs driving HNSCC. The differentially expressed HOX cluster-embedded microRNAs (DEHMs) in HNSCC and their association with HOX-target genes were evaluated to construct a regulatory network of the HOX cluster in HNSCC. Our analysis identified sixteen DEHGs in HNSCC and determined their importance in stage stratification and HPV infection. We found a total of 55 HNSCC driver genes that were identified as targets of DEHGs. The involvement of DEHGs and their targets in cancer-associated signaling mechanisms have confirmed their role in pathophysiology. Further, we found that their oncogenic nature could be targeted by using the novel and approved anti-neoplastic drugs in HNSCC. Construction of the regulatory network depicted the interaction between DEHGs, DEHMs and their targets genes in HNSCC. Hence, aberrantly expressed HOX cluster genes function in a coordinated manner to drive HNSCC. It could provide a broad perspective to carry out the experimental investigation, to understand the underlying oncogenic mechanism and allow the discovery of new clinical biomarkers for HNSCC.

TNFRSF13B is a potential contributor to prostate cancer

Background

Immunodeficiencies are genetic diseases known to predispose an individual to cancer owing to defective immunity towards malignant cells. However, the link between immunodeficiency and prostate cancer progression remains unclear. Therefore, the aim of this study was to evaluate the effects of common genetic variants among eight immunodeficiency pathway-related genes on disease recurrence in prostate cancer patients treated with radical prostatectomy.

Methods

Genetic and bioinformatic analyses on 19 haplotype-tagging single-nucleotide polymorphisms in eight immunodeficiency pathway-related genes were conducted in 458 patients with prostate cancer after receiving radical prostatectomy. Furthermore, the TNFRSF13B was knocked down in 22Rv1 and PC-3 human prostate cancer cell lines via transfecting short hairpin RNAs and cell proliferation and colony formation assays were performed. The molecular mechanisms underlying the effects of TNFRSF13B were further explored by microarray gene expression profiling.

Results

TNFRSF13B rs4792800 was found to be significantly associated with biochemical recurrence even after adjustment for clinical predictors and false discovery rate correction (adjusted hazard ratio 1.78, 95% confidence interval 1.16–2.71, p = 0.008), and the G allele was associated with higher TNFRSF13B expression (p = 0.038). Increased TNFRSF13B expression suggested poor prognosis in four independent prostate cancer datasets. Furthermore, silencing TNFRSF13B expression resulted in decreased colony formation of 22Rv1 and PC-3 cells through modulating the cell cycle and p53 signalling pathways.

Conclusions

The present study suggests the potential role of immunodeficiency pathway-related genes, primarily TNFRSF13B, in prostate cancer progression.

Supplementary information

The online version contains supplementary material available at 10.1186/s12935-022-02590-2.

Survival-related genes are diversified across cancers but generally enriched in cancer hallmark pathways

Background

Pan-cancer studies have disclosed many commonalities and differences in mutations, copy number variations, and gene expression alterations among cancers. Some of these features are significantly associated with clinical outcomes, and many prognosis-predictive biomarkers or biosignatures have been proposed for specific cancer types. Here, we systematically explored the biological functions and the distribution of survival-related genes (SRGs) across cancers.

Results

We carried out two different statistical survival models on the mRNA expression profiles in 33 cancer types from TCGA. We identified SRGs in each cancer type based on the Cox proportional hazards model and the log-rank test. We found a large difference in the number of SRGs among different cancer types, and most of the identified SRGs were specific to a particular cancer type. While these SRGs were unique to each cancer type, they were found mostly enriched in cancer hallmark pathways, e.g., cell proliferation, cell differentiation, DNA metabolism, and RNA metabolism. We also analyzed the association between cancer driver genes and SRGs and did not find significant over-representation amongst most cancers.

Conclusions

In summary, our work identified all the SRGs for 33 cancer types from TCGA. In addition, the pan-cancer analysis revealed the similarities and the differences in the biological functions of SRGs across cancers. Given the potential of SRGs in clinical utility, our results can serve as a resource for basic research and biotech applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08581-x.

Combined GWAS and Transcriptome Analyses Provide New Insights Into the Response Mechanisms of Sunflower Against Drought Stress

Sunflower is one of the most important oil crops in the world, and drought stress can severely limit its production and quality. To understand the underlying mechanism of drought tolerance, and identify candidate genes for drought tolerance breeding, we conducted a combined genome-wide association studies (GWAS) and RNA-seq analysis. A total of 226 sunflower inbred lines were collected from different regions of China and other countries. Eight phenotypic traits were evaluated under control and drought stress conditions. Genotyping was performed using a Specific-Locus Amplified Fragment Sequencing (SLAF-seq) approach. A total of 934.08 M paired-end reads were generated, with an average Q30 of 91.97%. Based on the 243,291 polymorphic SLAF tags, a total of 94,162 high-quality SNPs were identified. Subsequent analysis of linkage disequilibrium (LD) and population structure in the 226 accessions was carried out based on the 94,162 high-quality SNPs. The average LD decay across the genome was 20 kb. Admixture analysis indicated that the entire population most likely originated from 11 ancestors. GWAS was performed using three methods (MLM, FarmCPU, and BLINK) simultaneously. A total of 80 SNPs showed significant associations with the 8 traits (p < 1.062 × 10-6). Next, a total of 118 candidate genes were found. To obtain more reliable candidate genes, RNA-seq analysis was subsequently performed. An inbred line with the highest drought tolerance was selected according to phenotypic traits. RNA was extracted from leaves at 0, 7, and 14 days of drought treatment. A total of 18,922 differentially expressed genes were obtained. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed up-regulated genes were mainly enriched in the branched-chain amino acid catabolic process, while the down-regulated genes were mainly enriched in the photosynthesis-related process. Six DEGs were randomly selected from all DEGs for validation; these genes showed similar patterns in RNA-seq and RT-qPCR analysis, with a correlation coefficient of 0.8167. Through the integration of the genome-wide association study and the RNA-sequencing, 14 candidate genes were identified. Four of them (LOC110885273, LOC110872899, LOC110891369, LOC110920644) were abscisic acid related protein kinases and transcription factors. These genes may play an important role in sunflower drought response and will be used for further study. Our findings provide new insights into the response mechanisms of sunflowers against drought stress and contribute to further genetic breeding.

Long noncoding RNA LINC01606 protects colon cancer cells from ferroptotic cell death and promotes stemness by SCD1-Wnt/β-catenin-TFE3 feedback loop signalling

Background

Ferroptosis is principally caused by iron catalytic activity and intracellular lipid peroxidation. Long noncoding RNAs (lncRNAs) play crucial roles in tumorigenesis. However, the potential interplay between lncRNA LINC01606 and ferroptosis in colon cancer remains elusive.

Methods

The expression level of LNC01606 in colon cancer tissue was detected by quantitative real‐time polymerase chain reaction. The functional role of LNC01606 was investigated by gain‐ and loss‐of‐function assays both in vitro and in vivo. The LINC01606‐SCD1‐Wnt/β‐catenin‐TFE3 axis were screened and validated by DNA/RNA pull down, gas chromatography‐mass spectrometry, RNA immunoprecipitation and dual‐luciferase reporter.

Results

The expression of lncRNA LINC01606 was frequently upregulated in human colon cancer and strongly associated with a poor prognosis. LINC01606 functioned as an oncogene and promotes colon cancer cell growth, invasion and stemness both in vitro and in vivo. Moreover, LINC01606 protected colon cancer cells from ferroptosis by decreasing the concentration of iron, lipid reactive oxygen species, mitochondrial superoxide and increasing mitochondrial membrane potential. Mechanistically, LINC01606 enhanced the expression of stearoyl‐CoA desaturase 1 (SCD1), serving as a competing endogenous RNA to modulate miR‐423‐5p expression, subsequently activating the canonical Wnt/β‐catenin signaling, and transcription factor binding to IGHM enhancer 3 (TFE3) increased LINC01606 transcription after recruitment to the promoter regions of LINC01606. Furthermore, we confirmed that upregulated LINC01606 and Wnt/β‐catenin formed a positive feedback regulatory loop, further inhibiting ferroptosis and enhancing stemness.

Conclusions

LINC01606 functions as an oncogene to facilitate tumor cell stemness, proliferation and inhibit ferroptosis and is a promising therapeutic target for colon cancer.

Identification of neoantigens for individualized therapeutic cancer vaccines

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.

Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis

Simple Summary

Prostate cancer (PC) is the second most common cancer worldwide and steroid hormone plays an important role in prostate carcinogenesis. Most patients with PC are initially sensitive to androgen deprivation therapy (ADT) but eventually become hormone refractory and reflect disease progression. The aim of the study was to investigate the genes which regulate the steroid hormone functional pathways and associate with the disease progression of PC. We identified a panel of eight-gene signatures that modulated steroid-hormone pathways and predicted the prognosis of PC using integrative bioinformatics analysis of multiple datasets validated from external cohorts. This panel could be used for predicting the prognosis of PC patients and might be associated with the drug response of hormonal therapies. Moreover, these genes in the signature could be potential targets to develop a novel treatment for castration-resistant PC therapy.

Abstract

The importance of anti-androgen therapy for prostate cancer (PC) has been well recognized. However, the mechanisms underlying prostate cancer resistance to anti-androgens are not completely understood. Therefore, identifying pharmacological targets in driving the development of castration-resistant PC is necessary. In the present study, we sought to identify core genes in regulating steroid hormone pathways and associating them with the disease progression of PC. The selection of steroid hormone-associated genes was identified from functional databases, including gene ontology, KEGG, and Reactome. The gene expression profiles and relevant clinical information of patients with PC were obtained from TCGA and used to examine the genes associated with steroid hormone. The machine-learning algorithm was performed for key feature selection and signature construction. With the integrative bioinformatics analysis, an eight-gene signature, including CA2, CYP2E1, HSD17B, SSTR3, SULT1E1, TUBB3, UCN, and UGT2B7 was established. Patients with higher expression of this gene signature had worse progression-free interval in both univariate and multivariate cox models adjusted for clinical variables. The expression of the gene signatures also showed the aggressiveness consistently in two external cohorts, PCS and PAM50. Our findings demonstrated a validated eight-gene signature could successfully predict PC prognosis and regulate the steroid hormone pathway.

Siglec-9, a Putative Immune Checkpoint Marker for Cancer Progression Across Multiple Cancer Types

Siglec-9, a cell surface transmembrane receptor mainly expressed on B cells, CD56⁺ NK cells, and CD4⁺ and CD8⁺ T cells, is strongly related to the tumor immune microenvironment. However, the expression pattern of Siglec-9 and its prognostic potential have not been investigated in a pan-cancer perspective. This study aimed to explore the association of Siglec-9 with prognosis, tumor stage, molecular subtype, and the immune microenvironment in pan-cancer. The mRNA expression of Siglec-9 was obtained from The Cancer Genome Atlas (TCGA), the Broad Institute Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx). The relationship between Siglec-9 mRNA expression and prognosis was evaluated by the Kaplan–Meier analysis. The correlation between Siglec-9 and tumor-infiltrating immune cells, immune subtype, and molecular subtype was evaluated on Tumor Immune Estimation Resource (TIMER) and Integrated Repository Portal for Tumor-Immune System Interactions (TISIDB). The correlation between Siglec-9 expression and immune checkpoint, mismatch repair (MMR), DNA methyltransferase (DNMT), tumor mutation burden (TMB), and microsatellite instability (MSI) was also analyzed. It showed that Siglec-9 expression was significantly altered in most TCGA tumors. Siglec-9 expression was associated with the prognosis of patients with adrenocortical carcinoma (ACC), lung adenocarcinoma (LUSC), thymoma (THYM), colon adenocarcinoma (COAD), glioblastoma multiforme (GBM), prostate adenocarcinoma (PRAD), esophageal carcinoma (ESCA), and brain lower-grade glioma (LGG). Particularly, increased Siglec-9 expression was strongly correlated with poor prognosis in LGG. Correlation between Siglec-9 expression and tumor stage was also observed in various cancers. In addition, Siglec-9 was positively associated with infiltration of immune cells including neutrophils, dendritic cells (DCs), macrophage, and CD4⁺ and CD8⁺ T cells. Moreover, a significant correlation between Siglec-9 and MSI, TMB, MMR, DNMT, immune checkpoint, immune subtype, molecular subtype, and immunomodulators was observed in multiple cancers. Specifically, poor prognostic value and strong correlation to immune cell infiltration were verified with the LGG dataset from the Chinese Glioma Genome Atlas (CGGA). These findings indicated that Siglec-9 can be a novel biomarker and a potential target for cancer immunotherapy.

Semaphorin 4C promotes motility and immunosuppressive activity of cancer cells via CRMP3 and PD-L1

Semaphorins (SEMAs) are membrane-bound or soluble proteins that participate in organ development and cancer progression, however, the detailed role of SEMAs in carcinogenesis is not fully elucidated yet. Our in silico analysis showed among the differentially expressed SEMAs in colon cancer tissues, patients with higher SEMA4C expression tumors had worse survival. The migration and invasion of the HCT116 and CT26 colon cancer cells were significantly suppressed by SEMA4C neutralizing antibody treatment; while enhanced by ectopic expression of SEMA4C. Subsequently, RNA sequencing study revealed microtubule polymerization- and nucleation-related genes are highly enriched in SEMA4C overexpression HCT116 cells. Western blotting showed the negative correlation between the levels of SEMA4C expression and tubulin acetylation. Mechanistic study showed SEMA4C interacted with and stabilized collapsin response mediator protein 3 (CRMP3), a novel deacetylase, to increase α-tubulin deacetylation and cell motility, which could be effectively attenuated after HDAC inhibitors treatment. We also found that a tumor-suppressive miRNA let-7b can target SEMA4C and act synergistically with SEMA4C neutralizing antibody to suppress the motility of colon cancer cells. In addition, blockade of SEMA4C could attenuate the expression of program death ligand 1 (PD-L1). Collectively, our results highlight that SEMA4C may promote colon cancer progression through modulating CRMP3-mediated tubulin deacetylation and PD-L1-mediated immunosuppression.

ERCC6L is a biomarker and therapeutic target for non-small cell lung adenocarcinoma

BACKGROUND

Non-small cell lung carcinoma (NSCLC) accounts for the majority of lung cancer which is one of the most common cancer types and results in high percentage of cancer-related deaths. Although NSCLC patients have been benefiting from the existing standard treatments, more candidate biomarkers for effective diagnosis and targets for therapy are still required to be uncovered. The expression pattern and biological function of Excision repair cross-complementation group 6 like (ERCC6L) in NSCLC are ill-investigated.

METHODS

We performed bioinformatic analyses in NSCLC patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC), respectively. Patient survival determination and meta-analysis were carried out to check the clinical significance of ERCC6L. Datamining was also performed to evaluate the ERCC6L mRNA and protein expression levels in patients with LUAD and the correlation with immune cell infiltration. In silico prediction indicated the potential interacting proteins and correlated pathways of ERCC6L in LUAD. Loss-of-function studies were performed to determine the role of ERCC6L in LUAD cells.

RESULTS

Here, we found that ERCC6L is upregulated in patients with LUAD and LUSC and is strongly associated with poor outcomes of LUAD, but not LUSC, patients. In addition, ERCC6L mRNA and protein were shown to be more expressed in patients with advanced stages of LUAD. Finally, functional analyses reveal the promoting effects of ERCC6L on LUAD cell survival, migration and invasion.

CONCLUSIONS

Cohort data analysis and experimental validation shed light on the promising prognostic and therapeutic application of ERCC6L in LUAD, but maybe not LUSC, patients.

miR-4759 suppresses breast cancer through immune checkpoint blockade

Programmed cell death protein 1 (PD-1)/ programmed cell death protein ligand 1 (PD-L1) is the key immune checkpoint governing evasion of advanced cancer from immune surveillance. Immuno-oncology (IO) therapy targeting PD-1/PD-L1 with traditional antibodies is a promising approach to multiple cancer types but to which the response rate remains moderate in breast cancer, calling for the need of exploring alternative IO targeting approaches. A miRNA-gene network was integrated by a bioinformatics approach and corroborated with The Cancer Genome Atlas (TCGA) to screen miRNAs regulating immune checkpoint genes and associated with patient survival. Here we show the identification of a novel microRNA miR-4759 which repressed RNA expression of the PD-L1 gene. miR-4759 targeted the PD-L1 gene through two binding motifs in the 3′ untranslated region (3′-UTR) of PD-L1. Reconstitution of miR-4759 inhibited PD-L1 expression and sensitized breast cancer cells to killing by immune cells. Treatment with miR-4759 suppressed tumor growth of orthotopic xenografts and promoted tumor infiltration of CD8⁺ T lymphocytes in immunocompetent mice. In contrast, miR-4759 had no effect to tumor growth in immunodeficient mice. In patients with breast cancer, expression of miR-4759 was preferentially downregulated in tumors compared to normal tissues and was associated with poor overall survival. Together, our results demonstrated miR-4759 as a novel non-coding RNA which promotes anti-tumor immunity of breast cancer.

Using bioinformatics approaches to identify survival-related oncomiRs as potential targets of miRNA-based treatments for lung adenocarcinoma

Lung cancer is a major cause of cancer-associated deaths worldwide, and lung adenocarcinoma (LUAD) is the most common lung cancer subtype. Micro RNAs (miRNAs) regulate the pattern of gene expression in multiple cancer types and have been explored as potential drug development targets. To develop an oncomiR-based panel, we identified miRNA candidates that show differential expression patterns and are relevant to the worse 5-year overall survival outcomes in LUAD patient samples. We further evaluated various combinations of miRNA candidates for association with 5-year overall survival and identified a four-miRNA panel: miR-9-5p, miR-1246, miR-31-3p, and miR-3136-5p. The combination of these four miRNAs outperformed any single miRNA for predicting 5-year overall survival (hazard ratio [HR]: 3.47, log-rank p-value = 0.000271). Experiments were performed on lung cancer cell lines and animal models to validate the effects of these miRNAs. The results showed that singly transfected antagomiRs largely inhibited cell growth, migration, and invasion, and the combination of all four antagomiRs considerably reduced cell numbers, which is twice as effective as any single miRNA-targeted transfected. The in vivo studies revealed that antagomiR-mediated knockdown of all four miRNAs significantly reduced tumor growth and metastatic ability of lung cancer cells compared to the negative control group. The success of these in vivo and in vitro experiments suggested that these four identified oncomiRs may have therapeutic potential.

Comprehensive Analysis of Subtype-Specific Molecular Characteristics of Colon Cancer: Specific Genes, Driver Genes, Signaling Pathways, and Immunotherapy Responses

Colon cancer is a complex, heterogeneous disease. The Colorectal Cancer Subtyping Consortium reported a novel classification system for colon cancer in 2015 to better understand its heterogeneity. This molecular classification system divided colon cancer into four distinct consensus molecular subtypes (CMS 1, 2, 3, and 4). However, the characteristics of different colon cancer molecular subtypes have not been fully elucidated. This study comprehensively analyzed the molecular characteristics of varying colon cancer subtypes using multiple databases and algorithms, including The Cancer Genome Atlas (TCGA) database, DriverDBv3 database, CIBERSORT, and MCP-counter algorithms. We analyzed the alterations in the subtype-specific genes of different colon cancer subtypes, such as the RNA levels and DNA alterations, and showed that specific subtype-specific genes significantly affected prognosis. We also explored the changes in colon cancer driver genes and representative genes of 10 signaling pathways in different subtypes. We identified genes that were altered in specific subtypes. We further detected the infiltration of 22 immune cell types in four colon cancer subtypes and the infiltration level of primary immune cells among these subtypes. Additionally, we explored changes in immune checkpoint genes (ICGs) and immunotherapy responses among different colon cancer subtypes. This study may provide clues for the molecular mechanism of tumorigenesis and progression in colon cancer. It also offers potential biomarkers and targets for the clinical diagnosis and treatment of different colon cancer subtypes.

Identification of DNA Damage Repair-Associated Prognostic Biomarkers for Prostate Cancer Using Transcriptomic Data Analysis

In the recent decade, the importance of DNA damage repair (DDR) and its clinical application have been firmly recognized in prostate cancer (PC). For example, olaparib was just approved in May 2020 to treat metastatic castration-resistant PC with homologous recombination repair-mutated genes; however, not all patients can benefit from olaparib, and the treatment response depends on patient-specific mutations. This highlights the need to understand the detailed DDR biology further and develop DDR-based biomarkers. In this study, we establish a four-gene panel of which the expression is significantly associated with overall survival (OS) and progression-free survival (PFS) in PC patients from the TCGA-PRAD database. This panel includes DNTT, EXO1, NEIL3, and EME2 genes. Patients with higher expression of the four identified genes have significantly worse OS and PFS. This significance also exists in a multivariate Cox regression model adjusting for age, PSA, TNM stages, and Gleason scores. Moreover, the expression of the four-gene panel is highly correlated with aggressiveness based on well-known PAM50 and PCS subtyping classifiers. Using publicly available databases, we successfully validate the four-gene panel as having the potential to serve as a prognostic and predictive biomarker for PC specifically based on DDR biology.

Identification of Three Core Secretome Genes Associated with Immune Infiltration in High Tumor Mutation Burden Across 14 Major Solid Tumors

Background

Secretome genes, encoding proteins secreted from the cell, are involved in the tumor immune response and correlated with levels of tumor mutation burden (TMB) in multiple tumors. This study aimed to identify core secretome genes and their potential association with immunomodulators and immune infiltration in high TMB groups across 14 major solid tumors through bioinformatics analysis.

Methods

Multi-omics data for 14 major solid tumors were downloaded from The Cancer Genome Atlas (TCGA) database. Patients were divided into high TMB (TMB-high) and low TMB (TMB-low) groups using the median TMB values for each of the solid tumors. The CIBERSORT algorithm was conducted to estimate the proportion of 22 tumor-infiltrating immune cells (TIICs). Kaplan-Meier analysis and the log-rank test were utilized to screened prognosis-related genes. The correlations between core secretome genes and TIICs were analyzed using Spearman correlation coefficients.

Results

In TMB-high groups, multi-omics data analysis revealed that secretome genes were strongly associated with clinical characteristics, and 65 prognosis-related secretome genes were screened. Among the prognosis-related genes, 21 core secretome genes were identified, and strongly associated with five types of TIICs, namely activated NK cells, follicular helper T cells, CD8 T cells, and macrophages M0 and M2. Notably, three secretome genes (ADAMTS12, COL12A1, and COL5A2) were significantly related to immunomodulators and TIICs in multiple solid tumors. In addition, 12 core secretome genes were significantly differentially expressed between responding and non-responding patients receiving immunotherapy. Furthermore, core secretome genes may be involved in the PI3K/AKT signaling pathway.

Conclusion

We examined the prognostic significance of secretome genes and their potential association with immunomodulators and immune infiltration across 14 major solid tumors. In summary, three secretome genes (ADAMTS12, COL12A1, and COL5A2) may be pivotal mediators of immune infiltration in TMB-high patients, which may help to identify patients who could benefit from immunotherapy.

Co-targeting BET bromodomain BRD4 and RAC1 suppresses growth, stemness and tumorigenesis by disrupting the c-MYC-G9a-FTH1axis and downregulating HDAC1 in molecular subtypes of breast cancer

BET bromodomain BRD4 and RAC1 oncogenes are considered important therapeutic targets for cancer and play key roles in tumorigenesis, survival and metastasis. However, combined inhibition of BRD4-RAC1 signaling pathways in different molecular subtypes of breast cancer including luminal-A, HER-2 positive and triple-negative breast (TNBC) largely remains unknown. Here, we demonstrated a new co-targeting strategy by combined inhibition of BRD4-RAC1 oncogenic signaling in different molecular subtypes of breast cancer in a context-dependent manner. We show that combined treatment of JQ1 (inhibitor of BRD4) and NSC23766 (inhibitor of RAC1) suppresses cell growth, clonogenic potential, cell migration and mammary stem cells expansion and induces autophagy and cellular senescence in molecular subtypes of breast cancer cells. Mechanistically, JQ1/NSC23766 combined treatment disrupts MYC/G9a axis and subsequently enhances FTH1 to exert antitumor effects. Furthermore, combined treatment targets HDAC1/Ac-H3K9 axis, thus suggesting a role of this combination in histone modification and chromatin modeling. C-MYC depletion and co-treatment with vitamin-C sensitizes different molecular subtypes of breast cancer cells to JQ1/NSC23766 combination and further reduces cell growth, cell migration and mammosphere formation. Importantly, co-targeting RAC1-BRD4 suppresses breast tumor growth in vivo using xenograft mouse model. Clinically, RAC1 and BRD4 expression positively correlates in breast cancer patient's samples and show high expression patterns across different molecular subtypes of breast cancer. Both RAC1 and BRD4 proteins predict poor survival in breast cancer patients. Taken together, our results suggest that combined inhibition of BRD4-RAC1 pathways represents a novel and potential therapeutic approach in different molecular subtypes of breast cancer and highlights the importance of co-targeting RAC1-BRD4 signaling in breast tumorigenesis via disruption of C-MYC/G9a/FTH1 axis and down regulation of HDAC1.

Analysis of the Expression and Prognostic Potential of a Novel Metabolic Regulator ANGPTL8/Betatrophin in Human Cancers

The angiopoietin-like protein (ANGPTL) family members, except for the novel atypical member ANGPTL8/betatrophin, have been reported to participate in angiogenesis, inflammation and cancer. ANGPTL8/betatrophin is a metabolic regulator that is involved in lipid metabolism and glucose homeostasis. However, little is known about the expression and prognostic value of ANGPTL8/betatrophin in human cancers. In this study, we first conducted detailed analyses of ANGPTL8/betatrophin expression in cancer/normal samples via the Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis (GEPIA), DriverDBv3, ENCORI and UALCAN databases. ANGPTL8/betatrophin showed high tissue specificity (enriched in the liver) and cell-type specificity (enriched in HepG2 and MCF7 cell lines). More than one databases demonstrated that the gene expression of ANGPTL8/betatrophin was significantly lower in cholangiocarcinoma (CHOL), breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC), and significantly higher in kidney renal clear cell carcinoma (KIRC) compared with that in normal samples. However, the protein expression of ANGPTL8/betatrophin displayed opposite results in clear cell renal cell carcinoma (ccRCC)/KIRC. Based on the expression profiles, the prognostic value was evaluated with the GEPIA, DriverDBv3, Kaplan Meier plotter and ENCORI databases. Two or more databases demonstrated that ANGPTL8/betatrophin significantly affected the survival of KIRC, uterine corpus endometrial carcinoma (UCEC), pheochromocytoma and paraganglioma (PCPG) and sarcoma (SARC); patients with PCPG and SARC may benifit from high ANGPTL8/betatrophin expression while high ANGPTL8/betatrophin expression was associated with poor prognosis in KIRC and UCEC. Functional analyses with the GeneMANIA, Metascape and STRING databases suggested that ANGPTL8/betatrophin was mainly involved in lipid homeostasis, especially triglyceride and cholesterol metabolism; glucose homeostasis, especially insulin resistance; AMPK signaling pathway; PI3K/Akt signaling pathway; PPAR signaling pathway; mTOR signaling pathway; HIF-1 signaling pathway; autophagy; regulation of inflammatory response. ANGPTL8/betatrophin may be a promising prognostic biomarker and therapeutic target, thus providing evidence to support further exploration of its role in defined human cancers.