Pan-cancer analysis of the CASP gene family in relation to survival, tumor-infiltrating immune cells and therapeutic targets

Mai

et al. 2021

Front. Mol. Biosci.

C1ORF112 is an evolutionarily conserved gene across vertebrates. Over the last decade, studies have suggested that C1ORF112 may play a role in tumorigenesis. Using The Cancer Genome Atlas datasets, we explored the role of C1ORF112 across various tumor types in this study. In most tumor types, C1ORF112 expression was increased in tumor tissues compared to corresponding non-tumor tissues. In patients with certain tumor types, higher C1ORF112 expression was correlated with shorter overall survival, disease-free survival, and progression-free survival. Further analyses of C1ORF112 genetic alteration data showed that C1ORF112 amplification and mutations may have an impact on liver hepatocellular carcinoma and uterine corpus endometrial carcinoma prognosis. In cancers including lower grade glioma and adrenocortical carcinoma, C1ORF112 expression was linked to cancer-associated fibroblast infiltration. Gene Ontology analysis showed that C1ORF112 was co-expressed with genes involved in biological processes such as cell cycle and mitotic regulation. The protein interaction network demonstrated that C1ORF112 physically interacted with RAD51, DMC1, and FIGNL1, which have well characterized functions in DNA repair and cell cycle regulation. This pan-cancer study revealed the prognostic value and oncogenic role of C1ORF112 across multiple tumor types.

Section: Discussionmentioning

confidence: 99%

Pan-Cancer Analysis Identified C1ORF112 as a Potential Biomarker for Multiple Tumor Types

Mai

et al. 2021

Front. Mol. Biosci.

“…Evaluating all possible associations indicates that these two variants were also associated with false negatives of CA19-9 diagnosis, and this was exploited by our model to improve CA19-9 prediction. This was not a surprise due to several studies linking B4GALT5 to glycosylation and then to the elevation of CA19-9 values [ 33 ]. Generally, B4GALT5 is a membrane-bound glycoprotein, and it was linked with pancreatic cancer through MUC4 [ 36 , 40 ].…”

Section: Discussionmentioning

confidence: 91%

“…Wang et al discuss GSDMD’s potential as a cancer target due to its role in pyroptosis gasdermin mediated programmed cell death [ 32 ]. It is highly connected with the CASP gene family (see Figure S1_6a ), which is strongly related to the poor prognosis of pancreatic cancer [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Improves Pancreatic Cancer Diagnosis Using RNA-Based Variants

Al-Fatlawi

Malekian

García

et al. 2021

Cancers

For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.

“…Tumor-infiltrating immune cells are a major component of the tumor microenvironment and affect the clinical outcomes and pathological staging of multiple tumor types (8,9). Some studies suggest lncRNA can affect tumor development and tumor immune cell microenvironment through the ceRNA network (10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

“…Current research reveals that lncRNA plays an important role in the genesis and development of tumors through interactions with competing endogenous RNA (ceRNA) or by acting as microRNA sponges (7). Tumor-infiltrating immune cells are a major component of the tumor microenvironment and affect the clinical outcomes and pathological staging of multiple tumor types (8, 9). Some studies suggest lncRNA can affect tumor development and tumor immune cell microenvironment through the ceRNA network (10-12).…”

Section: Introductionmentioning

confidence: 99%

The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Deng

Wang

et al. 2021

Preprint

Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patients' quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear. Methods: We constructed a ceRNA network associated with KIRC by analyzing the long noncoding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from the Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through "The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts". Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism. Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [Area Under Curves (AUCs) of 1, 3 and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747 and 0.772; AUCs of 1, 3 and 5-year survivals in nomogram based on immune cells: 0.603, 0.642 and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells. Conclusions: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.