BackgroundMitochondria-nuclear cross talk and mitochondrial retrograde regulation are involved in the genesis and development of breast cancer (BC). Therefore, mitochondria can be regarded as a promising target for BC therapeutic strategies. In the present study, we aimed to construct regulating network and seek the potential biomarkers of BC diagnosis, prognosis and also the molecular therapeutic targets from the perspective of mitochondrial dysfunction. MethodsThe microarray data of mitochondria-related encoding genes of BC were downloaded from GEO including GSE128610 and GSE72319. GSE128610 was treated as test set and validation sets consisted of GSE72319 and TCGA, which were used for identifying mitochondria-related differential expressed genes (mrDEGs). We performed enrichment analysis, PPI network, hub mrDEGs, and overall survival analysis and constructed transcription factor (TF)-miRNA-hub mrDEGs network. ResultsA total of 23 up-regulated and 71 down-regulated mrDEGs were identified and validated. Enrichment analyses indicated that mrDEGs were associated with several cancer-related biological processes, Moreover, 9 hub mrDEGs were identified and validated in tissues. Finally, 5 hub coregulated mrDEGs, 21 miRNA and 117 TF were used to construct TF-miRNA-hub mrDEGs network. MAZ, HDGF and SP2 could regulate 3 hub mrDEGs. hsa-mir-21-5p, hsa-mir-1-3p, hsa-mir-218-5p, hsa-mir-26a-5p, and hsa-mir-335-5p regulated 2 hub mrDEGs. Overall survival analysis suggested that the up-regulated FN1 and down-regulated DDR2 conferred to poor BC prognosis. ConclusionTF-miRNA-hub mrDEGs has instruction significance for the etiology exploration of BC. The identified hub mrDEGs, such as FN1 and DDR2, were likely to regulate mitochondrial function and might be novel biomarkers of BC diagnosis and prognosis as well as the therapeutic targets.

Background: Recently, the incidence of cholangiocarcinoma (CCA) has gradually increased. As CCA has a poor prognosis, the ideal survival rate is scarce for patients. The abnormal expressed tsRNA may regulate the progression of a variety of tumors, and tsRNA is expected to become a new diagnostic marker of cancer. However, the expression of tsRNA is obscure and should be elucidated in CCA.Methods: We collected CCA tissues and adjacent normal tissues from three patients. High-throughput RNA-seq was utilized to determine the overall expression profiles of tsRNA in CCA and adjacent normal tissues and to screen the tsRNAs that were differentially expressed. The biological effects and potential signaling pathways of dysregulated tsRNAs between the CCA and adjacent normal tissues were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.Results: High-throughput RNA-seq totally demonstrated 535 dysregulated tsRNAs, of which 241 tsRNAs were upregulated and 294 tsRNAs were downregulated in CCA compared with adjacent normal tissues (|log2 (fold change)| >=1 and p value< 0.05). GO and KEGG enrichment analyses indicated that the target genes of dysregulated tRFs (tRF-34-JJ6RRNLIK898HR, tRF-38-0668K87SERM492V, tRF-39-0668K87SERM492E2) were mainly enriched in the Notch signaling pathway, Hippo signaling pathway, and cAMP signaling pathway and in growth hormone synthesis, secretion and action.Conclusion: Differentially expressed tRFs in CCA are enriched in many pathways associated with neoplasms, which may impact the progression of CCA.

Background: Heat shock proteins (HSPs) are a class of molecular chaperones that function in protein folding and maintain protein structure and function. No study has performed a comprehensive pan-cancer analysis of HSPs. Here we carried out a panoramic analysis of the expression and prognosis of HSP110, HSP90, HSP70 and HSP60 families in 33 tumors, with the aim of deepening the systematic understanding of HSPs in cancer. Materials and Methods: Next-generation sequencing data of multiple tumors were downloaded from TCGA, CCLE and Oncomine databases. RStudio 3.6.1 was used to analyze the expressions, mutations, copy number variations (CNVs), cancer-related signaling pathways, immune cell infiltration and prognosis profiles of HSP110, HSP90, HSP70, and HSP60 families in 33 tumors. Results: HSPA6 and HSPA7 were generally highly expressed while HSPA12A, HSPA12B, HSPA2 and HSPA4L were mainly expressed at low levels in different cancer tissues. The results revealed mainly positive correlations among the expressions of HSPs in different cancers. Expressions of HSP family members were generally associated with poor prognosis in respiratory, digestive, urinary, and reproductive system tumors and associated with good prognosis in cholangiocarcinoma, pheochromocytoma and paraganglioma. TCGA mutation analysis showed that HSP gene mutation rate in cancers was 0­%–23%. CCLE mutation analysis indicated that HSP gene mutation rate in 828 cell lines from 15 tumors was 0%–17%. CNV analysis revealed that HSPs have different degrees of gene amplifications and deletions in cancers. Gene mutations of 15 HSPs influenced their protein expressions in different cancers. Copy number amplifications and deletions of 22 HSPs also impacted protein expression levels in pan-cancer. HSP gene mutation was generally a poor prognosis factor in cancers, except for uterine corpus endometrial carcinoma. CNVs in 14 HSPs showed varying influences on survival status in different cancers. HSPs may be involved in the activation and inhibition of multiple cancer-related pathways. HSP expressions were closely correlated with 22 immune cell infiltrations in different cancers. Conclusion: Our results show that HSP families play an important role in the occurrence and development of various tumors and are potential tumor diagnostic and prognostic biomarkers as well as anti-cancer therapeutic targets.

Background The expression of pepsinogen C (PGC) is considered an ideal negative biomarker of gastric cancer, but its pathological mechanisms remain unclear. This study aims to analyze competing endogenous RNA (ceRNA) networks related to PGC expression at a post-transcriptional level and build an experimental basis for studying the role of PGC in the progression of gastric cancer. Materials and methods RNA sequencing technology was used to detect the differential expression profiles of PGC-related long non-coding (lnc)RNAs, circular (circ)RNAs, and mRNAs. The online database, STRING, was used to construct protein–protein interaction (PPI) networks of differentially expressed (DE) mRNAs. A ggcorrplot R package and online database were used to construct DElncRNAs/DEcircRNAs co-mediated PGC expression–related ceRNA networks. In vivo and in vitro validations were performed using quantitative reverse transcription–PCR. Results RNA sequencing found 637 DEmRNAs, 698 DElncRNAs, and 38 DEcircRNAs. The PPI network of PGC expression–related mRNAs consisted of 503 nodes and 1179 edges. CFH, PPARG, and MUC6 directly interacted with PGC. Enrichment analysis suggested that DEmRNAs were mainly enriched in cancer-related pathways. Eleven DElncRNAs, 13 circRNAs, and 35 miRNA–mRNA pairs were used to construct ceRNA networks co-mediated by DElncRNAs and DEcircRNAs that were PGC expression–related. The network directly related to PGC was as follows: SNHG16/hsa_circ_0008197–hsa-mir-98-5p/hsa-let-7f-5p/hsa-let-7c-5p–PGC. Quantitative reverse transcriptase PCR validation results showed that PGC, PPARG, SNHG16, and hsa_circ_0008197 were differentially expressed in gastric cancer cells and tissues: PGC positively correlated with PPARG (r = 0.276, P = 0.009), SNHG16 (r = 0.35, P = 0.002), and hsa_circ_0008197 (r = 0.346, P = 0.005). Conclusion PGC-related DElncRNAs and DEcircRNAs co-mediated complicated ceRNA networks to regulate PGC expression, thus affecting the occurrence and development of gastric cancer at a post-transcriptional level. Of these, the network directly associated with PGC expression was a SNHG16/hsa_circ_0008197–mir-98-5p/hsa-let-7f-5p/hsa-let-7c-5p – PGC axis. This study may form a foundation for the subsequent exploration of the possible regulatory mechanisms of PGC in gastric cancer.