Cancer Genome Research Articles

Association of COL4A2 indel polymorphism with the development of stomach adenocarcinoma in Chinese populations

Objective The objective of the study was to assess the potential association between the indel polymorphism (rs34802628) located within the intron of the collagen type ⅳ alpha 2 gene (COL4A2) and the susceptibility to stomach adenocarcinoma (STAD) within a Chinese population. Methods Peripheral venous blood samples were collected from a total of 497 STAD patients and 804 healthy control individuals to extract genomic DNA. The genotyping of the COL4A2 rs34802628 polymorphism was carried out using a polymerase chain reaction assay. Additionally, statistical analyses were conducted on the expression levels of COL4A2 mRNA using the GEPIA database. Meanwhile, the expression of COL4A2 mRNA was also validated by Real-time PCR using STAD tissue samples. Then, based on an analysis of patient tumor RNA seq data available from the Cancer Genome Atlas (TCGA), we assessed the prognostic value of mRNA expression of the COL4A2 gene in STAD patients using K–M plotter. Results The study presented compelling evidence supporting an association between the rs34802628 polymorphism in the COL4A2 gene and susceptibility to STAD. Logistic regression analysis revealed that both the heterozygote and homozygote 4-bp del/del genotypes were significantly associated with a decreased risk of STAD, even after controlling for other variables (adjusted odds ratio [OR] = 0.663, 95% confidence interval [CI] 0.519–0.848, p = 0.037; OR = 0.422, 95% CI 0.290–0.614, p = 0.000005, respectively). Importantly, individuals carrying the 4-bp deletion allele demonstrated a notably lower risk of developing the disease (OR = 0.696, 95% CI 0.591–0.820, p = 0.000014). Furthermore, Genotype-phenotype correlation studies in human STAD tissue samples demonstrated that the higher mRNA expression levels of COL4A2 were associated with the ins allele of rs34802628. Bioinformatics analysis revealed that higher expression of the COL4A2 gene was significant with development and poor prognosis of STAD. Conclusion The results of our study provide strong evidence indicating a potential involvement of genetic variants in the COL4A2 gene in the development of STAD. Nonetheless, to validate and consolidate these findings, additional investigations incorporating larger sample sizes and functional experiments are necessary.

Prediction of the 3D cancer genome from whole-genome sequencing using InfoHiC

The 3D genome prediction in cancer is crucial for uncovering the impact of structural variations (SVs) on tumorigenesis, especially when they are present in noncoding regions. We present InfoHiC, a systemic framework for predicting the 3D cancer genome directly from whole-genome sequencing (WGS). InfoHiC utilizes contig-specific copy number encoding on the SV contig assembly, and performs a contig-to-total Hi-C conversion for the cancer Hi-C prediction from multiple SV contigs. We showed that InfoHiC can predict 3D genome folding from all types of SVs using breast cancer cell line data. We applied it to WGS data of patients with breast cancer and pediatric patients with medulloblastoma, and identified neo topologically associating domains. For breast cancer, we discovered super-enhancer hijacking events associated with oncogenic overexpression and poor survival outcomes. For medulloblastoma, we found SVs in noncoding regions that caused super-enhancer hijacking events of medulloblastoma driver genes (GFI1, GFI1B, and PRDM6). In addition, we provide trained models for cancer Hi-C prediction from WGS at https://github.com/dmcb-gist/InfoHiC, uncovering the impacts of SVs in cancer patients and revealing novel therapeutic targets.

A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery.

This study introduces a novel graph-informed modeling framework for improving the statistical analysis of gene expression data, particularly in the context of identifying differentially expressed gene pathways and gene expression-assisted disease classification in a high-dimensional data setting. By integrating gene regulatory network information into hypothesis testing for the difference between mean vectors and linear discriminant analysis, we aim to effectively capture and utilize previously validated external gene interaction information. Our method leverages a block-coordinate descent approach which enables us to incorporate mixed graph information into linear structural equation modeling, accommodating directed/undirected edges and potential cycles in gene regulatory networks. Extensive simulations under various data scenarios have demonstrated the effectiveness of our approach with improved power for gene pathway tests and disease classification over existing methods. An application to a lung cancer dataset from the Cancer Genome Atlas Program (TCGA) further exemplifies the potential of our graph-informed approach in empowering the detection of differentially expressed gene pathways and gene expression-based classification of different lung cancer stages. Our findings underscore the potential utility of incorporating gene regulatory network information in gene pathway analysis, setting the stage for future advancements in gene pathway discovery, disease diagnosis, and treatment strategies.

Long non-coding RNA FAM87A is associated with overall survival and promotes cell migration and invasion in gastric cancer.

The role of long non-coding RNAs (lncRNAs) in the invasion and metastasis of gastric cancer remains largely unclear. Integrating transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differentially expressed genes were identified in gastric cancer. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) database-curated gene set, lncRNAs associated with invasion and metastasis were identified. The Cox analyses were performed to identify prognostic lncRNAs. The competing endogenous RNA (ceRNA) regulation network was constructed to identify hub lncRNAs in gastric cancer. Functional and pathway analyses were used to investigate the function of identified lncRNAs. RT-qPCR and Transwell assays were used to investigate the expression in gastric cancer tissues and functions in gastric cancer cell lines. Based on GEO and TCGA databases, 111 differentially expressed lncRNAs were identified between gastric cancer and normal samples. A total of 43 lncRNAs were significantly correlated with hallmark genes of cancer invasion and metastasis. Among them, as a hub lncRNA in the invasion-related ceRNA regulation network, FAM87A showed potential regulation on MAPK signaling and transforming growth factor (TGF) signaling cascade, such as TGFB2, TGFBR1, and TGFBR2. Furthermore, FAM87A also showed a significant correlation with cell adhesion molecules, such as Integrin alpha 6 (ITGA6) and Contactin-1 (CNTN1). RT-qPCR experiments showed that FAM87A expression was upregulated in gastric cancer tissues compared to normal samples (n = 30). Transwell assays showed that FAM87A knockdown inhibited the migration and invasion abilities of gastric cancer cells in vitro. Notably, clinical data analysis showed that lncRNA FAM87A could be an independent factor for the overall survival of patients with gastric cancer. LncRNA FAM87A may play a pivotal role in regulating migration and invasion of gastric cancer cells. FAM87A could be a potential biomarker for the overall survival of patients with gastric cancer.

CT45A1-mediated MLC2 (MYL9) phosphorylation promotes natural killer cell resistance and outer cell fate in a cell-in-cell structure, potentiating the progression of microsatellite instability-high colorectal cancer.

Patients with microsatellite instability-high (MSI-H) colorectal cancer (CRC) have high tumor mutation burden and tumor immunogenicity, exhibiting a higher response rate to immunotherapy and better survival. However, a portion of MSI-H CRC patients still experience adverse disease outcomes. We aimed to identify the tumor-autonomous regulators determining these heterogeneous clinical outcomes. The Cancer Genome Atlas (TCGA) dataset was used to identify regulators in MSI-H CRC patients with unfavorable outcomes. Stable CRC tumor clones expressing targeted regulators were established to evaluate migratory and stemness properties, immune cell vulnerability, and cell-in-cell (CIC) structure formation. RNA-sequencing (RNA-seq) was used to identify enriched biological pathways in stable CRC tumor clones. Clinicopathological characterization of formalin-fixed paraffin-embedded (FFPE) MSI-H CRC specimens was performed to explore the underlying mechanisms involved. We showed that cancer/testis antigen family 45 member A1 (CT45A1) expression was upregulated in MSI-H CRC patients with poor survival outcomes. CT45A1-expressing microsatellite stable (MSS) CRC cells showed enhanced migratory ability. However, CT45A1-expressing MSI-H CRC cells, but not MSS CRC cells, showed higher resistance to natural killer (NK) cell cytotoxicity and served as outer cells in homotypic CIC structures, preventing exogenous or therapeutic antibody access to inner CRCcells. Inactivating RHO-ROCK/MLCK-MLC2 signaling with small-molecule inhibitors or short-hairpin RNAs (shRNAs) targeting myosin light chain kinase (MYLK) abolished NK cell resistance and reduced the outer cell fate of CT45A1-expressing MSI-H CRC cells. In MSI-H CRC patients, CT45A1-positive tumors exhibited increased MLC2 phosphorylation, increased outer cell fate, and decreased survival. We demonstrated that CT45A1 potentiates the advanced progression of MSI-H CRC, and targeting MLC2 phosphorylation may enhance immunotherapy efficacy in CT45A1-positive MSI-H CRC patients.

Pyroptosis-related genes features on prediction of the prognosis in liver cancer: An integrated analysis of bulk and single-cell RNA sequencing

ObjectiveThis study explores the impact of pyroptosis-related genes (PRG) on the prognosis of liver cancer (LC). Methods421 samples (371 tumor samples and 50 normal samples) from the Cancer Genome Atlas (TCGA) were included in this study. GSE14520 dataset (data of RNA expression and relevant clinicopathological features), GSE125449 dataset (single-cell data in LC) and HCCDB18 dataset (validation on the reliability of the model) were downloaded as appropriate. Download the PRG and its corresponding pathway information from the gene set enrichment analysis (GSEA) website. The consensus clustering was performed by ConsensusClusterPlus package. Differentially expressed genes (DEGs) were identified using limma package, and prognostic features were constructed using un/multivariate and Lasso Cox regression. Pathway enrichment analysis was conducted by ssGSEA method. Receiver Operating Characteristic and the survival analysis were conducted by timeROC and Survminer packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, following the quantification on the key genes via reverse-transcription quantitative PCR, the Transwell and scratch assays were applied to evaluate the in-vitro invasion and migration of LC cells Huh-7. Results12 prognosis-related genes were identified to be related to the progression of LC. Three subtypes including C1, C2 and C3 were categorized using the 12 prognosis-related genes and PRGs significantly related to the prognosis of LC patients. The worst and best prognosis was seen in C3 subtype and C2 subtype, respectively. Hallmark pathway enrichment analysis has shown the concurrent immunoactivation and immune escape in C3 subtype. A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes. The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set. A nomogram model combining the risk score had the extreme higher benefit. Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score was seen in Monocytic-Macrophages. The quantification on the key genes has suggested the high expressions of KPNA2, UCK2, CBX2, RAB32, HMMR and S100A9 and the low expressions of FTCD and ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded and migrated LC cells in vitro. ConclusionThe risk model associated with pyproptosis is crucial for the tumor immunity of LC and may serve as a prognostic indicator for patients suffering from LC. Our findings will offer new perspectives for immunotherapies targeting LC.

Real-world genome profiling in Japanese patients with pancreatic ductal adenocarcinoma focusing on HRD implications.

Pancreatic ductal adenocarcinoma (PDAC) poses significant challenges due to its high mortality, making it a critical area of research. This retrospective observational study aimed to analyze real-world data from comprehensive genome profiling (CGP) of Japanese patients with PDAC, mainly focusing on differences in gene detection rates among panels and the implications for homologous recombination deficiency (HRD) status. This study enrolled 2568 patients with PDAC who had undergone CGP between June 2019 and December 2021 using data from the nationwide Center for Cancer Genomics and Advanced Therapeutics database. Two types of CGP assays (tissue and liquid biopsies) were compared and a higher detection rate of genetic abnormalities in tissue specimens was revealed. HRD-related gene alterations were detected in 23% of patients, with BRCA1/2 mutations accounting for 0.9% and 2.9% of patients, respectively. Treatment outcome analysis indicated that patients with BRCA1/2 mutations had a longer time to treatment discontinuation with FOLFIRINOX than gemcitabine plus nab-paclitaxel as first-line therapy (9.3 vs. 5.6 months, p = 0.028). However, no significant differences were observed in the treatment response among the other HRD-related genes. Logistic regression analysis identified younger age and family history of breast, prostate, and ovarian cancers as predictive factors for HRD-related gene alterations. Despite the lack of progression-free survival data and the inability to discriminate between germline and somatic mutations, this study provides valuable insights into the clinical implications of CGP in Japanese patients with PDAC. Further research is warranted to optimize panel selection and elucidate the efficacy of platinum-based therapies depending on the HRD status.

The role of pyroptosis-related genes in breast cancer progression

Breast cancer (BC) is one of the most common malignant cancers affecting females worldwide. Pyroptosis, a form of programmed cell death associated with inflammation and triggered by pro-inflammatory signals, is not well understood in the context of BC. This study aimed to investigate the role of pyroptosis in BC patients. Paired tumor and adjacent normal tissue samples were obtained from The Cancer Genome Atlas. Using a least absolute shrinkage and selection operator Cox analysis, we identified 15 prognostic genes associated with BC. Among these, fibrinogen C domain-containing 1, calcium voltage-gated channel subunit alpha1 H, heat shock protein family B (small) member 8, and peroxidasin-like (PXDNL) were classified as high-risk genes in BC. In contrast, the remaining 11 genes, such as proteasome activator subunit 2 and DIRAS (DIRAS family GTPase 3), were low-risk genes. Kyoto Encyclopedia of Genes and Genomes and Gene ontology analyses revealed that immune-related genes were enriched but showed reduced immunological status in the high-risk group, indicating that pro-inflammatory factors and immune antigens were produced during cell death. Consequently, targeting immune antigens with new immunosuppressants may offer a novel approach to treating BC. Moreover, the expression levels of the prognostic genes PXDNL, armadillo-like helical domain-containing 1 (ARMH1), APOBEC3D, and APOBEC3F in BC cell lines were assessed using quantitative polymerase chain reaction and western blotting. PXDNL and ARMH1 exhibited high levels of expression in BC, suggesting they could be potential therapeutic targets.

Oxidative stress gene signature construction to identify subtypes and prognosis of patients with lung adenocarcinoma

BackgroundAlthough oxidative stress and malignancies are intimately connected, it is unknown how lung adenocarcinoma (LUAD) is affected by oxidative stress response-related genes (OSRGs).Our goal in this work was to create a genetic signature based on OSRGs that might both predict prognosis and hint to potential treatment options for LUAD. MethodsClinicopathological and transcriptome information on LUAD patients was obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A model for predicting risk was created using LASSO regression. The TCGA, GSE72094, and GSE41271 cohorts all demonstrated the risk model's prediction ability. Immune cell infiltration was measured using the CIBERSORT method, and the TIDE platform was implemented to evaluate the therapeutic efficacy of immune checkpoint inhibition (ICI). Chemotherapy sensitivity was predicted using drug activity data by the Genomics of Drug Sensitivity. An investigation into gene expression was conducted using qRT-PCR. CCK-8 and transwell assays were employed to look into how DKK1 affected the migration and proliferation of LUAD cells. ResultsA gene signature consisting of ANLN, FAM83A, DKK1, LOXL2, RHOV, IGFBP1, CCR2, GNG7, and C11orf16 was efficiently determined and used to calculate a patient-specific risk score, this functioned as a stand-alone biomarker for prediction. Correlations were found between risk scores and immune cell infiltration frequency, ICI therapy response rate, estimated chemotherapeutic drug susceptibility and autophagy-related genes.Furthermore, DKK1 knockdown reduced the ability of LUAD cells to multiply and migrate. ConclusionOur thorough transcriptome study of OSRGs generated a biological framework effective in forecasting outcome and responsiveness to therapy in LUAD patients.

Pangenome-Informed Language Models for Privacy-Preserving Synthetic Genome Sequence Generation.

The public availability of genome datasets, such as The Human Genome Project (HGP), The 1000 Genomes Project, The Cancer Genome Atlas, and the International HapMap Project, has significantly advanced scientific research and medical understanding. Here our goal is to share such genomic information for downstream analysis while protecting the privacy of individuals through Differential Privacy (DP). We introduce synthetic DNA data generation based on pangenomes in combination with Pretrained-Language Models (PTLMs). We introduce two novel tokenization schemes based on pangenome graphs to enhance the modeling of DNA. We evaluated these tokenization methods, and compared them with classical single nucleotide and k -mer tokenizations. We find k -mer tokenization schemes, indicating that our tokenization schemes boost the model's performance consistency with long effective context length (covering longer sequences with the same number of tokens). Additionally, we propose a method to utilize the pangenome graph and make it comply with DP privacy standards. We assess the performance of DP training on the quality of generated sequences with discussion of the trade-offs between privacy and model accuracy. The source code for our work will be published under a free and open source license soon.

Cell Migration-Proliferation Dichotomy in Cancer: Biological Fact or Experimental Artefact?

The migration-proliferation dichotomy (MPD) has long been observed in cultured cancer cells. This phenomenon is not only relevant to tumour progression but may also have therapeutic significance in clinical cancer. However, MPD has rarely been investigated in primary cancer. This study aimed to either confirm or disprove the existence of MPD in primary cancer. Using primary gastric, colorectal and prostate cancer (GC, CRC and PCa) cohorts from the Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center, this study interrogated the MPD phenomenon by utilising RNA-Seq-based proliferation (CIN70 signature) and migration (epithelial-mesenchymal transition) indices, as well as gene set enrichment analyses (GSEA). Alternative hypothetical migration-proliferation models-The simultaneous migration-proliferation (SMP) and phenotype-refractory (PR) models-were compared to the MPD model by probing the migration-proliferation relationships within cancer stages and between early- and late-stage diseases using chi-square and independent T tests, z-score statistics and GSEA. The results revealed an inverse relationship between migration and proliferation signatures overall in the GC, CRC and PCa cohorts, as well as in early- and late-stage diseases. Additionally, a shift in proliferation- to migration dominance was observed from early- to late-stage diseases in the GC and CRC cohorts but not in the PCa cohorts, which showed enhanced proliferation dominance in metastatic tumours compared to primary cancers. The above features exhibited by the cancer cohorts are in keeping with the MPD model of the migration-proliferation relationship at the cellular level and exclude the SMP and PR migration-proliferation models.

Construction of an endoplasmic reticulum stress and cuproptosis -related lncRNAs signature in chemosensitivity in hepatocellular carcinoma by comprehensive bioinformatics analysis

Endoplasmic reticulum stress (ERS) and cuproptosis have remarkable effects on hepatocellular carcinoma (HCC) leading to a poor prognosis. The current study aimed to explore credible signature for predicting the prognosis of HCC based on ERS and cuproptosis-related lncRNAs. In our study, clinical and transcriptomic profiles of HCC patients were obtained from the Cancer Genome Atlas (TCGA) database. An ERS and cuproptosis-related lncRNA prognostic signature, including NRAV, SNHG3, LINC00839 and AC004687.1, was determined by correlation tests, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) methods. Survival and predictive value were evaluated using Kaplan–Meier and receiver operating characteristic (ROC) curves, while calibration and nomograms curves were developed. Besides the enrichment analyses for ERS and cuproptosis-related lncRNAs, mutational status and immune status were assessed with TMB and ESTIMATE. Additionally, consensus cluster analysis was employed to compare cancer subtype differences, while drug sensitivity and immunologic efficacy were evaluated for further exploration. qRT-PCR and CCK-8 were utilized to verify the alteration of the prognostic lncRNAs expression and proliferation in vitro. High-risk groups exhibited poorer prognosis. The signature exhibited robust predictive value as an independent prognostic indicator and showed significant correlation with clinicopathological features. In the enriched analysis, biological membrane pathways were enriched. Low-risk patients had lower TMB and higher immune status. A cluster analysis revealed that cluster 2 had the best clinical immunological efficacy and most active immune function. In brief, our constructed signature with ERS and cuproptosis-related lncRNAs was associated survival outcomes of HCC, and can be used to predict the clinical classification and curative effect.

IPFMC: an iterative pathway fusion approach for enhanced multi-omics clustering in cancer research.

Using multi-omics data for clustering (cancer subtyping) is crucial for precision medicine research. Despite numerous methods having been proposed, current approaches either do not perform satisfactorily or lack biological interpretability, limiting the practical application of these methods. Based on the biological hypothesis that patients with the same subtype may exhibit similar dysregulated pathways, we developed an Iterative Pathway Fusion approach for enhanced Multi-omics Clustering (IPFMC), a novel multi-omics clustering method involving two data fusion stages. In the first stage, omics data are partitioned at each layer using pathway information, with crucial pathways iteratively selected to represent samples. Ultimately, the representation information from multiple pathways is integrated. In the second stage, similarity network fusion was applied to integrate the representation information from multiple omics. Comparative experiments with nine cancer datasets from The Cancer Genome Atlas (TCGA), involving systematic comparisons with 10 representative methods, reveal that IPFMC outperforms these methods. Additionally, the biological pathways and genes identified by our approach hold biological significance, affirming not only its excellent clustering performance but also its biological interpretability.

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding of complex diseases. To this end, researchers have developed various network-based prediction techniques targeting specific molecular associations. However, limitations imposed by reductionism and network representation learning have led existing studies to narrowly focus on high prediction efficiency within single association type, thereby glossing over the discovery of unknown types of associations. Additionally, effectively utilizing network structure to fit the interaction properties of regulatory networks and combining specific case biomarker validations remains an unresolved issue in cancer biomarker prediction methods. To overcome these limitations, we propose a multi-view learning framework, CeRVE, based on directed graph neural networks (DGNN) for predicting unknown type cancer biomarkers. CeRVE effectively extracts and integrates subgraph information through multi-view feature learning. Subsequently, CeRVE utilizes DGNN to simulate the entire regulatory network, propagating node attribute features and extracting various interaction relationships between molecules. Furthermore, CeRVE constructed a comparative analysis matrix of three cancers and adjacent normal tissues through The Cancer Genome Atlas and identified multiple types of potential cancer biomarkers through differential expression analysis of mRNA, microRNA, and long noncoding RNA. Computational testing of multiple types of biomarkers for 72 cancers demonstrates that CeRVE exhibits superior performance in cancer biomarker prediction, providing a powerful tool and insightful approach for AI-assisted disease biomarker discovery.

Comprehensive analysis of NOTCH pathway with tumor environment in pancreatic adenocarcinoma

Abstract Objectives Pancreatic adenocarcinoma (PAAD) ranks among the most prevalent malignant neoplasms, and multiple pathways are involved in its pathogenesis, including the NOTCH pathway. However, the variable biological functions of the pathway in PAAD are controversial. Methods RNA-seq data for PAAD was analyzed using data from The Cancer Genome Atlas and Genotype-Tissue Expression databases. Utilizing Kaplan-Meier survival curves and Cox regression analyses, we examined the prognostic significance. The tumor microenvironment and immunotherapy responses were investigated using ssGSEA, ESTIMATE, and TIDE models. Functional enrichment analysis was used to explore gene functions. Results We identified NOTCH2, JAG1, NOTCH4, and DLL3 as high-priority members of the NOTCH pathway that modulates PAAD. Elevated NOTCH2 and JAG1 levels were markedly linked to reduced overall survival (OS), while increased NOTCH4 and DLL3 levels were significantly related to extended OS. Immune analyses showed that NOTCH-based scores were closely related to the immune microenvironment. NOTCH scores were not only closely correlated with tumor-infiltrating immune cells, but also with immunologically activated and immune checkpoint gene expression. The high NOTCH score group had a higher proportion of tumor-infiltrating immune cells and had better responses to immune checkpoint inhibitor therapy. Conclusions These data indicate that NOTCH2, JAG1, NOTCH4, and DLL3 could function as efficient prognostic biomarkers and therapeutic targets in PAAD, and patients with a high NOTCH score may have a significant response to immune checkpoint inhibitor treatment.

Deep contrastive learning for predicting cancer prognosis using gene expression values.

Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor transcriptomes and clinical data to learn feature representations in a low-dimensional space. We then utilized these learned features to train a classifier to categorize tumors into a high- or low-risk group of recurrence. Using data from The Cancer Genome Atlas (TCGA), we demonstrated that CL can significantly improve classification accuracy. Specifically, our CL-based classifiers achieved an area under the receiver operating characteristic curve (AUC) greater than 0.8 for 14 types of cancer, and an AUC greater than 0.9 for 3 types of cancer. We also developed CL-based Cox (CLCox) models for predicting cancer prognosis. Our CLCox models trained with the TCGA data outperformed existing methods significantly in predicting the prognosis of 19 types of cancer under consideration. The performance of CLCox models and CL-based classifiers trained with TCGA lung and prostate cancer data were validated using the data from two independent cohorts. We also show that the CLCox model trained with the whole transcriptome significantly outperforms the Cox model trained with the 16 genes of Oncotype DX that is in clinical use for breast cancer patients. The trained models and the Python codes are publicly accessible and provide a valuable resource that will potentially find clinical applications for many types of cancer.

Tumor microbiota of renal cell carcinoma affects clinical prognosis by influencing the tumor immune microenvironment

Despite reported influences of the intratumoral microbiome on cancer progression, its role in this subtype remains unclear. This study aimed to characterize the microbial landscape and signatures of kidney renal clear cell carcinoma using RNA-Seq data from The Cancer Genome Atlas. Following microbial decontamination, differential microbial analysis was conducted between tumorous and adjacent non-tumorous samples. Compared to non-tumorous samples, tumorous microbiota exhibited reduced α and β diversity and distinct phylum-level communities. Differential microbial analysis between patients exhibiting long and short overall survival revealed ten significant differential microbial genera, with six genera correlating with a positive prognosis (Plasmodium, Babesia, Toxoplasma, Cytobacillus, Alicyclobacillus, Verrucomicrobium) and four with a negative prognosis (Colletotrichum, Leuconostoc, Gluconobacter, and Parabacteroides). Employing Cox regression analysis and support vector machines, a prognosis-related microbiome risk signature was developed, achieving an AUC of 0.809. Based on this risk signature, two microbiome-based subtypes were found to be significantly associated with distinct clinical prognoses and immune microenvironments. These findings were corroborated by significant correlations between prognostic-relevant microorganisms and 30 immune-related differentially expressed genes. Specifically, microbial genera associated with a negative prognosis were linked to a pro-tumor acute inflammatory immune response, whereas genera related to a positive prognosis were associated with an anti-tumor adaptive immune response. In conclusion, microbiome-based subtyping revealed correlations between tumor microbiome, clinical prognosis, and tumor microenvironment, indicating intratumoral microbiota as a promising prognostic biomarker for kidney renal clear cell carcinoma.

A comprehensive in silico analysis and experimental validation of miRNAs capable of discriminating between lung adenocarcinoma and squamous cell carcinoma.

Accurate differentiation between lung adenocarcinoma (AC) and lung squamous cell carcinoma (SCC) is crucial owing to their distinct therapeutic approaches. MicroRNAs (miRNAs) exhibit variable expression across subtypes, making them promising biomarkers for discrimination. This study aimed to identify miRNAs with robust discriminatory potential between AC and SCC and elucidate their clinical significance. MiRNA expression profiles for AC and SCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and supervised machine learning methods (Support Vector Machine, Decision trees and Naïve Bayes) were employed. Clinical significance was assessed through receiver operating characteristic (ROC) curve analysis, survival analysis, and correlation with clinicopathological features. Validation was conducted using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, signaling pathway and gene ontology enrichment analyses were conducted to unveil biological functions. Five miRNAs (miR-205-3p, miR-205-5p, miR-944, miR-375 and miR-326) emerged as potential discriminative markers. The combination of miR-944 and miR-326 yielded an impressive area under the curve of 0.985. RT-qPCR validation confirmed their biomarker potential. miR-326 and miR-375 were identified as prognostic factors in AC, while miR-326 and miR-944 correlated significantly with survival outcomes in SCC. Additionally, exploration of signaling pathways implicated their involvement in key pathways including PI3K-Akt, MAPK, FoxO, and Ras. This study enhances our understanding of miRNAs as discriminative markers between AC and SCC, shedding light on their role as prognostic indicators and their association with clinicopathological characteristics. Moreover, it highlights their potential involvement in signaling pathways crucial in non-small cell lung cancer pathogenesis.

Ancestry and somatic profile predict acral melanoma origin and prognosis.

Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, here we characterise the genome and transcriptome of 128 acral melanoma tumours from 96 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower number of structural variants. These BRAF-mutated tumours have a transcriptional profile similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and transcriptional profiling defined three expression clusters; these characteristics were associated with overall survival. We highlight novel low-frequency drivers, such as SPHKAP, which correlate with a distinct genomic profile and clinical characteristics. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

MIR4435-2HG: A novel biomarker for triple-negative breast cancer diagnosis and prognosis, activating cancer-associated fibroblasts and driving tumor invasion through EMT associated with JNK/c-Jun and p38 MAPK signaling pathway activation

BackgroundBreast cancer has the highest incidence rate and causes the most fatalities among all female cancers worldwide. Triple-negative breast cancer (TNBC) is known for its strong invasiveness and higher rates of recurrence. In this research, we aimed to identify MIR4435-2HG as a promising long non-coding RNA (lncRNA) biomarker and therapeutic target for TNBC. MethodsUtilizing clinicopathological information and transcriptome data from The Cancer Genome Atlas (TCGA) database, we assessed the clinical relevance of MIR4435-2HG in breast cancer through univariate and multivariate COX regression, receiver operating characteristic (ROC) analysis, as well as Kaplan-Meier survival analysis. To investigate the biological role of MIR4435-2HG in TNBC, we conducted gene set enrichment analysis (GSEA), as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Additionally, we constructed and validated a nomogram to predict disease-free survival (DFS). Both the R package “pRRophetic” and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were employed to forecast the sensitivity to different therapeutics between the high- and low-MIR4435-2HG groups. We employed single-cell RNA sequencing analysis and tumor microenvironment infiltration analysis to investigate the potential involvement of MIR4435-2HG in the TNBC tumor microenvironment. Cellular biological behaviors were assessed utilizing CCK-8, transwell assays, and wound-healing assays. Furthermore, we performed RNA-seq, qRT-PCR, and western blotting analyses to elucidate and confirm the specific mechanisms underlying the role of MIR4435-2HG in TNBC. ResultsIn our study, we have identified MIR4435-2HG as a significant diagnostic and prognostic factor for TNBC. We observed that MIR4435-2HG is widely expressed and might have a significant impact on the reshaping of the TNBC tumor microenvironment. Patients with TNBC in the high-MIR4435-2HG group may show reduced sensitivity to cisplatin, doxorubicin, and gemcitabine and have an increased propensity for immune escape. Knockdown of MIR4435-2HG inhibits cancer-associated fibroblasts (CAFs) activation. Notably, MIR4435-2HG predominantly enhances the migratory and invasive capabilities of TNBC cells through the epithelial-mesenchymal transition (EMT) process. Mechanistically, we validated that MIR4435-2HG activates the JNK/c-Jun and p38 non-classical MAPK signaling pathway in TNBC cells. ConclusionsOur findings highlight the significant potential of MIR4435-2HG as a highly promising biomarker for TNBC. Targeting MIR4435-2HG could represent an appealing therapeutic approach for TNBC.