PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Multimodal ChatGPT-4V for ECG Interpretation: Promise and Limitations.

UNSTRUCTURED: Electrocardiogram (ECG) interpretation is an essential skill in cardiovascular medicine. This study evaluated the capabilities of newly released ChatGPT-4V, a large language model with visual recognition abilities, in interpreting ECG waveforms and answering related multiple-choice questions. A total of 62 ECG-related multiple-choice questions were collected from reputable medical exams. ChatGPT was prompted to answer the questions by analyzing the accompanying ECG images. Requiring at least 1 of 3 responses to be correct, ChatGPT achieved an overall accuracy of 83.87% across all question types. ChatGPT demonstrated significantly lower performance on counting-based questions like calculating QT intervals compared to diagnostic and treatment recommendation questions. The findings indicate that while ChatGPT shows promising potential in ECG interpretation and decision-making, its diagnostic reliability and quantitative analysis abilities need improvement before real clinical use. Further large-scale studies are warranted to fully evaluate ChatGPT's capabilities and track its progress as the model accumulates more medical knowledge through ongoing training. With technological advancements, multimodal AI like ChatGPT may one day play an important role in assisting clinicians with ECG interpretation and cardiovascular care.

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

ChatGPT's ability to generate realistic experimental images poses a new challenge to academic integrity.

The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT's writing capabilities, recent updates have integrated DALL-E 3's image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT's nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding "invisible watermarks" to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.

Development and validation of a radiosensitivity model to evaluate radiotherapy benefits in pan-cancer.

Radiotherapy, one of the most fundamental cancer treatments, is confronted with the dilemma of treatment failure due to radioresistance. To predict the radiosensitivity and improve tumor treatment efficiency in pan-cancer, we developed a model called Radiation Intrinsic Sensitivity Evaluation (RISE). The RISE model was built using cell line-based mRNA sequencing data from five tumor types with varying radiation sensitivity. Through four cell-derived datasets, two public tissue-derived cohorts, and one local cohort of 42 nasopharyngeal carcinoma patients, we demonstrated that RISE could effectively predict the level of radiation sensitivity (area under the ROC curve [AUC] from 0.666 to 1 across different datasets). After the verification by the colony formation assay and flow cytometric analysis of apoptosis, our four well-established radioresistant cell models successfully proved higher RISE values in radioresistant cells by RT-qPCR experiments. We also explored the prognostic value of RISE in five independent TCGA cohorts consisting of 1137 patients who received radiation therapy and found that RISE was an independent adverse prognostic factor (pooled multivariate Cox regression hazard ratio [HR]: 1.84, 95% CI 1.39-2.42; p < 0.01). RISE showed a promising ability to evaluate the radiotherapy benefit while predicting the prognosis of cancer patients, enabling clinicians to make individualized radiotherapy strategies in the future and improve the success rate of radiotherapy.

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.

CTLs heterogeneity and plasticity: implications for cancer immunotherapy.

Cytotoxic T lymphocytes (CTLs) play critical antitumor roles, encompassing diverse subsets including CD4+, NK, and γδ T cells beyond conventional CD8+ CTLs. However, definitive CTLs biomarkers remain elusive, as cytotoxicity-molecule expression does not necessarily confer cytotoxic capacity. CTLs differentiation involves transcriptional regulation by factors such as T-bet and Blimp-1, although epigenetic regulation of CTLs is less clear. CTLs promote tumor killing through cytotoxic granules and death receptor pathways, but may also stimulate tumorigenesis in some contexts. Given that CTLs cytotoxicity varies across tumors, enhancing this function is critical. This review summarizes current knowledge on CTLs subsets, biomarkers, differentiation mechanisms, cancer-related functions, and strategies for improving cytotoxicity. Key outstanding questions include refining the CTLs definition, characterizing subtype diversity, elucidating differentiation and senescence pathways, delineating CTL-microbe relationships, and enabling multi-omics profiling. A more comprehensive understanding of CTLs biology will facilitate optimization of their immunotherapy applications. Overall, this review synthesizes the heterogeneity, regulation, functional roles, and enhancement strategies of CTLs in antitumor immunity, highlighting gaps in our knowledge of subtype diversity, definitive biomarkers, epigenetic control, microbial interactions, and multi-omics characterization. Addressing these questions will refine our understanding of CTLs immunology to better leverage cytotoxic functions against cancer.

Role of body mass index and weight change in the risk of cancer: A systematic review and meta-analysis of 66 cohort studies.

Background: This study was designed to evaluate the effects of body mass index (BMI) and weight change on the risk of developing cancer overall and cancer at different sites. Methods: We searched PubMed and other databases up to July 2023 using the keywords related to 'risk', 'cancer', 'weight', 'overweight', and 'obesity'. We identified eligible studies, and the inclusion criteria encompassed cohort studies in English that focused on cancer diagnosis and included BMI or weight change as an exposure factor. Multiple authors performed data extraction and quality assessment, and statistical analyses were carried out using RevMan and R software. We used random- or fixed-effects models to calculate the pooled relative risk (RR) or hazard ratio along with 95% confidence intervals (CIs). We used the Newcastle-Ottawa Scale to assess study quality. Results: Analysis included 66 cohort studies. Compared to underweight or normal weight, overweight or obesity was associated with an increased risk of endometrial cancer, kidney cancer, and liver cancer but a decreased risk of prostate cancer and lung cancer. Being underweight was associated with an increased risk of gastric cancer and lung cancer but not that of postmenopausal breast cancer or female reproductive cancer. In addition, weight loss of more than five kg was protective against overall cancer risk. Conclusions: Overweight and obesity increase the risk of most cancers, and weight loss of >5 kg reduces overall cancer risk. These findings provide insights for cancer prevention and help to elucidate the mechanisms underlying cancer development. Registration: Reviewregistry1786.

Potential anti-tumor effects of regulatory T cells in the tumor microenvironment: a review.

Regulatory T cells (Tregs) expressing the transcription factor FoxP3 are essential for maintaining immunological balance and are a significant component of the immunosuppressive tumor microenvironment (TME). Single-cell RNA sequencing (ScRNA-seq) technology has shown that Tregs exhibit significant plasticity and functional diversity in various tumors within the TME. This results in Tregs playing a dual role in the TME, which is not always centered around supporting tumor progression as typically believed. Abundant data confirms the anti-tumor activities of Tregs and their correlation with enhanced patient prognosis in specific types of malignancies. In this review, we summarize the potential anti-tumor actions of Tregs, including suppressing tumor-promoting inflammatory responses and boosting anti-tumor immunity. In addition, this study outlines the spatial and temporal variations in Tregs function to emphasize that their predictive significance in malignancies may change. It is essential to comprehend the functional diversity and potential anti-tumor effects of Tregs to improve tumor therapy strategies.

Correlation between second and first primary cancer: systematic review and meta-analysis of 9 million cancer patients.

BACKGROUND: Many survivors of a first primary cancer (FPCs) are at risk of developing a second primary cancer (SPC), with effects on patient prognosis. Primary cancers have different frequencies of specific SPC development and the development of SPCs may be closely related to the FPC. The aim of this study was to explore possible correlations between SPCs and FPCs. METHODS: Relevant literature on SPCs was retrospectively searched and screened from four databases, namely, PubMed, EMBASE, Web of Science, and PMC. Data on the number of patients with SPC in 28 different organ sites were also collected from The Surveillance, Epidemiology, and End Results (SEER) 8 Registry and NHANES database. RESULTS: A total of 9 617 643 patients with an FPC and 677 430 patients with an SPC were included in the meta-analysis. Patients with a first primary gynaecological cancer and thyroid cancer frequently developed a second primary breast cancer and colorectal cancer. Moreover, those with a first primary head and neck cancer, anal cancer and oesophageal cancer developed a second primary lung cancer more frequently. A second primary lung cancer and prostate cancer was also common among patients with first primary bladder cancer and penile cancer. Patients with second primary bladder cancer accounted for 56% of first primary ureteral cancer patients with SPCs. CONCLUSIONS: This study recommends close clinical follow-up, monitoring and appropriate interventions in patients with relevant FPCs for better screening and early diagnosis of SPCs.

FvDFR2 rather than FvDFR1 play key roles for anthocyanin synthesis in strawberry petioles.

The accumulation of anthocyanins can be found in both the fruit and petioles of strawberries, but the fruit appears red while the petioles appear purple-red. Additionally, in the white-fruited diploid strawberries, the petioles can accumulate anthocyanins normally, suggesting a different synthesis pattern between the petioles and fruits. We screened the EMS mutagenized population of a red-fruited diploid strawberry 'Ruegen' and discovered a mutant which showed no anthocyanin accumulation in the petioles but normal accumulation in the fruit. Through BSA sequencing and allelic test, it was found that a mutation in FvDFR2 was responsible for this phenotype. Furthermore, the complex formed by the interaction between the petiole-specific FvMYB10L and FvTT8 only binds the promoter of FvDFR2 but not FvDFR1, resulting in the expression of only FvDFR2 in the petiole. FvDFR2 can catalyze the conversion of DHQ and eventually the formation of cyanidin and peonidin, giving the petiole a purplish-red color. In the fruit, however, both FvDFR1 and FvDFR2 can be expressed, which can mediate the synthesis of cyanidin and pelargonidin. Our study clearly reveals different regulation of FvDFR1 and FvDFR2 in mediating anthocyanin synthesis in petioles and fruits.

Correlation between IL3 signaling pathway-related genes and immune checkpoint inhibitor efficacy in patients with renal cell carcinoma.

BACKGROUND: There is a lack of effective biomarkers that predict immunotherapy efficacy in clear cell renal cell carcinoma(KIRC). OBJECTIVE: We aimed to identify biomarkers that would predict the efficacy of KIRC treatment with immune checkpoint inhibitors (ICIs). METHODS: Cohort data of KIRC patients with somatic mutations, mRNA expression and survival data from The Cancer Genome Atlas (TCGA) database and immunotherapy cohort and Genomics of Drug Sensitivity in Cancer (GDSC) database were analyzed and divided into interleukin 3 (IL3) pathway-related genes high expression (IL3-High) and IL3 pathway-related genes low expression (IL3-Low) groups according to pathway expression status to assess the relationship between the IL3 pathway-related genes activation status and the prognosis of KIRC patients treated with ICIs. The data were validated by immunohistochemistry experiments, and possible mechanisms of action were explored at the level of gene mutation landscape, immune microenvironment characteristics, transcriptome and copy number variation(CNV) characteristicsRESULTS: The IL3 pathway-related genes was an independent predictor of the efficacy of ICIs in KIRC patients, and the IL3-High group had a longer overall survival (OS); KIRC patients in the IL3-High group had increased levels of chemokines, cytolysis, immune checkpoint gene expression and abundant immunity. The IL3-Low group had poor immune cell infiltration and significant downregulation of complement activation, cytophagy, B-cell activation, and humoral immune response pathways. The high group was more sensitive to targeted drugs of some signaling pathways, and its efficacy in combining these drugs with immunity has been predicted in the published literature. CONCLUSION: The IL3 pathway-related genes can be used as a predictor of the efficacy of ICIs in KIRC. The IL3 pathway-related genes may affect the therapeutic efficacy of ICIs by affecting the expression of immune-related molecules, immune cell infiltration, and the level of immune response pathways.

Mutations in the DNA polymerase binding pathway affect the immune microenvironment of patients with small-cell lung cancer and enhance the efficacy of platinum-based chemotherapy.

Background: Small-cell lung cancer (SCLC) is characterized by its high malignancy and is associated with a poor prognosis. In the early stages of the disease, platinum-based chemotherapy is the recommended first-line treatment and has demonstrated efficacy. However, SCLC is prone to recurrence and is generally resistant to chemotherapy in its later stages. Methods: Here, we collected samples from SCLC patients who received platinum-based chemotherapy, performed genomic and transcriptomic analyses, and validated our results with publicly available data. Results: SCLC patients with DNA polymerase binding pathway mutations had an improved prognosis after platinum chemotherapy compared with patients without such mutations. Patients in the mutant (MT) group had higher infiltration of T cells, B cells, and M1 macrophages compared with patients without DNA polymerase binding pathway mutations. Conclusions: DNA polymerase binding pathway mutations can be used as prognostic markers for platinum-based chemotherapy in SCLC.

CACNA1C mutation as a prognosis predictor of immune checkpoint inhibitor in skin cutaneous melanoma.

Aims: There is an urgent need for appropriate biomarkers that can precisely and reliably predict immunotherapy efficacy, as immunotherapy responses can differ in skin cutaneous melanoma (SKCM) patients. Methods: In this study, univariate regression models and survival analysis were used to examine the link between calcium voltage-gated channel subunit alpha 1C (CACNA1C) mutation status and immunotherapy outcome in SKCM patients receiving immunotherapy. Mutational landscape, immunogenicity, tumor microenvironment and pathway-enrichment analyses were also performed. Results: The CACNA1C mutation group had a better prognosis, higher immunogenicity, lower endothelial cell infiltration, significant enrichment of antitumor immune response pathways and significant downregulation of protumor pathways. Conclusion: CACNA1C mutation status is anticipated to be a biomarker for predicting melanoma immunotherapy effectiveness.

Aims: The treatment to make the immune system work better is also used to treat a skin cancer called skin cutaneous melanoma (SKCM). We need new ways to predict if the treatment will work. Methods: We looked at two groups of people getting the treatment to make the immune system work better. One group had a special change in their bodies, and the other group did not. We looked at how this change affected the patients. We also looked at how to make their immune system stronger. Results: We found that people with mutations tend to have better chances of getting better from their sickness. Conclusion: We think that this might be a good way to tell if immunotherapy will work well for this type of SKCM.