LPCAT1 acts as an independent prognostic biomarker correlated with immune infiltration in hepatocellular carcinoma.

BACKGROUND: Lysophosphatidylcholine acyltransferase 1 (LPCAT1) is overexpressed in multiple human tumors. However, the role of LPCAT1 in hepatocellular carcinoma (HCC) has not been understood. We aim to explore the relationships between LPCAT1 expression and prognosis, clinicopathological features, tumor microenvironment (TME), immune cell infiltration, immune checkpoint gene expression, and related signaling pathways in HCC. Furthermore, we also explored the relationship between LPCAT1 expression and drug sensitivity to HCC treatment. METHODS: The expression profiles were acquired from the Cancer Genome Atlas (TCGA) and the Human Protein Atlas (THPA). Immune status and infiltration in cancer tissues were explored using the single sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm. RESULTS: LPCAT1 was overexpressed in HCC, and its expression was related to poor prognosis, LPCAT1 was an independent prognostic biomarker in HCC. Expression of LPCAT1 increased statistically with the increase of clinical stage and grade of HCC patients. GO and KEGG network analysis revealed that LPCAT1 positively associated molecules were mostly enriched in functions related to cell adhesion. The TME score of high-LPCAT1 group was significantly higher than that of low-LPCAT1 group. Immune infiltrating cells positively correlated with LPCAT1 expression were Macrophages M0, B cells memory, Dendritic cells activated, T cells regulatory and T cells gamma delta in HCC. We found a positive correlation between LPCAT1 and most immune checkpoint gene expression. The IC50 of 5-Fluorouracil, Gemcitabine, Mitomycin C, Sorafenib and Cabozantinib in patients with high-LPCAT1 expression was lower than that in patients with low-LPCAT1 expression. Our findings provide a wealth of information for further understanding of the biological functions and signaling pathways of LPCAT1 in HCC. CONCLUSIONS: LPCAT1 is an independent prognostic biomarker and associated with tumor microenvironment, immune cell infiltration, immune checkpoint expression and drug sensitivity in hepatocellular carcinoma.

Characterization of alternative splicing events and prognostic signatures in breast cancer.

BACKGROUND: Breast cancer (BRCA) is one of the most common cancers worldwide. Abnormal alternative splicing (AS) frequently observed in cancers. This study aims to demonstrate AS events and signatures that might serve as prognostic indicators for BRCA. METHODS: Original data for all seven types of splice events were obtained from TCGA SpliceSeq database. RNA-seq and clinical data of BRCA cohorts were downloaded from TCGA database. Survival-associated AS events in BRCA were analyzed by univariate COX proportional hazards regression model. Prognostic signatures were constructed for prognosis prediction in patients with BRCA based on survival-associated AS events. Pearson correlation analysis was performed to measure the correlation between the expression of splicing factors (SFs) and the percent spliced in (PSI) values of AS events. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to demonstrate pathways in which survival-associated AS event is enriched. RESULTS: A total of 45,421 AS events in 21,232 genes were identified. Among them, 1121 AS events in 931 genes significantly correlated with survival for BRCA. The established AS prognostic signatures of seven types could accurately predict BRCA prognosis. The comprehensive AS signature could serve as independent prognostic factor for BRCA. A SF-AS regulatory network was therefore established based on the correlation between the expression levels of SFs and PSI values of AS events. CONCLUSIONS: This study revealed survival-associated AS events and signatures that may help predict the survival outcomes of patients with BRCA. Additionally, the constructed SF-AS networks in BRCA can reveal the underlying regulatory mechanisms in BRCA.

Unmet needs from the first diagnosis of cancer until the end of medical treatment: A longitudinal study.

OBJECTIVE: This study aimed to examine (1) The courses of Chinese cancer patients' unmet supportive care needs (psychological, physical and daily living, health system and information, patient care and support, and sexual) from the first cancer diagnosis until the end of medical treatment; (2) The predictive role of socio-demographic and medical characteristics (i.e., age, gender, and cancer stage) in the courses of unmet needs; and (3) The associations of courses of unmet needs with courses of depressive and anxiety symptoms. METHODS: A longitudinal study was performed at Shaanxi Provincial Tumour Hospital in Xi'an, China. A total of 153 heterogeneous cancer patients were assessed after the first diagnosis (T1), at the beginning (T2) and the end (T3) of the receipt of medical treatment. Latent growth curve models were used to examine the research questions. RESULTS: Psychological needs and health system and information needs showed a decrease over time, whereas physical needs, patient care needs and sexual needs remained stable. Younger and female patients tended to report higher levels of unmet psychological needs at T1 and experienced slower decreases from T1 to T3. Only the courses of unmet psychological needs were associated with the courses of depressive and anxiety symptoms from T1 to T3. CONCLUSIONS: More attention can be given to young and female cancer patients, as they were more likely to suffer from high unmet psychological needs over the disease trajectory. Future research may focus more on addressing unmet needs reported by Chinese cancer patients.

Survival-Associated Alternative Splicing Events and Prognostic Signatures in Pancreatic Cancer.

BACKGROUND: Alternative splicing (AS) is reported to be related to the biological process of multiple malignancies. This study is conducted to identify survival-associated AS events and prognostic signatures that may serve as prognostic indicators for pancreatic cancer (PC). METHODS: Univariate Cox analysis was used to determine the survival-associated AS events in PC. Prognostic signatures were constructed by LASSO Cox analysis based on seven types of survival-associated AS events. The correlation between the expression of splicing factors (SFs) and the percent spliced in values of AS events was analyzed by Pearson correlation analysis. Risk scores were calculated to determine high- or low-risk patients with different types of AS events. Gene functional annotation analysis was performed to reveal pathways in which prognostic AS is enriched. RESULTS: A total of 45,313 AS events in 10,624 genes were observed, and there were 1,565 AS events in 1,223 genes significantly correlated with overall survival for PC. Kaplan-Meier analysis, receiver-operator characteristic curve, univariate and multivariate Cox analyses showed that AS prognostic signatures could effectively predict prognosis of patients with PC. Splicing factors-AS regulatory networks were established to demonstrate the interaction between AS events and SFs. CONCLUSION: The survival-associated AS events and prognostic signatures identified in this study can serve as useful tool for predicting prognosis of patients with PC. Moreover, the SF-AS regulatory networks may provide clues for the mechanisms underlying AS in PC.

Prognostic role of alternative splicing events in head and neck squamous cell carcinoma.

BACKGROUND: Aberrant alternative splicing (AS) is implicated in biological processes of cancer. This study aims to reveal prognostic AS events and signatures that may serve as prognostic predictors for head and neck squamous cell carcinoma (HNSCC). METHODS: Prognostic AS events in HNSCC were identified by univariate COX analysis. Prognostic signatures comprising prognostic AS events were constructed for prognosis prediction in patients with HNSCC. The correlation between the percent spliced in (PSI) values of AS events and the expression of splicing factors (SFs) was analyzed by Pearson correlation analysis. Gene functional annotation analysis was performed to reveal pathways in which prognostic AS is enriched. RESULTS: A total of 27,611 AS events in 15,873 genes were observed, and there were 3433 AS events in 2624 genes significantly associated with overall survival (OS) for HNSCC. Moreover, we found that AS prognostic signatures could accurately predict HNSCC prognosis. SF-AS regulatory networks were constructed according to the correlation between PSI values of AS events and the expression levels of SFs. CONCLUSIONS: Our study identified prognostic AS events and signatures. Furthermore, it established SF-AS networks in HNSCC that were valuable in predicting the prognosis of patients with HNSCC and elucidating the regulatory mechanisms underlying AS in HNSCC.