Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/660175

Hsa-mir-210 as a novel signature predicts survival in lung squamous cell carcinoma using bioinformatics analysis

Liu, Y.·Chen, Y.·Li, L.-L.·Wang, S.-N.·Xia, S.
View original source ↗Export metadata ↓

Abstract

In recent years, more and more studies have shown that the expression of miRNAs is closely related to the occurrence of tumors and plays an irreplaceable role in the development and metastasis of tumors. The research was focused on lung squamous cell carcinoma. The data information is downloaded from the TCGA database and analyzed for variance, which is then verified in the GEO database. Then differential expression of miRNAs was found and survival analysis was performed, through the cut - off standard(P<0.05,|logFC|[&ge;]2), we screen the 38 up-regulated miRNAs and 14 down-regulated miRNAs from the TCGA. Finally, after the verification on the GEO database, four up-regulated miRNAs (hsa-mir-205, hsa-mir-210, hsa-mir-182, hsa-mir-224) and one down-regulated miRNA (hsa-mir-451) were obtained, which provide a new direction for the diagnosis of lung squamous cell carcinoma. In the survival analysis, it was found that the expression state of hsamir-210 was significantly correlated with patient survival. The results in the univariate and multivariate Cox analysis indicated that hsa-mir-210 was an independent prognostic factor on lung squamous cell carcinoma. The functional enrichment analysis showed that hsa-mir-210 was closely related to positive and negative regulation of cell proliferation, DNA transcription, VEGF signaling pathway, MAPK signaling pathway, hif-1 signaling pathway and choline metabolic pathway. In summary, this study suggested that hsa-mir-210 could be a potential prognostic factor for lung squamous cell carcinoma.\n\nAuthor SummaryMicroRNAs are single-stranded small molecular RNA that participate in the regulation of various biological functions through indirect regulation of gene expression, and have been reported to play an important role in the occurrence, development, invasion and metastasis of tumors. In recent years, the research on miRNAs has become increasingly hot, and the role of miRNAs in tumor has been proved more and more. The subjects of this study were squamous cell carcinoma in lung cancer with relatively few studies on miRNAs. Through high-throughput data analysis, miRNAs with differential expression between lung squamous cell carcinoma tissues and normal tissues were found. These differentially expressed miRNAs provide a new direction for the early diagnosis of patients. Then, survival analysis was conducted to find the miRNAs significantly correlated with the total survival time of patients, and multi-factor analysis was conducted to exclude the influence of other clinical factors, and independent risk factors (miRNAs) affecting the survival of patients were determined, so as to provide new targets for the treatment and survival prediction of patients.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Liu, Y., Chen, Y., Li, L.-L., Wang, S.-N., Xia, S.. 2019-06-04. Hsa-mir-210 as a novel signature predicts survival in lung squamous cell carcinoma using bioinformatics analysis. https://doi.org/10.1101/660175

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗
bioRxiv2026

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗
bioRxiv2026

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗