Scientific figures interpreted by ChatGPT: strengths in plot recognition and limits in color perception.

Emerging studies underscore the promising capabilities of large language model-based chatbots in conducting basic bioinformatics data analyses. The recent feature of accepting image inputs by ChatGPT, also known as GPT-4V(ision), motivated us to explore its efficacy in deciphering bioinformatics scientific figures. Our evaluation with examples in cancer research, including sequencing data analysis, multimodal network-based drug repositioning, and tumor clonal evolution, revealed that ChatGPT can proficiently explain different plot types and apply biological knowledge to enrich interpretations.

Bioinformatics Illustrations Decoded by ChatGPT: The Good, The Bad, and The Ugly.

Emerging studies underscore the promising capabilities of large language model-based chatbots in conducting fundamental bioinformatics data analyses. The recent feature of accepting image-inputs by ChatGPT motivated us to explore its efficacy in deciphering bioinformatics illustrations. Our evaluation with examples in cancer research, including sequencing data analysis, multimodal network-based drug repositioning, and tumor clonal evolution, revealed that ChatGPT can proficiently explain different plot types and apply biological knowledge to enrich interpretations.

MicroRNA-Based Discovery of Biomarkers, Therapeutic Targets, and Repositioning Drugs for Breast Cancer.

Breast cancer treatment can be improved with biomarkers for early detection and individualized therapy. A set of 86 microRNAs (miRNAs) were identified to separate breast cancer tumors from normal breast tissues ( = 52) with an overall accuracy of 90.4%.

Expression-Based Diagnosis, Treatment Selection, and Drug Development for Breast Cancer.

There is currently no gene expression assay that can assess if premalignant lesions will develop into invasive breast cancer. This study sought to identify biomarkers for selecting patients with a high potential for developing invasive carcinoma in the breast with normal histology, benign lesions, or premalignant lesions. A set of 26-gene mRNA expression profiles were used to identify invasive ductal carcinomas from histologically normal tissue and benign lesions and to select those with a higher potential for future cancer development (ADHC) in the breast associated with atypical ductal hyperplasia (ADH).

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets.

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets.

Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks.

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and survival in early-stage NSCLC patients.

Multi-Omics Immune Interaction Networks in Lung Cancer Tumorigenesis, Proliferation, and Survival.

There are currently no effective biomarkers for prognosis and optimal treatment selection to improve non-small cell lung cancer (NSCLC) survival outcomes. This study further validated a seven-gene panel for diagnosis and prognosis of NSCLC using RNA sequencing and proteomic profiles of patient tumors. Within the seven-gene panel, expression combined with dendritic cell activities defined NSCLC patient subgroups ( = 966) with distinct survival outcomes ( = 0.

Redox imbalance in COVID-19 pathophysiology.

Background: The pathophysiologic significance of redox imbalance is unquestionable as numerous reports and topic reviews indicate alterations in redox parameters during corona virus disease 2019 (COVID-19). However, a more comprehensive understanding of redox-related parameters in the context of COVID-19-mediated inflammation and pathophysiology is required.

Methods: COVID-19 subjects (n = 64) and control subjects (n = 19) were enrolled, and blood was drawn within 72 h of diagnosis.

Single B Cell Gene Co-Expression Networks Implicated in Prognosis, Proliferation, and Therapeutic Responses in Non-Small Cell Lung Cancer Bulk Tumors.

In NSCLC, there is a pressing need for immunotherapy predictive biomarkers. The processes underlying B-cell dysfunction, as well as their prognostic importance in NSCLC, are unknown. Tumor-specific B-cell gene co-expression networks were constructed by comparing the Boolean implication modeling of single-cell RNA sequencing of NSCLC tumor B cells and normal B cells.

A Multi-Omics Network of a Seven-Gene Prognostic Signature for Non-Small Cell Lung Cancer.

There is an unmet clinical need to identify patients with early-stage non-small cell lung cancer (NSCLC) who are likely to develop recurrence and to predict their therapeutic responses. Our previous study developed a qRT-PCR-based seven-gene microfluidic assay to predict the recurrence risk and the clinical benefits of chemotherapy. This study showed it was feasible to apply this seven-gene panel in RNA sequencing profiles of The Cancer Genome Atlas (TCGA) NSCLC patients ( = 923) in randomly partitioned feasibility-training and validation sets ( < 0.

Immune-Omics Networks of , , and in Non-Small Cell Lung Cancer.

To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. Major immune-checkpoint inhibitors (ICIs) have more DNA copy number variations (CNV) than mutations in The Cancer Genome Atlas (TCGA) NSCLC tumors. Nevertheless, CNV-mediated dysregulated gene expression in NSCLC is not well understood.

Identification of Prognostic and Chemopredictive microRNAs for Non-Small-Cell Lung Cancer by Integrating SEER-Medicare Data.

This study developed a novel methodology to correlate genome-scale microRNA (miRNA) expression profiles in a lung squamous cell carcinoma (LUSC) cohort ( = 57) with Surveillance, Epidemiology, and End Results (SEER)-Medicare LUSC patients ( = 33,897) as a function of composite tumor progression indicators of T, N, and M cancer stage and tumor grade. The selected prognostic and chemopredictive miRNAs were extensively validated with miRNA expression profiles of non-small-cell lung cancer (NSCLC) patient samples collected from US hospitals ( = 156) and public consortia including NCI-60, The Cancer Genome Atlas (TCGA; = 1016), and Cancer Cell Line Encyclopedia (CCLE; = 117). Hsa-miR-142-3p was associated with good prognosis and chemosensitivity in all the studied datasets.

Molecular Analysis of in Non-Small-Cell Lung Cancer.

Our previous study found that zinc finger protein 71 (ZNF71) mRNA expression was associated with chemosensitivity and its protein expression was prognostic of non-small-cell lung cancer (NSCLC). The Krüppel associated box (KRAB) transcriptional repression domain is commonly present in human zinc finger proteins, which are linked to imprinting, silencing of repetitive elements, proliferation, apoptosis, and cancer. This study revealed that had a significantly higher expression than the -less isoform in NSCLC tumors ( = 197) and cell lines ( = 117).

Pilot deep RNA sequencing of worker blood samples from Singapore printing industry for occupational risk assessment.

Several engineered nanomaterials (ENMs) are used in toner-based printing equipment (TPE) including laser printers and photocopiers to improve toner performance. High concentration of airborne nanoparticles due to TPE emissions has been documented in copy centers and chamber studies. Recent animal inhalation studies by our group suggested exposure to laser printer-emitted nanoparticles (PEPs) increased cardiovascular risk by impairing ventricular performance and inducing hypertension and arrhythmia, consistent with global transcriptomic and metabolomic profiling results.

Integrated Transcriptomics, Metabolomics, and Lipidomics Profiling in Rat Lung, Blood, and Serum for Assessment of Laser Printer-Emitted Nanoparticle Inhalation Exposure-Induced Disease Risks.

Laser printer-emitted nanoparticles (PEPs) generated from toners during printing represent one of the most common types of life cycle released particulate matter from nano-enabled products. Toxicological assessment of PEPs is therefore important for occupational and consumer health protection. Our group recently reported exposure to PEPs induces adverse cardiovascular responses including hypertension and arrythmia via monitoring left ventricular pressure and electrocardiogram in rats.

A Predictive 7-Gene Assay and Prognostic Protein Biomarkers for Non-small Cell Lung Cancer.

Purpose: This study aims to develop a multi-gene assay predictive of the clinical benefits of chemotherapy in non-small cell lung cancer (NSCLC) patients, and substantiate their protein expression as potential therapeutic targets.

Patients And Methods: The mRNA expression of 160 genes identified from microarray was analyzed in qRT-PCR assays of independent 337 snap-frozen NSCLC tumors to develop a predictive signature. A clinical trial JBR.

Network-based identification of biomarkers coexpressed with multiple pathways.

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication.

Combining COPD with clinical, pathological and demographic information refines prognosis and treatment response prediction of non-small cell lung cancer.

Background: Accurate assessment of a patient's risk of recurrence and treatment response is an important prerequisite of personalized therapy in lung cancer. This study extends a previously described non-small cell lung cancer prognostic model by the addition of chemotherapy and co-morbidities through the use of linked SEER-Medicare data.

Methodology/principal Findings: Data on 34,203 lung adenocarcinoma and 26,967 squamous cell lung carcinoma patients were used to determine the contribution of Chronic Obstructive Pulmonary Disease (COPD) to prognostication in 30 treatment combinations.

Integrated miRNA and mRNA Analysis of Time Series Microarray Data.

The dynamic temporal regulatory effects of microRNA are not well known. We introduce a technique for integrating miRNA and mRNA time series microarray data with known disease pathology. The integrated analysis includes identifying both mRNA and miRNA that are signi cantly similar to the quantitative pathology.

The microRNA-200 family targets multiple non-small cell lung cancer prognostic markers in H1299 cells and BEAS-2B cells.

Lung cancer remains the leading cause of cancer-related mortality for both men and women. Tumor recurrence and metastasis is the major cause of lung cancer treatment failure and death. The microRNA‑200 (miR-200) family is a powerful regulator of the epithelial-mesenchymal transition (EMT) process, which is essential in tumor metastasis.

A smoking-associated 7-gene signature for lung cancer diagnosis and prognosis.

Smoking is responsible for 90% of lung cancer cases. There is currently no clinically available gene test for early detection of lung cancer in smokers, or an effective patient selection strategy for adjuvant chemotherapy in lung cancer treatment. In this study, concurrent coexpression with multiple signaling pathways was modeled among a set of genes associated with smoking and lung cancer survival.

Pathway-based identification of a smoking associated 6-gene signature predictive of lung cancer risk and survival.

Objective: Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers.