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Background As the second most common malignancy in adults, papillary renal cell carcinoma (PRCC) has shown an increasing trend in both incidence and mortality. Effective treatment for advanced metastatic PRCC is still lacking. In this study, we aimed to establish competitive endogenous RNA (ceRNA) networks related to PRCC tumorigenesis, and analyze the specific role of differentially expressed ceRNA components and infiltrating immune cells in tumorigenesis. Methods CeRNA networks were established to identify the key ceRNAs related to PRCC tumorigenesis based on the 318 samples from The Cancer Genome Atlas database (TCGA), including 285 PRCC and 33 normal control samples. The R package, “CIBERSORT,” was used to evaluate the infiltration of 22 types of immune cells. Then we identified the significant ceRNAs and immune cells, based on which two nomograms were obtained for predicting the prognosis in PRCC patients. Finally, we investigated the co‐expression of PRCC‐specific immune cells and core ceRNAs via Pearson correlation test. Results COL1A1, H19, ITPKB, LDLR, TCF4, and WNK3 were identified as hub genes in ceRNA networks. Four prognostic‐related tumor‐infiltrating immune cells, including T cells CD4 memory resting, Macrophages M1, and Macrophages M2 were revealed. Pearson correlation test indicated that Macrophage M1 was negatively related with COL1A1 (p, We constructed two nomograms for predicting the survival of papillary renal cell carcinoma patients.H19‐miR‐29c‐3p‐COL1A1 axis might promote the polarization of M2 macrophages and inhibit M1 macrophage activation through Wnt signaling pathway, collaborating to promote PRCC tumorigenesis and lead to poor overall survival of PRCC patients.

Circular RNAs (circRNAs) are novel biomarkers of various cancers. CircRNAs can sponge miRNAs and regulate target mRNAs, which was called competing endogenous RNAs (ceRNA). This study was designed to identify circRNAs related to patients with clear cell renal cell carcinoma (ccRCC) and the first to select three independent Gene Expression Omnibus microarrays covering circRNAs, miRNAs, and mRNAs for multiple analyses. The data of clinical cases applied in our study were obtained from The Cancer Genome Atlas. We successfully conducted a circRNA/miRNA/mRNA ceRNA network related to ccRCC patients via R software and Cytoscape including 8 circRNAs, 6 miRNAs, and 49 mRNAs. The prognosis‐associated subnet covered 8 circRNAs, 6 miRNAs, and 22 mRNAs. Quantitative real‐time PCR was applied to measure our prediction in three renal cell lines and 23 pairs of tissues. Small interfering RNA targeting the back‐splice region of hsa_circ_0001167 was further implied to confirm the regulation. Ultimately, hsa_circ_0001167/hsa‐miR‐595/CCDC8 regulatory axis was identified in this study, which may serve as prognostic indicators. Lower levels of hsa_circ_0001167 and CCDC8 were potentially correlated with worse patient survival., Based on the analyzation of three Gene Expression Omnibus microarrays and The Cancer Genome Atlas cases, we were the first to construct a ccRCC centred ceRNA network and its prognosis‐associated subnet. We further validated a hsa_circ_0001167/ has‐miR‐595/CCDC8 axis in renal cell lines, which may serve as prognostic indictors.

Background Microsatellite instability (MSI) predetermines responses to adjuvant 5‐fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning model that could preoperatively predict the MSI status of rectal cancer based on magnetic resonance images. Methods This single‐center retrospective study included 491 rectal cancer patients with pathologically proven microsatellite status. Patients were randomly divided into the training/validation cohort (n = 395) and the testing cohort (n = 96). A clinical model using logistic regression was constructed to discriminate MSI status using only clinical factors. Based on a modified MobileNetV2 architecture, deep learning models were tested for the predictive ability of MSI status from magnetic resonance images, with or without integrating clinical factors. Results The clinical model correctly classified 37.5% of MSI status in the testing cohort, with an AUC value of 0.573 (95% confidence interval [CI], 0.468 ~ 0.674). The pure imaging‐based model and the combined model correctly classified 75.0% and 85.4% of MSI status in the testing cohort, with AUC values of 0.820 (95% CI, 0.718 ~ 0.884) and 0.868 (95% CI, 0.784 ~ 0.929), respectively. Both deep learning models performed better than the clinical model (p, Microsatellite instability (MSI) serves as a prognostic biomarker for clinical outcomes. We developed and validated a deep learning model that could preoperatively predict the MSI status of rectal cancer based on MR and found deep learning based on MRI showed a good predictive performance for MSI status in rectal cancer patients. The proposed model may help to identify patients who would benefit from chemotherapy or immunotherapy and determine individualized therapeutic strategies for these patients.

Background Prognostic indicators in lung adenocarcinoma (LUAD) have been seeking under database analysis, and remarkable advance is on the way. Methods This study calculated the scores of stromal and immune components of the tumor microenvironment (TME) in 551 LUAD samples using the ESTIMATE algorithm on The Cancer Genome Atlas (TCGA) database. R package ''limma'' was used to selected differentially expressed genes (DEG). We have analyzed the DEGs by means of Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments. The protein‐protein network, univariate Cox analysis, and Lasso regression analysis were performed to selected survival‐related genes. Gene Set Enrichment Analysis (GSEA) represented the enriched pathway of CC chemokine receptor 2 (CCR2). The ratios of immune cells in the TME of each LUAD sample were obtained using the R package "limma" and CIBERSORT algorithm in R 4.0.2. Results The ImmuneScore was positively correlated with prognosis regarding survival rate, T classification of TNM stages, and clinicopathological staging characteristics. GO and KEGG enrichments showed DEGs were associated with immune‐related activities. Three genes of LUAD were selected from the PPI network and Cox proportional hazards regression analysis. CCR2 was the most survival correlated gene by Lasso regression analysis. GSEA results showed that C2 kegg gene sets in the CCR2 high‐expression group were mainly enriched in the B cell or T cell receptor signaling pathway and natural killer cell‐mediated cytotoxicity. Correlation of CCR2 expression with prognosis was conducted, implicating a positive correlation with the prognosis of survival rate and M classification, negative correlation with the prognosis of T and N classifications. The correlation between CCR2 and tumor‐infiltrating immune cells (TICs) was analyzed, and 14 kinds of TICs were found closely correlated with CCR2 expression through difference analysis. Conclusion Therefore, CCR2 has prognostic value as an immune indicator in LUAD., The violin diagram showed nine kinds of TICs were positively correlated with CCR2 expression, including memory B cell , CD8+ T cells, gamma delta T cells, monocytes, resting and actived T cells memory, macrophage M1, resting dendritic cells, and resting mast cells; five kinds of TICs were negatively correlated with CCR2 expression, including follicular helper T cells, activated NK cells, macrophage M0, activated dendritic cells, and activated mast cells. These results further supported that the levels of CCR2 affected the immune activity of TME. ​

In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer‐related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high‐value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration., Artificial intelligence (AI) in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread and the impact of AI on patient outcomes remains uncertain. Overcoming implementation barriers for AI in oncology will require training and educating the oncology workforce in AI; standardizing datasets, research reporting, validation methods, and regulatory standards; and funding and conducting prospective clinical trials that demonstrate improvement in patient outcomes.

Background Recent outbreaks of highly pathogenic avian influenza and multiple occurrences of zoonotic infection and deaths in humans have sparked a dramatic increase in influenza research. In order to rapidly identify and help prevent future influenza outbreaks, numerous laboratories around the world are working to develop new nucleotide-based diagnostics for identifying and subtyping influenza viruses. While there are several databases that have been developed for manipulating the vast amount of influenza genetic data that have been produced, significant progress can still be made in developing tools for translating the genetic data into effective diagnostics. Description The Influenza Primer Design Resource (IPDR) is the combination of a comprehensive database of influenza nucleotide sequences and a web interface that provides several important tools that aid in the development of oligonucleotides that may be used to develop better diagnostics. IPDR’s database can be searched using a variety of criteria, allowing the user to align the subset of influenza sequences that they are interested in. In addition, IPDR reports a consensus sequence for the alignment along with sequence polymorphism information, a summary of most published primers and probes that match the consensus sequence, and a Primer3 analysis of potential primers and probes that could be used for amplifying the sequence subset. Conclusions The IPDR is a unique combination of bioinformatics tools that will greatly aid researchers in translating influenza genetic data into diagnostics, which can effectively identify and subtype influenza strains. The website is freely available at http://www.ipdr.mcw.edu.