Abstract

   Disulfidptosis a new cell death mode, which can cause the death of
   Hepatocellular Carcinoma (HCC) cells. However, the significance of
   disulfidptosis-related Long non-coding RNAs (DRLs) in the prognosis and
   immunotherapy of HCC remains unclear. Based on The Cancer Genome Atlas
   (TCGA) database, we used Least Absolute Shrinkage and Selection
   Operator (LASSO) and Cox regression model to construct DRL Prognostic
   Signature (DRLPS)-based risk scores and performed Gene Expression
   Omnibus outside validation. Survival analysis was performed and a
   nomogram was constructed. Moreover, we performed functional enrichment
   annotation, immune infiltration and drug sensitivity analyses. Five
   DRLs ([42]AL590705.3, [43]AC072054.1, [44]AC069307.1, [45]AC107959.3
   and ZNF232-AS1) were identified to construct prognostic signature.
   DRLPS-based risk scores exhibited better predictive efficacy of
   survival than conventional clinical features. The nomogram showed high
   congruence between the predicted survival and observed survival. Gene
   set were mainly enriched in cell proliferation, differentiation and
   growth function related pathways. Immune cell infiltration in the
   low-risk group was significantly higher than that in the high-risk
   group. Additionally, the high-risk group exhibited higher sensitivity
   to Afatinib, Fulvestrant, Gefitinib, Osimertinib, Sapitinib, and
   Taselisib. In conclusion, our study highlighted the potential utility
   of the constructed DRLPS in the prognosis prediction of HCC patients,
   which demonstrated promising clinical application value.

   Keywords: LncRNA, Hepatocellular carcinoma cells, Disulfidptosis,
   Immune microenvironment, Prognostic signature

   Subject terms: Cancer, Computational biology and bioinformatics

Introduction

   According to Global Cancer Statistics^[46]1, in 2020, there were
   905,677 new cases of liver cancer and 830,180 new deaths, accounting
   for 4.7% of all cancer cases and 8.3% of all cancer-related deaths
   respectively, and ranking as the third cause of cancer-related
   mortality. Live cancer mainly includes hepatocellular carcinoma (HCC),
   intrahepatic cholangiocarcinoma, and mixed liver cancer, among which
   HCC is the most commonly diagnosed primary cancer of the liver,
   comprising 75–85% of cases of all liver cancer cases. Presently,
   although hepatic resection, liver transplantation, radiotherapy,
   chemotherapy and targeted therapy have improved the clinical prognosis
   for some HCC patients, the prognosis of HCC remains poor, with a
   post-operative recurrence rate of up to 70%^[47]2. Therefore, exploring
   novel prognostic models and potential therapeutic targets is of great
   clinical significance.

   In March 2023, Liu et al.^[48]3 first reported in the journal Nat Cell
   Bio a new form of programmed cell death, which may serve as a new
   targeted therapeutic approach. The study found that cell death still
   occurred after the knock-down of genes that regulated apoptosis and
   iron death, but was inhibited by the use of the thiol-based reducing
   reagent Tris (2-Carboxyethyl) Phosphine (TCEP). Further analysis
   revealed that SLC7A11, which was highly expressed in cancer cells,
   mediated the uptake of cystine and consequently the synthesis of
   glutathione to counteract the oxidative effect caused by high
   expression. However, under glucose starvation, the supply of
   nicotinamide adenine dinucleotide phosphate (NADPH) generated through
   the pentose phosphate pathway was insufficient, and would lead to the
   inhibition of the cysteine reduction to cysteine pathway and the
   increase of intermolecular disulphide bonds of actin, which induced
   disulphide stress and activated the Rac-WAVE regulatory complex
   (WRC)-actin-related protein (Arp2/3) signaling axis, triggering
   cytoskeletal disorders and cell death. In this process, excessive
   cystine intake combined with glucose starvation may induce cell death.
   Due to such stress characteristics of disulfide compounds, this form of
   cell death was termed disulfidptosis. It reported that GYS1, NDUFS1,
   OXSM, LRPPRC, NDUFA11, NUBPL, NCKAP1, RPN1, SLC3A2, and SLC7A11 were
   closely associated with disulfidptosis.

   Long non-coding RNAs (lncRNAs) refer to a type of non-coding RNAs more
   than 200 nucleotides in length, which are distributed in the nucleus
   and cytoplasm and can regulate the biological behavior of cancer cells
   by binding to DNA, RNA and protein, such as apoptosis, proliferation
   and metastasis. Numerous studies have reported that lncRNAs are closely
   associated with the occurrence and development of hepatocellular
   carcinoma. Zhou et al.^[49]4 found that lncRNA PART1 promoted HCC
   proliferation, migration and invasion by regulating miRNA-149-5p/MAP2K1
   Axis. Wei et al.^[50]5 found that lncRNA PAARH inhibited HCC apoptosis
   and promoted HCC migration, invasion and tumor growth by upregulating
   HOTTIP and activating HIF-1α/VEGF pathway. In addition^[51]6, lncRNAs
   are found to be closely associated with programmed cell death. A study
   found that the prognostic model based on ferroptosis-related lncRNAs
   (POLH-AS139384, MKLN1-AS, [52]AC090772.3, etc.) could accurately
   predict the prognosis of HCC patients. The above studies have
   demonstrated the potential critical role of lncRNAs in the treatment
   and prediction of HCC, but the role of DRL in HCC remains to be
   elucidated.

   At present, in studies on the modes of cell death of HCC,
   Ferroptosis-Related lncRNA^[53]7, Cuproptosis-Related lncRNA^[54]8,
   pyroptosis-related lncRNA^[55]9, necroptosis-related lncRNAs^[56]10 all
   provide valuable prognostic biomarkers and therapeutic targets for HCC.
   However, DRLs remain unclear, so comprehensive evaluation of DRLs and
   construction of prognostic markers are essential for accurate treatment
   of clinical diagnosis, such as monitoring of prognostic markers, early
   detection of disease changes, and adoption of appropriate therapeutic
   measures to improve the survival and life quality of patients.
   Therefore, in this study, we used R to perform a comprehensive analysis
   of TCGA-LIHC-RNA-seq data and clinical data, identifying
   prognostic-related DRLs in HCC and constructing a prognostic
   risk-scoring model. In addition, a nomogram was constructed based on
   DRLPS risk scores to assess OS in HCC patients. Finally, we evaluated
   immune infiltration and drug sensitivity in high- and low-risk groups.
   These results were expected to provide references for the prognosis,