Purpose: Autophagy is a major catabolic system by which eukaryotic cells undergo self-degradation of damaged, defective, or unwanted intracellular components. An abnormal autophagic level is implicated in the pathogenesis of multiple diseases, including cancers. The aim of this study is to explore the prognostic value of autophagy in bladder cancer (BC), which is a major cause of cancer-related death globally. Patients and methods: First, 27 differentially expressed autophagy-related genes (ARGs) were identified in BC patients based on The Cancer Genome Atlas (TCGA) database. Functional enrichment analyses hinted that autophagy may act in a tumor-suppressive role in the initiation of BC. Then, the Cox proportional hazard regression model were employed to identify three key prognostic ARGs (JUN, MYC, and ITGA3), which were related with overall survival (OS) significantly in BC. The three genes represented important clinical significance and prognostic value in BC. Then a prognostic index (PI) was constructed. Results: The PI was constructed based on the three genes, and significantly stratified BC patients into high- and low-risk groups in terms of OS (HR=1.610, 95% CI=1.200–2.160, P =0.002). PI remained as an independent prognostic factor in multivariate analyses (HR=2.355, 95% CI=1.483–3.739, P <0.001). When integrated with clinical characteristics of age and stage, an autophagy-clinical prognostic index (ACPI) was finally validated, which had improved performance in predicting OS of BC patients (HR=2.669, 95% CI=1.986–3.587, P <0.001). The ACPI was confirmed in datasets of GSE13507 (HR=7.389, 95% CI=3.645–14.980, P <0.001) and GSE31684 (HR=1.665, 95% CI=0.872–3.179, P =0.122). Conclusion: This study provides a potential prognostic signature for predicting prognosis of BC patients and molecular insights of autophagy in BC.

Breast cancer (BC) has a complex etiology and pathogenesis, and is the most common malignant tumor type in females, in USA in 2018, yet its relevant molecular mechanisms remain largely unknown. The collagen type V α-1 chain (COL5A1) gene is differentially expressed in renal and ovarian cancer. Using bioinformatics methods, COL5A1 was determined to also be a significant gene in BC, but its association with BC has not been sufficiently reported. COL5A1 microarray and relevant clinical data were collected from the Gene Expression Omnibus, The Cancer Genome Atlas and other databases to summarize COL5A1 expression in BC and its subtypes at the mRNA and protein levels. All associated information was comprehensively analyzed by various software. The clinical significance of the mutation was obtained via the cBioPortal. Furthermore, Gene Ontology functional annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were also performed to investigate the mechanism of COL5A1 in BC. Immunohistochemistry was also conducted to detect and confirm COL5A1 expression. It was determined that COL5A1 was highly expressed in BC tissues, compared with normal tissues at the mRNA level [standard mean difference, 0.84; 95% confidence interval (CI), 0.60-1.07; P=0.108]. The area under the summary receiver operator characteristic curve for COL5A1 was 0.87 (95% CI, 0.84-0.90). COL5A1 expression was altered in 32/817 (4%) sequenced samples. KEGG analysis confirmed the most notable pathways, including focal adhesion, extracellular matrix-receptor interaction and regulation of the actin cytoskeleton. Immunohistochemical detection was used to verify the expression of COL5A1 in 136 selected cases of invasive BC tissues and 55 cases of adjacent normal tissues, while the rate of high expression of COL5A1 in BC was up to 90.4%. These results indicated that COL5A1 is highly expressed at the mRNA and protein levels in BC, and the prognosis of patients with BC with high COL5A1 expression may be reduced; therefore, COL5A1 may be used independently or combined with other detection factors in BC diagnosis.

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Alternative splicing (AS) is crucial a mechanism by which the complexity of mammalian and viral proteom increased overwhelmingly. There lacks systematic and comprehensive analysis of the prognostic significance of AS profiling landscape for uteri corpus endometrial carcinoma (UCEC). In this study, univariate and multivariate Cox regression analyses were conducted to identify candidate survival-associated AS events curated from SpliceSeq for the construction of prognostic index (PI) models. A correlation network between splicing factor-related AS events and significant survival-associated AS events were constructed using Cytoscape 3.5. As consequence, 28281 AS events from 8137 genes were detected from 506 UCEC patients, including 2630 survival-associated AS events. Kaplan Meier survival analysis revealed that six of the seven PI models (AD, AP, AT, ME, RI and ALL) exhibited good performance in stratifying the prognosis of low risk and high risk group (P<0.05). Among the six PI models, PI-AT performed best with an area under curves (AUC) value of 0.758 from time-dependent receiver operating characteristic. Correlation network implicated potential regulatory mechanism of AS events in UCEC. PI models based on survival-associated AS events for UCEC in this study showed preferable prognosis-predicting ability and may be promising prognostic indicators for UCEC patients.Summary: This is the first study to systematically investigate the prognostic value of AS in UCEC. Findings in the presents study supported the clinical potential of AS for UCEC and shed light on the potential AS-associated molecular basis of UCEC.

Papillary renal cell carcinoma (PRCC) accounts for 15–20% of all kidney neoplasms and continually attracts attention due to the increase in the incidents in which it occurs. The molecular mechanism of PRCC remains unclear and the efficacy of drugs that treat PRCC lacks sufficient evidence in clinical trials. Therefore, it is necessary to investigate the underlying mechanism in the development of PRCC and identify additional potential anti-PRCC drugs for its treatment. The differently expressed genes (DEGs) of PRCC were identified, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for functional annotation. Then, potential drugs for PRCC treatment were predicted by Connectivity Map (Cmap) based on DEGs. Furthermore, the latent function of query drugs in PRCC was explored by integrating drug-target, drug-pathway and drug-protein interactions. In total, 627 genes were screened as DEGs, and these DEGs were annotated using KEGG pathway analyses and were clearly associated with the complement and coagulation cascades, amongst others. Then, 60 candidate drugs, as predicted based on DEGs, were obtained from the Cmap database. Vorinostat was considered as the most promising drug for detailed discussion. Following protein-protein interaction (PPI) analysis and molecular docking, vorinostat was observed to interact with C3 and ANXN1 proteins, which are the upregulated hub genes and may serve as oncologic therapeutic targets in PRCC. Among the top 20 metabolic pathways, several significant pathways, such as complement and coagulation cascades and cell adhesion molecules, may greatly contribute to the development and progression of PRCC. Following the performance of the PPI network and molecular docking tests, vorinostat exhibited a considerable and promising application in PRCC treatment by targeting C3 and ANXN1.

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Splicing factors (SFs) have been increasingly documented to perturb the genome of cancers. However, little is known about the alterations of SFs in hepatocellular carcinoma (HCC). This study comprehensively delineated the genomic and epigenomic characteristics of 404 SFs in HCC based on the multi-omics data from the Cancer Genome Atlas database. The analysis revealed several clinically relevant SFs that could be effective biomarkers for monitoring the onset and prognosis of HCC (such as, HSPB1, DDX39A, and NELFE, which were the three most significant clinically relevant SFs). Functional enrichment analysis of these indicators showed the enrichment of pathways related to splicing and mRNA processes. Furthermore, the study found that SF copy number variation is common in HCC and could be a typical characteristic of hepatocarcinogenesis; the complex expression regulation of SFs was significantly affected by copy number variant and methylation. Several SFs with significant mutation patterns were identified (such as, RNF213, SF3B1, SPEN, NOVA1, and EEF1A1), and the potential regulatory network of SFs was constructed to identify their potential mechanisms for regulating clinically relevant alternative splicing events. Therefore, this study established a foundation to uncover the broad molecular spectrum of SFs for future functional and therapeutic studies of HCC.

ObjectivesThe present study aimed to determine the feasibility of the American College of Radiology's (ACR) contrast‐enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI‐RADS) (version 2017) in examinations using Sonazoid and compare its diagnostic performance with that of modified LI‐RADS in patients at high risk of hepatocellular carcinoma (HCC).MethodsThis retrospective study's sample population consisted of 137 participants with a total of 140 nodules who underwent CEUS with Sonazoid and pathological confirmation via surgery or biopsy from January 2020 to February 2022. The lesions were evaluated and classified based on the reference standards (ie, ACR CEUS LI‐RADS and modified LI‐RADS). The overall diagnostic capabilities of the two systems were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% confidence intervals (CIs).ResultsThe participants had a median age of 51 years and an interquartile range of 43–58 years. Regarding LR‐5 as a predictor of HCC, the accuracy results of the ACR LI‐RADS and modified LI‐RADS algorithms were 72.9 and 71.4%, respectively (P = .50). The sensitivity of both systems was the same (69.7%; 95% CI: 60.7–77.8%). Regarding LR‐M as a predictor of non‐HCC malignancy, the diagnostic performance of the algorithms was the same, with accuracy and sensitivity results of 76.4 and 73.3%, respectively (95% CI: 44.9–92.2%).ConclusionThe findings indicate that modified LI‐RADS had a moderate level of diagnostic performance for HCC in examinations using Sonazoid, which was comparable to ACR LI‐RADS.