The dysregulation of membrane protein expression has been implicated in tumorigenesis and progression, including hepatocellular carcinoma (HCC). In this study, we aimed to identify membrane proteins that modulate HCC viability. To achieve this, we performed a CRISPR activation screen targeting human genes encoding membrane-associated proteins, revealing TMX2 as a potential driver of HCC cell viability. Gain- and loss-of-function experiments demonstrated that TMX2 promoted growth and tumorigenesis of HCC. Clinically, TMX2 was an independent prognostic factor for HCC patients. It was significantly upregulated in HCC tissues and associated with poor prognosis of HCC patients. Mechanistically, TMX2 was demonstrated to promote macroautophagy/autophagy by facilitating KPNB1 nuclear export and TFEB nuclear import. In addition, TMX2 interacted with VDAC2 and VADC3, assisting in the recruitment of PRKN to defective mitochondria to promote cytoprotective mitophagy during oxidative stress. Most interestingly, HCC cells responded to oxidative stress by upregulating TMX2 expression and cell autophagy. Knockdown of TMX2 enhanced the anti-tumor effect of lenvatinib. In conclusion, our findings emphasize the pivotal role of TMX2 in driving the HCC cell viability by promoting both autophagy and mitophagy. These results suggest that TMX2 May serve as a prognostic marker and promising therapeutic target for HCC treatment. Abbreviation: CCCP: Carbonyl cyanide 3-chlorophenylhydrazone; Co-IP: co-immunoprecipitation; CRISPR: clustered regularly interspaced short palindromic repeat; ER: endoplasmic reticulum; HCC: hepatocellular carcinoma; KPNB1: karyopherin subunit beta 1; PRKN: parkin RBR E3 ubiquitin protein ligase; ROS: reactive oxygen species; TFEB: transcription factor EB; TMX2: thioredoxin related transmembrane protein 2; VDAC2: voltage dependent anion channel 2; VDAC3: voltage dependent anion channel 3; WB: western blot.

The dysregulation of membrane protein expression has been implicated in tumorigenesis and progression, including hepatocellular carcinoma (HCC). In this study, we aimed to identify membrane proteins that modulate HCC viability. To achieve this, we performed a CRISPR activation screen targeting human genes encoding membrane-associated proteins, revealing TMX2 as a potential driver of HCC cell viability. Gain- and loss-of-function experiments demonstrated that TMX2 promoted growth and tumorigenesis of HCC. Clinically, TMX2 was an independent prognostic factor for HCC patients. It was significantly upregulated in HCC tissues and associated with poor prognosis of HCC patients. Mechanistically, TMX2 was demonstrated to promote macroautophagy/autophagy by facilitating KPNB1 nuclear export and TFEB nuclear import. In addition, TMX2 interacted with VDAC2 and VADC3, assisting in the recruitment of PRKN to defective mitochondria to promote cytoprotective mitophagy during oxidative stress. Most interestingly, HCC cells responded to oxidative stress by upregulating TMX2 expression and cell autophagy. Knockdown of TMX2 enhanced the anti-tumor effect of lenvatinib. In conclusion, our findings emphasize the pivotal role of TMX2 in driving the HCC cell viability by promoting both autophagy and mitophagy. These results suggest that TMX2 May serve as a prognostic marker and promising therapeutic target for HCC treatment. Abbreviation: CCCP: Carbonyl cyanide 3-chlorophenylhydrazone; Co-IP: co-immunoprecipitation; CRISPR: clustered regularly interspaced short palindromic repeat; ER: endoplasmic reticulum; HCC: hepatocellular carcinoma; KPNB1: karyopherin subunit beta 1; PRKN: parkin RBR E3 ubiquitin protein ligase; ROS: reactive oxygen species; TFEB: transcription factor EB; TMX2: thioredoxin related transmembrane protein 2; VDAC2: voltage dependent anion channel 2; VDAC3: voltage dependent anion channel 3; WB: western blot.

Educational question answering is vital for online education, with Educational Knowledge Graph Question Answering (EKGQA) focusing on leveraging knowledge bases to answer questions. However, the lack of real Chinese annotated datasets hampers EKGQA's progress. Current datasets often feature direct questions, but actual educational questions reflect varied cognitive expressions and language styles due to differing experiences and backgrounds. To address this, in this paper, we propose to benchmark such challenges for EKGQA by establishing a more realistic dataset EDUCEQ. It contains 236.3k questions in 12 categories. Compared with existing datasets, EDUCEQ features two more authentic features - diverse cognitive expressions and various language styles.Additionally, we have evaluated various categories using the EDUCEQ dataset and developed a robust baseline EKGQA method that establishes competitive benchmarks for future research.The EDUCEQ is the first to portray a more realistic view of educational phenomena from a cognitive and computational linguistics perspective, which is beneficial to educational knowledge graph question answering and also the online education field in general.

Educational question answering is vital for online education, with Educational Knowledge Graph Question Answering (EKGQA) focusing on leveraging knowledge bases to answer questions. However, the lack of real Chinese annotated datasets hampers EKGQA's progress. Current datasets often feature direct questions, but actual educational questions reflect varied cognitive expressions and language styles due to differing experiences and backgrounds. To address this, in this paper, we propose to benchmark such challenges for EKGQA by establishing a more realistic dataset EDUCEQ. It contains 236.3k questions in 12 categories. Compared with existing datasets, EDUCEQ features two more authentic features - diverse cognitive expressions and various language styles.Additionally, we have evaluated various categories using the EDUCEQ dataset and developed a robust baseline EKGQA method that establishes competitive benchmarks for future research.The EDUCEQ is the first to portray a more realistic view of educational phenomena from a cognitive and computational linguistics perspective, which is beneficial to educational knowledge graph question answering and also the online education field in general.

Educational question answering is vital for online education, with Educational Knowledge Graph Question Answering (EKGQA) focusing on leveraging knowledge bases to answer questions. However, the lack of real Chinese annotated datasets hampers EKGQA's progress. Current datasets often feature direct questions, but actual educational questions reflect varied cognitive expressions and language styles due to differing experiences and backgrounds. To address this, in this paper, we propose to benchmark such challenges for EKGQA by establishing a more realistic dataset EDUCEQ. It contains 236.3k questions in 12 categories. Compared with existing datasets, EDUCEQ features two more authentic features - diverse cognitive expressions and various language styles.Additionally, we have evaluated various categories using the EDUCEQ dataset and developed a robust baseline EKGQA method that establishes competitive benchmarks for future research.The EDUCEQ is the first to portray a more realistic view of educational phenomena from a cognitive and computational linguistics perspective, which is beneficial to educational knowledge graph question answering and also the online education field in general.

Educational question answering is vital for online education, with Educational Knowledge Graph Question Answering (EKGQA) focusing on leveraging knowledge bases to answer questions. However, the lack of real Chinese annotated datasets hampers EKGQA's progress. Current datasets often feature direct questions, but actual educational questions reflect varied cognitive expressions and language styles due to differing experiences and backgrounds. To address this, in this paper, we propose to benchmark such challenges for EKGQA by establishing a more realistic dataset EDUCEQ. It contains 236.3k questions in 12 categories. Compared with existing datasets, EDUCEQ features two more authentic features - diverse cognitive expressions and various language styles.Additionally, we have evaluated various categories using the EDUCEQ dataset and developed a robust baseline EKGQA method that establishes competitive benchmarks for future research.The EDUCEQ is the first to portray a more realistic view of educational phenomena from a cognitive and computational linguistics perspective, which is beneficial to educational knowledge graph question answering and also the online education field in general.

Educational question answering is vital for online education, with Educational Knowledge Graph Question Answering (EKGQA) focusing on leveraging knowledge bases to answer questions. However, the lack of real Chinese annotated datasets hampers EKGQA's progress. Current datasets often feature direct questions, but actual educational questions reflect varied cognitive expressions and language styles due to differing experiences and backgrounds. To address this, in this paper, we propose to benchmark such challenges for EKGQA by establishing a more realistic dataset EDUCEQ. It contains 236.3k questions in 12 categories. Compared with existing datasets, EDUCEQ features two more authentic features - diverse cognitive expressions and various language styles.Additionally, we have evaluated various categories using the EDUCEQ dataset and developed a robust baseline EKGQA method that establishes competitive benchmarks for future research.The EDUCEQ is the first to portray a more realistic view of educational phenomena from a cognitive and computational linguistics perspective, which is beneficial to educational knowledge graph question answering and also the online education field in general.