COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients

Díez-Villanueva, Anna; Sanz‐Pamplona, Rebeca; Solé, Xavier; Cordero, David; Crous‐Bou, Marta; Guinó, Elisabet; López-Dóriga, Adriana; Berenguer, Antoni; Aussó, Susanna; Paré, Laia; Obón-Santacana, Mireia; Moratalla-Navarro, Ferrán; Salazar, Ramón; Sanjuán, Xavier; Santos, Cristina; Biondo, Sebastiano; Díez-Obrero, Virginia; Garcia‐Serrano, Ainhoa; Alonso, M. Henar; Carreras‐Torres, Robert; Closa, Adrià

doi:10.1038/s41597-022-01697-5

Cited by 9 publications

(6 citation statements)

References 39 publications

(54 reference statements)

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“…In addition to small RNA-seq, it has microarray expression data for the same subjects. Sample processing, quality control and normalization are described elsewhere [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

Moratalla-Navarro

Díez-Villanueva

Serrano

et al. 2023

Cancers

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We aimed to identify and validate a set of miRNAs that could serve as a prognostic signature useful to determine the recurrence risk for patients with COAD. Small RNAs from tumors of 100 stage II, untreated, MSS colon cancer patients were sequenced for the discovery step. For this purpose, we built an miRNA score using an elastic net Cox regression model based on the disease-free survival status. Patients were grouped into high or low recurrence risk categories based on the median value of the score. We then validated these results in an independent sample of stage II microsatellite stable tumor tissues, with a hazard ratio of 3.24, (CI95% = 1.05–10.0) and a 10-year area under the receiver operating characteristic curve of 0.67. Functional analysis of the miRNAs present in the signature identified key pathways in cancer progression. In conclusion, the proposed signature of 12 miRNAs can contribute to improving the prediction of disease relapse in patients with stage II MSS colorectal cancer, and might be useful in deciding which patients may benefit from adjuvant chemotherapy.

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“…In addition to small RNA-seq, it has microarray expression data for the same subjects. Sample processing, quality control and normalization are described elsewhere [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

Moratalla-Navarro

Díez-Villanueva

Serrano

et al. 2023

Cancers

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“…To test our signature we employed six independent datasets. These comprise 126 Colon Cancer WES obtained from TCGA; 72 colon WES samples from Genentech (Seshagiri et al, 2012); 42 samples taken from Colonomics (Díez-Villanueva et al, 2022); 101 Breast Cancer WES samples obtained from dbGaP (NCBI, 2021); 291 colon samples from COCA-CN study taken from ICGC (Zhang et al, 2019) and 338 samples from LINC-JP taken from ICGC. The first three datasets were analyzed with our pipeline starting from the BAM files.…”

Section: Datasetsmentioning

confidence: 99%

TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures

Privitera,

Alaimo,

Caruso

et al. 2024

Front. Genet.

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Background:In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology.Methods:This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA.Results:Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES).Conclusion:We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice.

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“…We searched the CRC dataset from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/, accessed on 10 November 2023) using the keyword "Colorectal cancer". As the training dataset obtained from our retrieval, the GSE44076 dataset contained data from 98 CRC samples and 50 healthy control samples [49]. All the data analyzed in this study were extracted from the GEO database; thus, no ethical approval or informed consent was required.…”

Section: Acquisition Of Crc-related Targetsmentioning

confidence: 99%

Potential Mechanism of Tibetan Medicine Liuwei Muxiang Pills against Colorectal Cancer: Network Pharmacology and Bioinformatics Analyses

Qi,

Liang,

Wang

et al. 2024

Pharmaceuticals

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This study aimed to explore the mechanism through which Tibetan medicine Liuwei Muxiang (LWMX) pills acts against colorectal cancer (CRC). We firstly retrieved the active ingredients and the correlated targets of LWMX pills from public databases. The CRC-related targets were determined through bioinformatic analysis of a public CRC dataset. By computing the intersection of the drug-specific and disease-related targets, LWMX pill–CRC interaction networks were constructed using the protein–protein interaction (PPI) method and functional enrichment analysis. Subsequently, we determined the hub genes using machine learning tools and further verified their critical roles in CRC treatment via immune infiltration analysis and molecular docking studies. We identified 81 active ingredients in LWMX pills with 614 correlated targets, 1877 differentially expressed genes, and 9534 coexpression module genes related to CRC. A total of 5 target hub genes were identified among the 108 intersecting genes using machine learning algorithms. The immune infiltration analysis results suggested that LWMX pills could affect the CRC immune infiltration microenvironment by regulating the expression of the target hub genes. Finally, the molecular docking outcomes revealed stable binding affinity between all target hub proteins and the primary active ingredients of LWMX pills. Our findings illustrate the anti-CRC potential and the mechanism of action of LWMX pills and provide novel insights into multitarget medication for CRC treatment.

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COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients

Cited by 9 publications

References 39 publications

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures

Potential Mechanism of Tibetan Medicine Liuwei Muxiang Pills against Colorectal Cancer: Network Pharmacology and Bioinformatics Analyses

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