Background Papillary thyroid cancer (PTC) is the most common type of thyroid cancer. However, due to the lack of reliable prognostic biomarkers for PTC, overtreatment has been on the rise. Therefore, our research aims to identify new and promising prognostic biomarkers and provide fresh perspectives for clinical decision making. Methods The RNA‐seq data and clinical data of PTC samples were obtained from The Cancer Genome Atlas data portal. GSE64912 and GSE83520 datasets were downloaded through the GEOquery R package. The difference in the expression of oxoglutarate dehydrogenase like ( OGDHL ) between PTC and normal tissues was explored by the Wilcoxon test. Kaplan–Meier (KM) and Cox regression analyses were used to further explore the prognostic value of OGDHL . The tumor microenvironments of PTC patients were explored based on ssGSEA and Tumor Immune Estimation Resource online database. Gene Set Enrichment Analysis (GSEA) was performed to explore the biological processes associated with OGDHL . Results The expression level of OGDHL in PTC was significantly altered compared to that in normal tissues ( p < 0.05). Various biological processes associated with OGDHL were also explored through GSEA. KM analysis suggested that the low‐ OGDHL group had a better overall survival [OS, p = 3.49e‐03, hazard ratio (HR) = 4.567]. The receiver operating characteristic curve also indicated the favorable prognostic potential of OGDHL . Moreover, OGDHL was proved to be an independent prognostic indicator in Cox analysis ( p = 1.33e‐02, HR = 0.152). In the analysis of the tumor microenvironment, the low‐ OGDHL group showed a lower immune score and stromal score, while tumor purity was higher. The expression of OGDHL was also closely correlated with the infiltration of immune cells. Conclusion Our study elucidated the influence of OGDHL on the prognosis of PTC and demonstrated its potential as a novel biomarker, which would provide new insights into the prognosis monitoring and clinical decision making in PTC patients.

Melanoma is a skin cancer with great metastatic potential, which is responsible for the major deaths in skin cancer. Although the prognosis of melanoma patients has been improved with the comprehensive treatment, for patients with metastasis, the complexity and heterogeneity of diffuse diseases make prognosis prediction and systematic treatment difficult and ineffective. Therefore, we established a novel personalized immune-related gene pairs index (IRGPI) to predict the prognosis of patients with metastatic melanoma, which was conducive to provide new insights into clinical decision-making and prognostic monitoring for metastatic melanoma. Through complex analysis and filtering, we identified 24 immune-related gene pairs to build the model and obtained the optimal cut-off value from receiver operating characteristic curves, which divided the patients into high and low immune-risk groups. Meantime, the Kaplan–Meier analysis, Cox regression analysis and subgroup analysis showed that IRGPI had excellent prognostic value. Furthermore, IRGPI was shown that was closely associated with immune system in the subsequent tumor microenvironment analysis and gene set enrichment analysis. In addition, we broken through the data processing limitations of traditional researches in different platforms through the application of gene pairs, which would provide great credibility for our model. We believe that our research would provide a new perspective for clinical decision-making and prognostic monitoring in metastatic melanoma.

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Nephroblastoma, also known as Wilms' tumor (WT), is the most common renal tumor that occurs in children. Although the efficacy of treatment has been significantly improved by a series of comprehensive treatments, some patients still have poor prognosis. Myelin and lymphocyte (MAL) protein, a highly hydrophobic integrated membrane‐bound protein, has been implicated in many tumors and is also closely linked to kidney development. However, the relationship between MAL and WT has not yet been elucidated. Therefore, we attempted to evaluate the feasibility of MAL as a promising prognosis factor for WT. The differential expression of MAL was investigated using TARGET database and was verified using the Gene Expression Omnibus database and real‐time quantitative PCR. The prognostic ability of MAL was determined using Kaplan–Meier and Cox regression analyses. Pearson correlation analysis was applied to explore the relationship between MAL expression and methylation sites. The ESTIMATE and CIBERSORT algorithms showed that MAL expression was associated with the WT tumor microenvironment. Gene Set Enrichment Analysis (GSEA) indicated that multiple signaling pathways closely associated with tumorigenesis were differentially enriched between the high‐ and low‐MAL groups. In conclusion, our study comprehensively explored the potential of MAL as a prognosis factor for WT. Meanwhile, we also demonstrated that MAL, as a prognostic factor for WT, may be closely related to the tumor microenvironment.

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