Esophageal squamous cell carcinoma (ESCC) remains a common aggressive malignancy in the world. Several long non-coding RNAs (lncRNAs) are reported to predict the prognosis of ESCC. Therefore, an in-depth research is urgently needed to further investigate the prognostic value of lncRNAs in ESCC. From the training set, we identified a eight-lncRNA signature (including AP000487, AC011997, LINC01592, LINC01497, LINC01711, FENDRR, AC087045, AC137770) which separated the patients into two groups with significantly different overall survival (hazard ratio, HR = 3.79, 95% confidence interval, 95% CI [2.56-5.62]; P < 0.001). The signature was applied to the validation set (HR = 2.73, 95%CI [1.65-4.53]; P < 0.001) and showed similar prognostic values. Stratified, univariate and multivariate Cox regression analysis indicated that the signature was an independent prognostic factor for patients with ESCC. A nomogram based on the lncRNAs signature, age, grade and stage was developed and showed good accuracy for predicting 1-, 3- and 5-year survival probability of ESCC patients. We found a strong correlation between the gene significance for the survival time and T stage. Eight modules were constructed, among which the key module most closely associated with clinical information was identified. Our eight-lincRNA signature and nomogram could be practical and reliable prognostic tools for esophageal squamous cell carcinoma. We downloaded the lncRNA expression profiles of ESCC patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets and separated to training and validation cohort. The univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identify a lncRNA-based signature. The predictive value of the signature was assessed using the Kaplan-Meier method, receiver operating characteristic (ROC) curves and area under curve (AUC). Weighted gene co-expression network analysis (WGCNA) was applied to predict the intrinsic relationship between gene expressions. In addition, we further explored the combination of clinical information and module construction.

Chaperonin containing TCP-1 (T-complex protein 1) (CCT) is a large molecular weight complex that contains nine subunits (TCP1, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT6B, CCT7, CCT8). This study aimed to reveal key genes which encode CCT subunits for prognosis and establish prognostic gene signatures based on CCT subunit genes. The data was downloaded from The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus. CCT subunit gene expression levels between tumor and normal tissues were compared. Corresponding Kaplan-Meier analysis displayed a distinct separation in the overall survival of CCT subunit genes. Correlation analysis, protein-protein interaction network, Gene Ontology analysis, immune cells infiltration analysis, and transcription factor network were performed. A nomogram was constructed for the prediction of prognosis. Based on multivariate Cox regression analysis and shrinkage and selection method for linear regression model, a three-gene signature comprising CCT4, CCT6A, and CCT6B was constructed in the training set and significantly associated with prognosis as an independent prognostic factor. The prognostic value of the signature was then validated in the validation and testing set. Nomogram including the signature showed some clinical benefit for overall survival prediction. In all, we built a novel three-gene signature and nomogram from CCT subunit genes to predict the prognosis of hepatocellular carcinoma, which may support the medical decision for HCC therapy.

Postsurgical recurrence within 2 years is the major cause of poor survival of hepatocellular carcinoma (HCC) patients. However, the molecular mechanism underlying HCC recurrence remains unclear. Here, we distinguish the function and mechanism of Sec62 in promoting HCC recurrence. The correlation between Sec62 and early recurrence was demonstrated in 60 HCC samples from a prospective study. HCC cells with Sec62 knockdown (Sec62^KD) or overexpression (Sec62^OE) were used to determine the potential of Sec62 in cell migration in vitro. Microarray analysis comparing Sec62^KD or Sec62^OE to their control counterparts was used to explore the mechanisms of Sec62-induced recurrence. A luciferase-labelled orthotopic nude mouse model of HCC with Sec62^KD or Sec62^OE was used to validate the potential of Sec62 in early HCC recurrence in vivo. We found that high expression of Sec62 was positively correlated with surgical recurrence in clinical HCC samples. Multivariate analysis revealed that Sec62 was an independent prognostic factor for early recurrence in postoperative HCC patients. Moreover, Sec62 promoted migration and invasion of HCC cells in vitro and postsurgical recurrence in vivo. Mechanically, integrinα/CAV1 signalling was identified as one of the targets of Sec62 in cell movement. Overexpression of integrin α partially rescued the Sec62 knockdown-induced inhibition of cell migration. Sec62 is a potentially prognostic factor for early recurrence in postoperative HCC patients and promotes HCC metastasis through integrinα/CAV1 signalling. Sec62 might be an attractive drug target for combating HCC postsurgical recurrence.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and N6-methyladenosine (m⁶A) is a predominant internal modification of RNA in various cancers. We obtained the expression profiles of m⁶A-related genes for HCC patients from the International Cancer Genome Consortium and The Cancer Genome Atlas datasets. Most of the m⁶A RNA methylation regulators were confirmed to be differentially expressed among groups stratified by clinical characteristics and tissues. The clinical factors (including stage, grade, and gender) were correlated with the two subgroups (cluster 1/2). We identified an m⁶A RNA methylation regulator-based signature (including METTL3, YTHDC2, and YTHDF2) that could effectively stratify a high-risk subset of these patients by univariate and LASSO Cox regression, and receiver operating characteristic (ROC) analysis indicated that the signature had a powerful predictive ability. Immune cell analysis revealed that the genes in the signature were correlated with B cell, CD4 T cell, CD8 T cell, dendritic cell, macrophage, and neutrophil. Functional enrichment analysis suggested that these three genes may be involved in genetic and epigenetic events with known links to HCC. Moreover, the nomogram was established based on the signature integrated with clinicopathological features. The calibration curve and the area under ROC also demonstrated the good performance of the nomogram in predicting 3- and 5-year OS in the ICGC and TCGA cohorts. In summary, we demonstrated the vital role of m⁶A RNA methylation regulators in the initial presentation and progression of HCC and constructed a nomogram which would predict the clinical outcome and provide a basis for individualized therapy.

Ferroptosis exerts a pivotal role in the formation and dissemination processes of hepatocellular carcinoma (HCC). The heterogeneity of ferroptosis and the link between ferroptosis and immune responses have remained elusive. Based on ferroptosis-related genes (FRGs) and HCC patients from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) cohorts, we comprehensively explored the heterogeneous ferroptosis subtypes. The genetic alterations, consensus clustering and survival analysis, immune infiltration, pathway enrichment analysis, integrated signature development, and nomogram building were further investigated. Kaplan–Meier plotter confirmed statistically differential probabilities of survival among the three subclusters. Immune infiltration analysis showed there were clear differences among the types of immune cell infiltration, the expression of PD-L1, and the distribution of TP53 mutations among the three clusters. Univariate Cox regression analysis, random survival forest, and multivariate Cox analysis were used to identify the prognostic integrated signature, including MED8, PIGU, PPM1G, RAN, and SNRPB. Kaplan–Meier analysis and time-dependent receiver operating characteristic (ROC) curves revealed the satisfactory predictive potential of the five-gene model. Subsequently, a nomogram was established, which combined the signature with clinical factors. The nomogram including the ferroptosis-based signature was conducted and showed some clinical net benefits. These results facilitated an understanding of ferroptosis and immune responses for HCC.