Title: Contribution of classification based on ferroptosis-related genes to the heterogeneity of
MAFLD
Version: 3 Date: 12 Nov 2021
Reviewer:  Tasnuva D Kabir
Reviewer's report:
The 60 gene signature for ferroptosis was selected from a published work on human liver
cancer (HCC) from the TCGA dataset. MAFLD is a major risk factor for HCC but to associate
ferroptosis to MAFLD development and consequences I would like the researchers to use an
established  ferroptosis signature from C2 molecular signature database which has a list of 40
genes of ferroptosis which could be used to find its association with disease pathogenesis. The 60
gene signature from TCGA could be used as a supplementary validation as it is more associated
with ferroptosis related to HCC  progression. I believe may be this is one of the reasons why
only 2 genes validated to be significantly up in the validation cohort. The design of the study
otherwise was all right.
Also the K clustering used to define 2 clusters need to be increased. Based on Fig 1B which looks
very similar to elbow method of cluster determination and supplementary figure 1 I feel at least
6 clusters could classify the disease better than 2. I understand the authors are trying to classify
the disease based on presence and absence of steatohepatitis. But I guess the disease could be
sub-typed more in detail depending on how much clinical information they have such as diabetic
origin, obesity, metabolic disorders, cirrhosis, steatohepatitis, plus minus inflammation etc.
Regarding looking for immune cell enrichment is it possible for the authors to deconvolve the
bulk microarray data and look for enrichment of immune cell markers using tools similar to
Cibersort ? There is single cell sequencing data available for NAFLD/NASH. The author could
use those data to deconvolve their microarray data and look for immune cell enrichment markers
which I believe will be more accurate.
In the validation cohort is there any more clinical information available? Please tabulate them
and add it and show if the gene expression signature can define MAFLD subtypes.
Title: Contribution of classification based on ferroptosis-related genes to the heterogeneity of
MAFLD
Version: 3 Date: 12 Nov 2021
Reviewer:  Tasnuva D Kabir
Reviewer's report:
The 60 gene signature for ferroptosis was selected from a published work on human liver
cancer (HCC) from the TCGA dataset. MAFLD is a major risk factor for HCC but to associate
ferroptosis to MAFLD development and consequences I would like the researchers to use an
established  ferroptosis signature from C2 molecular signature database which has a list of 40
genes of ferroptosis which could be used to find its association with disease pathogenesis. The 60
gene signature from TCGA could be used as a supplementary validation as it is more associated
with ferroptosis related to HCC  progression. I believe may be this is one of the reasons why
only 2 genes validated to be significantly up in the validation cohort. The design of the study
otherwise was all right.
Also the K clustering used to define 2 clusters need to be increased. Based on Fig 1B which looks
very similar to elbow method of cluster determination and supplementary figure 1 I feel at least
6 clusters could classify the disease better than 2. I understand the authors are trying to classify
the disease based on presence and absence of steatohepatitis. But I guess the disease could be
sub-typed more in detail depending on how much clinical information they have such as diabetic
origin, obesity, metabolic disorders, cirrhosis, steatohepatitis, plus minus inflammation etc.
Regarding looking for immune cell enrichment is it possible for the authors to deconvolve the
bulk microarray data and look for enrichment of immune cell markers using tools similar to
Cibersort ? There is single cell sequencing data available for NAFLD/NASH. The author could
use those data to deconvolve their microarray data and look for immune cell enrichment markers
which I believe will be more accurate.
In the validation cohort is there any more clinical information available? Please tabulate them
and add it and show if the gene expression signature can define MAFLD subtypes.
Please review this paper.
user
Title: Contribution of classification based on ferroptosis-related genes to the heterogeneity of
MAFLD
Version: 3 Date: 12 Nov 2021
Reviewer:  Tasnuva D Kabir
Reviewer's report:
The 60 gene signature for ferroptosis was selected from a published work on human liver
cancer (HCC) from the TCGA dataset. MAFLD is a major risk factor for HCC but to associate
ferroptosis to MAFLD development and consequences I would like the researchers to use an
established  ferroptosis signature from C2 molecular signature database which has a list of 40
genes of ferroptosis which could be used to find its association with disease pathogenesis. The 60
gene signature from TCGA could be used as a supplementary validation as it is more associated
with ferroptosis related to HCC  progression. I believe may be this is one of the reasons why
only 2 genes validated to be significantly up in the validation cohort. The design of the study
otherwise was all right.
Also the K clustering used to define 2 clusters need to be increased. Based on Fig 1B which looks
very similar to elbow method of cluster determination and supplementary figure 1 I feel at least
6 clusters could classify the disease better than 2. I understand the authors are trying to classify
the disease based on presence and absence of steatohepatitis. But I guess the disease could be
sub-typed more in detail depending on how much clinical information they have such as diabetic
origin, obesity, metabolic disorders, cirrhosis, steatohepatitis, plus minus inflammation etc.
Regarding looking for immune cell enrichment is it possible for the authors to deconvolve the
bulk microarray data and look for enrichment of immune cell markers using tools similar to
Cibersort ? There is single cell sequencing data available for NAFLD/NASH. The author could
use those data to deconvolve their microarray data and look for immune cell enrichment markers
which I believe will be more accurate.
In the validation cohort is there any more clinical information available? Please tabulate them
and add it and show if the gene expression signature can define MAFLD subtypes.
assistant