Background: Reliable noninvasive biomarkers for hepatocellular carcinoma (HCC)⁷ diagnosis and prognosis are urgently needed. We explored the potential of not only microRNAs (miRNAs) but other types of noncoding RNAs (ncRNAs) as HCC biomarkers. Methods: Peripheral blood samples were collected from 77 individuals; among them, 57 plasma cell-free RNA transcriptomes and 20 exosomal RNA transcriptomes were profiled. Significantly upregulated ncRNAs and published potential HCC biomarkers were validated with reverse transcription (RT)-qPCR in an independent validation cohort (60–150 samples). We particularly investigated the diagnosis and prognosis performance and biological function for 1 ncRNA biomarker, RN7SL1,⁸ and its S fragment. Results: We identified certain circulating ncRNAs escaping from RNase degradation, possibly through binding with RNA-binding proteins: 899 ncRNAs were highly upregulated in HCC patients. Among them, 337 genes were fragmented long noncoding RNAs, 252 genes were small nucleolar RNAs, and 134 genes were piwi-interacting RNAs. Forty-eight candidates were selected and validated with RT-qPCR, of which, 16 ncRNAs were verified to be significantly upregulated in HCC, including RN7SL1, SNHG1, ZFAS1, and LINC01359. Particularly, the abundance of RN7SL1 S fragment discriminated HCC samples from negative controls (area under the curve, 0.87; 95% CI, 0.817–0.920). HCC patients with higher concentrations of RN7SL1 S fragment had lower survival rates. Furthermore, RN7SL1 S fragment alone promoted cancer cell proliferation and clonogenic growth. Conclusions: Our results show that various ncRNA species, not only miRNAs, identified in the small RNA sequencing of plasma are also able to serve as noninvasive biomarkers. Particularly, we identified a domain of srpRNA RN7SL1 with reliable clinical performance for HCC diagnosis and prognosis.

Combination immunotherapy is promising to overcome the limited objective response rates of immune checkpoint blockade (ICB) therapy. Here, a tumor immunological phenotype (TIP) gene signature and high-throughput sequencing–based high-throughput screening (HTS²) were combined to identify combination immunotherapy compounds. We firstly defined a TIP gene signature distinguishing “cold” tumors from “hot” tumors. After screening thousands of compounds, we identified that aurora kinase inhibitors (AKIs) could reprogram the expression pattern of TIP genes in triple-negative breast cancer (TNBC) cells. AKIs treatments up-regulate expression of chemokine genes CXCL10 and CXCL11 through inhibiting aurora kinase A (AURKA)–signal transducer and activator of transcription 3 (STAT3) signaling pathway, which promotes effective T cells infiltrating into tumor microenvironment and improves anti-programmed cell death 1 (PD-1) efficacy in preclinical models. Our study established a novel strategy to discover combination immunotherapy compounds and suggested the therapeutic potential of combining AKIs with ICB for the treatment of TNBC.

The Warburg effect is a prominent metabolic feature associated with neoplastic diseases; however, the underlying mechanism remains incompletely understood. TAp73, a structural homolog of the tumor suppressor p53, is frequently overexpressed in human tumors, indicating a proliferative advantage that it can confer to tumor cells. Here we show that TAp73 stimulates the expression of phosphofructokinase-1, liver type (PFKL), which catalyzes the committed step in glycolysis. Through this regulation, TAp73 enhances glucose consumption and lactate excretion, promoting the Warburg effect. By activating PFKL, TAp73 also increases ATP production and bolsters anti-oxidant defense. TAp73 deficiency results in a pronounced reduction in tumorigenic potential, which can be rescued by forced PFKL expression. These findings establish TAp73 as a critical regulator of glycolysis and reveal a mechanism by which tumor cells achieve the Warburg effect to enable oncogenic growth.

Hepatocellular carcinoma (HCC) is the predominant subtype of primary liver cancer and represents a highly heterogeneous disease, making it hard to predict the prognosis and therapy efficacy. Here, we established a novel tumor immunological phenotype-related gene index (TIPRGPI) consisting of 11 genes by Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict HCC prognosis and immunotherapy response. TIPRGPI was validated in multiple datasets and exhibited outstanding performance in predicting the overall survival of HCC. Multivariate analysis verified it as an independent predictor and a TIPRGPI-integrated nomogram was constructed to provide a quantitative tool for clinical practice. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in tumor microenvironment were shown between the TIPRGPI high and low-risk groups. Notably, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade (ICB) therapy of the low-risk group. Besides, six potential drugs binding to the core target of the TIPRGPI signature were predicted via molecular docking. Taken together, our study shows that the proposed TIPRGPI was a reliable signature to predict the risk classification, immunotherapy response, and drugs candidate with potential application in the clinical decision and treatment of HCC. The novel “TIP genes”-guided strategy for predicting the survival and immunotherapy efficacy, we reported here, might be also applied to more cancers other than HCC.